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Pan Y, Bi C, Kochunov P, Shardell M, Smith JC, McCoy RG, Ye Z, Yu J, Lu T, Yang Y, Lee H, Liu S, Gao S, Ma Y, Li Y, Chen C, Ma T, Wang Z, Nichols T, Hong LE, Chen S. Brain-wide functional connectome analysis of 40,000 individuals reveals brain networks that show aging effects in older adults. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.17.594743. [PMID: 38798606 PMCID: PMC11118564 DOI: 10.1101/2024.05.17.594743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
The functional connectome changes with aging. We systematically evaluated aging related alterations in the functional connectome using a whole-brain connectome network analysis in 39,675 participants in UK Biobank project. We used adaptive dense network discovery tools to identify networks directly associated with aging from resting-state fMRI data. We replicated our findings in 499 participants from the Lifespan Human Connectome Project in Aging study. The results consistently revealed two motor-related subnetworks (both permutation test p-values <0.001) that showed a decline in resting-state functional connectivity (rsFC) with increasing age. The first network primarily comprises sensorimotor and dorsal/ventral attention regions from precentral gyrus, postcentral gyrus, superior temporal gyrus, and insular gyrus, while the second network is exclusively composed of basal ganglia regions, namely the caudate, putamen, and globus pallidus. Path analysis indicates that white matter fractional anisotropy mediates 19.6% (p<0.001, 95% CI [7.6% 36.0%]) and 11.5% (p<0.001, 95% CI [6.3% 17.0%]) of the age-related decrease in both networks, respectively. The total volume of white matter hyperintensity mediates 32.1% (p<0.001, 95% CI [16.8% 53.0%]) of the aging-related effect on rsFC in the first subnetwork.
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Affiliation(s)
- Yezhi Pan
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Chuan Bi
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Peter Kochunov
- Department of Psychiatry and Behavioral Science, University of Texas Health Science Center Houston, Houston, United States of America
| | - Michelle Shardell
- Department of Epidemiology and Public Health and Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - J. Carson Smith
- Department of Kinesiology, University of Maryland, College Park, Maryland, United States of America
| | - Rozalina G. McCoy
- Division of Endocrinology, Diabetes, & Nutrition, Department of Medicine, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Zhenyao Ye
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Jiaao Yu
- Department of Mathematics, University of Maryland, College Park, Maryland, United States of America
| | - Tong Lu
- Department of Mathematics, University of Maryland, College Park, Maryland, United States of America
| | - Yifan Yang
- Department of Mathematics, University of Maryland, College Park, Maryland, United States of America
| | - Hwiyoung Lee
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Song Liu
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
| | - Si Gao
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Yizhou Ma
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Yiran Li
- Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Chixiang Chen
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America
| | - Ze Wang
- Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Thomas Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - L. Elliot Hong
- Department of Psychiatry and Behavioral Science, University of Texas Health Science Center Houston, Houston, United States of America
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
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Boerwinkle VL, Manjón I, Sussman BL, McGary A, Mirea L, Gillette K, Broman-Fulks J, Cediel EG, Arhin M, Hunter SE, Wyckoff SN, Allred K, Tom D. Resting-State Functional Magnetic Resonance Imaging Network Association With Mortality, Epilepsy, Cognition, and Motor Two-Year Outcomes in Suspected Severe Neonatal Acute Brain Injury. Pediatr Neurol 2024; 152:41-55. [PMID: 38198979 DOI: 10.1016/j.pediatrneurol.2023.12.003] [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: 06/12/2023] [Revised: 11/14/2023] [Accepted: 12/06/2023] [Indexed: 01/12/2024]
Abstract
BACKGROUND AND OBJECTIVES In acute brain injury of neonates, resting-state functional magnetic resonance imaging (MRI) (RS) showed incremental association with consciousness, mortality, cognitive and motor development, and epilepsy, with correction for multiple comparisons, at six months postgestation in neonates with suspected acute brain injury (ABI). However, there are relatively few developmental milestones at six months to benchmark against, thus, we extended this cohort study to evaluate two-year outcomes. METHODS In 40 consecutive neonates with ABI and RS, ordinal scores of resting-state networks; MRI, magnetic resonance spectroscopy, and electroencephalography; and up to 42-month outcomes of mortality, general and motor development, Pediatric Cerebral Performance Category Scale (PCPC), and epilepsy informed associations between tests and outcomes. RESULTS Mean gestational age was 37.8 weeks, 68% were male, and 60% had hypoxic-ischemic encephalopathy. Three died in-hospital, four at six to 42 months, and five were lost to follow-up. Associations included basal ganglia network with PCPC (P = 0.0003), all-mortality (P = 0.005), and motor (P = 0.0004); language/frontoparietal network with developmental delay (P = 0.009), PCPC (P = 0.006), and all-mortality (P = 0.01); default mode network with developmental delay (P = 0.003), PCPC (P = 0.004), neonatal intensive care unit mortality (P = 0.01), and motor (P = 0.009); RS seizure onset zone with epilepsy (P = 0.01); and anatomic MRI with epilepsy (P = 0.01). CONCLUSION For the first time, at any age, resting state functional MRI in ABI is associated with long-term epilepsy and RSNs predicted mortality in neonates. Severity of RSN abnormality was associated with incrementally worsened neurodevelopment including cognition, language, and motor function over two years.
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Affiliation(s)
- Varina L Boerwinkle
- Division of Child Neurology, University of North Carolina Medical School, Chapel Hill, North Carolina.
| | - Iliana Manjón
- University of Arizona College of Medicine - Tucson, Tucson, Arizona
| | - Bethany L Sussman
- Division of Neuroscience Research, Barrow Neurological Institute at Phoenix Children's Hospital, Phoenix, Arizona
| | - Alyssa McGary
- Department of Clinical Research, Phoenix Children's Hospital, Phoenix, Arizona
| | - Lucia Mirea
- Department of Clinical Research, Phoenix Children's Hospital, Phoenix, Arizona
| | - Kirsten Gillette
- Division of Child Neurology, University of North Carolina Medical School, Chapel Hill, North Carolina
| | - Jordan Broman-Fulks
- Division of Child Neurology, University of North Carolina Medical School, Chapel Hill, North Carolina
| | - Emilio G Cediel
- Division of Child Neurology, University of North Carolina Medical School, Chapel Hill, North Carolina
| | - Martin Arhin
- Division of Child Neurology, University of North Carolina Medical School, Chapel Hill, North Carolina
| | - Senyene E Hunter
- Division of Child Neurology, University of North Carolina Medical School, Chapel Hill, North Carolina
| | - Sarah N Wyckoff
- Division of Neuroscience Research, Barrow Neurological Institute at Phoenix Children's Hospital, Phoenix, Arizona
| | - Kimberlee Allred
- Division of Neonatology, Phoenix Children's Hospital, Phoenix, Arizona
| | - Deborah Tom
- Division of Neonatology, Phoenix Children's Hospital, Phoenix, Arizona
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Wang Z, Yang J, Zheng Z, Cao W, Dong L, Li H, Wen X, Luo C, Cai Q, Jian W, Yao D. Trait- and State-Dependent Changes in Cortical-Subcortical Functional Networks Across the Adult Lifespan. J Magn Reson Imaging 2023; 58:720-731. [PMID: 36637029 DOI: 10.1002/jmri.28599] [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: 10/07/2022] [Revised: 12/29/2022] [Accepted: 01/03/2023] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND How the functional interactions of the basal ganglia/thalamus with the cerebral cortex and the cerebellum change over the adult lifespan in movie-watching and resting-state is less clear. PURPOSE To investigate the functional changes in the organization of the human cortical-subcortical functional networks over the adult lifespan using movie-watching and resting-state fMRI data. STUDY TYPE Cohort. SUBJECTS Healthy 467 adults (cross-sectional individuals aged 18-88 years) from the Cambridge Centre for Ageing and Neuroscience (www.cam-can.com). FIELD STRENGTH/SEQUENCE: fMRI using a gradient-echo echo-planar imaging (EPI) sequence at 3 T. ASSESSMENT Functional connectivities (FCs) of the subcortical subregions (i.e. the basal ganglia and thalamus) with both the cerebral cortex and cerebellum were examined in fMRI data acquired during resting state and movie-watching. And, fluid intelligence scores were also assessed. STATISTICAL TESTS Student's t-tests, false discovery rate (FDR) corrected. RESULTS As age increased, FCs that mainly within the basal ganglia and thalamus, and between the basal ganglia/thalamus and cortical networks (including the dorsal attention, ventral attention, and limbic networks) were both increased/decreased during movie-watching and resting states. However, FCs showed a state-dependent component with advancing age. During the movie-watching state, the FCs between the basal ganglia/thalamus and cerebellum/frontoparietal control networks were mainly increased with age, and the FCs in the somatomotor network were decreased with age. During the resting state, the FCs between the basal ganglia/thalamus and default mode/visual networks were mainly increased with age, and the FCs in the cerebellum were mainly decreased with age. Moreover, inverse relationships between FCs and fluid intelligence were mainly found in these network regions. DATA CONCLUSION Our study may suggest that changes in cortical-subcortical functional networks across the adult lifespan were both state-dependent and stable traits, and that aging fMRI studies should consider the effects of both physiological characteristics and individual situations. EVIDENCE LEVEL 2. TECHNICAL EFFICACY Stage 3.
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Affiliation(s)
- Ziqi Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jie Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zihao Zheng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Weifang Cao
- Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, China
| | - Hechun Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xin Wen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China
| | - Qingyan Cai
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Wei Jian
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, China
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Jackson TB, Bernard JA. Cerebello-basal Ganglia Networks and Cortical Network Global Efficiency. CEREBELLUM (LONDON, ENGLAND) 2023; 22:588-600. [PMID: 35661099 PMCID: PMC11223677 DOI: 10.1007/s12311-022-01418-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
The cerebellum (CB) and basal ganglia (BG) each have topographically distinct functional subregions that are functionally and anatomically interconnected with cortical regions through discrete thalamic loops and with each other via disynaptic connections, with previous work detailing high levels of functional connectivity between these phylogenetically ancient regions. It was posited that this CB-BG network provides support for cortical systems processing, spanning cognitive, emotional, and motor domains, implying that subcortical network measures are strongly related to cortical network measures (Bostan & Strick, 2018); however, it is currently unknown how network measures within distinct CB-BG networks relate to cortical network measures. Here, 122 regions of interest comprising cognitive and motor CB-BG networks and 7 canonical cortical resting-state were used to investigate whether the integration (quantified using global efficiency, GE) of cognitive CB-BG network (CCBN) nodes and their segregation from motor CB-BG network (MCBN) nodes is related to cortical network GE and segregation in 233 non-related, right-handed participants (Human Connectome Project-1200). CCBN GE positively correlated with GE in the default mode, motor, and auditory networks and MCBN GE positively correlated with GE in all networks, except the default mode and emotional. MCBN segregation was related to motor network segregation. These findings highlight the CB-BG network's potential role in cortical networks associated with executive function, task switching, and verbal working memory. This work has implications for understanding cortical network organization and cortical-subcortical interactions in healthy adults and may help in determining biomarkers and deciphering subcortical differences seen in disease states.
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Affiliation(s)
- T Bryan Jackson
- Department of Psychological and Brain Sciences, Texas A&M University, 4235 TAMU, College Station, TX, 77843, USA.
| | - Jessica A Bernard
- Department of Psychological and Brain Sciences, Texas A&M University, 4235 TAMU, College Station, TX, 77843, USA
- Texas A&M Institute for Neuroscience, Texas A&M University, 4235 TAMU, College Station, TX, 77843, USA
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Sussman BL, Wyckoff SN, Heim J, Wilfong AA, Adelson PD, Kruer MC, Gonzalez MJ, Boerwinkle VL. Is Resting State Functional MRI Effective Connectivity in Movement Disorders Helpful? A Focused Review Across Lifespan and Disease. Front Neurol 2022; 13:847834. [PMID: 35493815 PMCID: PMC9046695 DOI: 10.3389/fneur.2022.847834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 03/23/2022] [Indexed: 11/20/2022] Open
Abstract
In the evolving modern era of neuromodulation for movement disorders in adults and children, much progress has been made recently characterizing the human motor network (MN) with potentially important treatment implications. Herein is a focused review of relevant resting state fMRI functional and effective connectivity of the human motor network across the lifespan in health and disease. The goal is to examine how the transition from functional connectivity to dynamic effective connectivity may be especially informative of network-targeted movement disorder therapies, with hopeful implications for children.
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Affiliation(s)
- Bethany L. Sussman
- Division of Neuroscience, Barrow Neurological Institute at Phoenix Children's Hospital, Phoenix, AZ, United States
- *Correspondence: Bethany L. Sussman
| | - Sarah N. Wyckoff
- Division of Neuroscience, Barrow Neurological Institute at Phoenix Children's Hospital, Phoenix, AZ, United States
- Department of Research, Phoenix Children's Hospital, Phoenix, AZ, United States
| | - Jennifer Heim
- Division of Pediatric Neurology, Barrow Neurological Institute at Phoenix Children's Hospital, Phoenix, AZ, United States
| | - Angus A. Wilfong
- Division of Pediatric Neurology, Barrow Neurological Institute at Phoenix Children's Hospital, Phoenix, AZ, United States
| | - P. David Adelson
- Division of Pediatric Neurosurgery, Barrow Neurological Institute at Phoenix Children's Hospital, Phoenix, AZ, United States
| | - Michael C. Kruer
- Division of Pediatric Neurology, Barrow Neurological Institute at Phoenix Children's Hospital, Phoenix, AZ, United States
- Departments of Child Health, Neurology, Genetics and Cellular & Molecular Medicine, University of Arizona College of Medicine – Phoenix, Phoenix, AZ, United States
| | | | - Varina L. Boerwinkle
- Division of Pediatric Neurology, Barrow Neurological Institute at Phoenix Children's Hospital, Phoenix, AZ, United States
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Angioni D, Virecoulon Giudici K, Montoya Martinez M, Rolland Y, Vellas B, de Souto Barreto P. Neuroimaging markers of chronic fatigue in older people: a narrative review. Aging Clin Exp Res 2021; 33:1487-1492. [PMID: 32734575 DOI: 10.1007/s40520-020-01666-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 07/18/2020] [Indexed: 10/24/2022]
Abstract
BACKGROUND Chronic fatigue is a common symptom in older adults. Although some studies have attempted to identify the neuronal correlates of fatigue associated with chronic diseases, the scientific evidence is scarce regarding fatigue in older people not suffering from a specific disease. AIMS To gather available evidence of neuroimaging studies investigating the associations between fatigue and brain health in older adults out of the context of a specific disease, and to identify potential brain structures associated with this symptom. METHODS Studies considering exclusively patients with a specific disease and/or studies focusing on physiological mechanisms of acute fatigue induced by the realization of cognitive and physical tasks were excluded. RESULTS Very few studies on the associations of fatigue with neuroimaging markers are currently available. Fatigue was associated with reduced hippocampus volumes and with hippocampal amyloid deposition. Regarding the association between fatigue and the circuit of basal ganglia, putamen and thalamus were associated with physical fatigability, whereas amygdala and thalamus with mental fatigability. Very limited evidence about white matter integrity found that healthy individuals with high levels of fatigue had a greater total volume of leukoaraiosis. CONCLUSION This review suggests that hippocampus damage and potentially loss of function in basal ganglia networks could play a role on chronic fatigue during aging. Further studies are needed to assess the associations of fatigue with white matter alterations.
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Tivarus ME, Zhuang Y, Wang L, Murray KD, Venkataraman A, Weber MT, Zhong J, Qiu X, Schifitto G. Mitochondrial toxicity before and after combination antiretroviral therapy, a Magnetic Resonance Spectroscopy study. NEUROIMAGE-CLINICAL 2021; 31:102693. [PMID: 34020161 PMCID: PMC8144469 DOI: 10.1016/j.nicl.2021.102693] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 03/21/2021] [Accepted: 05/04/2021] [Indexed: 11/29/2022]
Abstract
The aim of this study was to quantify, via Magnetic Resonance Spectroscopy (MRS), the effect of combination antiretroviral therapy (cART) on brain metabolites and characterize any possible associations between changes in metabolites, age, blood biomarkers of neuronal damage, functional connectivity and cognitive performance. As cART has dramatically increased the life expectancy of HIV-infected (HIV + ) individuals and unmasked an increase in HIV-associated neurocognitive disorders, it is still not clear whether cART neurotoxicity contributes to these disorders. We hypothesized a bimodal effect, with early cART treatment of HIV infection decreasing inflammation as measured by MRS metabolites and improving cognitive performance, and chronic exposure to cART contributing to persistence of cognitive impairment via its effect on mitochondrial function. Basal ganglia metabolites, functional connectivity, cognitive scores, as well as plasma levels of neurofilament light chain (NfL) and tau protein were measured before and after 12 weeks, 1 year and 2 years of cART in a cohort of 50 cART-naïve HIV + subjects and 72 age matched HIV- healthy controls. Glutamate (Glu) levels were lower in the cART naïve patients than in healthy controls and were inversely correlated with plasma levels of NfL. There were no other significant metabolite differences between HIV + and uninfected individuals. Treatment improved Glu levels in HIV+, however, no associations were found between Glu, functional connectivity and cognitive performance. Stable brain metabolites and plasma levels of NfL and Tau over two-years of follow-ups suggest there are no signs of cART neurotoxicity in this relatively young cohort of HIV + individuals.
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Affiliation(s)
- Madalina E Tivarus
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester NY, USA; Department of Neuroscience, University of Rochester Medical Center, Rochester NY, USA.
| | - Yuchuan Zhuang
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, USA
| | - Lu Wang
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester NY, USA
| | - Kyle D Murray
- Department of Physics and Astronomy, University of Rochester, Rochester NY, USA
| | - Arun Venkataraman
- Department of Physics and Astronomy, University of Rochester, Rochester NY, USA
| | - Miriam T Weber
- Department of Neurology, University of Rochester Medical Center, Rochester NY, USA
| | - Jianhui Zhong
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester NY, USA; Department of Physics and Astronomy, University of Rochester, Rochester NY, USA; Department of Biomedical Engineering, University of Rochester, Rochester NY, USA
| | - Xing Qiu
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester NY, USA
| | - Giovanni Schifitto
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester NY, USA; Department of Neurology, University of Rochester Medical Center, Rochester NY, USA
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Creo AL, Cortes TM, Jo HJ, Huebner AR, Dasari S, Tillema JM, Lteif AN, Klaus KA, Ruegsegger GN, Kudva YC, Petersen RC, Port JD, Nair KS. Brain functions and cognition on transient insulin deprivation in type 1 diabetes. JCI Insight 2021; 6:144014. [PMID: 33561011 PMCID: PMC8021100 DOI: 10.1172/jci.insight.144014] [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: 09/04/2020] [Accepted: 02/03/2021] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Type 1 diabetes (T1D) is a risk factor for dementia and structural brain changes. It remains to be determined whether transient insulin deprivation that frequently occurs in insulin-treated individuals with T1D alters brain function. METHODS We therefore performed functional and structural magnetic resonance imaging, magnetic resonance spectroscopy, and neuropsychological testing at baseline and following 5.4 ± 0.6 hours of insulin deprivation in 14 individuals with T1D and compared results with those from 14 age-, sex-, and BMI-matched nondiabetic (ND) participants with no interventions. RESULTS Insulin deprivation in T1D increased blood glucose, and β-hydroxybutyrate, while reducing bicarbonate levels. Participants with T1D showed lower baseline brain N-acetyl aspartate and myo-inositol levels but higher cortical fractional anisotropy, suggesting unhealthy neurons and brain microstructure. Although cognitive functions did not differ between participants with T1D and ND participants at baseline, significant changes in fine motor speed as well as attention and short-term memory occurred following insulin deprivation in participants with T1D. Insulin deprivation also reduced brain adenosine triphosphate levels and altered the phosphocreatine/adenosine triphosphate ratio. Baseline differences in functional connectivity in brain regions between participants with T1D and ND participants were noted, and on insulin deprivation further alterations in functional connectivity between regions, especially cortical and hippocampus-caudate regions, were observed. These alterations in functional connectivity correlated to brain metabolites and to changes in cognition. CONCLUSION Transient insulin deprivation therefore caused alterations in executive aspects of cognitive function concurrent with functional connectivity between memory regions and the sensory cortex. These findings have important clinical implications, as many patients with T1D inadvertently have periods of transient insulin deprivation. TRIAL REGISTRATION ClinicalTrials.gov NCT03392441. FUNDING Clinical and Translational Science Award (UL1 TR002377) from the National Center for Advancing Translational Science; NIH grants (R21 AG60139 and R01 AG62859); the Mayo Foundation.
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Affiliation(s)
- Ana L Creo
- Division of Pediatric Endocrinology and Metabolism
| | | | | | | | | | | | - Aida N Lteif
- Division of Pediatric Endocrinology and Metabolism
| | | | | | - Yogish C Kudva
- Division of Endocrinology, Diabetes, Metabolism, and Nutrition
| | | | - John D Port
- Division of Neuroradiology, Mayo Clinic, Rochester, Minnesota, USA
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Karim HT, Rosso A, Aizenstein HJ, Bohnen NI, Studenski S, Rosano C. Resting state connectivity within the basal ganglia and gait speed in older adults with cerebral small vessel disease and locomotor risk factors. Neuroimage Clin 2020; 28:102401. [PMID: 32932053 PMCID: PMC7495101 DOI: 10.1016/j.nicl.2020.102401] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 07/31/2020] [Accepted: 08/25/2020] [Indexed: 12/19/2022]
Abstract
BACKGROUND AND AIM The basal ganglia are critical for planned locomotion, but their role in age-related gait slowing is not well known. Spontaneous regional co-activation of brain activity at rest, known as resting state connectivity, is emerging as a biomarker of functional neural specialization of varying human processes, including gait. We hypothesized that greater connectivity amongst regions of the basal ganglia would be associated with faster gait speed in the elderly. We further investigated whether this association was similar in strength to that of other risk factors for gait slowing, specifically white matter hyperintensities (WMH). METHODS A cohort of 269 adults (79-90 years, 146 females, 164 White) were assessed for gait speed (m/sec) via stopwatch; brain activation during resting state functional magnetic resonance imaging, WMH, and gray matter volume (GMV) normalized by intracranial volume via 3T neuroimaging; and risk factors of poorer locomotion via clinical exams (body mass index (BMI), muscle strength, vision, musculoskeletal pain, cardiometabolic conditions, depressive symptoms, and cognitive function). To understand whether basal ganglia connectivity shows distinct clusters of connectivity, we conducted a k-means clustering analysis of regional co-activation among the substantia nigra, nucleus accumbens, subthalamic nucleus, putamen, pallidum, and caudate. We conducted two multivariable linear regression models: (1) with gait speed as the dependent variable and connectivity, demographics, WMH, GMV, and locomotor risk factors as independent variables and (2) with basal ganglia connectivity as the dependent variable and demographics, WMH, GMV, and locomotor risk factors as independent variables. RESULTS We identified two clusters of basal ganglia connectivity: high and low without a distinct spatial distribution allowing us to compute an average connectivity index of the entire basal ganglia regional connectivity (representing a continuous measure). Lower connectivity was associated with slower gait, independent of other locomotor risk factors, including WMH; the coefficient of this association was similar to those of other locomotor risk factors. Lower connectivity was significantly associated with lower BMI and greater WMH. CONCLUSIONS Lower resting state basal ganglia connectivity is associated with slower gait speed. Its contribution appears comparable to WMH and other locomotor risk factors. Future studies should assess whether promoting higher basal ganglia connectivity in older adults may reduce age-related gait slowing.
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Affiliation(s)
- H T Karim
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States.
| | - A Rosso
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, United States
| | - H J Aizenstein
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - N I Bohnen
- Departments of Radiology & Neurology, University of Michigan, Ann Arbor, MI, United States; Neurology Service & Geriatric Research Education and Clinical Center, VA Ann Arbor Healthcare System, Ann Arbor, MI, United States
| | - S Studenski
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - C Rosano
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, United States
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10
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Griffanti L, Klein JC, Szewczyk-Krolikowski K, Menke RAL, Rolinski M, Barber TR, Lawton M, Evetts SG, Begeti F, Crabbe M, Rumbold J, Wade-Martins R, Hu MT, Mackay C. Cohort profile: the Oxford Parkinson's Disease Centre Discovery Cohort MRI substudy (OPDC-MRI). BMJ Open 2020; 10:e034110. [PMID: 32792423 PMCID: PMC7430482 DOI: 10.1136/bmjopen-2019-034110] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
PURPOSE The Oxford Parkinson's Disease Centre (OPDC) Discovery Cohort MRI substudy (OPDC-MRI) collects high-quality multimodal brain MRI together with deep longitudinal clinical phenotyping in patients with Parkinson's, at-risk individuals and healthy elderly participants. The primary aim is to detect pathological changes in brain structure and function, and develop, together with the clinical data, biomarkers to stratify, predict and chart progression in early-stage Parkinson's and at-risk individuals. PARTICIPANTS Participants are recruited from the OPDC Discovery Cohort, a prospective, longitudinal study. Baseline MRI data are currently available for 290 participants: 119 patients with early idiopathic Parkinson's, 15 Parkinson's patients with pathogenic mutations of the leucine-rich repeat kinase 2 or glucocerebrosidase (GBA) genes, 68 healthy controls and 87 individuals at risk of Parkinson's (asymptomatic carriers of GBA mutation and patients with idiopathic rapid eye movement sleep behaviour disorder-RBD). FINDINGS TO DATE Differences in brain structure in early Parkinson's were found to be subtle, with small changes in the shape of the globus pallidus and evidence of alterations in microstructural integrity in the prefrontal cortex that correlated with performance on executive function tests. Brain function, as assayed with resting fMRI yielded more substantial differences, with basal ganglia connectivity reduced in early Parkinson'sand RBD. Imaging of the substantia nigra with the more recent adoption of sequences sensitive to iron and neuromelanin content shows promising results in identifying early signs of Parkinsonian disease. FUTURE PLANS Ongoing studies include the integration of multimodal MRI measures to improve discrimination power. Follow-up clinical data are now accumulating and will allow us to correlate baseline imaging measures to clinical disease progression. Follow-up MRI scanning started in 2015 and is currently ongoing, providing the opportunity for future longitudinal imaging analyses with parallel clinical phenotyping.
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Affiliation(s)
- Ludovica Griffanti
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, Oxfordshire, UK
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
| | - Johannes C Klein
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Konrad Szewczyk-Krolikowski
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Ricarda A L Menke
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Michal Rolinski
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
- Institute of Clinical Neurosciences, University of Bristol, Bristol, UK
| | - Thomas R Barber
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, Oxfordshire, UK
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Michael Lawton
- Population Health Sciences, University of Bristol, Bristol, UK
| | - Samuel G Evetts
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Faye Begeti
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Marie Crabbe
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Jane Rumbold
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Richard Wade-Martins
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, Oxfordshire, UK
| | - Michele T Hu
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Clare Mackay
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, Oxfordshire, UK
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Oxford Health, NHS Foundation Trust, Oxford, Oxfordshire, UK
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11
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Bertino S, Basile GA, Bramanti A, Anastasi GP, Quartarone A, Milardi D, Cacciola A. Spatially coherent and topographically organized pathways of the human globus pallidus. Hum Brain Mapp 2020; 41:4641-4661. [PMID: 32757349 PMCID: PMC7555102 DOI: 10.1002/hbm.25147] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 06/29/2020] [Accepted: 07/12/2020] [Indexed: 12/18/2022] Open
Abstract
Internal and external segments of globus pallidus (GP) exert different functions in basal ganglia circuitry, despite their main connectional systems share the same topographical organization, delineating limbic, associative, and sensorimotor territories. The identification of internal GP sensorimotor territory has therapeutic implications in functional neurosurgery settings. This study is aimed at assessing the spatial coherence of striatopallidal, subthalamopallidal, and pallidothalamic pathways by using tractography‐derived connectivity‐based parcellation (CBP) on high quality diffusion MRI data of 100 unrelated healthy subjects from the Human Connectome Project. A two‐stage hypothesis‐driven CBP approach has been carried out on the internal and external GP. Dice coefficient between functionally homologous pairs of pallidal maps has been computed. In addition, reproducibility of parcellation according to different pathways of interest has been investigated, as well as spatial relations between connectivity maps and existing optimal stimulation points for dystonic patients. The spatial organization of connectivity clusters revealed anterior limbic, intermediate associative and posterior sensorimotor maps within both internal and external GP. Dice coefficients showed high degree of coherence between functionally similar maps derived from the different bundles of interest. Sensorimotor maps derived from the subthalamopallidal pathway resulted to be the nearest to known optimal pallidal stimulation sites for dystonic patients. Our findings suggest that functionally homologous afferent and efferent connections may share similar spatial territory within the GP and that subcortical pallidal connectional systems may have distinct implications in the treatment of movement disorders.
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Affiliation(s)
- Salvatore Bertino
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Gianpaolo Antonio Basile
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | | | - Giuseppe Pio Anastasi
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Angelo Quartarone
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Demetrio Milardi
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy.,IRCCS Centro Neurolesi "Bonino Pulejo", Messina, Italy
| | - Alberto Cacciola
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
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Cao X, Wang X, Xue C, Zhang S, Huang Q, Liu W. A Radiomics Approach to Predicting Parkinson's Disease by Incorporating Whole-Brain Functional Activity and Gray Matter Structure. Front Neurosci 2020; 14:751. [PMID: 32760248 PMCID: PMC7373781 DOI: 10.3389/fnins.2020.00751] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 06/25/2020] [Indexed: 02/06/2023] Open
Abstract
Parkinson's disease (PD) is a progressive, chronic, and neurodegenerative disorder that is primarily diagnosed by clinical examinations and magnetic resonance imaging (MRI). In this study, we proposed a machine learning based radiomics method to predict PD. Fifty healthy controls (HC) along with 70 PD patients underwent resting-state magnetic resonance imaging (rs-fMRI). For all subjects, we extracted five types of 6664 features, including mean amplitude of low-frequency fluctuation (mALFF), mean regional homogeneity (mReHo), resting-state functional connectivity (RSFC), voxel-mirrored homotopic connectivity (VMHC) and gray matter (GM) volume. After conducting dimension reduction utilizing Least absolute shrinkage and selection operator (LASSO), fifty-three radiomic features including 46 RSFCs, 1 mALFF, 3 mReHos, 1 VMHC, 2 GM volumes and 1 clinical factor were retained. The selected features also indicated the most discriminative regions for PD. We further conducted model fitting procedure for classifying subjects in the training set employing random forest and support volume machine (SVM) to evaluate the performance of the two methods. After cross-validation, both methods achieved 100% accuracy and area under curve (AUC) for distinguishing between PD and HC in the training set. In the testing set, SVM performed better than random forest with the accuracy, true positive rate (TPR) and AUC being 85%, 1 and 0.97, respectively. These findings demonstrate the radiomics technique has the potential to support radiological diagnosis and to achieve high classification accuracy for clinical diagnostic systems for patients with PD.
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Affiliation(s)
- Xuan Cao
- Division of Statistics and Data Science, Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH, United States
| | - Xiao Wang
- Department of Radiology, Affiliated Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Chen Xue
- Department of Radiology, Affiliated Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Shaojun Zhang
- Department of Statistics, University of Florida, Gainesville, FL, United States
| | - Qingling Huang
- Department of Radiology, Affiliated Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Weiguo Liu
- Department of Neurology, Affiliated Brain Hospital, Nanjing Medical University, Nanjing, China
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Abstract
The global population is ageing at an accelerating speed. The ability to perform working memory tasks together with rapid processing becomes increasingly difficult with increases in age. With increasing national average life spans and a rise in the prevalence of age-related disease, it is pertinent to discuss the unique perspectives that can be gained from imaging the aged brain. Differences in structure, function, blood flow, and neurovascular coupling are present in both healthy aged brains and in diseased brains and have not yet been explored to their full depth in contemporary imaging studies. Imaging methods ranging from optical imaging to magnetic resonance imaging (MRI) to newer technologies such as photoacoustic tomography each offer unique advantages and challenges in imaging the aged brain. This paper will summarize first the importance and challenges of imaging the aged brain and then offer analysis of potential imaging modalities and their representative applications. The potential breakthroughs in brain imaging are also envisioned.
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Affiliation(s)
- Hannah Humayun
- Photoacoustic Imaging Laboratory, Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Junjie Yao
- Photoacoustic Imaging Laboratory, Department of Biomedical Engineering, Duke University, Durham, NC, USA
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