1
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Chumin EJ, Cutts SA, Risacher SL, Apostolova LG, Farlow MR, McDonald BC, Wu YC, Betzel R, Saykin AJ, Sporns O. Edge time series components of functional connectivity and cognitive function in Alzheimer's disease. Brain Imaging Behav 2024; 18:243-255. [PMID: 38008852 PMCID: PMC10844434 DOI: 10.1007/s11682-023-00822-1] [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] [Accepted: 11/04/2023] [Indexed: 11/28/2023]
Abstract
Understanding the interrelationships of brain function as measured by resting-state magnetic resonance imaging and neuropsychological/behavioral measures in Alzheimer's disease is key for advancement of neuroimaging analysis methods in clinical research. The edge time-series framework recently developed in the field of network neuroscience, in combination with other network science methods, allows for investigations of brain-behavior relationships that are not possible with conventional functional connectivity methods. Data from the Indiana Alzheimer's Disease Research Center sample (53 cognitively normal control, 47 subjective cognitive decline, 32 mild cognitive impairment, and 20 Alzheimer's disease participants) were used to investigate relationships between functional connectivity components, each derived from a subset of time points based on co-fluctuation of regional signals, and measures of domain-specific neuropsychological functions. Multiple relationships were identified with the component approach that were not found with conventional functional connectivity. These involved attentional, limbic, frontoparietal, and default mode systems and their interactions, which were shown to couple with cognitive, executive, language, and attention neuropsychological domains. Additionally, overlapping results were obtained with two different statistical strategies (network contingency correlation analysis and network-based statistics correlation). Results demonstrate that connectivity components derived from edge time-series based on co-fluctuation reveal disease-relevant relationships not observed with conventional static functional connectivity.
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Affiliation(s)
- Evgeny J Chumin
- Department of Psychological and Brain Sciences, Indiana University (IU), Psychology Building 308, 1101 E 10th St, Bloomington, IN, 47405, USA.
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA.
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA.
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA.
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA.
| | - Sarah A Cutts
- Department of Psychological and Brain Sciences, Indiana University (IU), Psychology Building 308, 1101 E 10th St, Bloomington, IN, 47405, USA
- Program in Neuroscience, IU, Bloomington, IN, USA
| | - Shannon L Risacher
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA
| | - Liana G Apostolova
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA
- Department of Neurology, IUSM, Indianapolis, IN, USA
| | - Martin R Farlow
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Department of Neurology, IUSM, Indianapolis, IN, USA
| | - Brenna C McDonald
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA
- Department of Neurology, IUSM, Indianapolis, IN, USA
| | - Yu-Chien Wu
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA
| | - Richard Betzel
- Department of Psychological and Brain Sciences, Indiana University (IU), Psychology Building 308, 1101 E 10th St, Bloomington, IN, 47405, USA
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA
- Program in Neuroscience, IU, Bloomington, IN, USA
| | - Andrew J Saykin
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA
- Department of Neurology, IUSM, Indianapolis, IN, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University (IU), Psychology Building 308, 1101 E 10th St, Bloomington, IN, 47405, USA
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Program in Neuroscience, IU, Bloomington, IN, USA
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA
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2
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Chumin EJ, Cutts SA, Risacher SL, Apostolova LG, Farlow MR, McDonald BC, Wu YC, Betzel R, Saykin AJ, Sporns O. Edge Time Series Components of Functional Connectivity and Cognitive Function in Alzheimer's Disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.13.23289936. [PMID: 38014005 PMCID: PMC10680898 DOI: 10.1101/2023.05.13.23289936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Understanding the interrelationships of brain function as measured by resting-state magnetic resonance imaging and neuropsychological/behavioral measures in Alzheimer's disease is key for advancement of neuroimaging analysis methods in clinical research. The edge time-series framework recently developed in the field of network neuroscience, in combination with other network science methods, allows for investigations of brain-behavior relationships that are not possible with conventional functional connectivity methods. Data from the Indiana Alzheimer's Disease Research Center sample (53 cognitively normal control, 47 subjective cognitive decline, 32 mild cognitive impairment, and 20 Alzheimer's disease participants) were used to investigate relationships between functional connectivity components, each derived from a subset of time points based on co-fluctuation of regional signals, and measures of domain-specific neuropsychological functions. Multiple relationships were identified with the component approach that were not found with conventional functional connectivity. These involved attentional, limbic, frontoparietal, and default mode systems and their interactions, which were shown to couple with cognitive, executive, language, and attention neuropsychological domains. Additionally, overlapping results were obtained with two different statistical strategies (network contingency correlation analysis and network-based statistics correlation). Results demonstrate that connectivity components derived from edge time-series based on co-fluctuation reveal disease-relevant relationships not observed with conventional static functional connectivity.
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Affiliation(s)
- Evgeny J. Chumin
- Department of Psychological and Brain Sciences, Indiana University (IU), Bloomington, IN, United States
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
| | - Sarah A. Cutts
- Department of Psychological and Brain Sciences, Indiana University (IU), Bloomington, IN, United States
- Program in Neuroscience, IU, Bloomington, IN, United States
| | - Shannon L. Risacher
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
| | - Liana G. Apostolova
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
- Department of Neurology, IUSM, Indianapolis, IN, United States
| | - Martin R. Farlow
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Neurology, IUSM, Indianapolis, IN, United States
| | - Brenna C. McDonald
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
- Department of Neurology, IUSM, Indianapolis, IN, United States
| | - Yu-Chien Wu
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
| | - Richard Betzel
- Department of Psychological and Brain Sciences, Indiana University (IU), Bloomington, IN, United States
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Program in Neuroscience, IU, Bloomington, IN, United States
| | - Andrew J. Saykin
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
- Department of Neurology, IUSM, Indianapolis, IN, United States
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University (IU), Bloomington, IN, United States
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Program in Neuroscience, IU, Bloomington, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
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3
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Abbas K, Liu M, Wang M, Duong-Tran D, Tipnis U, Amico E, Kaplan AD, Dzemidzic M, Kareken D, Ances BM, Harezlak J, Goñi J. Tangent functional connectomes uncover more unique phenotypic traits. iScience 2023; 26:107624. [PMID: 37694156 PMCID: PMC10483051 DOI: 10.1016/j.isci.2023.107624] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 07/31/2023] [Accepted: 08/10/2023] [Indexed: 09/12/2023] Open
Abstract
Functional connectomes (FCs) containing pairwise estimations of functional couplings between pairs of brain regions are commonly represented by correlation matrices. As symmetric positive definite matrices, FCs can be transformed via tangent space projections, resulting into tangent-FCs. Tangent-FCs have led to more accurate models predicting brain conditions or aging. Motivated by the fact that tangent-FCs seem to be better biomarkers than FCs, we hypothesized that tangent-FCs have also a higher fingerprint. We explored the effects of six factors: fMRI condition, scan length, parcellation granularity, reference matrix, main-diagonal regularization, and distance metric. Our results showed that identification rates are systematically higher when using tangent-FCs across the "fingerprint gradient" (here including test-retest, monozygotic and dizygotic twins). Highest identification rates were achieved when minimally (0.01) regularizing FCs while performing tangent space projection using Riemann reference matrix and using correlation distance to compare the resulting tangent-FCs. Such configuration was validated in a second dataset (resting-state).
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Affiliation(s)
- Kausar Abbas
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Mintao Liu
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Michael Wang
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Duy Duong-Tran
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Mathematics, United States Naval Academy, Annapolis, MD, USA
| | - Uttara Tipnis
- Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Alan D. Kaplan
- Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Mario Dzemidzic
- Department of Neurology, Indiana University School of Medicine, Indiana Alcohol Research Center, Indianapolis, IN, USA
| | - David Kareken
- Department of Neurology, Indiana University School of Medicine, Indiana Alcohol Research Center, Indianapolis, IN, USA
| | - Beau M. Ances
- Department of Neurology, Washington University in Saint Louis, School of Medicine, St Louis, MO, USA
| | - Jaroslaw Harezlak
- Department of Epidemiology and Biostatistics, Indiana University, Bloomington, IN, USA
| | - Joaquín Goñi
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
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4
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Orlichenko A, Qu G, Su KJ, Liu A, Shen H, Deng HW, Wang YP. Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results. ARXIV 2023:arXiv:2308.01451v1. [PMID: 37576121 PMCID: PMC10418521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Functional magnetic resonance (fMRI) is an invaluable tool in studying cognitive processes in vivo. Many recent studies use functional connectivity (FC), partial correlation connectivity (PC), or fMRI-derived brain networks to predict phenotypes with results that sometimes cannot be replicated. At the same time, FC can be used to identify the same subject from different scans with great accuracy. In this paper, we show a method by which one can unknowingly inflate classification results from 61% accuracy to 86% accuracy by treating longitudinal or contemporaneous scans of the same subject as independent data points. Using the UK Biobank dataset, we find one can achieve the same level of variance explained with 50 training subjects by exploiting identifiability as with 10,000 training subjects without double-dipping. We replicate this effect in four different datasets: the UK Biobank (UKB), the Philadelphia Neurodevelopmental Cohort (PNC), the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP), and an OpenNeuro Fibromyalgia dataset (Fibro). The unintentional improvement ranges between 7% and 25% in the four datasets. Additionally, we find that by using dynamic functional connectivity (dFC), one can apply this method even when one is limited to a single scan per subject. One major problem is that features such as ROIs or connectivities that are reported alongside inflated results may confuse future work. This article hopes to shed light on how even minor pipeline anomalies may lead to unexpectedly superb results.
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Affiliation(s)
- Anton Orlichenko
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
| | - Gang Qu
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
| | - Kuan-Jui Su
- School of Medicine, Tulane University, New Orleans, LA, USA
| | - Anqi Liu
- School of Medicine, Tulane University, New Orleans, LA, USA
| | - Hui Shen
- School of Medicine, Tulane University, New Orleans, LA, USA
| | - Hong-Wen Deng
- School of Medicine, Tulane University, New Orleans, LA, USA
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
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5
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Mahzarnia A, Stout JA, Anderson RJ, Moon HS, Yar Han Z, Beck K, Browndyke JN, Dunson DB, Johnson KG, O’Brien RJ, Badea A. Identifying vulnerable brain networks associated with Alzheimer's disease risk. Cereb Cortex 2023; 33:5307-5322. [PMID: 36320163 PMCID: PMC10399292 DOI: 10.1093/cercor/bhac419] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 09/21/2022] [Accepted: 09/23/2022] [Indexed: 12/23/2022] Open
Abstract
The selective vulnerability of brain networks in individuals at risk for Alzheimer's disease (AD) may help differentiate pathological from normal aging at asymptomatic stages, allowing the implementation of more effective interventions. We used a sample of 72 people across the age span, enriched for the APOE4 genotype to reveal vulnerable networks associated with a composite AD risk factor including age, genotype, and sex. Sparse canonical correlation analysis (CCA) revealed a high weight associated with genotype, and subgraphs involving the cuneus, temporal, cingulate cortices, and cerebellum. Adding cognitive metrics to the risk factor revealed the highest cumulative degree of connectivity for the pericalcarine cortex, insula, banks of the superior sulcus, and the cerebellum. To enable scaling up our approach, we extended tensor network principal component analysis, introducing CCA components. We developed sparse regression predictive models with errors of 17% for genotype, 24% for family risk factor for AD, and 5 years for age. Age prediction in groups including cognitively impaired subjects revealed regions not found using only normal subjects, i.e. middle and transverse temporal, paracentral and superior banks of temporal sulcus, as well as the amygdala and parahippocampal gyrus. These modeling approaches represent stepping stones towards single subject prediction.
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Affiliation(s)
- Ali Mahzarnia
- Radiology Department, Duke University Medical School, Durham, 27710 NC, USA
| | - Jacques A Stout
- Brain Imaging and Analysis Center, Duke University Medical School, Durham, 27710 NC, USA
| | - Robert J Anderson
- Radiology Department, Duke University Medical School, Durham, 27710 NC, USA
| | - Hae Sol Moon
- Biomedical Engineering Department, Pratt School of Engineering, Duke University, Durham, 27710 NC, USA
| | - Zay Yar Han
- Radiology Department, Duke University Medical School, Durham, 27710 NC, USA
| | - Kate Beck
- Neurology Department, Duke University Medical School, Durham, 27710 NC, USA
| | - Jeffrey N Browndyke
- Psychiatry and Behavioral Sciences Department, Duke University Medical School, Durham, 27710 NC, USA
| | - David B Dunson
- Statistical Sciences, Trinity College, Duke University, Durham, 27710 NC, USA
| | - Kim G Johnson
- Neurology Department, Duke University Medical School, Durham, 27710 NC, USA
| | - Richard J O’Brien
- Neurology Department, Duke University Medical School, Durham, 27710 NC, USA
| | - Alexandra Badea
- Radiology Department, Duke University Medical School, Durham, 27710 NC, USA
- Brain Imaging and Analysis Center, Duke University Medical School, Durham, 27710 NC, USA
- Biomedical Engineering Department, Pratt School of Engineering, Duke University, Durham, 27710 NC, USA
- Neurology Department, Duke University Medical School, Durham, 27710 NC, USA
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6
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Tepper Á, Vásquez Núñez J, Ramirez-Mahaluf JP, Aguirre JM, Barbagelata D, Maldonado E, Díaz Dellarossa C, Nachar R, González-Valderrama A, Undurraga J, Goñi J, Crossley N. Intra and inter-individual variability in functional connectomes of patients with First Episode of Psychosis. Neuroimage Clin 2023; 38:103391. [PMID: 37003128 PMCID: PMC10102560 DOI: 10.1016/j.nicl.2023.103391] [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: 10/06/2022] [Revised: 03/24/2023] [Accepted: 03/25/2023] [Indexed: 03/31/2023]
Abstract
Patients with Schizophrenia may show different clinical presentations, not only regarding inter-individual comparisons but also in one specific subject over time. In fMRI studies, functional connectomes have been shown to carry valuable individual level information, which can be associated with cognitive and behavioral variables. Moreover, functional connectomes have been used to identify subjects within a group, as if they were fingerprints. For the particular case of Schizophrenia, it has been shown that there is reduced connectome stability as well as higher inter-individual variability. Here, we studied inter and intra-individual heterogeneity by exploring functional connectomes' variability and related it with clinical variables (PANSS Total scores and antipsychotic's doses). Our sample consisted of 30 patients with First Episode of Psychosis and 32 Healthy Controls, with a test-retest approach of two resting-state fMRI scanning sessions. In our patients' group, we found increased deviation from healthy functional connectomes and increased intragroup inter-subject variability, which was positively correlated to symptoms' levels in six subnetworks (visual, somatomotor, dorsal attention, ventral attention, frontoparietal and DMN). Moreover, changes in symptom severity were positively related to changes in deviation from healthy functional connectomes. Regarding intra-subject variability, we were unable to replicate previous findings of reduced connectome stability (i.e., increased intra-subject variability), but we found a trend suggesting that result. Our findings highlight the relevance of variability characterization in Schizophrenia, and they can be related to evidence of Schizophrenia patients having a noisy functional connectome.
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Affiliation(s)
- Ángeles Tepper
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile; Millennium Institute for Intelligent Healthcare Engineering, iHEALTH, Santiago, Chile
| | | | | | - Juan Manuel Aguirre
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Daniella Barbagelata
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Elisa Maldonado
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile
| | | | - Ruben Nachar
- Psychiatric Institute "Dr. José Horwitz Barak", Santiago. Chile
| | - Alfonso González-Valderrama
- Psychiatric Institute "Dr. José Horwitz Barak", Santiago. Chile; School of Medicine, Universidad Finis Terrae, Santiago, Chile
| | - Juan Undurraga
- Psychiatric Institute "Dr. José Horwitz Barak", Santiago. Chile; Department of Neurology and Psychiatry, Faculty of Medicine, Clínica Alemana Universidad del Desarrollo, Santiago, Chile
| | - Joaquín Goñi
- CONNplexity Lab. Purdue University. West Lafayette, Indiana USA
| | - Nicolás Crossley
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile; Millennium Institute for Intelligent Healthcare Engineering, iHEALTH, Santiago, Chile; Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
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7
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Cutts SA, Faskowitz J, Betzel RF, Sporns O. Uncovering individual differences in fine-scale dynamics of functional connectivity. Cereb Cortex 2023; 33:2375-2394. [PMID: 35690591 DOI: 10.1093/cercor/bhac214] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 05/07/2022] [Accepted: 05/08/2022] [Indexed: 01/01/2023] Open
Abstract
Functional connectivity (FC) profiles contain subject-specific features that are conserved across time and have potential to capture brain-behavior relationships. Most prior work has focused on spatial features (nodes and systems) of these FC fingerprints, computed over entire imaging sessions. We propose a method for temporally filtering FC, which allows selecting specific moments in time while also maintaining the spatial pattern of node-based activity. To this end, we leverage a recently proposed decomposition of FC into edge time series (eTS). We systematically analyze functional magnetic resonance imaging frames to define features that enhance identifiability across multiple fingerprinting metrics, similarity metrics, and data sets. Results show that these metrics characteristically vary with eTS cofluctuation amplitude, similarity of frames within a run, transition velocity, and expression of functional systems. We further show that data-driven optimization of features that maximize fingerprinting metrics isolates multiple spatial patterns of system expression at specific moments in time. Selecting just 10% of the data can yield stronger fingerprints than are obtained from the full data set. Our findings support the idea that FC fingerprints are differentially expressed across time and suggest that multiple distinct fingerprints can be identified when spatial and temporal characteristics are considered simultaneously.
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Affiliation(s)
- Sarah A Cutts
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States.,Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States.,Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States
| | - Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States.,Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States.,Network Science Institute, Indiana University, Bloomington, IN 47408, United States.,Cognitive Science Program, Indiana University, Bloomington, IN 47405, United States
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States.,Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States.,Network Science Institute, Indiana University, Bloomington, IN 47408, United States.,Cognitive Science Program, Indiana University, Bloomington, IN 47405, United States
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8
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Xu F, Garai S, Duong-Tran D, Saykin AJ, Zhao Y, Shen L. Consistency of Graph Theoretical Measurements of Alzheimer's Disease Fiber Density Connectomes Across Multiple Parcellation Scales. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2022; 2022:1323-1328. [PMID: 37041884 PMCID: PMC10082965 DOI: 10.1109/bibm55620.2022.9995657] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Graph theoretical measures have frequently been used to study disrupted connectivity in Alzheimer's disease human brain connectomes. However, prior studies have noted that differences in graph creation methods are confounding factors that may alter the topological observations found in these measures. In this study, we conduct a novel investigation regarding the effect of parcellation scale on graph theoretical measures computed for fiber density networks derived from diffusion tensor imaging. We computed 4 network-wide graph theoretical measures of average clustering coefficient, transitivity, characteristic path length, and global efficiency, and we tested whether these measures are able to consistently identify group differences among healthy control (HC), mild cognitive impairment (MCI), and AD groups in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort across 5 scales of the Lausanne parcellation. We found that the segregative measure of transtivity offered the greatest consistency across scales in distinguishing between healthy and diseased groups, while the other measures were impacted by the selection of scale to varying degrees. Global efficiency was the second most consistent measure that we tested, where the measure could distinguish between HC and MCI in all 5 scales and between HC and AD in 3 out of 5 scales. Characteristic path length was highly sensitive to the variation in scale, corroborating previous findings, and could not identify group differences in many of the scales. Average clustering coefficient was also greatly impacted by scale, as it consistently failed to identify group differences in the higher resolution parcellations. From these results, we conclude that many graph theoretical measures are sensitive to the selection of parcellation scale, and further development in methodology is needed to offer a more robust characterization of AD's relationship with disrupted connectivity.
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Affiliation(s)
- Frederick Xu
- Department of Bioengineering, University of Pennsylvania, Philadelphia, USA
| | - Sumita Garai
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Duy Duong-Tran
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, USA
| | - Yize Zhao
- Department of Biostatistics, Yale University School of Public Health, NJ, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
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9
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Li Y, Yu X, Ma Y, Su J, Li Y, Zhu S, Bai T, Wei Q, Becker B, Ding Z, Wang K, Tian Y, Wang J. Neural signatures of default mode network in major depression disorder after electroconvulsive therapy. Cereb Cortex 2022; 33:3840-3852. [PMID: 36089839 DOI: 10.1093/cercor/bhac311] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 06/17/2022] [Accepted: 07/08/2022] [Indexed: 11/12/2022] Open
Abstract
Functional abnormalities of default mode network (DMN) have been well documented in major depressive disorder (MDD). However, the association of DMN functional reorganization with antidepressant treatment and gene expression is unclear. Moreover, whether the functional interactions of DMN could predict treatment efficacy is also unknown. Here, we investigated the link of treatment response with functional alterations of DMN and gene expression with a comparably large sample including 46 individuals with MDD before and after electroconvulsive therapy (ECT) and 46 age- and sex-matched healthy controls. Static and dynamic functional connectivity (dFC) analyses showed increased intrinsic/static but decreased dynamic functional couplings of inter- and intra-subsystems and between nodes of DMN. The changes of static functional connections of DMN were spatially correlated with brain gene expression profiles. Moreover, static and dFC of the DMN before treatment as features could predict depressive symptom improvement following ECT. Taken together, these results shed light on the underlying neural and genetic basis of antidepressant effect of ECT and the intrinsic functional connectivity of DMN have the potential to serve as prognostic biomarkers to guide accurate personalized treatment.
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Affiliation(s)
- Yuanyuan Li
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 625014, China
| | - Xiaohui Yu
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
| | - Yingzi Ma
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
| | - Jing Su
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
| | - Yue Li
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 625014, China
| | - Shunli Zhu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 625014, China
| | - Tongjian Bai
- Department of Neurology, The First Hospital of Anhui Medical University, Hefei 230022, China
| | - Qiang Wei
- Department of Neurology, The First Hospital of Anhui Medical University, Hefei 230022, China
| | - Benjamin Becker
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 625014, China
| | - Zhiyong Ding
- Medical Imaging Department, Maternal and Child Health-care Hospital of Qujing, Qujing 655000, China
| | - Kai Wang
- Department of Neurology, The First Hospital of Anhui Medical University, Hefei 230022, China.,Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei 230022, China.,Anhui Medical University, School of Mental Health and Psychological Sciences, Hefei 230022, China.,Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei 230022, China.,Anhui Province Clinical Research Center for Neurological Disease, Hefei 230022, China
| | - Yanghua Tian
- Department of Neurology, The First Hospital of Anhui Medical University, Hefei 230022, China.,Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China.,Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei 230022, China.,Anhui Medical University, School of Mental Health and Psychological Sciences, Hefei 230022, China.,Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei 230022, China.,Anhui Province Clinical Research Center for Neurological Disease, Hefei 230022, China.,Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China
| | - Jiaojian Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China.,Yunnan Key Laboratory of Primate Biomedical Research, Kunming, Yunnan 650500, China
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10
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Tong X, Xie H, Carlisle N, Fonzo GA, Oathes DJ, Jiang J, Zhang Y. Transdiagnostic connectome signatures from resting-state fMRI predict individual-level intellectual capacity. Transl Psychiatry 2022; 12:367. [PMID: 36068228 PMCID: PMC9448815 DOI: 10.1038/s41398-022-02134-2] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 08/18/2022] [Accepted: 08/22/2022] [Indexed: 11/22/2022] Open
Abstract
Medication and other therapies for psychiatric disorders show unsatisfying efficacy, in part due to the significant clinical/ biological heterogeneity within each disorder and our over-reliance on categorical clinical diagnoses. Alternatively, dimensional transdiagnostic studies have provided a promising pathway toward realizing personalized medicine and improved treatment outcomes. One factor that may influence response to psychiatric treatments is cognitive function, which is reflected in one's intellectual capacity. Intellectual capacity is also reflected in the organization and structure of intrinsic brain networks. Using a large transdiagnostic cohort (n = 1721), we sought to discover neuroimaging biomarkers by developing a resting-state functional connectome-based prediction model for a key intellectual capacity measure, Full-Scale Intelligence Quotient (FSIQ), across the diagnostic spectrum. Our cross-validated model yielded an excellent prediction accuracy (r = 0.5573, p < 0.001). The robustness and generalizability of our model was further validated on three independent cohorts (n = 2641). We identified key transdiagnostic connectome signatures underlying FSIQ capacity involving the dorsal-attention, frontoparietal and default-mode networks. Meanwhile, diagnosis groups showed disorder-specific biomarker patterns. Our findings advance the neurobiological understanding of cognitive functioning across traditional diagnostic categories and provide a new avenue for neuropathological classification of psychiatric disorders.
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Affiliation(s)
- Xiaoyu Tong
- grid.259029.50000 0004 1936 746XDepartment of Bioengineering, Lehigh University, Bethlehem, PA USA
| | - Hua Xie
- grid.164295.d0000 0001 0941 7177Department of Psychology, University of Maryland, College Park, MD USA
| | - Nancy Carlisle
- grid.259029.50000 0004 1936 746XDepartment of Psychology, Lehigh University, Bethlehem, PA USA
| | - Gregory A. Fonzo
- grid.89336.370000 0004 1936 9924Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, Austin, TX USA
| | - Desmond J. Oathes
- grid.25879.310000 0004 1936 8972Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA USA
| | - Jing Jiang
- grid.214572.70000 0004 1936 8294Departments of Pediatrics and Psychiatry, Carver College of Medicine, University of Iowa, Iowa, IA USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA.
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11
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Romano A, Trosi Lopez E, Liparoti M, Polverino A, Minino R, Trojsi F, Bonavita S, Mandolesi L, Granata C, Amico E, Sorrentino G, Sorrentino P. The progressive loss of brain network fingerprints in Amyotrophic Lateral Sclerosis predicts clinical impairment. Neuroimage Clin 2022; 35:103095. [PMID: 35764029 PMCID: PMC9241102 DOI: 10.1016/j.nicl.2022.103095] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 05/31/2022] [Accepted: 06/19/2022] [Indexed: 10/25/2022]
Abstract
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterised by functional connectivity alterations in both motor and extra-motor brain regions. Within the framework of network analysis, fingerprinting represents a reliable approach to assess subject-specific connectivity features within a given population (healthy or diseased). Here, we applied the Clinical Connectome Fingerprint (CCF) analysis to source-reconstructed magnetoencephalography (MEG) signals in a cohort of seventy-eight subjects: thirty-nine ALS patients and thirty-nine healthy controls. We set out to develop an identifiability matrix to assess the extent to which each patient was recognisable based on his/her connectome, as compared to healthy controls. The analysis was performed in the five canonical frequency bands. Then, we built a multilinear regression model to test the ability of the "clinical fingerprint" to predict the clinical evolution of the disease, as assessed by the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-r), the King's disease staging system, and the Milano-Torino Staging (MiToS) disease staging system. We found a drop in the identifiability of patients in the alpha band compared to the healthy controls. Furthermore, the "clinical fingerprint" was predictive of the ALSFRS-r (p = 0.0397; β = 32.8), the King's (p = 0.0001; β = -7.40), and the MiToS (p = 0.0025; β = -4.9) scores. Accordingly, it negatively correlated with the King's (Spearman's rho = -0.6041, p = 0.0003) and MiToS scales (Spearman's rho = -0.4953, p = 0.0040). Our results demonstrated the ability of the CCF approach to predict the individual motor impairment in patients affected by ALS. Given the subject-specificity of our approach, we hope to further exploit it to improve disease management.
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Affiliation(s)
- Antonella Romano
- Department of Motor Sciences and Wellness - University of Naples "Parthenope", via Medina 40, 80133 Naples, Italy
| | - Emahnuel Trosi Lopez
- Department of Motor Sciences and Wellness - University of Naples "Parthenope", via Medina 40, 80133 Naples, Italy
| | - Marianna Liparoti
- Department of Social and Developmental Psychology, University of Rome "Sapienza", Italy
| | - Arianna Polverino
- Institute of Diagnosis and Treatment Hermitage Capodimonte, via Cupa delle Tozzole 2, 80131 Naples, Italy
| | - Roberta Minino
- Department of Motor Sciences and Wellness - University of Naples "Parthenope", via Medina 40, 80133 Naples, Italy
| | - Francesca Trojsi
- Department of Advanced Medical and Surgical Sciences, Division of Neurology, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Simona Bonavita
- Department of Advanced Medical and Surgical Sciences, Division of Neurology, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Laura Mandolesi
- Department of Humanistic Studies, University of Naples Federico II, via Porta di Massa 1, 80133, Naples, Italy
| | - Carmine Granata
- Institute of Applied Sciences and Intelligent Systems, CNR, via Campi Flegrei 34, 80078 Pozzuoli, NA, Italy
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Giuseppe Sorrentino
- Department of Motor Sciences and Wellness - University of Naples "Parthenope", via Medina 40, 80133 Naples, Italy; Institute of Diagnosis and Treatment Hermitage Capodimonte, via Cupa delle Tozzole 2, 80131 Naples, Italy; Institute of Applied Sciences and Intelligent Systems, CNR, via Campi Flegrei 34, 80078 Pozzuoli, NA, Italy.
| | - Pierpaolo Sorrentino
- Institute of Applied Sciences and Intelligent Systems, CNR, via Campi Flegrei 34, 80078 Pozzuoli, NA, Italy; Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France
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12
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Svaldi DO, Goñi J, Abbas K, Amico E, Clark DG, Muralidharan C, Dzemidzic M, West JD, Risacher SL, Saykin AJ, Apostolova LG. Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease. Hum Brain Mapp 2021; 42:3500-3516. [PMID: 33949732 PMCID: PMC8249900 DOI: 10.1002/hbm.25448] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 03/07/2021] [Accepted: 04/06/2021] [Indexed: 12/29/2022] Open
Abstract
Functional connectivity, as estimated using resting state functional MRI, has shown potential in bridging the gap between pathophysiology and cognition. However, clinical use of functional connectivity biomarkers is impeded by unreliable estimates of individual functional connectomes and lack of generalizability of models predicting cognitive outcomes from connectivity. To address these issues, we combine the frameworks of connectome predictive modeling and differential identifiability. Using the combined framework, we show that enhancing the individual fingerprint of resting state functional connectomes leads to robust identification of functional networks associated to cognitive outcomes and also improves prediction of cognitive outcomes from functional connectomes. Using a comprehensive spectrum of cognitive outcomes associated to Alzheimer's disease (AD), we identify and characterize functional networks associated to specific cognitive deficits exhibited in AD. This combined framework is an important step in making individual level predictions of cognition from resting state functional connectomes and in understanding the relationship between cognition and connectivity.
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Affiliation(s)
| | - Joaquín Goñi
- School of Industrial EngineeringPurdue UniversityWest LafayetteIndianaUSA
- Purdue Institute for Integrative Neuroscience, Purdue UniversityWest LafayetteIndianaUSA
- Weldon School of Biomedical EngineeringPurdue UniversityWest LafayetteIndianaUSA
| | - Kausar Abbas
- School of Industrial EngineeringPurdue UniversityWest LafayetteIndianaUSA
- Purdue Institute for Integrative Neuroscience, Purdue UniversityWest LafayetteIndianaUSA
| | - Enrico Amico
- School of Industrial EngineeringPurdue UniversityWest LafayetteIndianaUSA
- Purdue Institute for Integrative Neuroscience, Purdue UniversityWest LafayetteIndianaUSA
| | - David G. Clark
- Indiana University School of MedicineIndianapolisIndianaUSA
| | | | | | - John D. West
- Indiana University School of MedicineIndianapolisIndianaUSA
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