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Noor L, Hoffmann J, Meller T, Gaser C, Nenadić I. Amygdala functional connectivity in borderline personality disorder. Psychiatry Res Neuroimaging 2024; 340:111808. [PMID: 38492542 DOI: 10.1016/j.pscychresns.2024.111808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 03/08/2024] [Accepted: 03/09/2024] [Indexed: 03/18/2024]
Abstract
Borderline personality disorder (BPD) is characterised by structural and functional brain alterations. Yet, there is little data on functional connectivity (FC) across different levels of brain networks and parameters. In this study, we applied a multi-level approach to analyse abnormal functional connectivity. We analysed resting-state functional magnetic resonance imaging (fMRI) data sets of 69 subjects: 17 female BPD patients and 51 age-matched psychiatrically healthy female controls. fMRI was analysed using CONN toolbox including: a) seed-based FC analysis of amygdala connectivity, b) independent component analysis (ICA) based network analysis of intra- and inter-network FC of selected resting-state networks (DMN, SN, FPN), as well as c) graph-theory based measures of network-level characteristics. We show group-level seed FC differences with higher amygdala to contralateral (superior) occipital cortex connectivity in BPD, which correlated with schema-therapy derived measures of symptoms/traits across the entire cohort. While there was no significant group effect on DMN, SN, or FPN intra-network or inter-network FC, we show a significant group difference for local efficiency and cluster coefficient for a DMN-linked cerebellum cluster. Our findings demonstrate BPD-linked changes in FC across multiple levels of observation, which supports a multi-level analysis for future studies to consider different aspects of functional connectome alterations.
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Affiliation(s)
- Laila Noor
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Marburg, Germany; Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Jonas Hoffmann
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Marburg, Germany
| | - Tina Meller
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Marburg, Germany; Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Christian Gaser
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany; Department of Neurology, Jena University Hospital, Jena, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Marburg, Germany; Center for Mind, Brain and Behavior (CMBB), Marburg, Germany.
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Lahnakoski JM, Nolte T, Solway A, Vilares I, Hula A, Feigenbaum J, Lohrenz T, King-Casas B, Fonagy P, Montague PR, Schilbach L. A machine-learning approach for differentiating borderline personality disorder from community participants with brain-wide functional connectivity. J Affect Disord 2024:S0165-0327(24)00868-1. [PMID: 38806064 DOI: 10.1016/j.jad.2024.05.125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 05/23/2024] [Accepted: 05/24/2024] [Indexed: 05/30/2024]
Abstract
BACKGROUND Functional connectivity has garnered interest as a potential biomarker of psychiatric disorders including borderline personality disorder (BPD). However, small sample sizes and lack of within-study replications have led to divergent findings with no clear spatial foci. AIMS Evaluate discriminative performance and generalizability of functional connectivity markers for BPD. METHOD Whole-brain fMRI resting state functional connectivity in matched subsamples of 116 BPD and 72 control individuals defined by three grouping strategies. We predicted BPD status using classifiers with repeated cross-validation based on multiscale functional connectivity within and between regions of interest (ROIs) covering the whole brain-global ROI-based network, seed-based ROI-connectivity, functional consistency, and voxel-to-voxel connectivity-and evaluated the generalizability of the classification in the left-out portion of non-matched data. RESULTS Full-brain connectivity allowed classification (~70 %) of BPD patients vs. controls in matched inner cross-validation. The classification remained significant when applied to unmatched out-of-sample data (~61-70 %). Highest seed-based accuracies were in a similar range to global accuracies (~70-75 %), but spatially more specific. The most discriminative seed regions included midline, temporal and somatomotor regions. Univariate connectivity values were not predictive of BPD after multiple comparison corrections, but weak local effects coincided with the most discriminative seed-ROIs. Highest accuracies were achieved with a full clinical interview while self-report results remained at chance level. LIMITATIONS The accuracies vary considerably between random sub-samples of the population, global signal and covariates limiting the practical applicability. CONCLUSIONS Spatially distributed functional connectivity patterns are moderately predictive of BPD despite heterogeneity of the patient population.
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Affiliation(s)
- Juha M Lahnakoski
- Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, 52428 Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, 40225 Düsseldorf, Germany.
| | - Tobias Nolte
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom; Anna Freud National Centre for Children and Families, London, United Kingdom
| | - Alec Solway
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA
| | - Iris Vilares
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Andreas Hula
- Austrian Institute of Technology, Vienna, Austria
| | - Janet Feigenbaum
- Research Department of Clinical, Educational and Health Psychology, University College London, London, United Kingdom
| | - Terry Lohrenz
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA
| | - Brooks King-Casas
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA; Department of Psychology, Virginia Tech, Blacksburg, VA, USA
| | - Peter Fonagy
- Anna Freud National Centre for Children and Families, London, United Kingdom; Research Department of Clinical, Educational and Health Psychology, University College London, London, United Kingdom
| | - P Read Montague
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom; Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA; Department of Physics, Virginia Tech, Blacksburg, VA, USA; Department of Psychiatry and Behavioral Medicine, Virginia Tech Carilion School of Medicine, Virginia Tech, Roanoke, VA, USA
| | - Leonhard Schilbach
- Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany; Department of Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany
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Faryadras M, Burles F, Iaria G, Davidsen J. Functional brain networks in Developmental Topographical Disorientation. Cereb Cortex 2024; 34:bhae104. [PMID: 38566506 PMCID: PMC10987990 DOI: 10.1093/cercor/bhae104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 02/27/2024] [Accepted: 02/29/2024] [Indexed: 04/04/2024] Open
Abstract
Despite a decade-long study on Developmental Topographical Disorientation, the underlying mechanism behind this neurological condition remains unknown. This lifelong selective inability in orientation, which causes these individuals to get lost even in familiar surroundings, is present in the absence of any other neurological disorder or acquired brain damage. Herein, we report an analysis of the functional brain network of individuals with Developmental Topographical Disorientation ($n = 19$) compared against that of healthy controls ($n = 21$), all of whom underwent resting-state functional magnetic resonance imaging, to identify if and how their underlying functional brain network is altered. While the established resting-state networks (RSNs) are confirmed in both groups, there is, on average, a greater connectivity and connectivity strength, in addition to increased global and local efficiency in the overall functional network of the Developmental Topographical Disorientation group. In particular, there is an enhanced connectivity between some RSNs facilitated through indirect functional paths. We identify a handful of nodes that encode part of these differences. Overall, our findings provide strong evidence that the brain networks of individuals suffering from Developmental Topographical Disorientation are modified by compensatory mechanisms, which might open the door for new diagnostic tools.
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Affiliation(s)
- Mahsa Faryadras
- Department of Physics and Astronomy, University of Calgary, 2500 University Drive NW, Calgary, T2N 1N4 AB, Canada
| | - Ford Burles
- Department of Psychology, University of Calgary, 2500 University Drive NW, Calgary, T2N 1N4 AB, Canada
| | - Giuseppe Iaria
- Department of Psychology, University of Calgary, 2500 University Drive NW, Calgary, T2N 1N4 AB, Canada
- Hotchkiss Brain Institute, University of Calgary, 3330 Hospital Drive NW, Calgary, T2N 4N1 AB, Canada
| | - Jörn Davidsen
- Department of Physics and Astronomy, University of Calgary, 2500 University Drive NW, Calgary, T2N 1N4 AB, Canada
- Hotchkiss Brain Institute, University of Calgary, 3330 Hospital Drive NW, Calgary, T2N 4N1 AB, Canada
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Al-Juhani A, Alzahrani MJ, Abdullah A Z, Alnefaie AN, Alnowaisser LN, Alhadi W, Alghamdi JK, Bauthman MS. Neuroimaging and Brain-Based Markers Identifying Neurobiological Markers Associated With Criminal Behaviour, Personality Disorders, and Mental Health: A Narrative Review. Cureus 2024; 16:e58814. [PMID: 38784339 PMCID: PMC11113083 DOI: 10.7759/cureus.58814] [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: 04/23/2024] [Indexed: 05/25/2024] Open
Abstract
We begin the review by pointing to the common stigma associated with mental health issues, which often derives from a lack of understanding or incomplete knowledge. Neurobiological research provides us with a new lens to help challenge and dispel common assumptions and misunderstandings and gives an understanding of sexual behaviours that influence society. As such, it generates substantial evidence for the structural and functional asymmetry of the brains of individuals with mental disorders. However, this type of representation poses many challenges to traditional thinking and constantly provokes change in perspective and empathy towards those individuals. In the review, we go deeper into the effects of neurobiological findings on understanding criminal behaviours and personality disorders, looking further beyond behavioural health. These problems, which were once mainly discussed as moral ones or viewed from the perspective of character flaws, are analysed today through neurological considerations pointing to their complexity. When the root of bipolar disorder is revealed to be neurological, society will react with more information and understanding, hence reducing the stigmatisation and discrimination meted out to people with these problems. At a macro level, findings from neurobiology affect society in ways that go beyond individuals; social attitudes, laws, and policies about the services rendered are influenced. Operating as a catalyst within the community, neurobiological research helps to initiate social change through the creation of an informed, understanding public forum. Thus, it creates broader value for those dealing with behavioural and mental health challenges. The first and most important question of this narrative review is focused on identifying identifiable neurobiological markers that are closely related to criminal conduct, personality disorders, and mental health disorders. Through this review, we aim to present detailed insights into the neurological foundations that anchor these phenomena via a narrative analysis of contemporary literature. The potential implications are finding problems early to apply specific treatment and learning an advanced strategy for social attitudes. This will promote a more humanistic approach based on adequate information on the behavioural and mental health issues involved.
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Affiliation(s)
| | | | | | | | | | - Wajd Alhadi
- College of Medicine, King Khalid University, Abha, SAU
| | | | - Moayyad S Bauthman
- Internal Medicine, King Abdulaziz University Faculty of Medicine, Rabigh, SAU
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Riccardi N, Zhao X, den Ouden DB, Fridriksson J, Desai RH, Wang Y. Network-based statistics distinguish anomic and Broca's aphasia. Brain Struct Funct 2023:10.1007/s00429-023-02738-4. [PMID: 38160205 DOI: 10.1007/s00429-023-02738-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 11/21/2023] [Indexed: 01/03/2024]
Abstract
INTRODUCTION Aphasia is a speech-language impairment commonly caused by damage to the left hemisphere. The neural mechanisms that underpin different types of aphasia and their symptoms are still not fully understood. This study aims to identify differences in resting-state functional connectivity between anomic and Broca's aphasia measured through resting-state functional magnetic resonance imaging (rs-fMRI). METHODS We used the network-based statistic (NBS) method, as well as voxel- and connectome-based lesion symptom mapping (V-, CLSM), to identify distinct neural correlates of the anomic and Broca's groups. To control for lesion effect, we included lesion volume as a covariate in both the NBS method and LSM. RESULTS NBS identified a subnetwork located in the dorsal language stream bilaterally, including supramarginal gyrus, primary sensory, motor, and auditory cortices, and insula. The connections in the subnetwork were weaker in the Broca's group than the anomic group. The properties of the subnetwork were examined through complex network measures, which indicated that regions in right inferior frontal sulcus, right paracentral lobule, and bilateral superior temporal gyrus exhibit intensive interaction. Left superior temporal gyrus, right postcentral gyrus, and left supramarginal gyrus play an important role in information flow and overall communication efficiency. Disruption of this network underlies the constellation of symptoms associated with Broca's aphasia. Whole-brain CLSM did not detect any significant connections, suggesting an advantage of NBS when thousands of connections are considered. However, CLSM identified connections that differentiated Broca's from anomic aphasia when analysis was restricted to a hypothesized network of interest. DISCUSSION We identified novel signatures of resting-state brain network differences between groups of individuals with anomic and Broca's aphasia. We identified a subnetwork of connections that statistically differentiated the resting-state brain networks of the two groups, in comparison with standard CLSM results that yielded isolated connections. Network-level analyses are useful tools for the investigation of the neural correlates of language deficits post-stroke.
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Affiliation(s)
- Nicholas Riccardi
- Department of Psychology, University of South Carolina, Columbia, SC, USA
| | - Xingpei Zhao
- Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA
| | - Dirk-Bart den Ouden
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA
| | - Julius Fridriksson
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA
| | - Rutvik H Desai
- Department of Psychology, University of South Carolina, Columbia, SC, USA
| | - Yuan Wang
- Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA.
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Langerbeck M, Baggio T, Messina I, Bhat S, Grecucci A. Borderline shades: Morphometric features predict borderline personality traits but not histrionic traits. Neuroimage Clin 2023; 40:103530. [PMID: 37879232 PMCID: PMC10618757 DOI: 10.1016/j.nicl.2023.103530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 10/09/2023] [Accepted: 10/12/2023] [Indexed: 10/27/2023]
Abstract
Borderline personality disorder (BPD) is one of the most diagnosed disorders in clinical settings. Besides the fully diagnosed disorder, borderline personality traits (BPT) are quite common in the general population. Prior studies have investigated the neural correlates of BPD but not of BPT. This paper investigates the neural correlates of BPT in a subclinical population using a supervised machine learning method known as Kernel Ridge Regression (KRR) to build predictive models. Additionally, we want to determine whether the same brain areas involved in BPD are also involved in subclinical BPT. Recent attempts to characterize the specific role of resting state-derived macro networks in BPD have highlighted the role of the default mode network. However, it is not known if this extends to the subclinical population. Finally, we wanted to test the hypothesis that the same circuitry that predicts BPT can also predict histrionic personality traits. Histrionic personality is sometimes considered a milder form of BPD, and making a differential diagnosis between the two may be difficult. For the first time KRR was applied to structural images of 135 individuals to predict BPT, based on the whole brain, on a circuit previously found to correctly classify BPD, and on the five macro-networks. At a whole brain level, results show that frontal and parietal regions, as well as the Heschl's area, the thalamus, the cingulum, and the insula, are able to predict borderline traits. BPT predictions increase when considering only the regions limited to the brain circuit derived from a study on BPD, confirming a certain overlap in brain structure between subclinical and clinical samples. Of all the five macro networks, only the DMN successfully predicts BPD, confirming previous observations on its role in the BPD. Histrionic traits could not be predicted by the BPT circuit. The results have implications for the diagnosis of BPD and a dimensional model of personality.
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Affiliation(s)
- Miriam Langerbeck
- Faculty of Psychology and Neuroscience (FPN), Maastricht University, Netherlands
| | - Teresa Baggio
- Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Italy.
| | - Irene Messina
- Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Italy; Universitas Mercatorum, Rome, Italy.
| | - Salil Bhat
- Department of Cognitive Neuroscience, Faculty of Psychology and Cognitive Neuroscience (FPN), Maastricht University, Netherlands.
| | - Alessandro Grecucci
- Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Italy; Centre for Medical Sciences (CISMed), University of Trento, Italy.
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Cao L, Li L, Huang Z, Xia F, Huang R, Ma Y, Qin Y, Wu J, Tong L, Zhang C, Zhang Y, Ren Z. Functional network segregation is associated with higher functional connectivity in endurance runners. Neurosci Lett 2023; 812:137401. [PMID: 37460055 DOI: 10.1016/j.neulet.2023.137401] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/21/2023] [Accepted: 07/14/2023] [Indexed: 07/23/2023]
Abstract
Neuroimaging studies have identified significant differences in brain structure, function, and connectivity between endurance runners and healthy controls. However, the topological organization of large-scale functional brain networks remains unexplored in endurance runners. Using resting-state functional magnetic resonance imaging data, this study examined the differences in the topological organization of functional networks between endurance runners (n = 22) and healthy controls (n = 20). Endurance runners had significantly higher clustering coefficients in the whole-brain functional network than healthy controls, but the two did not differ regarding the shortest path length or small-world index. Using network-based statistics, we identified one subnetwork in endurance runners with higher functional connectivity than healthy controls, and the mean functional connectivity of the subnetwork significantly correlated with the three aforementioned small-world parameters. In this subnetwork, the mean clustering coefficient of nodes associated with short-range connections was higher in endurance runners than in healthy controls, but the mean clustering coefficient of nodes associated with long-range connections did not differ between the two groups. In conclusion, using graph theoretical approaches, we revealed significant differences in the topological organization of the whole-brain functional network and functional connectivity between endurance runners and healthy controls. The relationship between these two features suggests that a more segregated network may arise from the optimization of the identified subnetwork in endurance runners. These findings are possibly the neural basis underlying the good performance of endurance runners in endurance running.
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Affiliation(s)
- Long Cao
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, 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
| | - Lunxiong Li
- Key Laboratory of Brain, Cognition and Education Science, Ministry of Education, China; Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Zitong Huang
- 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
| | - Fengguang Xia
- Key Laboratory of Brain, Cognition and Education Science, Ministry of Education, China; Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Ruiwang Huang
- School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Yudan Ma
- School of Public Teaching, Shanwei Institute of Technology, Shanwei 516600, China
| | - Yifan Qin
- College of Physical Education, Shenzhen University, Shenzhen 518060, China
| | - Jinlong Wu
- College of physical education, Southwest University, Chongqing 400715, China
| | - Li Tong
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China
| | - Chi Zhang
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China
| | - Yuanchao Zhang
- 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.
| | - Zhanbing Ren
- College of Physical Education, Shenzhen University, Shenzhen 518060, China.
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Tooley UA, Latham A, Kenley JK, Alexopoulos D, Smyser T, Warner BB, Shimony JS, Neil JJ, Luby JL, Barch DM, Rogers CE, Smyser CD. Prenatal environment is associated with the pace of cortical network development over the first three years of life. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.18.552639. [PMID: 37662189 PMCID: PMC10473645 DOI: 10.1101/2023.08.18.552639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Environmental influences on brain structure and function during early development have been well-characterized. In pre-registered analyses, we test the theory that socioeconomic status (SES) is associated with differences in trajectories of intrinsic brain network development from birth to three years (n = 261). Prenatal SES is associated with developmental increases in cortical network segregation, with neonates and toddlers from lower-SES backgrounds showing a steeper increase in cortical network segregation with age, consistent with accelerated network development. Associations between SES and cortical network segregation occur at the local scale and conform to a sensorimotor-association hierarchy of cortical organization. SES-associated differences in cortical network segregation are associated with language abilities at two years, such that lower segregation is associated with improved language abilities. These results yield key insight into the timing and directionality of associations between the early environment and trajectories of cortical development.
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Affiliation(s)
- Ursula A. Tooley
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110
| | - Aidan Latham
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110
| | - Jeanette K. Kenley
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110
| | | | - Tara Smyser
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110
| | - Barbara B. Warner
- Department of Pediatrics, Washington University in St. Louis, St. Louis, MO 63110
| | - Joshua S. Shimony
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110
| | - Jeffrey J. Neil
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110
| | - Joan L. Luby
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110
| | - Deanna M. Barch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO 63110
| | - Cynthia E. Rogers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110
- Department of Pediatrics, Washington University in St. Louis, St. Louis, MO 63110
| | - Chris D. Smyser
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110
- Department of Pediatrics, Washington University in St. Louis, St. Louis, MO 63110
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110
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Yi X, Wang X, Fu Y, Jiang F, Zhang Z, Wang J, Han Z, Xiao Q, Chen BT. Altered resting-state functional connectivity and its association with executive function in adolescents with borderline personality disorder. Eur Child Adolesc Psychiatry 2023:10.1007/s00787-023-02277-7. [PMID: 37555869 DOI: 10.1007/s00787-023-02277-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 08/03/2023] [Indexed: 08/10/2023]
Abstract
Adolescents with borderline personality disorder (BPD) may have impaired executive functions. There are few functional MRI (fMRI) studies in adolescents with BPD and the neuroimaging markers of this disorder are unknown. The aim of this study was to investigate the functional connectivity (FC) of BPD in adolescents, and to explore the relationship between FC changes and executive function in adolescents with BPD. 50 adolescents aged 12 to 17 years with BPD and 21 gender-and-age matched healthy controls (HC) were enrolled into the study. Brain MRI scan including a 3D-T1 weighted structural sequence and a resting-state fMRI was acquired. A seed-based FC analysis was performed. We used the Stroop color-word test (SCWT) and the trail making test (TMT) to evaluate the executive function of the participants. Correlative analysis of FC alterations with executive function and clinical symptoms was also performed. Compared to the HCs, adolescents with BPD showed increased FC in the limbic-cortical circuit, such as the FC between the left hippocampus and right parahippocampal gyrus, between the right middle occipital gyrus and the left middle temporal gyrus, and between the left medial superior frontal gyrus and the right inferior temporal gyrus. FC in the default mode network (DMN) was decreased between the left angular gyrus and the left precuneus but increased between the left angular gyrus and the right anterior cingulate cortex (voxel P < 0.001, cluster P < 0.05, FWE corrected). The BPD group demonstrated significantly lower cognitive testing scores than the HC group on the SCWT-A (P < 0.001), SCWT-B (P < 0.001), and SCWT-C (P = 0.034). The FC alterations between limbic system and cortical regions were associated with SCWT and TMT (P < 0.05). FC alterations were noted in both limbic-cortical circuit and DMN in adolescents with BPD, which were associated with impaired executive function. This study implicated the FC alterations as the neural correlates of executive functioning in adolescents with BPD.
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Affiliation(s)
- Xiaoping Yi
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Changsha, 410008, Hunan, People's Republic of China
- National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha, 410008, Hunan, People's Republic of China
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China
- Hunan Engineering Research Center of Skin Health and Disease, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China
| | - Xueying Wang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Changsha, 410008, Hunan, People's Republic of China
- National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha, 410008, Hunan, People's Republic of China
| | - Yan Fu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Changsha, 410008, Hunan, People's Republic of China
- National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha, 410008, Hunan, People's Republic of China
| | - Furong Jiang
- Mental Health Center of Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, 410008, Hunan, People's Republic of China
| | - Zhejia Zhang
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China
| | - Jing Wang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China
| | - Zaide Han
- National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha, 410008, Hunan, People's Republic of China
| | - Qian Xiao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China.
- Mental Health Center of Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, 410008, Hunan, People's Republic of China.
| | - Bihong T Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, USA
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Yadav Y, Elumalai P, Williams N, Jost J, Samal A. Discrete Ricci curvatures capture age-related changes in human brain functional connectivity networks. Front Aging Neurosci 2023; 15:1120846. [PMID: 37293668 PMCID: PMC10244515 DOI: 10.3389/fnagi.2023.1120846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 05/02/2023] [Indexed: 06/10/2023] Open
Abstract
Introduction Geometry-inspired notions of discrete Ricci curvature have been successfully used as markers of disrupted brain connectivity in neuropsychiatric disorders, but their ability to characterize age-related changes in functional connectivity is unexplored. Methods We apply Forman-Ricci curvature and Ollivier-Ricci curvature to compare functional connectivity networks of healthy young and older subjects from the Max Planck Institute Leipzig Study for Mind-Body-Emotion Interactions (MPI-LEMON) dataset (N = 225). Results We found that both Forman-Ricci curvature and Ollivier-Ricci curvature can capture whole-brain and region-level age-related differences in functional connectivity. Meta-analysis decoding demonstrated that those brain regions with age-related curvature differences were associated with cognitive domains known to manifest age-related changes-movement, affective processing, and somatosensory processing. Moreover, the curvature values of some brain regions showing age-related differences exhibited correlations with behavioral scores of affective processing. Finally, we found an overlap between brain regions showing age-related curvature differences and those brain regions whose non-invasive stimulation resulted in improved movement performance in older adults. Discussion Our results suggest that both Forman-Ricci curvature and Ollivier-Ricci curvature correctly identify brain regions that are known to be functionally or clinically relevant. Our results add to a growing body of evidence demonstrating the sensitivity of discrete Ricci curvature measures to changes in the organization of functional connectivity networks, both in health and disease.
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Affiliation(s)
- Yasharth Yadav
- The Institute of Mathematical Sciences (IMSc), Chennai, India
- Indian Institute of Science Education and Research (IISER), Pune, India
| | | | - Nitin Williams
- Department of Computer Science, Helsinki Institute of Information Technology, Aalto University, Espoo, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Jürgen Jost
- Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
- The Santa Fe Institute, Santa Fe, NM, United States
| | - Areejit Samal
- The Institute of Mathematical Sciences (IMSc), Chennai, India
- Homi Bhabha National Institute (HBNI), Mumbai, India
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11
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D'Aurizio G, Di Stefano R, Socci V, Rossi A, Barlattani T, Pacitti F, Rossi R. The role of emotional instability in borderline personality disorder: a systematic review. Ann Gen Psychiatry 2023; 22:9. [PMID: 36918920 PMCID: PMC10011773 DOI: 10.1186/s12991-023-00439-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 02/25/2023] [Indexed: 03/15/2023] Open
Abstract
BACKGROUND The emotional regulation process plays a pivotal role in daily-life functioning, modulating goal-directed and adaptive behavior. Conversely, altering this cognitive function can disrupt self-regulation and bring emotional dysregulation. Emotional instability could represent a core characteristic of BPD, also modulating the BPD symptom's onset. This systematic review aims to summarize the existing literature reporting the role of emotional instability in BPD to better define the role of the impairment of the emotional processes in the onset of the cognitive and behavioral symptoms of this complex mental disorder. METHODS MEDLINE, Scopus and Web of Science were independently searched for relevant studies. Eligible studies had to be identifiable through database searching, published and accessible. This systematic review was conducted according to PRISMA guidelines. The search period was from 2012 to 14 September 2022. RESULTS A pool of 120 studies was identified, out of which 11 met the selection criteria and were included. Overall, the studies confirm a relationship between emotional instability and borderline personality disorder. CONCLUSIONS The evidences retrieved seem to point out the role of the emotional impairment not only in worsening of the disorder, but could also be one of the risk factors for its onset.
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Affiliation(s)
- Giulia D'Aurizio
- Chair of Psychiatry, Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, Via Vetoio (Coppito 2), 67100, L'Aquila, Italy
| | - Ramona Di Stefano
- Chair of Psychiatry, Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, Via Vetoio (Coppito 2), 67100, L'Aquila, Italy.
| | - Valentina Socci
- Chair of Psychiatry, Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, Via Vetoio (Coppito 2), 67100, L'Aquila, Italy
| | - Alessandro Rossi
- Chair of Psychiatry, Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, Via Vetoio (Coppito 2), 67100, L'Aquila, Italy
| | - Tommaso Barlattani
- Chair of Psychiatry, Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, Via Vetoio (Coppito 2), 67100, L'Aquila, Italy
| | - Francesca Pacitti
- Chair of Psychiatry, Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, Via Vetoio (Coppito 2), 67100, L'Aquila, Italy
| | - Rodolfo Rossi
- Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
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12
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Morand-Beaulieu S, Wu J, Mayes LC, Grantz H, Leckman JF, Crowley MJ, Sukhodolsky DG. Increased Alpha-Band Connectivity During Tic Suppression in Children With Tourette Syndrome Revealed by Source Electroencephalography Analyses. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:241-250. [PMID: 33991741 PMCID: PMC8589865 DOI: 10.1016/j.bpsc.2021.05.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 04/08/2021] [Accepted: 05/04/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Tourette syndrome (TS) is a neurodevelopmental disorder involving chronic motor and phonic tics. Most individuals with TS can suppress their tics for at least a short period of time. Yet, the brain correlates of tic suppression are still poorly understood. METHODS In the current study, high-density electroencephalography was recorded during a resting-state and a tic suppression session in 72 children with TS. Functional connectivity between cortical regions was assessed in the alpha band (8-13 Hz) using an electroencephalography source connectivity method. Graph theory and network-based statistics were used to assess the global network topology and to identify brain regions showing increased connectivity during tic suppression. RESULTS Graph theoretical analyses revealed distinctive global network topology during tic suppression, relative to rest. Using network-based statistics, we found a subnetwork of increased connectivity during tic suppression (p < .001). That subnetwork encompassed many cortical areas, including the right superior frontal gyrus and the left precuneus, which are involved in the default mode network. We also found a condition-by-age interaction, suggesting age-mediated increases in connectivity during tic suppression. CONCLUSIONS These results suggest that children with TS suppress their tics through a brain circuit involving distributed cortical regions, many of which are part of the default mode network. Brain connectivity during tic suppression also increases as youths with TS mature. These results highlight a mechanism by which children with TS may control their tics, which could be relevant for future treatment studies.
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Affiliation(s)
| | - Jia Wu
- Child Study Center, Yale University School of Medicine, New Haven, Connecticut
| | - Linda C Mayes
- Child Study Center, Yale University School of Medicine, New Haven, Connecticut
| | - Heidi Grantz
- Child Study Center, Yale University School of Medicine, New Haven, Connecticut
| | - James F Leckman
- Child Study Center, Yale University School of Medicine, New Haven, Connecticut
| | - Michael J Crowley
- Child Study Center, Yale University School of Medicine, New Haven, Connecticut
| | - Denis G Sukhodolsky
- Child Study Center, Yale University School of Medicine, New Haven, Connecticut.
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13
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Zhao X, Riccardi N, den Ouden DB, Desai RH, Fridriksson J, Wang Y. Network-based statistics distinguish anomic and Broca aphasia. ARXIV 2023:arXiv:2302.03250v2. [PMID: 36798458 PMCID: PMC9934734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
Aphasia is a speech-language impairment commonly caused by damage to the left hemisphere. Due to the complexity of speech-language processing, the neural mechanisms that underpin various symptoms between different types of aphasia are still not fully understood. We used the network-based statistic method to identify distinct subnetwork(s) of connections differentiating the resting-state functional networks of the anomic and Broca groups. We identified one such subnetwork that mainly involved the brain regions in the premotor, primary motor, primary auditory, and primary sensory cortices in both hemispheres. The majority of connections in the subnetwork were weaker in the Broca group than the anomic group. The network properties of the subnetwork were examined through complex network measures, which indicated that the regions in the superior temporal gyrus and auditory cortex bilaterally exhibit intensive interaction, and primary motor, premotor and primary sensory cortices in the left hemisphere play an important role in information flow and overall communication efficiency. These findings underlied articulatory difficulties and reduced repetition performance in Broca aphasia, which are rarely observed in anomic aphasia. This research provides novel findings into the resting-state brain network differences between groups of individuals with anomic and Broca aphasia. We identified a subnetwork of, rather than isolated, connections that statistically differentiate the resting-state brain networks of the two groups, in comparison with standard lesion symptom mapping results that yield isolated connections.
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Affiliation(s)
- Xingpei Zhao
- Department of Epidemiology and Biostatistics, University of South Carolina
| | | | - Dirk-Bart den Ouden
- Department of Communication Sciences and Disorders, University of South Carolina
| | | | - Julius Fridriksson
- Department of Communication Sciences and Disorders, University of South Carolina
| | - Yuan Wang
- Department of Epidemiology and Biostatistics, University of South Carolina
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14
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Tooley UA, Park AT, Leonard JA, Boroshok AL, McDermott CL, Tisdall MD, Bassett DS, Mackey AP. The Age of Reason: Functional Brain Network Development during Childhood. J Neurosci 2022; 42:8237-8251. [PMID: 36192151 PMCID: PMC9653278 DOI: 10.1523/jneurosci.0511-22.2022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 07/25/2022] [Accepted: 09/03/2022] [Indexed: 01/27/2023] Open
Abstract
Human childhood is characterized by dramatic changes in the mind and brain. However, little is known about the large-scale intrinsic cortical network changes that occur during childhood because of methodological challenges in scanning young children. Here, we overcome this barrier by using sophisticated acquisition and analysis tools to investigate functional network development in children between the ages of 4 and 10 years ([Formula: see text]; 50 female, 42 male). At multiple spatial scales, age is positively associated with brain network segregation. At the system level, age was associated with segregation of systems involved in attention from those involved in abstract cognition, and with integration among attentional and perceptual systems. Associations between age and functional connectivity are most pronounced in visual and medial prefrontal cortex, the two ends of a gradient from perceptual, externally oriented cortex to abstract, internally oriented cortex. These findings suggest that both ends of the sensory-association gradient may develop early, in contrast to the classical theories that cortical maturation proceeds from back to front, with sensory areas developing first and association areas developing last. More mature patterns of brain network architecture, controlling for age, were associated with better visuospatial reasoning abilities. Our results suggest that as cortical architecture becomes more specialized, children become more able to reason about the world and their place in it.SIGNIFICANCE STATEMENT Anthropologists have called the transition from early to middle childhood the "age of reason", when children across cultures become more independent. We employ cutting-edge neuroimaging acquisition and analysis approaches to investigate associations between age and functional brain architecture in childhood. Age was positively associated with segregation between cortical systems that process the external world and those that process abstract phenomena like the past, future, and minds of others. Surprisingly, we observed pronounced development at both ends of the sensory-association gradient, challenging the theory that sensory areas develop first and association areas develop last. Our results open new directions for research into how brains reorganize to support rapid gains in cognitive and socioemotional skills as children reach the age of reason.
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Affiliation(s)
- Ursula A Tooley
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Anne T Park
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Julia A Leonard
- Department of Psychology, Yale University, New Haven, Connecticut 06520
| | - Austin L Boroshok
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Cassidy L McDermott
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Matthew D Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Dani S Bassett
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Department of Physics and Astronomy, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Santa Fe Institute, Santa Fe, New Mexico 87501
| | - Allyson P Mackey
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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15
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Avvaru S, Parhi KK. Betweenness Centrality in Resting-State Functional Networks Distinguishes Parkinson's Disease. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:4785-4788. [PMID: 36086073 DOI: 10.1109/embc48229.2022.9870988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The goal of this paper is to use graph theory network measures derived from non-invasive electroencephalography (EEG) to develop neural decoders that can differentiate Parkinson's disease (PD) patients from healthy controls (HC). EEG signals from 27 patients and 27 demographically matched controls from New Mexico were analyzed by estimating their functional networks. Data recorded from the patients during ON and OFF levodopa sessions were included in the analysis for comparison. We used betweenness centrality of estimated functional networks to classify the HC and PD groups. The classifiers were evaluated using leave-one-out cross-validation. We observed that the PD patients (on and off medication) could be distinguished from healthy controls with 89% accuracy - approximately 4% higher than the state-of-the-art on the same dataset. This work shows that brain network analysis using extracranial resting-state EEG can discover patterns of interactions indicative of PD. This approach can also be extended to other neurological disorders.
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16
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Local inefficiency of the default mode network in young men with narcissistic personality disorder. Neurosci Lett 2022; 784:136720. [DOI: 10.1016/j.neulet.2022.136720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 05/17/2022] [Accepted: 06/06/2022] [Indexed: 11/23/2022]
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17
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Fusina F, Marino M, Spironelli C, Angrilli A. Ventral Attention Network Correlates With High Traits of Emotion Dysregulation in Community Women - A Resting-State EEG Study. Front Hum Neurosci 2022; 16:895034. [PMID: 35721362 PMCID: PMC9205637 DOI: 10.3389/fnhum.2022.895034] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 04/27/2022] [Indexed: 11/26/2022] Open
Abstract
In recent years, many studies have focused on resting-state brain activity, and especially on functional connectivity (FC), an approach that typically describes the statistical interdependence of activity in distant brain regions through specific networks. Our aim was to study the neurophysiological correlates of emotion dysregulation. Therefore, we expected that both the Default Mode Network (DMN), and the Ventral Attention Network (VAN) would have been involved. Indeed, the latter plays a role in the automatic orienting of attention towards biologically salient stimuli and includes key regions for emotion control and modulation. Starting from a community sample of 422 female students, we selected 25 women with high traits of emotion dysregulation (HD group) and 25 with low traits (LD group). They underwent a 64-channel EEG recording during a five-minute resting state with eyes open. Seed-based FC was computed on the EEG Alpha band (8-13 Hz) as a control band, and on EEG Gamma power (30-50 Hz) as the relevant measure. The power within each network and inter-network connectivity (Inter-NC) was also calculated. Analysis of the EEG Gamma band revealed, in the HD group, higher levels of Inter-NC between the VAN and all other resting-state networks as compared with the LD group, while no differences emerged in the Alpha band. Concerning correlations, Alpha power in the VAN was negatively correlated in the HD group with affective lability (ALS-18 questionnaire), both for total score (ρ = -0.52, p FDR < 0.01) and the Depression/Elation subscale) ρ = -0.45, p FDR < 0.05). Consistent with this, in the Gamma band, a positive correlation was found between VAN spectral power and the Depression/Elation subscale of ALS-18, again in the HD group only (ρ = 0.47, p FDR < 0.05). In conclusion, both resting state FC and network power in the VAN were found to be related to high emotion dysregulation, even in our non-clinical sample with high traits. Emotion dysregulation was characterized, in the EEG gamma band, by a VAN strongly connected to all other networks, a result that points, in women prone to emotion dysregulation, to a strong automatic orienting of attention towards their internal state, bodily sensations, and emotionally intense related thoughts.
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Affiliation(s)
- Francesca Fusina
- Padova Neuroscience Center, University of Padua, Padua, Italy
- Department of General Psychology, University of Padua, Padua, Italy
| | - Marco Marino
- Department of Movement Sciences, Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium
- IRCCS San Camillo Hospital, Venice, Italy
| | - Chiara Spironelli
- Padova Neuroscience Center, University of Padua, Padua, Italy
- Department of General Psychology, University of Padua, Padua, Italy
| | - Alessandro Angrilli
- Padova Neuroscience Center, University of Padua, Padua, Italy
- Department of General Psychology, University of Padua, Padua, Italy
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18
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Elumalai P, Yadav Y, Williams N, Saucan E, Jost J, Samal A. Graph Ricci curvatures reveal atypical functional connectivity in autism spectrum disorder. Sci Rep 2022; 12:8295. [PMID: 35585156 PMCID: PMC9117309 DOI: 10.1038/s41598-022-12171-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 05/04/2022] [Indexed: 11/20/2022] Open
Abstract
While standard graph-theoretic measures have been widely used to characterize atypical resting-state functional connectivity in autism spectrum disorder (ASD), geometry-inspired network measures have not been applied. In this study, we apply Forman–Ricci and Ollivier–Ricci curvatures to compare networks of ASD and typically developing individuals (N = 1112) from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. We find brain-wide and region-specific ASD-related differences for both Forman–Ricci and Ollivier–Ricci curvatures, with region-specific differences concentrated in Default Mode, Somatomotor and Ventral Attention networks for Forman–Ricci curvature. We use meta-analysis decoding to demonstrate that brain regions with curvature differences are associated to those cognitive domains known to be impaired in ASD. Further, we show that brain regions with curvature differences overlap with those brain regions whose non-invasive stimulation improves ASD-related symptoms. These results suggest the utility of graph Ricci curvatures in characterizing atypical connectivity of clinically relevant regions in ASD and other neurodevelopmental disorders.
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Affiliation(s)
| | - Yasharth Yadav
- The Institute of Mathematical Sciences (IMSc), Chennai, India.,Indian Institute of Science Education and Research (IISER), Pune, India
| | - Nitin Williams
- Department of Computer Science, Helsinki Institute of Information Technology, Aalto University, Espoo, Finland. .,Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland.
| | - Emil Saucan
- Department of Applied Mathematics, ORT Braude College, Karmiel, Israel
| | - Jürgen Jost
- Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany.,The Santa Fe Institute, Santa Fe, NM, USA
| | - Areejit Samal
- The Institute of Mathematical Sciences (IMSc), Chennai, India. .,Homi Bhabha National Institute (HBNI), Mumbai, India.
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Grecucci A, Lapomarda G, Messina I, Monachesi B, Sorella S, Siugzdaite R. Structural Features Related to Affective Instability Correctly Classify Patients With Borderline Personality Disorder. A Supervised Machine Learning Approach. Front Psychiatry 2022; 13:804440. [PMID: 35295769 PMCID: PMC8918568 DOI: 10.3389/fpsyt.2022.804440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 01/03/2022] [Indexed: 11/13/2022] Open
Abstract
Previous morphometric studies of Borderline Personality Disorder (BPD) reported inconsistent alterations in cortical and subcortical areas. However, these studies have investigated the brain at the voxel level using mass univariate methods or region of interest approaches, which are subject to several artifacts and do not enable detection of more complex patterns of structural alterations that may separate BPD from other clinical populations and healthy controls (HC). Multiple Kernel Learning (MKL) is a whole-brain multivariate supervised machine learning method able to classify individuals and predict an objective diagnosis based on structural features. As such, this method can help identifying objective biomarkers related to BPD pathophysiology and predict new cases. To this aim, we applied MKL to structural images of patients with BPD and matched HCs. Moreover, to ensure that results are specific for BPD and not for general psychological disorders, we also applied MKL to BPD against a group of patients with bipolar disorder, for their similarities in affective instability. Results showed that a circuit, including basal ganglia, amygdala, and portions of the temporal lobes and of the orbitofrontal cortex, correctly classified BPD against HC (80%). Notably, this circuit positively correlates with the affective sector of the Zanarini questionnaire, thus indicating an involvement of this circuit with affective disturbances. Moreover, by contrasting BPD with BD, the spurious regions were excluded, and a specific circuit for BPD was outlined. These results support that BPD is characterized by anomalies in a cortico-subcortical circuit related to affective instability and that this circuit discriminates BPD from controls and from other clinical populations.
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Affiliation(s)
- Alessandro Grecucci
- Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Rovereto, Italy
- Center for Medical Sciences - CISMed, University of Trento, Trento, Italy
| | - Gaia Lapomarda
- Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Rovereto, Italy
- Department of Psychology, Science Division, New York University of Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Irene Messina
- Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Rovereto, Italy
- Universitas Mercatorum, Rome, Italy
| | - Bianca Monachesi
- Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Rovereto, Italy
| | - Sara Sorella
- Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Rovereto, Italy
| | - Roma Siugzdaite
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
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20
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Dadomo H, Salvato G, Lapomarda G, Ciftci Z, Messina I, Grecucci A. Structural Features Predict Sexual Trauma and Interpersonal Problems in Borderline Personality Disorder but Not in Controls: A Multi-Voxel Pattern Analysis. Front Hum Neurosci 2022; 16:773593. [PMID: 35280205 PMCID: PMC8904389 DOI: 10.3389/fnhum.2022.773593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 01/17/2022] [Indexed: 12/01/2022] Open
Abstract
Child trauma plays an important role in the etiology of Bordeline Personality Disorder (BPD). Of all traumas, sexual trauma is the most common, severe and most associated with receiving a BPD diagnosis when adult. Etiologic models posit sexual abuse as a prognostic factor in BPD. Here we apply machine learning using Multiple Kernel Regression to the Magnetic Resonance Structural Images of 20 BPD and 13 healthy control (HC) to see whether their brain predicts five sources of traumas: sex abuse, emotion neglect, emotional abuse, physical neglect, physical abuse (Child Trauma Questionnaire; CTQ). We also applied the same analysis to predict symptom severity in five domains: affective, cognitive, impulsivity, interpersonal (Zanarini Rating Scale for Borderline Personality Disorder; Zan-BPD) for BPD patients only. Results indicate that CTQ sexual trauma is predicted by a set of areas including the amygdala, the Heschl area, the Caudate, the Putamen, and portions of the Cerebellum in BPD patients only. Importantly, interpersonal problems only in BPD patients were predicted by a set of areas including temporal lobe and cerebellar regions. Notably, sexual trauma and interpersonal problems were not predicted by structural features in matched healthy controls. This finding may help elucidate the brain circuit affected by traumatic experiences and connected with interpersonal problems BPD suffer from.
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Affiliation(s)
- Harold Dadomo
- Unit of Neuroscience, Department of Medicine and Surgery, University of Parma, Parma, Italy
- *Correspondence: Harold Dadomo,
| | - Gerardo Salvato
- Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Milan Center for Neuroscience, School of Medicine and Surgery, University of Milano Bicocca, Milan, Italy
- Cognitive Neuropsychology Centre, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Gaia Lapomarda
- Clinical and Affective Neuroscience Lab – Cli.A.N. Lab, Department of Psychology and Cognitive Science, University of Trento, Rovereto, Italy
| | - Zafer Ciftci
- Clinical and Affective Neuroscience Lab – Cli.A.N. Lab, Department of Psychology and Cognitive Science, University of Trento, Rovereto, Italy
| | | | - Alessandro Grecucci
- Clinical and Affective Neuroscience Lab – Cli.A.N. Lab, Department of Psychology and Cognitive Science, University of Trento, Rovereto, Italy
- Centre for Medical Sciences, CISMed, University of Trento, Trento, Italy
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21
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Multi-Region Local Field Potential Signatures in Response to the Formalin-induced Inflammatory Stimulus in Male Rats. Brain Res 2022; 1778:147779. [PMID: 35007546 DOI: 10.1016/j.brainres.2022.147779] [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: 12/09/2021] [Revised: 12/31/2021] [Accepted: 01/03/2022] [Indexed: 11/22/2022]
Abstract
Pain can be ignited by noxious chemical (e.g., acid), mechanical (e.g., pressure), and thermal (e.g., heat) stimuli and generated by the activation of sensory neurons and their axonal terminals called nociceptors in the periphery. Nociceptive information transmitted from the periphery is projected to the central nervous system (thalamus, somatosensory cortex, insular, anterior cingulate cortex, amygdala, periaqueductal grey, prefrontal cortex, etc.) to generate a unified experience of pain. Local field potential (LFP) recording is one of the neurophysiological tools to investigate the combined neuronal activity, ranging from several hundred micrometers to a few millimeters (radius), located around the embedded electrode. The advantage of recording LFP is that it provides stable simultaneous activities in various brain regions in response to external stimuli. In this study, differential LFP activities from the contralateral anterior cingulate cortex (ACC), ventral tegmental area (VTA), and bilateral amygdala in response to peripheral noxious formalin injection were recorded in anesthetized male rats. The results indicated increased power of delta, theta, alpha, beta, and gamma bands in the ACC and amygdala but no change of gamma-band in the right amygdala. Within the VTA, intensities of the delta, theta, and beta bands were only enhanced significantly after formalin injection. It was found that the connectivity (i.t. the coherence) among these brain regions reduced significantly under the formalin-induced nociception, which suggests a significant interruption within the brain. With further study, it will sort out the key combination of structures that will serve as the signature for pain state.
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Avvaru S, Provenza NR, Widge AS, Parhi KK. Decoding Human Cognitive Control Using Functional Connectivity of Local Field Potentials. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:451-454. [PMID: 34891330 DOI: 10.1109/embc46164.2021.9630706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Many patients with mental illnesses characterized by impaired cognitive control have no relief from gold-standard clinical treatments resulting in a pressing need for new alternatives. This paper develops a neural decoder to detect task engagement in ten human subjects during a conflict-based behavioral task known as the multi-source interference task (MSIT). Task engagement is of particular interest here because closed-loop brain stimulation during those states can augment decision-making. The functional connectivity patterns of the electrodes are extracted. A principal component analysis of these patterns is carried out and the ranked principal components are used as inputs to train subject-specific linear support vector machine classifiers. In this paper, we show that task engagement can be differentiated from background brain activity with a median accuracy of 89.7%. This was accomplished by constructing distributed functional networks from local field potentials recording during the task performance. A further challenge is that goal-directed efforts take place over higher temporal resolution. Task engagement must thus be detected at a similar rate for proactive intervention. We show that our algorithms can detect task engagement from neural recordings in less than 2 seconds; this can be further improved using an application-specific device.
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23
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Sobczak AM, Bohaterewicz B, Fafrowicz M, Domagalik A, Beldzik E, Oginska H, Golonka N, Rekas M, Bronicki D, Romanowska-Dixon B, Bolsega-Pacud J, Karwowski W, Farahani FV, Marek T. The Influence of Intraocular Lens Implantation and Alterations in Blue Light Transmittance Level on the Brain Functional Network Architecture Reorganization in Cataract Patients. Brain Sci 2021; 11:brainsci11111400. [PMID: 34827400 PMCID: PMC8615544 DOI: 10.3390/brainsci11111400] [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: 07/21/2021] [Revised: 10/16/2021] [Accepted: 10/19/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Cataract is one of the most common age-related vision deteriorations, leading to opacification of the lens and therefore visual impairment as well as blindness. Both cataract extraction and the implantation of blue light filtering lens are believed to improve not only vision but also overall functioning. METHODS Thirty-four cataract patients were subject to resting-state functional magnetic resonance imaging before and after cataract extraction and intraocular lens implantation (IOL). Global and local graph metrics were calculated in order to investigate the reorganization of functional network architecture associated with alterations in blue light transmittance. Psychomotor vigilance task (PVT) was conducted. RESULTS Graph theory-based analysis revealed decreased eigenvector centrality after the cataract extraction and IOL replacement in inferior occipital gyrus, superior parietal gyrus and many cerebellum regions as well as increased clustering coefficient in superior and inferior parietal gyrus, middle temporal gyrus and various cerebellum regions. PVT results revealed significant change between experimental sessions as patients responded faster after IOL replacement. Moreover, a few regions were correlated with the difference in blue light transmittance and the time reaction in PVT. CONCLUSION Current study revealed substantial functional network architecture reorganization associated with cataract extraction and alteration in blue light transmittance.
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Affiliation(s)
- Anna Maria Sobczak
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, 30-348 Kraków, Poland; (M.F.); (E.B.); (H.O.); (N.G.); (T.M.)
- Malopolska Centre of Biotechnology, Jagiellonian University, 30-387 Kraków, Poland;
- Correspondence: (A.M.S.); (B.B.)
| | - Bartosz Bohaterewicz
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, 30-348 Kraków, Poland; (M.F.); (E.B.); (H.O.); (N.G.); (T.M.)
- Department of Psychology of Individual Differences, Psychological Diagnosis, and Psychometrics, Institute of Psychology, University of Social Sciences and Humanities, 03-815 Warsaw, Poland
- Correspondence: (A.M.S.); (B.B.)
| | - Magdalena Fafrowicz
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, 30-348 Kraków, Poland; (M.F.); (E.B.); (H.O.); (N.G.); (T.M.)
- Malopolska Centre of Biotechnology, Jagiellonian University, 30-387 Kraków, Poland;
| | - Aleksandra Domagalik
- Malopolska Centre of Biotechnology, Jagiellonian University, 30-387 Kraków, Poland;
| | - Ewa Beldzik
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, 30-348 Kraków, Poland; (M.F.); (E.B.); (H.O.); (N.G.); (T.M.)
- Malopolska Centre of Biotechnology, Jagiellonian University, 30-387 Kraków, Poland;
| | - Halszka Oginska
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, 30-348 Kraków, Poland; (M.F.); (E.B.); (H.O.); (N.G.); (T.M.)
- Malopolska Centre of Biotechnology, Jagiellonian University, 30-387 Kraków, Poland;
| | - Natalia Golonka
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, 30-348 Kraków, Poland; (M.F.); (E.B.); (H.O.); (N.G.); (T.M.)
| | - Marek Rekas
- Ophthalmology Department, Military Institute of Medicine, 04-349 Warsaw, Poland; (M.R.); (D.B.)
| | - Dominik Bronicki
- Ophthalmology Department, Military Institute of Medicine, 04-349 Warsaw, Poland; (M.R.); (D.B.)
| | - Bożena Romanowska-Dixon
- Department of Ophthalmology and Ocular Oncology, Medical College, Jagiellonian University, 31-008 Kraków, Poland; (B.R.-D.); (J.B.-P.)
| | - Joanna Bolsega-Pacud
- Department of Ophthalmology and Ocular Oncology, Medical College, Jagiellonian University, 31-008 Kraków, Poland; (B.R.-D.); (J.B.-P.)
| | - Waldemar Karwowski
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering & Management Systems, University of Central Florida, Orlando, FL 32816, USA; (W.K.); (F.V.F.)
| | - Farzad V. Farahani
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering & Management Systems, University of Central Florida, Orlando, FL 32816, USA; (W.K.); (F.V.F.)
- Biostatistics Department, John Hopkins University, Baltimore, MD 21218, USA
| | - Tadeusz Marek
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, 30-348 Kraków, Poland; (M.F.); (E.B.); (H.O.); (N.G.); (T.M.)
- Malopolska Centre of Biotechnology, Jagiellonian University, 30-387 Kraków, Poland;
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Diffusion Tensor Imaging Changes Do Not Affect Long-Term Neurodevelopment following Early Erythropoietin among Extremely Preterm Infants in the Preterm Erythropoietin Neuroprotection Trial. Brain Sci 2021; 11:brainsci11101360. [PMID: 34679424 PMCID: PMC8533828 DOI: 10.3390/brainsci11101360] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 10/12/2021] [Accepted: 10/14/2021] [Indexed: 11/17/2022] Open
Abstract
We aimed to evaluate diffusion tensor imaging (DTI) in infants born extremely preterm, to determine the effect of erythropoietin (Epo) on DTI, and to correlate DTI with neurodevelopmental outcomes at 2 years of age for infants in the Preterm Erythropoietin Neuroprotection (PENUT) Trial. Infants who underwent MRI with DTI at 36 weeks postmenstrual age were included. Neurodevelopmental outcomes were evaluated by Bayley Scales of Infant and Toddler Development (BSID-III). Generalized linear models were used to assess the association between DTI parameters and treatment group, and then with neurodevelopmental outcomes. A total of 101 placebo- and 93 Epo-treated infants underwent MRI. DTI white matter mean diffusivity (MD) was lower in placebo- compared to Epo-treated infants in the cingulate and occipital regions, and occipital white matter fractional isotropy (FA) was lower in infants born at 24-25 weeks vs. 26-27 weeks. These values were not associated with lower BSID-III scores. Certain decreases in clustering coefficients tended to have lower BSID-III scores. Consistent with the PENUT Trial findings, there was no effect on long-term neurodevelopment in Epo-treated infants even in the presence of microstructural changes identified by DTI.
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25
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Diagnosis of obsessive-compulsive disorder via spatial similarity-aware learning and fused deep polynomial network. Med Image Anal 2021; 75:102244. [PMID: 34700244 DOI: 10.1016/j.media.2021.102244] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 08/22/2021] [Accepted: 09/14/2021] [Indexed: 11/22/2022]
Abstract
Obsessive-compulsive disorder (OCD) is a type of hereditary mental illness, which seriously affect the normal life of the patients. Sparse learning has been widely used in detecting brain diseases objectively by removing redundant information and retaining monitor valuable biological characteristics from the brain functional connectivity network (BFCN). However, most existing methods ignore the relationship between brain regions in each subject. To solve this problem, this paper proposes a spatial similarity-aware learning (SSL) model to build BFCNs. Specifically, we embrace the spatial relationship between adjacent or bilaterally symmetric brain regions via a smoothing regularization term in the model. We develop a novel fused deep polynomial network (FDPN) model to further learn the powerful information and attempt to solve the problem of curse of dimensionality using BFCN features. In the FDPN model, we stack a multi-layer deep polynomial network (DPN) and integrate the features from multiple output layers via the weighting mechanism. In this way, the FDPN method not only can identify the high-level informative features of BFCN but also can solve the problem of curse of dimensionality. A novel framework is proposed to detect OCD and unaffected first-degree relatives (UFDRs), which combines deep learning and traditional machine learning methods. We validate our algorithm in the resting-state functional magnetic resonance imaging (rs-fMRI) dataset collected by the local hospital and achieve promising performance.
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26
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Chu SH, Parhi KK, Westlund Schreiner M, Lenglet C, Mueller BA, Klimes-Dougan B, Cullen KR. Effect of SSRIs on Resting-State Functional Brain Networks in Adolescents with Major Depressive Disorder. J Clin Med 2021; 10:jcm10194322. [PMID: 34640340 PMCID: PMC8509847 DOI: 10.3390/jcm10194322] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 08/27/2021] [Accepted: 09/14/2021] [Indexed: 01/03/2023] Open
Abstract
Investigation of brain changes in functional connectivity and functional network topology from receiving 8-week selective serotonin reuptake inhibitor (SSRI) treatments is conducted in 12 unmedicated adolescents with major depressive disorder (MDD) by using wavelet-filtered resting-state functional magnetic resonance imaging (fMRI). Changes are observed in frontal-limbic, temporal, and default mode networks. In particular, topological analysis shows, at the global scale and in the 0.12–0.25 Hz band, that the normalized clustering coefficient and smallworldness of brain networks decreased after treatment. Regional changes in clustering coefficient and efficiency were observed in the bilateral caudal middle frontal gyrus, rostral middle frontal gyrus, superior temporal gyrus, left pars triangularis, putamen, and right superior frontal gyrus. Furthermore, changes of nodal centrality and changes of connectivity associated with these frontal and temporal regions confirm the global topological alternations. Moreover, frequency dependence is observed from FDR-controlled subnetworks for the limbic-cortical connectivity change. In the high-frequency band, the altered connections involve mostly frontal regions, while the altered connections in the low-frequency bands spread to parietal and temporal areas. Due to the limitation of small sample sizes and lack of placebo control, these preliminary findings require confirmation with future work using larger samples. Confirmation of biomarkers associated with treatment could suggest potential avenues for clinical applications such as tracking treatment response and neurobiologically informed treatment optimization.
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Affiliation(s)
- Shu-Hsien Chu
- Department of Electrical & Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA; (S.-H.C.); (K.K.P.); (C.L.)
| | - Keshab K. Parhi
- Department of Electrical & Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA; (S.-H.C.); (K.K.P.); (C.L.)
| | - Melinda Westlund Schreiner
- Department of Psychiatry, Huntsman Mental Health Institute, University of Utah, Salt Lake City, UT 84108, USA;
| | - Christophe Lenglet
- Department of Electrical & Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA; (S.-H.C.); (K.K.P.); (C.L.)
- Department of Radiology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Bryon A. Mueller
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55454, USA;
| | | | - Kathryn R. Cullen
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55454, USA;
- Correspondence:
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27
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Zarnowski O, Ziton S, Holmberg R, Musto S, Riegle S, Van Antwerp E, Santos-Nunez G. Functional MRI findings in personality disorders: A review. J Neuroimaging 2021; 31:1049-1066. [PMID: 34468063 DOI: 10.1111/jon.12924] [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: 06/27/2021] [Revised: 08/11/2021] [Accepted: 08/13/2021] [Indexed: 11/28/2022] Open
Abstract
Personality disorders (PDs) have a prevalence of approximately 10% in the United States, translating to over 30 million people affected in just one country. The true prevalence of these disorders may be even higher, as the paucity of objective diagnostic criteria could be leading to underdiagnosis. Because little is known about the underlying neuropathologies of these disorders, patients are diagnosed using subjective criteria and treated nonspecifically. To better understand the neural aberrancies responsible for these patients' symptoms, a review of functional MRI literature was performed. The findings reveal that each PD is characterized by a unique set of activation changes corresponding to individual structures or specific neural networks. While unique patterns of neural activity are distinguishable within each PD, aberrations of the limbic/paralimbic structures and default mode network are noted across several of them. In addition to identifying valuable activation patterns, this review reveals a void in research pertaining to paranoid, schizoid, histrionic, narcissistic, and dependent PDs. By delineating patterns in PD neuropathology, we can more effectively direct future research efforts toward enhancing objective diagnostic techniques and developing targeted treatment modalities. Furthermore, understanding why patients are manifesting certain symptoms can advance clinical awareness and improve patient outcomes.
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Affiliation(s)
- Oskar Zarnowski
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, Florida, USA
| | - Shirley Ziton
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, Florida, USA
| | - Rylan Holmberg
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, Florida, USA
| | - Sarafina Musto
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, Florida, USA
| | - Sean Riegle
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, Florida, USA
| | - Emily Van Antwerp
- West Virginia School of Osteopathic Medicine, Lewisburg, West Virginia, USA
| | - Gabriela Santos-Nunez
- University of Massachusetts Memorial Medical Center, Radiology Department, Worcester, Massachusetts, USA
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28
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Avvaru S, Peled N, Provenza NR, Widge AS, Parhi KK. Region-Level Functional and Effective Network Analysis of Human Brain During Cognitive Task Engagement. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1651-1660. [PMID: 34398758 PMCID: PMC8428572 DOI: 10.1109/tnsre.2021.3105432] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Mental disorders are a major source of disability, with few effective treatments. It has recently been argued that these diseases might be effectively treated by focusing on decision-making, and specifically remediating decision-making deficits that act as "ingredients" in these disorders. Prior work showed that direct electrical brain stimulation can enhance human cognitive control, and consequently decision-making. This raises a challenge of detecting cognitive control lapses directly from electrical brain activity. Here, we demonstrate approaches to overcome that challenge. We propose a novel method, referred to as maximal variance node merging (MVNM), that merges nodes within a brain region to construct informative inter-region brain networks. We employ this method to estimate functional (correlational) and effective (causal) networks using local field potentials (LFP) during a cognitive behavioral task. The effective networks computed using convergent cross mapping differentiate task engagement from background neural activity with 85% median classification accuracy. We also derive task engagement networks (TENs): networks that constitute the most discriminative inter-region connections. Subsequent graph analysis illustrates the crucial role of the dorsolateral prefrontal cortex (dlPFC) in task engagement, consistent with a widely accepted model for cognition. We also show that task engagement is linked to prefrontal cortex theta (4-8 Hz) oscillations. We, therefore, identify objective biomarkers associated with task engagement. These approaches may generalize to other cognitive functions, forming the basis of a network-based approach to detecting and rectifying decision deficits.
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29
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Huang L, Wu H, Yan H, Liang Y, Li Q, Shui J, Han Z, Tang S. Syphilis Testing as a Proxy Marker for a Subgroup of Men Who Have Sex With Men With a Central Role in HIV-1 Transmission in Guangzhou, China. Front Med (Lausanne) 2021; 8:662689. [PMID: 34307399 PMCID: PMC8293274 DOI: 10.3389/fmed.2021.662689] [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: 02/01/2021] [Accepted: 05/26/2021] [Indexed: 12/09/2022] Open
Abstract
Objectives: The objectives of this study were to distinguish the role of men who have sex with men (MSM) with or without syphilis testing in HIV-1 transmission and to provide molecular evidence of syphilis testing as a proxy marker for identifying the subgroup of MSM. Methods: HIV-1 transmission clusters were constructed by HIV-TRACE and Cluster Picker using HIV-1 pol sequences from 729 newly diagnosed HIV-infected MSM from 2008 to 2012 in Guangzhou, China. The role of MSM in HIV-1 transmission networks was determined by a node influence measurement and centrality analysis. The association between syphilis testing and factors related to HIV-1 transmission and antiretroviral treatment (ART) were analyzed by the Cox regression model. Results: Among HIV-infected MSM, 56.7% did not test for syphilis at the time of HIV-1 diagnosis. MSM without syphilis testing was a specific subgroup of MSM with a larger closeness centrality and clustering coefficient than the recipients of syphilis testing (P < 0.001), indicating their central position in the HIV-1 transmission networks. The median degree and radiality within HIV-1 transmission networks as well as the median K-shell scores were also greater for MSM without syphilis testing (P < 0.001), suggesting their relatively greater contribution in transmitting HIV-1 than the receipts of syphilis testing. MSM with syphilis testing usually did not disclose their occupation or were more likely to be unemployed or to take non-skilled jobs, to have a history of sexually transmitted infections (STIs), and to be AIDS patients when diagnosed with HIV-1 infection (P < 0.05). Multivariable Cox regression analysis indicated that syphilis testing per se did not promote the engagement of ART (P = 0.233) or affect the speed of CD4+ T cell count recovery after treatment (P = 0.256). Conclusions: Our study identifies syphilis testing as a proxy marker of a specific subgroup of HIV-infected MSM who refuse syphilis testing during HIV-1 diagnosis with an important role in HIV-1 transmission. Specific prevention and intervention targeting MSM without syphilis testing during HIV-1 care are urgently needed.
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Affiliation(s)
- Liping Huang
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Hao Wu
- Department of AIDS Control and Prevention, Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Huanchang Yan
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Yuanhao Liang
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Qingmei Li
- Department of AIDS Control and Prevention, Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Jingwei Shui
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Zhigang Han
- Department of AIDS Control and Prevention, Guangzhou Center for Disease Control and Prevention, Guangzhou, China.,Institute of Public Health, Guangzhou Medical University, Guangzhou, China
| | - Shixing Tang
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China.,Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, China
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30
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Sen B, Cullen KR, Parhi KK. Classification of Adolescent Major Depressive Disorder Via Static and Dynamic Connectivity. IEEE J Biomed Health Inform 2021; 25:2604-2614. [PMID: 33296316 DOI: 10.1109/jbhi.2020.3043427] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This paper introduces an approach for classifying adolescents suffering from MDD using resting-state fMRI. Accurate diagnosis of MDD involves interviews with adolescent patients and their parents, symptom rating scales based on Diagnostic and Statistical Manual of Mental Disorders (DSM), behavioral observation as well as the experience of a clinician. Discovering predictive biomarkers for diagnosing MDD patients using functional magnetic resonance imaging (fMRI) scans can assist the clinicians in their diagnostic assessments. This paper investigates various static and dynamic connectivity measures extracted from resting-state fMRI for assisting with MDD diagnosis. First, absolute Pearson correlation matrices from 85 brain regions are computed and they are used to calculate static features for predicting MDD. A predictive sub-network extracted using sub-graph entropy classifies adolescent MDD vs. typical healthy controls with high accuracy, sensitivity and specificity. Next, approaches utilizing dynamic connectivity are employed to extract tensor based, independent component based and principal component based subject specific attributes. Finally, features from static and dynamic approaches are combined to create a feature vector for classification. A leave-one-out cross-validation method is used for the final predictor performance. Out of 49 adolescents with MDD and 33 matched healthy controls, a support vector machine (SVM) classifier using a radial basis function (RBF) kernel using differential sub-graph entropy combined with dynamic connectivity features classifies MDD vs. healthy controls with an accuracy of 0.82 for leave-one-out cross-validation. This classifier has specificity and sensitivity of 0.79 and 0.84, respectively.
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31
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Tooley UA, Mackey AP, Ciric R, Ruparel K, Moore TM, Gur RC, Gur RE, Satterthwaite TD, Bassett DS. Associations between Neighborhood SES and Functional Brain Network Development. Cereb Cortex 2021; 30:1-19. [PMID: 31220218 PMCID: PMC7029704 DOI: 10.1093/cercor/bhz066] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Higher socioeconomic status (SES) in childhood is associated with stronger cognitive abilities, higher academic achievement, and lower incidence of mental illness later in development. While prior work has mapped the associations between neighborhood SES and brain structure, little is known about the relationship between SES and intrinsic neural dynamics. Here, we capitalize upon a large cross-sectional community-based sample (Philadelphia Neurodevelopmental Cohort, ages 8-22 years, n = 1012) to examine associations between age, SES, and functional brain network topology. We characterize this topology using a local measure of network segregation known as the clustering coefficient and find that it accounts for a greater degree of SES-associated variance than mesoscale segregation captured by modularity. High-SES youth displayed stronger positive associations between age and clustering than low-SES youth, and this effect was most pronounced for regions in the limbic, somatomotor, and ventral attention systems. The moderating effect of SES on positive associations between age and clustering was strongest for connections of intermediate length and was consistent with a stronger negative relationship between age and local connectivity in these regions in low-SES youth. Our findings suggest that, in late childhood and adolescence, neighborhood SES is associated with variation in the development of functional network structure in the human brain.
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Affiliation(s)
- Ursula A Tooley
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Allyson P Mackey
- Department of Psychology, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Rastko Ciric
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kosha Ruparel
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Tyler M Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Danielle S Bassett
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA.,Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA.,Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
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32
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Sheng X, Chen H, Shao P, Qin R, Zhao H, Xu Y, Bai F. Brain Structural Network Compensation Is Associated With Cognitive Impairment and Alzheimer's Disease Pathology. Front Neurosci 2021; 15:630278. [PMID: 33716654 PMCID: PMC7947929 DOI: 10.3389/fnins.2021.630278] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 01/26/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Structural network alterations in Alzheimer's disease (AD) are related to worse cognitive impairment. The aim of this study was to quantify the alterations in gray matter associated with impaired cognition and their pathological biomarkers in AD-spectrum patients. METHODS We extracted gray matter networks from 3D-T1 magnetic resonance imaging scans, and a graph theory analysis was used to explore alterations in the network metrics in 34 healthy controls, 70 mild cognitive impairment (MCI) patients, and 40 AD patients. Spearman correlation analysis was computed to investigate the relationships among network properties, neuropsychological performance, and cerebrospinal fluid pathological biomarkers (i.e., Aβ, t-tau, and p-tau) in these subjects. RESULTS AD-spectrum individuals demonstrated higher nodal properties and edge properties associated with impaired memory function, and lower amyloid-β or higher tau levels than the controls. Furthermore, these compensations at the brain regional level in AD-spectrum patients were mainly in the medial temporal lobe; however, the compensation at the whole-brain network level gradually extended from the frontal lobe to become widely distributed throughout the cortex with the progression of AD. CONCLUSION The findings provide insight into the alterations in the gray matter network related to impaired cognition and pathological biomarkers in the progression of AD. The possibility of compensation was detected in the structural networks in AD-spectrum patients; the compensatory patterns at regional and whole-brain levels were different and the clinical significance was highlighted.
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Affiliation(s)
- Xiaoning Sheng
- Department of Neurology, Affiliated Drum Tower Hospital of Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Haifeng Chen
- Department of Neurology, Affiliated Drum Tower Hospital of Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Pengfei Shao
- Department of Neurology, Affiliated Drum Tower Hospital of Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Ruomeng Qin
- Department of Neurology, Affiliated Drum Tower Hospital of Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Hui Zhao
- Department of Neurology, Affiliated Drum Tower Hospital of Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Yun Xu
- Department of Neurology, Affiliated Drum Tower Hospital of Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Feng Bai
- Department of Neurology, Affiliated Drum Tower Hospital of Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
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Martins D, Dipasquale O, Paloyelis Y. Oxytocin modulates local topography of human functional connectome in healthy men at rest. Commun Biol 2021; 4:68. [PMID: 33452496 PMCID: PMC7811009 DOI: 10.1038/s42003-020-01610-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 12/16/2020] [Indexed: 01/08/2023] Open
Abstract
Oxytocin has recently received remarkable attention for its role as a modulator of human behaviour. Here, we aimed to expand our knowledge of the neural circuits engaged by oxytocin by investigating the effects of intranasal and intravenous oxytocin on the functional connectome at rest in 16 healthy men. Oxytocin modulates the functional connectome within discrete neural systems, but does not affect the global capacity for information transfer. These local effects encompass key hubs of the oxytocin system (e.g. amygdala) but also regions overlooked in previous hypothesis-driven research (i.e. the visual circuits, temporal lobe and cerebellum). Increases in levels of oxytocin in systemic circulation induce broad effects on the functional connectome, yet we provide indirect evidence supporting the involvement of nose-to-brain pathways in at least some of the observed changes after intranasal oxytocin. Together, our results suggest that oxytocin effects on human behaviour entail modulation of multiple levels of brain processing distributed across different systems.
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Affiliation(s)
- Daniel Martins
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London, SE5 8AF, UK
| | - Ottavia Dipasquale
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London, SE5 8AF, UK
| | - Yannis Paloyelis
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London, SE5 8AF, UK.
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Aberrant functional connectivity profiles of brain regions associated with salience and reward processing in female patients with borderline personality disorder. Brain Imaging Behav 2021; 14:485-495. [PMID: 30847803 DOI: 10.1007/s11682-019-00065-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Recent resting-state functional connectivity studies have shown significant group differences in several networks between patients suffering from borderline personality disorder (BPD) and healthy controls. However, reliable and consistent findings have not been reported yet. Several methodological factors might be responsible for the discrepant findings, including the heterogeneity of patient samples in terms of symptom severity. In the current study, we combined investigations of the whole-brain resting-state functional connectivity patterns of BPD patients with seed-based connectivity measures and then computed the correlation of connectivity measures with borderline symptom severity. Correlation-based connectivity analysis was performed on resting-state functional magnetic resonance imaging (fMRI) data from 26 female BPD patients and 26 healthy controls. Increased intrinsic connectivity was found in clusters involving part of the caudate nucleus and the left insula in the patient group, indicating greater integration of each region. Further seed-based connectivity analyses revealed that with the caudate seed, the patient data exhibited an increased resting-state functional connectivity in the bilateral ventral striatum and the midline prefrontal regions extending to the ACC, a network associated with reward processing. The left insula seed showed significantly increased connectivity with the bilateral fronto-orbital/insula, the inferior parietal lobule and the mid-cingulate cortex, a network involved in attention and salience encoding, in the patient population. Moreover, symptom severity, as assessed with the BSL-95 outside the scanner, was negatively correlated with the coupling of the insula and the striatum in the BPD group. Overall, an increased functional connectivity within two large-scale circuitries underlying reward and salience processing was evident in patients, as compared to healthy participants. When correlated with borderline symptom severity, a reduced connectivity between key regions belonging to the reward system and salience network was observed in the patients. These findings may be helpful for facilitating further understanding of the potential mechanisms underlying the BPD pathophysiology and thereby delineate potential treatment targets.
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Cremers H, van Zutphen L, Duken S, Domes G, Sprenger A, Waldorp L, Arntz A. Borderline personality disorder classification based on brain network measures during emotion regulation. Eur Arch Psychiatry Clin Neurosci 2021; 271:1169-1178. [PMID: 33263789 PMCID: PMC8354902 DOI: 10.1007/s00406-020-01201-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 10/14/2020] [Indexed: 12/13/2022]
Abstract
Borderline Personality Disorder (BPD) is characterized by an increased emotional sensitivity and dysfunctional capacity to regulate emotions. While amygdala and prefrontal cortex interactions are regarded as the critical neural mechanisms underlying these problems, the empirical evidence hereof is inconsistent. In the current study, we aimed to systematically test different properties of brain connectivity and evaluate the predictive power to detect borderline personality disorder. Patients with borderline personality disorder (n = 51), cluster C personality disorder (n = 26) and non-patient controls (n = 44), performed an fMRI emotion regulation task. Brain network analyses focused on two properties of task-related connectivity: phasic refers to task-event dependent changes in connectivity, while tonic was defined as task-stable background connectivity. Three different network measures were estimated (strength, local efficiency, and participation coefficient) and entered as separate models in a nested cross-validated linear support vector machine classification analysis. Borderline personality disorder vs. non-patient controls classification showed a balanced accuracy of 55%, which was not significant under a permutation null-model, p = 0.23. Exploratory analyses did indicate that the tonic strength model was the highest performing model (balanced accuracy 62%), and the amygdala was one of the most important features. Despite being one of the largest data-sets in the field of BPD fMRI research, the sample size may have been limited for this type of classification analysis. The results and analytic procedures do provide starting points for future research, focusing on network measures of tonic connectivity, and potentially focusing on subgroups of BPD.
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Affiliation(s)
- Henk Cremers
- Department of Clinical Psychology, University of Amsterdam, Nieuwe Achtergracht 129B, 1001 NK, Amsterdam, The Netherlands.
| | - Linda van Zutphen
- grid.5012.60000 0001 0481 6099Department of Clinical Psychological Science, Maastricht University, Maastricht, The Netherlands
| | - Sascha Duken
- grid.7177.60000000084992262Department of Clinical Psychology, University of Amsterdam, Nieuwe Achtergracht 129B, 1001 NK Amsterdam, The Netherlands
| | - Gregor Domes
- grid.12391.380000 0001 2289 1527Department of Biological and Clinical Psychology, University of Trier, Trier, Germany
| | - Andreas Sprenger
- grid.4562.50000 0001 0057 2672Department of Neurology, University of Lübeck, Lübeck, Germany
| | - Lourens Waldorp
- grid.7177.60000000084992262Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Arnoud Arntz
- grid.7177.60000000084992262Department of Clinical Psychology, University of Amsterdam, Nieuwe Achtergracht 129B, 1001 NK Amsterdam, The Netherlands ,grid.5012.60000 0001 0481 6099Department of Clinical Psychological Science, Maastricht University, Maastricht, The Netherlands
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Common and distinct brain functional alterations in pharmacotherapy treatment-naïve female borderline personality disorder patients with and without auditory verbal hallucinations: a pilot study. Eur Arch Psychiatry Clin Neurosci 2021; 271:1149-1157. [PMID: 32009225 PMCID: PMC8354887 DOI: 10.1007/s00406-020-01102-5] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 01/21/2020] [Indexed: 02/06/2023]
Abstract
Auditory verbal hallucinations (AVHs) are experienced by approximately 25% of patients with borderline personality disorder (BPD). Despite the high incidence, the pathological features of AVH in BPD remain unclear. This study aimed to investigate whole-brain functional connectivity (FC), as measured by functional connectivity density (FCD), and its relationship with AVH in BPD. 65 pharmacotherapy treatment-naïve female BPD patients (30 with AVH and 35 without AVH), and 35 female healthy controls were investigated. Functional magnetic resonance imaging (fMRI) data were collected to assess whole-brain FC and functional connectivity density mapping (FCDM) was applied to the fMRI data to compute FCD features. Compared to the healthy controls, both BPD groups (BPD-AVH and BPD without AVH) exhibited significantly higher gFCD values in the bilateral prefrontal lobe, bilateral orbital lobule, and bilateral insula, and significantly lower gFCD values in the SMA, right anterior temporal lobule, and the ACC. These altered regions were significantly associated with AVH in the BPD subjects. Moreover, higher gFCD values were observed in the left posterior temporal lobule and posterior frontal lobule. Aberrant alterations also emerged in the left posterior temporal lobule and posterior frontal lobule, mainly in Broca and Wernicke regions. Nevertheless, there was no significant correlation between gFCD values and the severity of AVH as measured by the AVH scores. In summary, we have identified aberrations in the FC and brain metabolism of the aforementioned neural circuits/networks, which may provide new insights into BPD-AVH and facilitate the development of therapeutic approaches for treating AVH in BPD patients.
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Ramos-Loyo J, Juárez-García C, Llamas-Alonso LA, Angulo-Chavira AQ, Romo-Vázquez R, Vélez-Pérez H. Inhibitory control under emotional contexts in women with borderline personality disorder: An electrophysiological study. J Psychiatr Res 2021; 132:182-190. [PMID: 33132135 DOI: 10.1016/j.jpsychires.2020.10.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 09/11/2020] [Accepted: 10/12/2020] [Indexed: 01/18/2023]
Abstract
Borderline personality disorder (BPD) is characterized by emotional dysregulation and difficulties in cognitive control. Inhibitory control, meanwhile, is modulated by the presence of emotional stimuli. The objective of the present study was to examine the effects of implicit emotional contexts on response inhibition in BPD patients. Participants performed a response inhibition task (Go-NoGo) under 3 background context conditions: neutral, pleasant and unpleasant. Behavioral performance did not differed between groups. Significantly higher P3NoGo amplitudes, shorter N2 latencies and lower global connectivity were observed in the patients regardless of the emotional valence of the background images compared to controls. In addition, higher P3NoGo amplitudes were correlated with more pronounced psychopathological symptoms. Emotional contexts enhanced N2 amplitudes compared to neutral ones in both groups. Results indicate that BPD required greater neural effort to successfully perform the inhibitory task. Finally, BPD showed lower synchronization between cortical regions, which may indicate a disruption in the effective temporal coupling of distributed areas associated with emotional stimuli-processing during both response and response inhibition.
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Affiliation(s)
| | | | | | | | - Rebeca Romo-Vázquez
- Department of Computational Science, CUCEI, University of Guadalajara, Mexico
| | - Hugo Vélez-Pérez
- Department of Computational Science, CUCEI, University of Guadalajara, Mexico
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宋 志, 李 文, 毕 卉, 王 苏, 邹 凌. [Comparative research on brain networks of children with attention deficit hyperactivity disorder and normal children based on visual cognitive tasks]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2020; 37:749-755. [PMID: 33140597 PMCID: PMC10320550 DOI: 10.7507/1001-5515.201912058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Indexed: 11/03/2022]
Abstract
Aiming at the difference between the brain networks of children with attention deficit hyperactivity disorder (ADHD) and normal children in the task-executing state, this paper conducted a comparative study using the network features of the visual function area. Functional magnetic resonance imaging (fMRI) data of 23 children with ADHD [age: (8.27 ± 2.77) years] and 23 normal children [age: (8.70 ± 2.58) years] were obtained by the visual capture paradigm when the subjects were performing the guessing task. First, fMRI data were used to build a visual area brain function network. Then, the visual area brain function network characteristic indicators including degree distribution, average shortest path, network density, aggregation coefficient, intermediary, etc. were obtained and compared with the traditional whole brain network. Finally, support vector machines (SVM) and other classifiers in the machine learning algorithm were used to classify the feature indicators to distinguish ADHD children from normal children. In this study, visual brain function network features were used for classification, with a classification accuracy of up to 96%. Compared with the traditional method of constructing a whole brain network, the accuracy was improved by about 10%. The test results show that the use of visual area brain function network analysis can better distinguish ADHD children from normal children. This method has certain help to distinguish the brain network between ADHD children and normal children, and is helpful for the auxiliary diagnosis of ADHD children.
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Affiliation(s)
- 志伟 宋
- 常州大学 信息科学与工程学院(江苏常州 213164)School of Information Science and Engineering, Changzhou University, Changzhou, Jiangsu 213164, P.R China
- 常州大学 生物医学信息技术重点实验室(江苏常州 213164)Key Laboratory of Biomedical Information Technology, Changzhou University, Changzhou, Jiangsu 213164, P.R China
| | - 文杰 李
- 常州大学 信息科学与工程学院(江苏常州 213164)School of Information Science and Engineering, Changzhou University, Changzhou, Jiangsu 213164, P.R China
- 常州大学 生物医学信息技术重点实验室(江苏常州 213164)Key Laboratory of Biomedical Information Technology, Changzhou University, Changzhou, Jiangsu 213164, P.R China
| | - 卉 毕
- 常州大学 信息科学与工程学院(江苏常州 213164)School of Information Science and Engineering, Changzhou University, Changzhou, Jiangsu 213164, P.R China
- 常州大学 生物医学信息技术重点实验室(江苏常州 213164)Key Laboratory of Biomedical Information Technology, Changzhou University, Changzhou, Jiangsu 213164, P.R China
| | - 苏弘 王
- 常州大学 信息科学与工程学院(江苏常州 213164)School of Information Science and Engineering, Changzhou University, Changzhou, Jiangsu 213164, P.R China
| | - 凌 邹
- 常州大学 信息科学与工程学院(江苏常州 213164)School of Information Science and Engineering, Changzhou University, Changzhou, Jiangsu 213164, P.R China
- 常州大学 生物医学信息技术重点实验室(江苏常州 213164)Key Laboratory of Biomedical Information Technology, Changzhou University, Changzhou, Jiangsu 213164, P.R China
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Sen B, Parhi KK. Predicting Biological Gender and Intelligence From fMRI via Dynamic Functional Connectivity. IEEE Trans Biomed Eng 2020; 68:815-825. [PMID: 32746070 DOI: 10.1109/tbme.2020.3011363] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE This paper explores the predictive capability of dynamic functional connectivity extracted from functional magnetic resonance imaging (fMRI) of the human brain, in contrast to static connectivity used in past research. METHODS Several state-of-the-art features extracted from static functional connectivity of the brain are employed to predict biological gender and intelligence using publicly available Human Connectome Project (HCP) database. Next, a novel tensor parallel factor (PARAFAC) decomposition model is proposed to decompose sequence of dynamic connectivity matrices into common connectivity components that are orthonormal to each other, common time-courses, and corresponding distinct subject-wise weights. The subject-wise loading of the components are employed to predict biological gender and intelligence using a random forest classifier (respectively, regressor) using 5-fold cross-validation. RESULTS The results demonstrate that dynamic functional connectivity can indeed classify biological gender with a high accuracy (0.94, where male identification accuracy was 0.87 and female identification accuracy was 0.97). It can also predict intelligence with less normalized mean square error (0.139 for fluid intelligence and 0.031 for fluid ability metrics) compared with other functional connectivity measures (the nearest mean square error were 0.147 and 0.037 for fluid intelligence and fluid ability metrics, respectively, using static connectivity approaches). CONCLUSION Our work is an important milestone for the understanding of non-stationary behavior of hemodynamic blood-oxygen level dependent (BOLD) signal in brain and how they are associated with biological gender and intelligence. SIGNIFICANCE The paper demonstrates that dynamic behavior of brain can contribute substantially towards forming a fingerprint of biological gender and intelligence.
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Becq GJPC, Barbier EL, Achard S. Brain networks of rats under anesthesia using resting-state fMRI: comparison with dead rats, random noise and generative models of networks. J Neural Eng 2020; 17:045012. [PMID: 32580176 DOI: 10.1088/1741-2552/ab9fec] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
OBJECTIVE Connectivity networks are crucial to understand the brain resting-state activity using functional magnetic resonance imaging (rs-fMRI). Alterations of these brain networks may highlight important findings concerning the resilience of the brain to different disorders. The focus of this paper is to evaluate the robustness of brain network estimations, discriminate them under anesthesia and compare them to generative models. APPROACH The extraction of brain functional connectivity (FC) networks is difficult and biased due to the properties of the data: low signal to noise ratio, high dimension low sample size. We propose to use wavelet correlations to assess FC between brain areas under anesthesia using four anesthetics (isoflurane, etomidate, medetomidine, urethane). The networks are then deduced from the functional connectivity matrices by applying statistical thresholds computed using the number of samples at a given scale of wavelet decomposition. Graph measures are extracted and extensive comparisons with generative models of structured networks are conducted. MAIN RESULTS The sample size and filtering are critical to obtain significant correlations values and thereby detect connections between regions. This is necessary to construct networks different from random ones as shown using rs-fMRI brain networks of dead rats. Brain networks under anesthesia on rats have topological features that are mixing small-world, scale-free and random networks. Betweenness centrality indicates that hubs are present in brain networks obtained from anesthetized rats but locations of these hubs are altered by anesthesia. SIGNIFICANCE Understanding the effects of anesthesia on brain areas is of particular importance in the context of animal research since animal models are commonly used to explore functions, evaluate lesions or illnesses, and test new drugs. More generally, results indicate that the use of correlations in the context of fMRI signals is robust but must be treated with caution. Solutions are proposed in order to control spurious correlations by setting them to zero.
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Affiliation(s)
- G J-P C Becq
- University Grenoble Alpes, CNRS, Grenoble INP, Gipsa-lab, 38000, Grenoble, France
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41
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Aguilar-Ortiz S, Salgado-Pineda P, Vega D, Pascual JC, Marco-Pallarés J, Soler J, Brunel C, Martin-Blanco A, Soto A, Ribas J, Maristany T, Sarró S, Rodríguez-Fornells A, Salvador R, McKenna PJ, Pomarol-Clotet E. Evidence for default mode network dysfunction in borderline personality disorder. Psychol Med 2020; 50:1746-1754. [PMID: 31456534 DOI: 10.1017/s0033291719001880] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
BACKGROUND Although executive and other cognitive deficits have been found in patients with borderline personality disorder (BPD), whether these have brain functional correlates has been little studied. This study aimed to examine patterns of task-related activation and de-activation during the performance of a working memory task in patients with the disorder. METHODS Sixty-seven DSM-IV BPD patients and 67 healthy controls underwent fMRI during the performance of the n-back task. Linear models were used to obtain maps of within-group activations and areas of differential activation between the groups. RESULTS On corrected whole-brain analysis, there were no activation differences between the BPD patients and the healthy controls during the main 2-back v. baseline contrast, but reduced activation was seen in the precentral cortex bilaterally and the left inferior parietal cortex in the 2-back v. 1-back contrast. The patients showed failure of de-activation affecting the medial frontal cortex and the precuneus, plus in other areas. The changes did not appear to be attributable to previous history of depression, which was present in nearly half the sample. CONCLUSIONS In this study, there was some, though limited, evidence for lateral frontal hypoactivation in BPD during the performance of an executive task. BPD also appears to be associated with failure of de-activation in key regions of the default mode network.
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Affiliation(s)
- Salvatore Aguilar-Ortiz
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- Benito Menni Complex Assistencial en Salut Mental, Sant Boi de Llobregat, Barcelona, Spain
- Departament de Psiquiatria i Medicina Legal, PhD Programme, Doctorat en Psiquiatria, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
| | - Pilar Salgado-Pineda
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- CIBERSAM, Barcelona, Spain
| | - Daniel Vega
- Servei de Psiquiatria i Salut Mental, Consorci Sanitari de l'Anoia, Igualada, Spain
| | - Juan C Pascual
- CIBERSAM, Barcelona, Spain
- Department of Psychiatry, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Josep Marco-Pallarés
- Faculty of Psychology, University of Barcelona, Bellvitge Hospital, Barcelona, Spain
| | - Joaquim Soler
- CIBERSAM, Barcelona, Spain
- Department of Psychiatry, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Cristina Brunel
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- Benito Menni Complex Assistencial en Salut Mental, Sant Boi de Llobregat, Barcelona, Spain
| | - Ana Martin-Blanco
- Department of Psychiatry, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Angel Soto
- Servei de Psiquiatria i Salut Mental, Consorci Sanitari de l'Anoia, Igualada, Spain
| | - Joan Ribas
- Servei de Psiquiatria i Salut Mental, Consorci Sanitari de l'Anoia, Igualada, Spain
| | - Teresa Maristany
- Hospital Sant Joan de Déu, Esplugues de Llobregrat, Barcelona, Spain
| | - Salvador Sarró
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- CIBERSAM, Barcelona, Spain
| | | | - Raymond Salvador
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- CIBERSAM, Barcelona, Spain
| | - Peter J McKenna
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- CIBERSAM, Barcelona, Spain
| | - Edith Pomarol-Clotet
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- CIBERSAM, Barcelona, Spain
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Kurmukov A, Mussabaeva A, Denisova Y, Moyer D, Jahanshad N, Thompson PM, Gutman BA. Optimizing Connectivity-Driven Brain Parcellation Using Ensemble Clustering. Brain Connect 2020; 10:183-194. [PMID: 32264696 PMCID: PMC7247040 DOI: 10.1089/brain.2019.0722] [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] [Indexed: 11/12/2022] Open
Abstract
This work addresses the problem of constructing a unified, topologically optimal connectivity-based brain atlas. The proposed approach aggregates an ensemble partition from individual parcellations without label agreement, providing a balance between sufficiently flexible individual parcellations and intuitive representation of the average topological structure of the connectome. The methods exploit a previously proposed dense connectivity representation, first performing graph-based hierarchical parcellation of individual brains, and subsequently aggregating the individual parcellations into a consensus parcellation. The search for consensus—based on the hard ensemble (HE) algorithm—approximately minimizes the sum of cluster membership distances, effectively estimating a pseudo-Karcher mean of individual parcellations. Computational stability, graph structure preservation, and biological relevance of the simplified representation resulting from the proposed parcellation are assessed on the Human Connectome Project data set. These aspects are assessed using (1) edge weight distribution divergence with respect to the dense connectome representation, (2) interhemispheric symmetry, (3) network characteristics' stability and agreement with respect to individually and anatomically parcellated networks, and (4) performance of the simplified connectome in a biological sex classification task. Ensemble parcellation was found to be highly stable with respect to subject sampling, outperforming anatomical atlases and other connectome-based parcellations in classification as well as preserving global connectome properties. The HE-based parcellation also showed a degree of symmetry comparable with anatomical atlases and a high degree of spatial contiguity without using explicit priors.
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Affiliation(s)
- Anvar Kurmukov
- Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia.,Higher School of Economics, Moscow, Russia.,Department of Biomedical Engineering, Medical Imaging Research Center, Illinois Institute of Technology, Chicago, Illinois, USA
| | - Ayagoz Mussabaeva
- Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
| | - Yulia Denisova
- Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
| | - Daniel Moyer
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, California, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, California, USA
| | - Boris A Gutman
- Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia.,Department of Biomedical Engineering, Medical Imaging Research Center, Illinois Institute of Technology, Chicago, Illinois, USA
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Luo FF, Wang JB, Yuan LX, Zhou ZW, Xu H, Ma SH, Zang YF, Zhang M. Higher Sensitivity and Reproducibility of Wavelet-Based Amplitude of Resting-State fMRI. Front Neurosci 2020; 14:224. [PMID: 32300288 PMCID: PMC7145399 DOI: 10.3389/fnins.2020.00224] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 03/02/2020] [Indexed: 01/26/2023] Open
Abstract
The fast Fourier transform (FFT) is a widely used algorithm used to depict the amplitude of low-frequency fluctuation (ALFF) of resting-state functional magnetic resonance imaging (RS-fMRI). Wavelet transform (WT) is more effective in representing the complex waveform due to its adaptivity to non-stationary or local features of data and many varieties of wavelet functions with different shapes being available. However, there is a paucity of RS-fMRI studies that systematically compare between the results of FFT versus WT. The present study employed five cohorts of datasets and compared the sensitivity and reproducibility of FFT-ALFF with those of Wavelet-ALFF based on five mother wavelets (namely, db2, bior4.4, morl, meyr, and sym3). In addition to the conventional frequency band of 0.0117-0.0781 Hz, a comparison was performed in sub-bands, namely, Slow-6 (0-0.0117 Hz), Slow-5 (0.0117-0.0273 Hz), Slow-4 (0.0273-0.0742 Hz), Slow-3 (0.0742-0.1992 Hz), and Slow-2 (0.1992-0.25 Hz). The results indicated that the Wavelet-ALFF of all five mother wavelets was generally more sensitive and reproducible than FFT-ALFF in all frequency bands. Specifically, in the higher frequency band Slow-2 (0.1992-0.25 Hz), the mean sensitivity of db2-ALFF results was 1.54 times that of FFT-ALFF, and the reproducibility of db2-ALFF results was 2.95 times that of FFT-ALFF. The findings suggest that wavelet-ALFF can replace FFT-ALFF, especially in the higher frequency band. Future studies should test more mother wavelets on other RS-fMRI metrics and multiple datasets.
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Affiliation(s)
- Fei-Fei Luo
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Sciences and Technology, Xi'an Jiaotong University, Xi'an, China.,Department of Medical Imaging, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Jian-Bao Wang
- Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China.,Center for Cognition and Brain Disorders, The Affiliated Hospital, Hangzhou Normal University, Hangzhou, China
| | - Li-Xia Yuan
- Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China.,Center for Cognition and Brain Disorders, The Affiliated Hospital, Hangzhou Normal University, Hangzhou, China
| | - Zhi-Wei Zhou
- Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China.,Center for Cognition and Brain Disorders, The Affiliated Hospital, Hangzhou Normal University, Hangzhou, China
| | - Hui Xu
- Department of Medical Imaging, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Shao-Hui Ma
- Department of Medical Imaging, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Yu-Feng Zang
- Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China.,Center for Cognition and Brain Disorders, The Affiliated Hospital, Hangzhou Normal University, Hangzhou, China
| | - Ming Zhang
- Department of Medical Imaging, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
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de Celis-Alonso B, Hidalgo-Tobón SS, Barragán-Pérez E, Castro-Sierra E, Dies-Suárez P, Garcia J, Moreno-Barbosa E, Arias-Carrión O. Different Food Odors Control Brain Connectivity in Impulsive Children. CNS & NEUROLOGICAL DISORDERS-DRUG TARGETS 2020; 18:63-77. [PMID: 30394220 DOI: 10.2174/1871527317666181105105113] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Revised: 10/19/2018] [Accepted: 10/29/2018] [Indexed: 01/22/2023]
Abstract
BACKGROUND Impulsivity is a complex multi-dimensional combination of behaviors which include: ineffective impulse control, premature decision-making and inability to delay gratification. OBJECTIVE The aim of this work was to explore how food odor perception and its emotional value is affected in impulsive children. METHODS Here we compared two cohorts of impulsive and control children with ages between 10 and 16 years. Both groups underwent a functional magnetic resonance imaging experiment, in which foodrelated odor-cues were presented to all of them. RESULTS Differences in regions of blood oxygen level dependent activation, as well as connectivity, were calculated. Activations were significant for all odors in the impulsive group in the temporal lobe, cerebellum, supplementary motor area, frontal cortex, medial cingulate cortex, insula, precuneus, precentral, para-hippocampal and calcarine cortices. CONCLUSION Connectivity results showed that the expected emotional reward, based on odor perceived and processed in temporal lobes, was the main cue driving responses of impulsive children. This was followed by self-consciousness, the sensation of interaction with the surroundings and feelings of comfort and happiness, modulated by the precuneus together with somatosensory cortex and cingulum. Furthermore, reduced connectivity to frontal areas as well as to other sensory integration areas (piriform cortex), combined to show different sensory processing strategies for olfactory emotional cues in impulsive children. Finally, we hypothesize that the cerebellum plays a pivotal role in modulating decision-making for impulsive children.
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Affiliation(s)
- Benito de Celis-Alonso
- Facultad de Ciencias Fisico Matematicas, Benemerita Universidad Autonoma de Puebla. Puebla, Puebla, Mexico, Address: Avenida San Claudio y 18 Sur, Colonia San Manuel, Edificio FM1-101B, Ciudad Universitaria, C.P. 72570, Puebla, Mexico
| | - Silvia S Hidalgo-Tobón
- Departamento de Imagenologia, Hospital Infantil de Mexico "Federico Gomez", Ciudad de Mexico, Mexico, Address: Calle Dr. Marquez 162, Cuauhtemoc, 06720 Ciudad de Mexico, CDMX, Mexico.,Departamento de Fisica, Universidad Autonoma Metropolitana - Iztapalapa, Ciudad de Mexico, Mexico, Address: Av. San Rafael Atlixco 186, Leyes de Reforma 1ra Secc, 09340 Ciudad de Mexico, CDMX, Mexico
| | - Eduardo Barragán-Pérez
- Departamento de Neurologia, Hospital Infantil de Mexico "Federico Gomez", Ciudad de Mexico, Mexico, Address: Calle Dr. Marquez 162, Cuauhtemoc, 06720 Ciudad de Mexico, CDMX, Mexico
| | - Eduardo Castro-Sierra
- Departamento de Imagenologia, Hospital Infantil de Mexico "Federico Gomez", Ciudad de Mexico, Mexico, Address: Calle Dr. Marquez 162, Cuauhtemoc, 06720 Ciudad de Mexico, CDMX, Mexico
| | - Pilar Dies-Suárez
- Departamento de Imagenologia, Hospital Infantil de Mexico "Federico Gomez", Ciudad de Mexico, Mexico, Address: Calle Dr. Marquez 162, Cuauhtemoc, 06720 Ciudad de Mexico, CDMX, Mexico
| | - Julio Garcia
- Department of Cardiac Sciences, Stephenson Cardiac Imaging Centre, University of Calgary, Cumming School of Medicine, Calgary, AB, Canada, Address: 2500 University Dr. NW Calgary, Alberta, Canada
| | - Eduardo Moreno-Barbosa
- Facultad de Ciencias Fisico Matematicas, Benemerita Universidad Autonoma de Puebla. Puebla, Puebla, Mexico, Address: Avenida San Claudio y 18 Sur, Colonia San Manuel, Edificio FM1-101B, Ciudad Universitaria, C.P. 72570, Puebla, Mexico
| | - Oscar Arias-Carrión
- Unidad de Trastornos del Movimiento y Sueno/Centro de Innovacion Medica Aplicada, Hospital General "Dr. Manuel Gea Gonzalez", Address: Calzada de Tlalpan 4800, Belisario Dominguez Secc. 16, 14080 Tlalpan, CDMX, Mexico
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45
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Sen B, Bernstein GA, Mueller BA, Cullen KR, Parhi KK. Sub-graph entropy based network approaches for classifying adolescent obsessive-compulsive disorder from resting-state functional MRI. NEUROIMAGE-CLINICAL 2020; 26:102208. [PMID: 32065968 PMCID: PMC7025090 DOI: 10.1016/j.nicl.2020.102208] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2019] [Revised: 01/17/2020] [Accepted: 02/04/2020] [Indexed: 11/16/2022]
Abstract
The proposed classification scheme improves the best-known adolescent OCD vs. healthy controls classification accuracy from 78% to 89%. This paper validates the efficacy of sub-graph entropy for classifying OCD vs. healthy groups. The proposed technique identifies a predictive sub-network that partially consists of regions from well-known cortico-striato-thalamic-cortical (CSTC) network. For both predictive sub-network and CSTC sub-network, sub-graph entropy is significantly lower in OCD patients compared with healthy controls. Sub-graph entropy using 1-hop neighborhood is effective in the classification task. Sub-graph entropy using 2-hop neighborhood has a lower classification accuracy.
This paper presents a novel approach for classifying obsessive-compulsive disorder (OCD) in adolescents from resting-state fMRI data. Currently, the state-of-the-art for diagnosing OCD in youth involves interviews with adolescent patients and their parents by an experienced clinician, symptom rating scales based on Diagnostic and Statistical Manual of Mental Disorders (DSM), and behavioral observation. Discovering signal processing and network-based biomarkers from functional magnetic resonance imaging (fMRI) scans of patients has the potential to assist clinicians in their diagnostic assessments of adolescents suffering from OCD. This paper investigates the clinical diagnostic utility of a set of univariate, bivariate and multivariate features extracted from resting-state fMRI using an information-theoretic approach in 15 adolescents with OCD and 13 matched healthy controls. Results indicate that an information-theoretic approach based on sub-graph entropy is capable of classifying OCD vs. healthy subjects with high accuracy. Mean time-series were extracted from 85 brain regions and were used to calculate Shannon wavelet entropy, Pearson correlation matrix, network features and sub-graph entropy. In addition, two special cases of sub-graph entropy, namely node and edge entropy, were investigated to identify important brain regions and edges from OCD patients. A leave-one-out cross-validation method was used for the final predictor performance. The proposed methodology using differential sub-graph (edge) entropy achieved an accuracy of 0.89 with specificity 1 and sensitivity 0.80 using leave-one-out cross-validation with in-fold feature ranking and selection. The high classification accuracy indicates the predictive power of the sub-network as well as edge entropy metric.
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Affiliation(s)
- Bhaskar Sen
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis
| | - Gail A Bernstein
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis
| | - Bryon A Mueller
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis
| | - Kathryn R Cullen
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis
| | - Keshab K Parhi
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis.
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Sen B, Mueller B, Klimes-Dougan B, Cullen K, Parhi KK. Classification of Major Depressive Disorder from Resting-State fMRI. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:3511-3514. [PMID: 31946635 DOI: 10.1109/embc.2019.8856453] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Major Depressive Disorder (MDD) is a very serious mental illness that can affect the daily lives of patients. Accurate diagnosis of this disorder is necessary for planning individualized treatment. However, diagnosing MDD requires the clinicians to personally interview the subjects and rate the symptoms based on Diagnostic and Statistical Manual of Mental Disorders (DSM), which can be very time consuming. Discovering quantifiable signals and biomarkers associated with MDD using functional magnetic resonance imaging (fMRI) scans of patients have the potential to assist the clinicians in their assessment. This paper explores the use of resting-state functional connectivity and network features to classify MDD vs. healthy subjects. For each subject, mean time-series are extracted from 85 brain regions and they are decomposed to 4-frequency bands. Mean time-series for each of the frequency bands are utilized to compute the Pearson correlation and network characteristics. Features are selected separately from correlation and network characteristics using Minimum Redundancy Maximum Relevance (mRMR) to create the final classifier. The proposed scheme achieves 79% accuracy (65 out of 82 subjects classified correctly) with 86% sensitivity (42 out of 49 MDD subjects identified correctly) and 70% specificity (23 out of 33 controls identified correctly) using leave-one-out classification with in-fold feature selection. Pearson correlation had the highest discrimination in band 0.015-0.03 Hz and network based features had the highest discrimination in band 0.03-0.06 Hz for distinguishing MDD vs. healthy subjects.
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Mehraram R, Kaiser M, Cromarty R, Graziadio S, O'Brien JT, Killen A, Taylor JP, Peraza LR. Weighted network measures reveal differences between dementia types: An EEG study. Hum Brain Mapp 2019; 41:1573-1590. [PMID: 31816147 PMCID: PMC7267959 DOI: 10.1002/hbm.24896] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Revised: 09/03/2019] [Accepted: 11/29/2019] [Indexed: 11/18/2022] Open
Abstract
The diagnosis of dementia with Lewy bodies (DLB) versus Alzheimer's disease (AD) can be difficult especially early in the disease process. However, one inexpensive and non‐invasive biomarker which could help is electroencephalography (EEG). Previous studies have shown that the brain network architecture assessed by EEG is altered in AD patients compared with age‐matched healthy control people (HC). However, similar studies in Lewy body diseases, that is, DLB and Parkinson's disease dementia (PDD) are still lacking. In this work, we (a) compared brain network connectivity patterns across conditions, AD, DLB and PDD, in order to infer EEG network biomarkers that differentiate between these conditions, and (b) tested whether opting for weighted matrices led to more reliable results by better preserving the topology of the network. Our results indicate that dementia groups present with reduced connectivity in the EEG α band, whereas DLB shows weaker posterior–anterior patterns within the β‐band and greater network segregation within the θ‐band compared with AD. Weighted network measures were more consistent across global thresholding levels, and the network properties reflected reduction in connectivity strength in the dementia groups. In conclusion, β‐ and θ‐band network measures may be suitable as biomarkers for discriminating DLB from AD, whereas the α‐band network is similarly affected in DLB and PDD compared with HC. These variations may reflect the impairment of attentional networks in Parkinsonian diseases such as DLB and PDD.
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Affiliation(s)
- Ramtin Mehraram
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, UK.,NIHR Newcastle Biomedical Research Centre, Campus for Ageing and Vitality, Newcastle upon Tyne, UK.,Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Marcus Kaiser
- Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle University, Newcastle upon Tyne, UK.,Department of Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ruth Cromarty
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, UK
| | - Sara Graziadio
- NIHR Newcastle in vitro Diagnostics Co-operative, Newcastle-Upon-Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - John T O'Brien
- Department of Psychiatry, University of Cambridge School of Medicine, Cambridge, UK
| | - Alison Killen
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, UK
| | - John-Paul Taylor
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, UK
| | - Luis R Peraza
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, UK.,IXICO Plc, London, UK
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LA A, Rm H, M G, R K, Ja O, Sb V, S O, Ca S, Sd L, L L, B D. Altered brain connectivity in sudden unexpected death in epilepsy (SUDEP) revealed using resting-state fMRI. NEUROIMAGE-CLINICAL 2019; 24:102060. [PMID: 31722289 PMCID: PMC6849487 DOI: 10.1016/j.nicl.2019.102060] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 10/22/2019] [Accepted: 10/24/2019] [Indexed: 01/12/2023]
Abstract
The functional architecture among regulatory structures, and the whole brain, is less modular in confirmed cases of SUDEP and those at high-risk. Altered functional organisation may mean potential impairment of communication among key regulatory circuits. SUDEP is associated with regional connectivity disruptions among cortical and sub-cortical regulatory sites. Medial thalamic connectivity was significantly altered in SUDEP compared with all control groups, including those at high-risk. Increases in the number, and a shift in organisation, of hubs appears to relate to lower mortality risk.
The circumstances surrounding SUDEP suggest autonomic or respiratory collapse, implying central failure of regulation or recovery. Characterisation of the communication among brain areas mediating such processes may shed light on mechanisms and noninvasively indicate risk. We used rs-fMRI to examine network properties among brain structures in people with epilepsy who suffered SUDEP (n = 8) over an 8-year follow-up period, compared with matched high- and low-risk subjects (n = 16/group) who did not suffer SUDEP during that period, and a group of healthy controls (n = 16). Network analysis was employed to explore connectivity within a ‘regulatory-subnetwork’ of brain regions involved in autonomic and respiratory regulation, and over the whole-brain. Modularity, the extent of network organization into separate modules, was significantly reduced in the regulatory-subnetwork, and the whole-brain, in SUDEP and high-risk. Increased participation, a local measure of inter-modular belonging, was evident in SUDEP and high-risk groups, particularly among thalamic structures. The medial prefrontal thalamus was increased in SUDEP compared with all other control groups, including high-risk. Patterns of hub topology were similar in SUDEP and high-risk, but were more extensive in low-risk patients, who displayed greater hub prevalence and a radical reorganization of hubs in the subnetwork. SUDEP is associated with reduced functional organization among cortical and sub-cortical brain regions mediating autonomic and respiratory regulation. Living high-risk subjects demonstrated similar patterns, suggesting such network measures may provide prospective risk-indicating value, though a crucial difference between SUDEP and high-risk was altered connectivity of the medial thalamus in SUDEP, which was also elevated compared with all sub-groups. Disturbed thalamic connectivity may reflect a potential non-invasive marker of elevated SUDEP risk.
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Affiliation(s)
- Allen LA
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK; Epilepsy Society MRI Unit, Chalfont St Peter, Buckinghamshire, UK; The Center for SUDEP Research, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Harper Rm
- The Center for SUDEP Research, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA; UCLA Brain Research Institute, Los Angeles, CA, USA; Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Guye M
- Aix Marseille University, CNRS, CRMBM UMR 7339, Marseille, France
| | - Kumar R
- The Center for SUDEP Research, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA; Department of Anesthesiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Department of Bioengineering, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Ogren Ja
- The Center for SUDEP Research, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA; UCLA Brain Research Institute, Los Angeles, CA, USA
| | - Vos Sb
- The Center for SUDEP Research, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA; UCLA Brain Research Institute, Los Angeles, CA, USA; Wellcome / EPSRC Centre Interventional and Surgical Sciences, UCL, London, UK; Translational Imaging Group, Centre for Medical Image Computing, UCL, London, UK
| | - Ourselin S
- School of Biomedical Engineering and Imaging Sciences, St Thomas' Hospital, King's College London, London, UK
| | - Scott Ca
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK; The Center for SUDEP Research, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Lhatoo Sd
- Department of Neurology, University of Texas Health Sciences Center at Houston, Houston, TX, USA
| | - Lemieux L
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK; Epilepsy Society MRI Unit, Chalfont St Peter, Buckinghamshire, UK
| | - Diehl B
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK; Epilepsy Society MRI Unit, Chalfont St Peter, Buckinghamshire, UK; The Center for SUDEP Research, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
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Farahani FV, Karwowski W, Lighthall NR. Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review. Front Neurosci 2019; 13:585. [PMID: 31249501 PMCID: PMC6582769 DOI: 10.3389/fnins.2019.00585] [Citation(s) in RCA: 265] [Impact Index Per Article: 53.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 05/23/2019] [Indexed: 12/20/2022] Open
Abstract
Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.
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Affiliation(s)
- Farzad V Farahani
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Waldemar Karwowski
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Nichole R Lighthall
- Department of Psychology, University of Central Florida, Orlando, FL, United States
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Ranking Regions, Edges and Classifying Tasks in Functional Brain Graphs by Sub-Graph Entropy. Sci Rep 2019; 9:7628. [PMID: 31110317 PMCID: PMC6527859 DOI: 10.1038/s41598-019-44103-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 05/09/2019] [Indexed: 01/27/2023] Open
Abstract
This paper considers analysis of human brain networks or graphs constructed from time-series collected from functional magnetic resonance imaging (fMRI). In the network of time-series, the nodes describe the regions and the edge weights correspond to the absolute values of correlation coefficients of the time-series of the two nodes associated with the edges. The paper introduces a novel information-theoretic metric, referred as sub-graph entropy, to measure uncertainty associated with a sub-graph. Nodes and edges constitute two special cases of sub-graph structures. Node and edge entropies are used in this paper to rank regions and edges in a functional brain network. The paper analyzes task-fMRI data collected from 475 subjects in the Human Connectome Project (HCP) study for gambling and emotion tasks. The proposed approach is used to rank regions and edges associated with these tasks. The differential node (edge) entropy metric is defined as the difference of the node (edge) entropy corresponding to two different networks belonging to two different classes. Differential entropy of nodes and edges are used to rank top regions and edges associated with the two classes of data. Using top node and edge entropy features separately, two-class classifiers are designed using support vector machine (SVM) with radial basis function (RBF) kernel and leave-one-out method to classify time-series for emotion task vs. no-task, gambling task vs. no-task and emotion task vs. gambling task. Using node entropies, the SVM classifier achieves classification accuracies of 0.96, 0.97 and 0.98, respectively. Using edge entropies, the classifier achieves classification accuracies of 0.91, 0.96 and 0.94, respectively.
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