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Sala A, Lizarraga A, Caminiti SP, Calhoun VD, Eickhoff SB, Habeck C, Jamadar SD, Perani D, Pereira JB, Veronese M, Yakushev I. Brain connectomics: time for a molecular imaging perspective? Trends Cogn Sci 2023; 27:353-366. [PMID: 36621368 PMCID: PMC10432882 DOI: 10.1016/j.tics.2022.11.015] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 11/19/2022] [Accepted: 11/30/2022] [Indexed: 01/09/2023]
Abstract
In the past two decades brain connectomics has evolved into a major concept in neuroscience. However, the current perspective on brain connectivity and how it underpins brain function relies mainly on the hemodynamic signal of functional magnetic resonance imaging (MRI). Molecular imaging provides unique information inaccessible to MRI-based and electrophysiological techniques. Thus, positron emission tomography (PET) has been successfully applied to measure neural activity, neurotransmission, and proteinopathies in normal and pathological cognition. Here, we position molecular imaging within the brain connectivity framework from the perspective of timeliness, validity, reproducibility, and resolution. We encourage the neuroscientific community to take an integrative approach whereby MRI-based, electrophysiological techniques, and molecular imaging contribute to our understanding of the brain connectome.
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Affiliation(s)
- Arianna Sala
- Department of Nuclear Medicine, Klinikum Rechts der Isar, Technical University of Munich, School of Medicine, 81675 Munich, Germany; Coma Science Group, GIGA-Consciousness, University of Liege, 4000 Liege, Belgium; Centre du Cerveau(2), University Hospital of Liege, 4000 Liege, Belgium
| | - Aldana Lizarraga
- Department of Nuclear Medicine, Klinikum Rechts der Isar, Technical University of Munich, School of Medicine, 81675 Munich, Germany
| | - Silvia Paola Caminiti
- Vita-Salute San Raffaele University, 20132 Milan, Italy; In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain, and Behaviour (INM-7), Research Centre Jülich, 52428 Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany
| | - Christian Habeck
- Cognitive Neuroscience Division, Department of Neurology, Columbia University, New York, NY 10032, USA
| | - Sharna D Jamadar
- Turner Institute for Brain and Mental Health, Monash University, 3800 Melbourne, Australia; Monash Biomedical Imaging, Monash University, 3800 Melbourne, Australia
| | - Daniela Perani
- Vita-Salute San Raffaele University, 20132 Milan, Italy; In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, 20132 Milan, Italy; Nuclear Medicine Unit, San Raffaele Hospital, 20132 Milan, Italy
| | - Joana B Pereira
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, 14152 Stockholm, Sweden; Memory Research Unit, Department of Clinical Sciences, Malmö Lund University, 20502 Lund, Sweden
| | - Mattia Veronese
- Department of Neuroimaging, King's College London, London SE5 8AF, UK; Department of Information Engineering, University of Padua, 35131 Padua, Italy
| | - Igor Yakushev
- Department of Nuclear Medicine, Klinikum Rechts der Isar, Technical University of Munich, School of Medicine, 81675 Munich, Germany.
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Deery HA, Di Paolo R, Moran C, Egan GF, Jamadar SD. The older adult brain is less modular, more integrated, and less efficient at rest: A systematic review of large-scale resting-state functional brain networks in aging. Psychophysiology 2023; 60:e14159. [PMID: 36106762 PMCID: PMC10909558 DOI: 10.1111/psyp.14159] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 07/22/2022] [Accepted: 07/25/2022] [Indexed: 12/23/2022]
Abstract
The literature on large-scale resting-state functional brain networks across the adult lifespan was systematically reviewed. Studies published between 1986 and July 2021 were retrieved from PubMed. After reviewing 2938 records, 144 studies were included. Results on 11 network measures were summarized and assessed for certainty of the evidence using a modified GRADE method. The evidence provides high certainty that older adults display reduced within-network and increased between-network functional connectivity. Older adults also show lower segregation, modularity, efficiency and hub function, and decreased lateralization and a posterior to anterior shift at rest. Higher-order functional networks reliably showed age differences, whereas primary sensory and motor networks showed more variable results. The inflection point for network changes is often the third or fourth decade of life. Age effects were found with moderate certainty for within- and between-network altered patterns and speed of dynamic connectivity. Research on within-subject bold variability and connectivity using glucose uptake provides low certainty of age differences but warrants further study. Taken together, these age-related changes may contribute to the cognitive decline often seen in older adults.
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Affiliation(s)
- Hamish A. Deery
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Robert Di Paolo
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Chris Moran
- Peninsula Clinical School, Central Clinical SchoolMonash UniversityFrankstonVictoriaAustralia
- Department of Geriatric MedicinePeninsula HealthFrankstonVictoriaAustralia
| | - Gary F. Egan
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
- Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneVictoriaAustralia
| | - Sharna D. Jamadar
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
- Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneVictoriaAustralia
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Deng S, Franklin CG, O'Boyle M, Zhang W, Heyl BL, Jerabek PA, Lu H, Fox PT. Hemodynamic and metabolic correspondence of resting-state voxel-based physiological metrics in healthy adults. Neuroimage 2022; 250:118923. [PMID: 35066157 PMCID: PMC9201851 DOI: 10.1016/j.neuroimage.2022.118923] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 01/07/2022] [Accepted: 01/11/2022] [Indexed: 12/18/2022] Open
Abstract
Voxel-based physiological (VBP) variables derived from blood oxygen level dependent (BOLD) fMRI time-course variations include: amplitude of low frequency fluctuations (ALFF), fractional amplitude of low frequency fluctuations (fALFF) and regional homogeneity (ReHo). Although these BOLD-derived variables can detect between-group (e.g. disease vs control) spatial pattern differences, physiological interpretations are not well established. The primary objective of this study was to quantify spatial correspondences between BOLD VBP variables and PET measurements of cerebral metabolic rate and hemodynamics, being well-validated physiological standards. To this end, quantitative, whole-brain PET images of metabolic rate of glucose (MRGlu; 18FDG) and oxygen (MRO2; 15OO), blood flow (BF; H215O) and blood volume (BV; C15O) were obtained in 16 healthy controls. In the same subjects, BOLD time-courses were obtained for computation of ALFF, fALFF and ReHo images. PET variables were compared pair-wise with BOLD variables. In group-averaged, across-region analyses, ALFF corresponded significantly only with BV (R = 0.64; p < 0.0001). fALFF corresponded most strongly with MRGlu (R = 0.79; p < 0.0001), but also significantly (p < 0.0001) with MRO2 (R = 0.68), BF (R = 0.68) and BV (R=0.68). ReHo performed similarly to fALFF, with significant strong correspondence (p < 0.0001) with MRGlu (R = 0.78), MRO2 (R = 0.54), and, but less strongly with BF (R = 0.50) and BV (R=0.50). Mutual information analyses further clarified these physiological interpretations. When conditioned by BV, ALFF retained no significant MRGlu, MRO2 or BF information. When conditioned by MRGlu, fALFF and ReHo retained no significant MRO2, BF or BV information. Of concern, however, the strength of PET-BOLD correspondences varied markedly by brain region, which calls for future investigation on physiological interpretations at a regional and per-subject basis.
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Affiliation(s)
- Shengwen Deng
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA; Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Crystal G Franklin
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA
| | - Michael O'Boyle
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA
| | - Wei Zhang
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA
| | - Betty L Heyl
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA
| | - Paul A Jerabek
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA
| | - Hanzhang Lu
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; South Texas Veterans Health Care System, San Antonio, TX, USA.
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Feng B, Cao J, Yu Y, Yang H, Jiang Y, Liu Y, Wang R, Zhao Q. Gender-Related Differences in Regional Cerebral Glucose Metabolism in Normal Aging Brain. Front Aging Neurosci 2022; 14:809767. [PMID: 35221996 PMCID: PMC8866755 DOI: 10.3389/fnagi.2022.809767] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 01/12/2022] [Indexed: 12/30/2022] Open
Abstract
Objectives: This study was aimed to investigate the gender-related differences of regional cerebral glucose metabolism in healthy people along the age using 18F-FDG PET/CT. Methods: We recruited 344 healthy volunteers, including 217 males and 127 females (age range: 40–89 years old). All subjects underwent fluorine-18 fluorodeoxyglucose(18F-FDG) positron emission tomography (PET). All the data were divided into four groups for every 10 years old. Each participant was carefully screened from PET, MR, and other examinations in order to exclude the abnormalities, such as neurodegenerative or psychiatric disorders, alcohol/abuse, cerebral vascular disorders, metabolic diseases like diabetes mellitus and hyperthyroidism, and other systemic malignancies. The 40–50 years old group was set as the baseline group. Statistical parametric mapping (SPM) analysis was employed to illustrate the differences among groups. Results: Compared to the baseline group, whether in a cohort or different gender groups, the decrease of brain glucose metabolism was shown in the bilateral frontal lobe, anterior cingulate gyrus, and the bilateral temporal lobe. In males, the regions of decreased metabolism were bilateral frontal lobe, caudate nucleus, and cingulate gyrus, whereas that of females were left occipital lobe, cerebellum, and the thalamus. However, the overall decrease of brain metabolism in men and women began from the age of 60s, an aggravated decrease from 70s was only observed in males. Conclusion: (1) An obviously decreased brain metabolism was found from 60 years old, especially in the bilateral frontal lobe, bilateral temporal lobe, and inferior cingulate gyrus; (2) We found specific brain metabolic differences between genders, including the caudate nucleus region in males and the occipital lobe region in females; and (3) The aging trend is different between genders.
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Affiliation(s)
- Bei Feng
- Department of Nuclear Medicine, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Jiang Cao
- Department of Nuclear Medicine, General Hospital of Ningxia Medical University, Yinchuan, China
- Department of Nuclear Medicine, Xi’an Central Hospital, Xi’an, China
| | - YaPing Yu
- Department of Nuclear Medicine, General Hospital of Ningxia Medical University, Yinchuan, China
| | - HaiYan Yang
- Obstetrics and Gynecology Center Functional Examination Department, General Hospital of Ningxia Medical University, Yinchuan, China
| | - YangHongYan Jiang
- Department of Nuclear Medicine, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Ying Liu
- Department of Nuclear Medicine, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Rong Wang
- Department of Nuclear Medicine, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Qian Zhao
- Department of Nuclear Medicine, General Hospital of Ningxia Medical University, Yinchuan, China
- *Correspondence: Qian Zhao
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Gupta SK, Rutherford N, Dolja-Gore X, Watson T, Nair BR. Regional changes with global brain hypometabolism indicates a physiological triage phenomenon and can explain shared pathophysiological events in Alzheimer's & small vessel diseases and delirium. AMERICAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING 2021; 11:492-506. [PMID: 35003887 PMCID: PMC8727878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 10/09/2021] [Indexed: 06/14/2023]
Abstract
While reduced global brain metabolism is known in aging, Alzheimer's disease (AD), small vessel disease (SVD) and delirium, explanation of regional brain metabolic (rBM) changes is a challenge. We hypothesized that this may be explained by "triage phenomenon", to preserve metabolic supply to vital brain areas. We studied changes in rBM in 69 patients with at least 5% decline in global brain metabolism during active lymphoma. There was significant decline in the rBM of the inferior parietal, precuneus, superior parietal, lateral occipital, primary visual cortices (P<0.001) and in the right lateral prefrontal cortex (P=0.01). Some areas showed no change; multiple areas had significantly increased rBM (e.g. medial prefrontal, anterior cingulate, pons, cerebellum and mesial temporal cortices; P<0.001). We conclude the existence of a physiological triage phenomenon and argue a new hypothetical model to explain the shared events in the pathophysiology of aging, AD, SVD and delirium.
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Affiliation(s)
- Sandeep K Gupta
- Department of Nuclear Medicine & PET, John Hunter and Calvary Mater Hospitals, Hunter Medical Research Institute (HMRI), University of NewcastleNewcastle, NSW, Australia
| | - Natalie Rutherford
- Department of Nuclear Medicine & PET, John Hunter and Calvary Mater Hospitals, Hunter Medical Research Institute (HMRI), University of NewcastleNewcastle, NSW, Australia
| | - Xenia Dolja-Gore
- Research Centre for Generational Health and Ageing (RCGHA), Faculty of Health and Medicine, University of NewcastleNewcastle, NSW, Australia
| | - Tahne Watson
- Department of Nuclear Medicine & PET, John Hunter and Calvary Mater Hospitals, Hunter Medical Research Institute (HMRI), University of NewcastleNewcastle, NSW, Australia
| | - Balakrishnan R Nair
- School of Medicine and Public Health, University of NewcastleNewcastle, NSW, Australia
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Frontal lobe hypometabolism associated with Sudden Unexpected Death in Epilepsy (SUDEP) risk: An objective PET study. Epilepsy Behav 2021; 122:108185. [PMID: 34252829 DOI: 10.1016/j.yebeh.2021.108185] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 06/20/2021] [Indexed: 12/11/2022]
Abstract
OBJECTIVE Abnormalities of brain structures and neuronal networks have been identified in MRI studies of patients with Sudden Unexpected Death in Epilepsy (SUDEP) as well as in those at elevated risk. The goal of this study was to identify common patterns of objectively detected brain glucose metabolic abnormalities associated with SUDEP patients and those at high SUDEP risk. METHODS Patients with refractory epilepsy (n = 78, age: 16-61 years, 44 females), who underwent comprehensive presurgical evaluation, were assessed for their risk of SUDEP using the revised SUDEP-7 inventory. From the 57 patients with low SUDEP risk, 35 were selected to match their demographic and clinical characteristics to those with high SUDEP risk (n = 21). [18F]fluoro-deoxy-glucose positron emission tomography (FDG-PET) abnormalities were evaluated in the high- and low-SUDEP risk subgroups compared to FDG-PET scans of a healthy adult control group using statistical parametric mapping (SPM). Individual FDG-PET scans of 4 additional patients, who died from SUDEP, were also analyzed by SPM. RESULTS Mean SUDEP-7 score was 6.1 in the high and 2.7 in the low SUDEP risk group. MRI showed no lesion in 36 patients (64%). Statistical parametric mapping analysis of the high SUDEP risk subgroup showed bilateral medial frontal and inferior frontal hypometabolism as a common pattern. The low-risk group showed no specific common metabolic abnormalities on SPM group analysis. Individual PET scans of all 4 patients who died from SUDEP also showed bilateral frontal lobe hypometabolism. CONCLUSIONS These data show that bilateral frontal lobe involvement on FDG-PET, especially the medial and inferior frontal cortex, may be a common metabolic pattern associated with high SUDEP risk and SUDEP itself, in patients with refractory focal epilepsy.
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Fang XT, Toyonaga T, Hillmer AT, Matuskey D, Holmes SE, Radhakrishnan R, Mecca AP, van Dyck CH, D’Souza DC, Esterlis I, Worhunsky PD, Carson RE. Identifying brain networks in synaptic density PET ( 11C-UCB-J) with independent component analysis. Neuroimage 2021; 237:118167. [PMID: 34000404 PMCID: PMC8452380 DOI: 10.1016/j.neuroimage.2021.118167] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 04/27/2021] [Accepted: 05/11/2021] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND The human brain is inherently organized into distinct networks, as reported widely by resting-state functional magnetic resonance imaging (rs-fMRI), which are based on blood-oxygen-level-dependent (BOLD) signal fluctuations. 11C-UCB-J PET maps synaptic density via synaptic vesicle protein 2A, which is a more direct structural measure underlying brain networks than BOLD rs-fMRI. METHODS The aim of this study was to identify maximally independent brain source networks, i.e., "spatial patterns with common covariance across subjects", in 11C-UCB-J data using independent component analysis (ICA), a data-driven analysis method. Using a population of 80 healthy controls, we applied ICA to two 40-sample subsets and compared source network replication across samples. We examined the identified source networks at multiple model orders, as the ideal number of maximally independent components (IC) is unknown. In addition, we investigated the relationship between the strength of the loading weights for each source network and age and sex. RESULTS Thirteen source networks replicated across both samples. We determined that a model order of 18 components provided stable, replicable components, whereas estimations above 18 were not stable. Effects of sex were found in two ICs. Nine ICs showed age-related change, with 4 remaining significant after correction for multiple comparison. CONCLUSION This study provides the first evidence that human brain synaptic density can be characterized into organized covariance patterns. Furthermore, we demonstrated that multiple synaptic density source networks are associated with age, which supports the potential utility of ICA to identify biologically relevant synaptic density source networks.
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Affiliation(s)
- Xiaotian T. Fang
- Yale PET Center, Department of Radiology and Biomedical Imaging, Yale University, 801 Howard Avenue, New Haven, CT 06520, USA,Corresponding author. (X.T. Fang)
| | - Takuya Toyonaga
- Yale PET Center, Department of Radiology and Biomedical Imaging, Yale University, 801 Howard Avenue, New Haven, CT 06520, USA
| | - Ansel T. Hillmer
- Yale PET Center, Department of Radiology and Biomedical Imaging, Yale University, 801 Howard Avenue, New Haven, CT 06520, USA,Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - David Matuskey
- Yale PET Center, Department of Radiology and Biomedical Imaging, Yale University, 801 Howard Avenue, New Haven, CT 06520, USA,Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA,Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Sophie E. Holmes
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | | | - Adam P. Mecca
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Christopher H. van Dyck
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA,Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | | | - Irina Esterlis
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | | | - Richard E. Carson
- Yale PET Center, Department of Radiology and Biomedical Imaging, Yale University, 801 Howard Avenue, New Haven, CT 06520, USA
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Premi E, Cristillo V, Gazzina S, Benussi A, Alberici A, Cotelli MS, Calhoun VD, Iraji A, Magoni M, Cotelli M, Micheli A, Gasparotti R, Padovani A, Borroni B. Expanding the role of education in frontotemporal dementia: a functional dynamic connectivity (the chronnectome) study. Neurobiol Aging 2020; 93:35-43. [DOI: 10.1016/j.neurobiolaging.2020.04.021] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 04/20/2020] [Accepted: 04/21/2020] [Indexed: 12/14/2022]
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Di X, Wölfer M, Amend M, Wehrl H, Ionescu TM, Pichler BJ, Biswal BB. Interregional causal influences of brain metabolic activity reveal the spread of aging effects during normal aging. Hum Brain Mapp 2019; 40:4657-4668. [PMID: 31389641 DOI: 10.1002/hbm.24728] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 07/08/2019] [Accepted: 07/09/2019] [Indexed: 11/08/2022] Open
Abstract
During healthy brain aging, different brain regions show anatomical or functional declines at different rates, and some regions may show compensatory increases in functional activity. However, few studies have explored interregional influences of brain activity during the aging process. We proposed a causality analysis framework combining high dimensionality independent component analysis (ICA), Granger causality, and least absolute shrinkage and selection operator regression on longitudinal brain metabolic activity data measured by Fludeoxyglucose positron emission tomography (FDG-PET). We analyzed FDG-PET images from healthy old subjects, who were scanned for at least five sessions with an averaged intersession interval of 1 year. The longitudinal data were concatenated across subjects to form a time series, and the first-order autoregressive model was used to measure interregional causality among the independent sources of metabolic activity identified using ICA. Several independent sources with reduced metabolic activity in aging, including the anterior temporal lobe and orbital frontal cortex, demonstrated causal influences over many widespread brain regions. On the other hand, the influenced regions were more distributed, and had smaller age-related declines or even relatively increased metabolic activity. The current data demonstrated interregional spreads of aging on metabolic activity at the scale of a year, and have identified key brain regions in the aging process that have strong influences over other regions.
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Affiliation(s)
- Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey.,School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Marie Wölfer
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey.,Clinical Affective Neuroimaging Laboratory (CANLAB), Otto-von-Guericke-University Magdeburg, Magdeburg, Germany.,Department for Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Mario Amend
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Hans Wehrl
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Tudor M Ionescu
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Bernd J Pichler
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey.,School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
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