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Hanania JU, Reimers E, Bevington CWJ, Sossi V. PET-based brain molecular connectivity in neurodegenerative disease. Curr Opin Neurol 2024; 37:353-360. [PMID: 38813843 DOI: 10.1097/wco.0000000000001283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Abstract
PURPOSE OF REVIEW Molecular imaging has traditionally been used and interpreted primarily in the context of localized and relatively static neurochemical processes. New understanding of brain function and development of novel molecular imaging protocols and analysis methods highlights the relevance of molecular networks that co-exist and interact with functional and structural networks. Although the concept and evidence of disease-specific metabolic brain patterns has existed for some time, only recently has such an approach been applied in the neurotransmitter domain and in the context of multitracer and multimodal studies. This review briefly summarizes initial findings and highlights emerging applications enabled by this new approach. RECENT FINDINGS Connectivity based approaches applied to molecular and multimodal imaging have uncovered molecular networks with neurodegeneration-related alterations to metabolism and neurotransmission that uniquely relate to clinical findings; better disease stratification paradigms; an improved understanding of the relationships between neurochemical and functional networks and their related alterations, although the directionality of these relationships are still unresolved; and a new understanding of the molecular underpinning of disease-related alteration in resting-state brain activity. SUMMARY Connectivity approaches are poised to greatly enhance the information that can be extracted from molecular imaging. While currently mostly contributing to enhancing understanding of brain function, they are highly likely to contribute to the identification of specific biomarkers that will improve disease management and clinical care.
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Affiliation(s)
| | - Erik Reimers
- Department of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada
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2
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Sun Z, Naismith SL, Meikle S, Calamante F. A novel method for PET connectomics guided by fibre-tracking MRI: Application to Alzheimer's disease. Hum Brain Mapp 2024; 45:e26659. [PMID: 38491564 PMCID: PMC10943179 DOI: 10.1002/hbm.26659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 02/20/2024] [Accepted: 02/29/2024] [Indexed: 03/18/2024] Open
Abstract
This study introduces a novel brain connectome matrix, track-weighted PET connectivity (twPC) matrix, which combines positron emission tomography (PET) and diffusion magnetic resonance imaging data to compute a PET-weighted connectome at the individual subject level. The new method is applied to characterise connectivity changes in the Alzheimer's disease (AD) continuum. The proposed twPC samples PET tracer uptake guided by the underlying white matter fibre-tracking streamline point-to-point connectivity calculated from diffusion MRI (dMRI). Using tau-PET, dMRI and T1-weighted MRI from the Alzheimer's Disease Neuroimaging Initiative database, structural connectivity (SC) and twPC matrices were computed and analysed using the network-based statistic (NBS) technique to examine topological alterations in early mild cognitive impairment (MCI), late MCI and AD participants. Correlation analysis was also performed to explore the coupling between SC and twPC. The NBS analysis revealed progressive topological alterations in both SC and twPC as cognitive decline progressed along the continuum. Compared to healthy controls, networks with decreased SC were identified in late MCI and AD, and networks with increased twPC were identified in early MCI, late MCI and AD. The altered network topologies were mostly different between twPC and SC, although with several common edges largely involving the bilateral hippocampus, fusiform gyrus and entorhinal cortex. Negative correlations were observed between twPC and SC across all subject groups, although displaying an overall reduction in the strength of anti-correlation with disease progression. twPC provides a new means for analysing subject-specific PET and MRI-derived information within a hybrid connectome using established network analysis methods, providing valuable insights into the relationship between structural connections and molecular distributions. PRACTITIONER POINTS: New method is proposed to compute patient-specific PET connectome guided by MRI fibre-tracking. Track-weighted PET connectivity (twPC) matrix allows to leverage PET and structural connectivity information. twPC was applied to dementia, to characterise the PET nework abnormalities in Alzheimer's disease and mild cognitive impairment.
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Affiliation(s)
- Zhuopin Sun
- School of Biomedical EngineeringThe University of SydneySydneyNew South WalesAustralia
| | - Sharon L. Naismith
- Brain and Mind CentreThe University of SydneySydneyNew South WalesAustralia
- Faculty of Science, School of PsychologyThe University of SydneySydneyNew South WalesAustralia
- Charles Perkins CenterThe University of SydneySydneyNew South WalesAustralia
| | - Steven Meikle
- Brain and Mind CentreThe University of SydneySydneyNew South WalesAustralia
- Sydney ImagingThe University of SydneySydneyNew South WalesAustralia
- School of Health SciencesThe University of SydneySydneyNew South WalesAustralia
| | - Fernando Calamante
- School of Biomedical EngineeringThe University of SydneySydneyNew South WalesAustralia
- Brain and Mind CentreThe University of SydneySydneyNew South WalesAustralia
- Sydney ImagingThe University of SydneySydneyNew South WalesAustralia
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3
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Wang T, Yan S, Lu J. The effects of noninvasive brain stimulation on cognitive function in patients with mild cognitive impairment and Alzheimer's disease using resting-state functional magnetic resonance imaging: A systematic review and meta-analysis. CNS Neurosci Ther 2023; 29:3160-3172. [PMID: 37349974 PMCID: PMC10580344 DOI: 10.1111/cns.14314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 05/08/2023] [Accepted: 06/06/2023] [Indexed: 06/24/2023] Open
Abstract
OBJECTIVE The aim of this systematic review and meta-analysis was to evaluate the efficacy of noninvasive brain stimulation (NIBS) on cognition using functional magnetic resonance imaging (fMRI) in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD), thus providing the neuroimaging mechanism of cognitive intervention. METHODS English articles published up to April 30, 2023 were searched in the PubMed, Web of Science, Embase, and Cochrane Library databases. We included randomized controlled trials where resting-state fMRI was used to observe the effect of NIBS in patients with MCI or AD. RevMan software was used to analyze the continuous variables, and SDM-PSI software was used to perform an fMRI data analysis. RESULTS A total of 17 studies comprising 258 patients in the treatment group and 256 in the control group were included. After NIBS, MCI patients in the treatment group showed hyperactivation in the right precuneus and decreased activity in the left cuneus and right supplementary motor area. In contrast, patients in the control group showed decreased activity in the right middle frontal gyrus and no hyperactivation. The clinical cognitive scores in MCI patients were significantly improved by NIBS, while not in AD. Some evidence regarding the modulation of NIBS in resting-state brain activity and functional brain networks in patients with AD was found. CONCLUSIONS NIBS could improve cognitive function in patients with MCI and AD. fMRI evaluations could be added to evaluate the contribution of specific NIBS treatment therapeutic effectiveness.
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Affiliation(s)
- Tao Wang
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijingChina
- Key Laboratory of Neurodegenerative DiseasesMinistry of EducationBeijingChina
| | - Shaozhen Yan
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijingChina
- Key Laboratory of Neurodegenerative DiseasesMinistry of EducationBeijingChina
| | - Jie Lu
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijingChina
- Key Laboratory of Neurodegenerative DiseasesMinistry of EducationBeijingChina
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Devrome M, Van Laere K, Koole M. Multiplex core of the human brain using structural, functional and metabolic connectivity derived from hybrid PET-MR imaging. FRONTIERS IN NEUROIMAGING 2023; 2:1115965. [PMID: 37645694 PMCID: PMC10461102 DOI: 10.3389/fnimg.2023.1115965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 07/06/2023] [Indexed: 08/31/2023]
Abstract
With the increasing success of mapping brain networks and availability of multiple MR- and PET-based connectivity measures, the need for novel methodologies to unravel the structure and function of the brain at multiple spatial and temporal scales is emerging. Therefore, in this work, we used hybrid PET-MR data of healthy volunteers (n = 67) to identify multiplex core nodes in the human brain. First, monoplex networks of structural, functional and metabolic connectivity were constructed, and consequently combined into a multiplex SC-FC-MC network by linking the same nodes categorically across layers. Taking into account the multiplex nature using a tensorial approach, we identified a set of core nodes in this multiplex network based on a combination of eigentensor centrality and overlapping degree. We introduced a coreness coefficient, which mitigates the effect of modeling parameters to obtain robust results. The proposed methodology was applied onto young and elderly healthy volunteers, where differences observed in the monoplex networks persisted in the multiplex as well. The multiplex core showed a decreased contribution to the default mode and salience network, while an increased contribution to the dorsal attention and somatosensory network was observed in the elderly population. Moreover, a clear distinction in eigentensor centrality was found between young and elderly healthy volunteers.
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Affiliation(s)
- Martijn Devrome
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, Katholieke Universiteit (KU) Leuven, Leuven, Belgium
| | - Koen Van Laere
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, Katholieke Universiteit (KU) Leuven, Leuven, Belgium
- Division of Nuclear Medicine, Universitair Ziekenhuis (UZ) Leuven, Leuven, Belgium
| | - Michel Koole
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, Katholieke Universiteit (KU) Leuven, Leuven, Belgium
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Yang Z, Chen Y, Hou X, Xu Y, Bai F. Topologically convergent and divergent large scale complex networks among Alzheimer's disease spectrum patients: A systematic review. Heliyon 2023; 9:e15389. [PMID: 37101638 PMCID: PMC10123263 DOI: 10.1016/j.heliyon.2023.e15389] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 03/16/2023] [Accepted: 04/05/2023] [Indexed: 04/28/2023] Open
Abstract
Alzheimer's disease (AD) is associated with disruption at the level of a large-scale complex network. To explore the underlying mechanisms in the progression of AD, graph theory was used to quantitatively analyze the topological properties of structural and functional connections. Although an increasing number of studies have shown altered global and nodal network properties, little is known about the topologically convergent and divergent patterns between structural and functional networks among AD-spectrum patients. In this review, we summarized the topological patterns of the large-scale complex networks using multimodal neuroimaging graph theory analysis in AD spectrum patients. Convergent deficits in the connectivity characteristics were primarily in the default mode network (DMN) itself both in the structural and functional networks, while a divergent changes in the neighboring regions of the DMN were also observed between the patient groups. Together, the application of graph theory to large-scale complex brain networks provides quantitative insights into topological principles of brain network organization, which may lead to increasing attention in identifying the underlying neuroimaging pathological changes and predicting the progression of AD.
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Affiliation(s)
- Zhiyuan Yang
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Ya Chen
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing 210008, China
| | - Xinle Hou
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
- Department of Neurology, Nanjing Drum Tower Hospital, State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing 210008, China
| | - Feng Bai
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
- Geriatric Medicine Center, Affiliated Taikang Xianlin Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
- Correspondence to: 321 Zhongshan Road, Nanjing, 210008, China.
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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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Protas H, Ghisays V, Goradia DD, Bauer R, Devadas V, Chen K, Reiman EM, Su Y. Individualized network analysis: A novel approach to investigate tau PET using graph theory in the Alzheimer's disease continuum. Front Neurosci 2023; 17:1089134. [PMID: 36937677 PMCID: PMC10017746 DOI: 10.3389/fnins.2023.1089134] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 02/14/2023] [Indexed: 03/06/2023] Open
Abstract
Introduction Tau PET imaging has emerged as an important tool to detect and monitor tangle burden in vivo in the study of Alzheimer's disease (AD). Previous studies demonstrated the association of tau burden with cognitive decline in probable AD cohorts. This study introduces a novel approach to analyze tau PET data by constructing individualized tau network structure and deriving its graph theory-based measures. We hypothesize that the network- based measures are a measure of the total tau load and the stage through disease. Methods Using tau PET data from the AD Neuroimaging Initiative from 369 participants, we determine the network measures, global efficiency, global strength, and limbic strength, and compare with two regional measures entorhinal and tau composite SUVR, in the ability to differentiate, cognitively unimpaired (CU), MCI and AD. We also investigate the correlation of these network and regional measures and a measure of memory performance, auditory verbal learning test for long-term recall memory (AVLT-LTM). Finally, we determine the stages based on global efficiency and limbic strength using conditional inference trees and compare with Braak staging. Results We demonstrate that the derived network measures are able to differentiate three clinical stages of AD, CU, MCI, and AD. We also demonstrate that these network measures are strongly correlated with memory performance overall. Unlike regional tau measurements, the tau network measures were significantly associated with AVLT-LTM even in cognitively unimpaired individuals. Stages determined from global efficiency and limbic strength, visually resembled Braak staging. Discussion The strong correlations with memory particularly in CU suggest the proposed technique may be used to characterize subtle early tau accumulation. Further investigation is ongoing to examine this technique in a longitudinal setting.
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Affiliation(s)
- Hillary Protas
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
- Arizona Alzheimer’s Consortium, Phoenix, AZ, United States
| | - Valentina Ghisays
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
- Arizona Alzheimer’s Consortium, Phoenix, AZ, United States
| | - Dhruman D. Goradia
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
- Arizona Alzheimer’s Consortium, Phoenix, AZ, United States
| | - Robert Bauer
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
- Arizona Alzheimer’s Consortium, Phoenix, AZ, United States
| | - Vivek Devadas
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
- Arizona Alzheimer’s Consortium, Phoenix, AZ, United States
| | - Kewei Chen
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
- Arizona Alzheimer’s Consortium, Phoenix, AZ, United States
- Department of Neurology, The University of Arizona, Tucson, AZ, United States
- Department of Psychiatry, The University of Arizona, Tucson, AZ, United States
- Department of Neuroscience, School of Computing and Augmented Intelligence, Biostatistical Core, School of Mathematics and Statistics, College of Health Solutions, Arizona State University, Tempe, AZ, United States
| | - Eric M. Reiman
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
- Arizona Alzheimer’s Consortium, Phoenix, AZ, United States
- Department of Neurology, The University of Arizona, Tucson, AZ, United States
- Department of Psychiatry, The University of Arizona, Tucson, AZ, United States
- Department of Neuroscience, School of Computing and Augmented Intelligence, Biostatistical Core, School of Mathematics and Statistics, College of Health Solutions, Arizona State University, Tempe, AZ, United States
- Translational Genomics Research Institute, Phoenix, AZ, United States
| | - Yi Su
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
- Arizona Alzheimer’s Consortium, Phoenix, AZ, United States
- Department of Neuroscience, School of Computing and Augmented Intelligence, Biostatistical Core, School of Mathematics and Statistics, College of Health Solutions, Arizona State University, Tempe, AZ, United States
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8
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Fathian A, Jamali Y, Raoufy MR. The trend of disruption in the functional brain network topology of Alzheimer's disease. Sci Rep 2022; 12:14998. [PMID: 36056059 PMCID: PMC9440254 DOI: 10.1038/s41598-022-18987-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 08/23/2022] [Indexed: 12/19/2022] Open
Abstract
Alzheimer's disease (AD) is a progressive disorder associated with cognitive dysfunction that alters the brain's functional connectivity. Assessing these alterations has become a topic of increasing interest. However, a few studies have examined different stages of AD from a complex network perspective that cover different topological scales. This study used resting state fMRI data to analyze the trend of functional connectivity alterations from a cognitively normal (CN) state through early and late mild cognitive impairment (EMCI and LMCI) and to Alzheimer's disease. The analyses had been done at the local (hubs and activated links and areas), meso (clustering, assortativity, and rich-club), and global (small-world, small-worldness, and efficiency) topological scales. The results showed that the trends of changes in the topological architecture of the functional brain network were not entirely proportional to the AD progression. There were network characteristics that have changed non-linearly regarding the disease progression, especially at the earliest stage of the disease, i.e., EMCI. Further, it has been indicated that the diseased groups engaged somatomotor, frontoparietal, and default mode modules compared to the CN group. The diseased groups also shifted the functional network towards more random architecture. In the end, the methods introduced in this paper enable us to gain an extensive understanding of the pathological changes of the AD process.
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Affiliation(s)
- Alireza Fathian
- Biomathematics Laboratory, Department of Applied Mathematics, School of Mathematical Science, Tarbiat Modares University, Tehran, Iran
| | - Yousef Jamali
- Biomathematics Laboratory, Department of Applied Mathematics, School of Mathematical Science, Tarbiat Modares University, Tehran, Iran.
- Applied Systems Biology, Leibniz-Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Jena, Germany.
| | - Mohammad Reza Raoufy
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
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9
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Sun T, Wang Z, Wu Y, Gu F, Li X, Bai Y, Shen C, Hu Z, Liang D, Liu X, Zheng H, Yang Y, El Fakhri G, Zhou Y, Wang M. Identifying the individual metabolic abnormities from a systemic perspective using whole-body PET imaging. Eur J Nucl Med Mol Imaging 2022; 49:2994-3004. [PMID: 35567627 PMCID: PMC9106794 DOI: 10.1007/s00259-022-05832-7] [Citation(s) in RCA: 3] [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: 01/14/2022] [Accepted: 05/01/2022] [Indexed: 12/28/2022]
Abstract
Introduction Distinct physiological states arise from complex interactions among the various organs present in the human body. PET is a non-invasive modality with numerous successful applications in oncology, neurology, and cardiology. However, while PET imaging has been applied extensively in detecting focal lesions or diseases, its potential in detecting systemic abnormalities is seldom explored, mostly because total-body imaging was not possible until recently. Methods In this context, the present study proposes a framework capable of constructing an individual metabolic abnormality network using a subject’s whole-body 18F-FDG SUV image and a normal control database. The developed framework was evaluated in the patients with lung cancer, the one discharged after suffering from Covid-19 disease, and the one that had gastrointestinal bleeding with the underlying cause unknown. Results The framework could successfully capture the deviation of these patients from healthy subjects at the level of both system and organ. The strength of the altered network edges revealed the abnormal metabolic connection between organs. The overall deviation of the network nodes was observed to be highly correlated to the organ SUV measures. Therefore, the molecular connectivity of glucose metabolism was characterized at a single subject level. Conclusion The proposed framework represents a significant step toward the use of PET imaging for identifying metabolic dysfunction from a systemic perspective. A better understanding of the underlying biological mechanisms and the physiological interpretation of the interregional connections identified in the present study warrant further research.
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Affiliation(s)
- Tao Sun
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China.
| | - Zhenguo Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China
| | - Yaping Wu
- Department of Medical Imaging, Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Fengyun Gu
- Central Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, People's Republic of China
- Department of Statistics, School of Mathematical Sciences, University College Cork, Cork, Ireland
| | - Xiaochen Li
- Department of Medical Imaging, Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Yan Bai
- Department of Medical Imaging, Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Chushu Shen
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China
| | - Zhanli Hu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China
| | - Xin Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China
| | - Yongfeng Yang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China
| | - Georges El Fakhri
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Yun Zhou
- Central Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, People's Republic of China
- School of Biomedical Engineering, Shanghai Tech University, Shanghai, People's Republic of China
| | - Meiyun Wang
- Department of Medical Imaging, Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Zhengzhou, People's Republic of China.
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10
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Canal-Garcia A, Gómez-Ruiz E, Mijalkov M, Chang YW, Volpe G, Pereira JB. Multiplex Connectome Changes across the Alzheimer’s Disease Spectrum Using Gray Matter and Amyloid Data. Cereb Cortex 2022; 32:3501-3515. [PMID: 35059722 PMCID: PMC9376877 DOI: 10.1093/cercor/bhab429] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 10/27/2021] [Accepted: 10/28/2021] [Indexed: 11/25/2022] Open
Abstract
The organization of the Alzheimer’s disease (AD) connectome has been studied using graph theory using single neuroimaging modalities such as positron emission tomography (PET) or structural magnetic resonance imaging (MRI). Although these modalities measure distinct pathological processes that occur in different stages in AD, there is evidence that they are not independent from each other. Therefore, to capture their interaction, in this study we integrated amyloid PET and gray matter MRI data into a multiplex connectome and assessed the changes across different AD stages. We included 135 cognitively normal (CN) individuals without amyloid-β pathology (Aβ−) in addition to 67 CN, 179 patients with mild cognitive impairment (MCI) and 132 patients with AD dementia who all had Aβ pathology (Aβ+) from the Alzheimer’s Disease Neuroimaging Initiative. We found widespread changes in the overlapping connectivity strength and the overlapping connections across Aβ-positive groups. Moreover, there was a reorganization of the multiplex communities in MCI Aβ + patients and changes in multiplex brain hubs in both MCI Aβ + and AD Aβ + groups. These findings offer a new insight into the interplay between amyloid-β pathology and brain atrophy over the course of AD that moves beyond traditional graph theory analyses based on single brain networks.
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Affiliation(s)
- Anna Canal-Garcia
- Address correspondence to Department of NVS, Division of Clinical Geriatrics, NEO seventh floor, Blickagången 16, 141 52 Huddinge, Sweden. ; Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
| | | | - Mite Mijalkov
- Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden
| | - Yu-Wei Chang
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Giovanni Volpe
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Joana B Pereira
- Address correspondence to Department of NVS, Division of Clinical Geriatrics, NEO seventh floor, Blickagången 16, 141 52 Huddinge, Sweden. ; Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
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11
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Liu Y, Li Z, Jiang X, Du W, Wang X, Sheng C, Jiang J, Han Y. Differences in Functional Brain Networks Between Subjective Cognitive Decline with and without Worry Groups: A Graph Theory Study from SILCODE. J Alzheimers Dis 2021; 84:1279-1289. [PMID: 34657889 DOI: 10.3233/jad-215156] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Evidence suggests that subjective cognitive decline (SCD) individuals with worry have a higher risk of cognitive decline. However, how SCD-related worry influences the functional brain network is still unknown. OBJECTIVE In this study, we aimed to explore the differences in functional brain networks between SCD subjects with and without worry. METHODS A total of 228 participants were enrolled from the Sino Longitudinal Study on Cognitive Decline (SILCODE), including 39 normal control (NC) subjects, 117 SCD subjects with worry, and 72 SCD subjects without worry. All subjects completed neuropsychological assessments, APOE genotyping, and resting-state functional magnetic resonance imaging (rs-fMRI). Graph theory was applied for functional brain network analysis based on both the whole brain and default mode network (DMN). Parameters including the clustering coefficient, shortest path length, local efficiency, and global efficiency were calculated. Two-sample T-tests and chi-square tests were used to analyze differences between two groups. In addition, a false discovery rate-corrected post hoc test was applied. RESULTS Our analysis showed that compared to the SCD without worry group, SCD with worry group had significantly increased functional connectivity and shortest path length (p = 0.002) and a decreased clustering coefficient (p = 0.013), global efficiency (p = 0.001), and local efficiency (p < 0.001). The above results appeared in both the whole brain and DMN. CONCLUSION There were significant differences in functional brain networks between SCD individuals with and without worry. We speculated that worry might result in alterations of the functional brain network for SCD individuals and then result in a higher risk of cognitive decline.
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Affiliation(s)
- Yi Liu
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Zhuoyuan Li
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Xueyan Jiang
- Biomedical Engineering Institute, Hainan University, Haikou, China.,German Center for Neurodegenerative Disease, Clinical Research Group, Bonn, Germany
| | - Wenying Du
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Xiaoqi Wang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Can Sheng
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Jiehui Jiang
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China.,Biomedical Engineering Institute, Hainan University, Haikou, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,National Clinical Research Center for Geriatric Disorders, Beijing, China
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12
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Relationships between frontal metabolites and Alzheimer's disease biomarkers in cognitively normal older adults. Neurobiol Aging 2021; 109:22-30. [PMID: 34638000 DOI: 10.1016/j.neurobiolaging.2021.09.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Revised: 09/12/2021] [Accepted: 09/13/2021] [Indexed: 12/18/2022]
Abstract
Elevated expression of β-amyloid (Aβ1-42) and tau are considered risk-factors for Alzheimer's disease in healthy older adults. We investigated the effect of aging and cerebrospinal fluid levels of Aβ1-42 and tau on 1) frontal metabolites measured with proton magnetic resonance spectroscopy (MRS) and 2) cognition in cognitively normal older adults (n = 144; age range 50-85). Levels of frontal gamma aminobutyric acid (GABA+) and myo-inositol relative to creatine (mI/tCr) were predicted by age. Levels of GABA+ predicted cognitive performance better than mI/tCr. Additionally, we found that frontal levels of n-acetylaspartate relative to creatine (tNAA/tCr) were predicted by levels of t-tau. In cognitively normal older adults, levels of frontal GABA+ and mI/tCr are predicted by aging, with levels of GABA+ decreasing with age and the opposite for mI/tCr. These results suggest that age- and biomarker-related changes in brain metabolites are not only located in the posterior cortex as suggested by previous studies and further demonstrate that MRS is a viable tool in the study of aging and biomarkers associated with pathological aging and Alzheimer's disease.
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13
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Peira E, Grazzini M, Bauckneht M, Sensi F, Bosco P, Arnaldi D, Morbelli S, Chincarini A, Pardini M, Nobili F. Probing the Role of a Regional Quantitative Assessment of Amyloid PET. J Alzheimers Dis 2021; 80:383-396. [PMID: 33554908 DOI: 10.3233/jad-201156] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND In clinical practice, the amy-PET is globally inspected to provide a binary outcome, but the role of a regional assessment has not been fully investigated yet. OBJECTIVE To deepen the role of regional amyloid burden and its implication on clinical-neuropsychological features. MATERIALS Amy-PET and a complete neuropsychological assessment (Trail Making Test, Rey Auditory Verbal Learning Test, semantic verbal fluency, Symbol Digit, Stroop, visuoconstruction) were available in 109 patients with clinical suspicion of Alzheimer's disease. By averaging the standardized uptake value ratio and ELBA, a regional quantification was calculated for each scan. Patients were grouped according to their overall amyloid load: correlation maps, based on regional quantification, were calculated and compared. A regression analysis between neuropsychological assessment and the regional amyloid-β (Aβ) load was carried out. RESULTS Significant differences were observed between the correlation maps of patients at increasing levels of Aβ and the overall dataset. The Aβ uptake of the subcortical gray matter resulted not related to other brain regions independently of the global Aβ level. A significant association of semantic verbal fluency was observed with ratios of cortical and subcortical distribution of Aβ which represent a coarse measure of differences in regional distribution of Aβ. CONCLUSION Our observations confirmed the different susceptibility to Aβ accumulation among brain regions. The association between cognition and Aβ distribution deserves further investigations: it is possibly due to a direct local effect or it represents a proxy marker of a more aggressive disease subtype. Regional Aβ assessment represents an available resource on amy-PET scan with possibly clinical and prognostic implications.
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Affiliation(s)
- Enrico Peira
- INFN, Genoa, Italy.,Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Child and Maternal Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Matteo Grazzini
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Child and Maternal Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Matteo Bauckneht
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy.,Nuclear Medicine Unit, Dept. of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | | | | | - Dario Arnaldi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Child and Maternal Health (DINOGMI), University of Genoa, Genoa, Italy.,Neurology Clinic, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Silvia Morbelli
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy.,Nuclear Medicine Unit, Dept. of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | | | - Matteo Pardini
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Child and Maternal Health (DINOGMI), University of Genoa, Genoa, Italy.,Neurology Clinic, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Flavio Nobili
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Child and Maternal Health (DINOGMI), University of Genoa, Genoa, Italy.,Neurology Clinic, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
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14
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Garcia-Cabello E, Gonzalez-Burgos L, Pereira JB, Hernández-Cabrera JA, Westman E, Volpe G, Barroso J, Ferreira D. The Cognitive Connectome in Healthy Aging. Front Aging Neurosci 2021; 13:694254. [PMID: 34489673 PMCID: PMC8416612 DOI: 10.3389/fnagi.2021.694254] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 07/23/2021] [Indexed: 11/17/2022] Open
Abstract
Objectives: Cognitive aging has been extensively investigated using both univariate and multivariate analyses. Sophisticated multivariate approaches such as graph theory could potentially capture unknown complex associations between multiple cognitive variables. The aim of this study was to assess whether cognition is organized into a structure that could be called the “cognitive connectome,” and whether such connectome differs between age groups. Methods: A total of 334 cognitively unimpaired individuals were stratified into early-middle-age (37–50 years, n = 110), late-middle-age (51–64 years, n = 106), and elderly (65–78 years, n = 118) groups. We built cognitive networks from 47 cognitive variables for each age group using graph theory and compared the groups using different global and nodal graph measures. Results: We identified a cognitive connectome characterized by five modules: verbal memory, visual memory—visuospatial abilities, procedural memory, executive—premotor functions, and processing speed. The elderly group showed reduced transitivity and average strength as well as increased global efficiency compared with the early-middle-age group. The late-middle-age group showed reduced global and local efficiency and modularity compared with the early-middle-age group. Nodal analyses showed the important role of executive functions and processing speed in explaining the differences between age groups. Conclusions: We identified a cognitive connectome that is rather stable during aging in cognitively healthy individuals, with the observed differences highlighting the important role of executive functions and processing speed. We translated the connectome concept from the neuroimaging field to cognitive data, demonstrating its potential to advance our understanding of the complexity of cognitive aging.
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Affiliation(s)
- Eloy Garcia-Cabello
- Department of Clinical Psychology, Psychobiology and Methodology, Faculty of Psychology, University of La Laguna, La Laguna, Spain
| | - Lissett Gonzalez-Burgos
- Department of Clinical Psychology, Psychobiology and Methodology, Faculty of Psychology, University of La Laguna, La Laguna, Spain.,Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden
| | - Joana B Pereira
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden.,Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Juan Andres Hernández-Cabrera
- Department of Clinical Psychology, Psychobiology and Methodology, Faculty of Psychology, University of La Laguna, La Laguna, Spain
| | - Eric Westman
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Giovanni Volpe
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - José Barroso
- Department of Clinical Psychology, Psychobiology and Methodology, Faculty of Psychology, University of La Laguna, La Laguna, Spain
| | - Daniel Ferreira
- Department of Clinical Psychology, Psychobiology and Methodology, Faculty of Psychology, University of La Laguna, La Laguna, Spain.,Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden.,Department of Radiology, Mayo Clinic, Rochester, MN, United States
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15
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Kapasi A, Yu L, Stewart C, Schneider JA, Bennett DA, Boyle PA. Association of Amyloid-β Pathology with Decision Making and Scam Susceptibility. J Alzheimers Dis 2021; 83:879-887. [PMID: 34366345 DOI: 10.3233/jad-210356] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND Recent findings suggest that poor decision making and increased scam susceptibility are harbingers of Alzheimer's disease (AD) dementia and may be among the earliest behavioral manifestations of pathologic cognitive aging. However, the degree to which poor decision making and scam susceptibility reflect accumulating Alzheimer's disease (AD) pathology remains unclear. OBJECTIVE To investigate the associations of AD pathology with decision making and scam susceptibility in older adults without dementia. METHODS Data came from 198 deceased participants without clinical dementia (mean age at death = 90 years; 69%women) from two ongoing studies of aging. All underwent annual clinical evaluations, completed assessments of healthcare and financial decision making and scam susceptibility, and brain donation. Neuropathologic evaluations quantified pathologic hallmarks of AD, amyloid-β and tau-tangles, Lewy body pathology, and TDP-43 proteinopathy. RESULTS In linear regression models adjusted for demographics, amyloid-β pathology was associated with lower decision making (estimate = -0.35; SE = 0.16, p = 0.03), particularly healthcare decision making (estimate = -0.20; SE = 0.09, p = 0.03), as well as greater scam susceptibility (estimate = 0.12; SE = 0.04, p = 0.003); tau-tangle pathology was not related. Further, TDP-43 pathology was associated with greater scam susceptibility (estimate = 0.10; SE = 0.04; p = 0.02). CONCLUSION Accumulating AD pathology, particularly amyloid-β, is associated with poor decision making and increased scam susceptibility among older persons without overt cognitive impairment. These findings provide compelling evidence that decision making and scam susceptibility are sensitive to the earliest pathological changes of AD.
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Affiliation(s)
- Alifiya Kapasi
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.,Department of Pathology (Neuropathology), Rush University Medical Center, Chicago, IL, USA
| | - Lei Yu
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.,Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Christopher Stewart
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Julie A Schneider
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.,Department of Pathology (Neuropathology), Rush University Medical Center, Chicago, IL, USA.,Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.,Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Patricia A Boyle
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.,Department of Behavioral Sciences, Rush University Medical Center, Chicago, IL, USA
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16
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Gonzalez-Escamilla G, Miederer I, Grothe MJ, Schreckenberger M, Muthuraman M, Groppa S. Metabolic and amyloid PET network reorganization in Alzheimer's disease: differential patterns and partial volume effects. Brain Imaging Behav 2021; 15:190-204. [PMID: 32125613 PMCID: PMC7835313 DOI: 10.1007/s11682-019-00247-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disorder, considered a disconnection syndrome with regional molecular pattern abnormalities quantifiable by the aid of PET imaging. Solutions for accurate quantification of network dysfunction are scarce. We evaluate the extent to which PET molecular markers reflect quantifiable network metrics derived through the graph theory framework and how partial volume effects (PVE)-correction (PVEc) affects these PET-derived metrics 75 AD patients and 126 cognitively normal older subjects (CN). Therefore our goal is twofold: 1) to evaluate the differential patterns of [18F]FDG- and [18F]AV45-PET data to depict AD pathology; and ii) to analyse the effects of PVEc on global uptake measures of [18F]FDG- and [18F]AV45-PET data and their derived covariance network reconstructions for differentiating between patients and normal older subjects. Network organization patterns were assessed using graph theory in terms of “degree”, “modularity”, and “efficiency”. PVEc evidenced effects on global uptake measures that are specific to either [18F]FDG- or [18F]AV45-PET, leading to increased statistical differences between the groups. PVEc was further shown to influence the topological characterization of PET-derived covariance brain networks, leading to an optimised characterization of network efficiency and modularisation. Partial-volume effects correction improves the interpretability of PET data in AD and leads to optimised characterization of network properties for organisation or disconnection.
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Affiliation(s)
- Gabriel Gonzalez-Escamilla
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131, Mainz, Germany.
| | - Isabelle Miederer
- Department of Nuclear Medicine, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Michel J Grothe
- German Center for Neurodegenerative Diseases (DZNE) - Rostock/Greifswald, Rostock, Germany
| | - Mathias Schreckenberger
- Department of Nuclear Medicine, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Muthuraman Muthuraman
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Sergiu Groppa
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131, Mainz, Germany
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17
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Jonkman LE, Steenwijk MD, Boesen N, Rozemuller AJM, Barkhof F, Geurts JJG, Douw L, van de Berg WDJ. Relationship between β-amyloid and structural network topology in decedents without dementia. Neurology 2020; 95:e532-e544. [PMID: 32661099 PMCID: PMC7455348 DOI: 10.1212/wnl.0000000000009910] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Accepted: 01/14/2020] [Indexed: 01/09/2023] Open
Abstract
OBJECTIVE To investigate the association between β-amyloid (Aβ) load and postmortem structural network topology in decedents without dementia. METHODS Fourteen decedents (mean age at death 72.6 ± 7.2 years) without known clinical diagnosis of neurodegenerative disease and meeting pathology criteria only for no or low Alzheimer disease (AD) pathologic change were selected from the Normal Aging Brain Collection Amsterdam database. In situ brain MRI included 3D T1-weighted images for anatomical registration and diffusion tensor imaging for probabilistic tractography with subsequent structural network construction. Network topologic measures of centrality (degree), integration (global efficiency), and segregation (clustering and local efficiency) were calculated. Tissue sections from 12 cortical regions were sampled and immunostained for Aβ and hyperphosphorylated tau (p-tau), and histopathologic burden was determined. Linear mixed effect models were used to assess the relationship between Aβ and p-tau load and network topologic measures. RESULTS Aβ was present in 79% of cases and predominantly consisted of diffuse plaques; p-tau was sparsely present. Linear mixed effect models showed independent negative associations between Aβ load and global efficiency (β = -0.83 × 10-3, p = 0.014), degree (β = -0.47, p = 0.034), and clustering (β = -0.55 × 10-2, p = 0.043). A positive association was present between Aβ load and local efficiency (β = 3.16 × 10-3, p = 0.035). Regionally, these results were significant in the posterior cingulate cortex (PCC) for degree (β = -2.22, p < 0.001) and local efficiency (β = 1.01 × 10-2, p = 0.014) and precuneus for clustering (β = -0.91 × 10-2, p = 0.017). There was no relationship between p-tau and network topology. CONCLUSION This study in deceased adults with AD-related pathologic change provides evidence for a relationship among early Aβ accumulation, predominantly of the diffuse type, and structural network topology, specifically of the PCC and precuneus.
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Affiliation(s)
- Laura E Jonkman
- From the Departments of Anatomy and Neurosciences (L.E.J., M.D.S., N.B., J.J.G.G., L.D., W.D.J.v.d.B.), Pathology (A.J.M.R.), and Radiology and Nuclear Medicine (F.B.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK.
| | - Martijn D Steenwijk
- From the Departments of Anatomy and Neurosciences (L.E.J., M.D.S., N.B., J.J.G.G., L.D., W.D.J.v.d.B.), Pathology (A.J.M.R.), and Radiology and Nuclear Medicine (F.B.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK
| | - Nicky Boesen
- From the Departments of Anatomy and Neurosciences (L.E.J., M.D.S., N.B., J.J.G.G., L.D., W.D.J.v.d.B.), Pathology (A.J.M.R.), and Radiology and Nuclear Medicine (F.B.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK
| | - Annemieke J M Rozemuller
- From the Departments of Anatomy and Neurosciences (L.E.J., M.D.S., N.B., J.J.G.G., L.D., W.D.J.v.d.B.), Pathology (A.J.M.R.), and Radiology and Nuclear Medicine (F.B.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK
| | - Frederik Barkhof
- From the Departments of Anatomy and Neurosciences (L.E.J., M.D.S., N.B., J.J.G.G., L.D., W.D.J.v.d.B.), Pathology (A.J.M.R.), and Radiology and Nuclear Medicine (F.B.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK
| | - Jeroen J G Geurts
- From the Departments of Anatomy and Neurosciences (L.E.J., M.D.S., N.B., J.J.G.G., L.D., W.D.J.v.d.B.), Pathology (A.J.M.R.), and Radiology and Nuclear Medicine (F.B.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK
| | - Linda Douw
- From the Departments of Anatomy and Neurosciences (L.E.J., M.D.S., N.B., J.J.G.G., L.D., W.D.J.v.d.B.), Pathology (A.J.M.R.), and Radiology and Nuclear Medicine (F.B.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK
| | - Wilma D J van de Berg
- From the Departments of Anatomy and Neurosciences (L.E.J., M.D.S., N.B., J.J.G.G., L.D., W.D.J.v.d.B.), Pathology (A.J.M.R.), and Radiology and Nuclear Medicine (F.B.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK
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18
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Kim S, Kim YW, Jeon H, Im CH, Lee SH. Altered Cortical Thickness-Based Individualized Structural Covariance Networks in Patients with Schizophrenia and Bipolar Disorder. J Clin Med 2020; 9:jcm9061846. [PMID: 32545747 PMCID: PMC7356298 DOI: 10.3390/jcm9061846] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Revised: 06/10/2020] [Accepted: 06/11/2020] [Indexed: 12/19/2022] Open
Abstract
Structural covariance is described as coordinated variation in brain morphological features, such as cortical thickness and volume, among brain structures functionally or anatomically interconnected to one another. Structural covariance networks, based on graph theory, have been studied in mental disorders. This analysis can help in understanding the brain mechanisms of schizophrenia and bipolar disorder. We investigated cortical thickness-based individualized structural covariance networks in patients with schizophrenia and bipolar disorder. T1-weighted magnetic resonance images were obtained from 39 patients with schizophrenia, 37 patients with bipolar disorder type I, and 32 healthy controls, and cortical thickness was analyzed via a surface-based morphometry analysis. The structural covariance of cortical thickness was calculated at the individual level, and covariance networks were analyzed based on graph theoretical indices: strength, clustering coefficient (CC), path length (PL) and efficiency. At the global level, both patient groups showed decreased strength, CC and efficiency, and increased PL, compared to healthy controls. In bipolar disorder, we found intermediate network measures among the groups. At the nodal level, schizophrenia patients showed decreased CCs in the left suborbital sulcus and the right superior frontal sulcus, compared to bipolar disorder patients. In addition, patient groups showed decreased CCs in the right insular cortex and the left superior occipital gyrus. Global-level network indices, including strength, CCs and efficiency, positively correlated, while PL negatively correlated, with the positive symptoms of the Positive and Negative Syndrome Scale for patients with schizophrenia. The nodal-level CC of the right insular cortex positively correlated with the positive symptoms of schizophrenia, while that of the left superior occipital gyrus positively correlated with the Young Mania Rating Scale scores for bipolar disorder. Altered cortical structural networks were revealed in patients, and particularly, the prefrontal regions were more altered in schizophrenia. Furthermore, altered cortical structural networks in both patient groups correlated with core pathological symptoms, indicating that the insular cortex is more vulnerable in schizophrenia, and the superior occipital gyrus is more vulnerable in bipolar disorder. Our individualized structural covariance network indices might be promising biomarkers for the evaluation of patients with schizophrenia and bipolar disorder.
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Affiliation(s)
- Sungkean Kim
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA;
| | - Yong-Wook Kim
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, Korea; (Y.-W.K.); (C.-H.I.)
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang 411-706, Korea;
| | - Hyeonjin Jeon
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang 411-706, Korea;
| | - Chang-Hwan Im
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, Korea; (Y.-W.K.); (C.-H.I.)
| | - Seung-Hwan Lee
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang 411-706, Korea;
- Department of Psychiatry, Ilsan Paik Hospital, College of Medicine, Inje University, Juhwa-ro 170, Ilsanseo-Gu, Goyang 411-706, Korea
- Correspondence: ; Tel.: +82-31-910-7260; Fax: +82-31-910-7268
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19
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Young PNE, Estarellas M, Coomans E, Srikrishna M, Beaumont H, Maass A, Venkataraman AV, Lissaman R, Jiménez D, Betts MJ, McGlinchey E, Berron D, O'Connor A, Fox NC, Pereira JB, Jagust W, Carter SF, Paterson RW, Schöll M. Imaging biomarkers in neurodegeneration: current and future practices. Alzheimers Res Ther 2020; 12:49. [PMID: 32340618 PMCID: PMC7187531 DOI: 10.1186/s13195-020-00612-7] [Citation(s) in RCA: 82] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 04/01/2020] [Indexed: 12/12/2022]
Abstract
There is an increasing role for biological markers (biomarkers) in the understanding and diagnosis of neurodegenerative disorders. The application of imaging biomarkers specifically for the in vivo investigation of neurodegenerative disorders has increased substantially over the past decades and continues to provide further benefits both to the diagnosis and understanding of these diseases. This review forms part of a series of articles which stem from the University College London/University of Gothenburg course "Biomarkers in neurodegenerative diseases". In this review, we focus on neuroimaging, specifically positron emission tomography (PET) and magnetic resonance imaging (MRI), giving an overview of the current established practices clinically and in research as well as new techniques being developed. We will also discuss the use of machine learning (ML) techniques within these fields to provide additional insights to early diagnosis and multimodal analysis.
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Affiliation(s)
- Peter N E Young
- Wallenberg Centre for Molecular and Translational Medicine and the Department of Psychiatry and Neurochemistry, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Mar Estarellas
- Centre for Medical Image Computing (CMIC), Department of Computer Science & Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Emma Coomans
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Meera Srikrishna
- Wallenberg Centre for Molecular and Translational Medicine and the Department of Psychiatry and Neurochemistry, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Helen Beaumont
- Neuroscience and Aphasia Research Unit, Division of Neuroscience and Experimental Psychology, The University of Manchester, Manchester, UK
| | - Anne Maass
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Ashwin V Venkataraman
- Division of Brain Sciences, Imperial College London, London, UK
- United Kingdom Dementia Research Institute, Imperial College London, London, UK
| | - Rikki Lissaman
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff, UK
| | - Daniel Jiménez
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK
- Department of Neurological Sciences, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Matthew J Betts
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
| | | | - David Berron
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Antoinette O'Connor
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK
| | - Nick C Fox
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK
| | - Joana B Pereira
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - William Jagust
- Helen Wills Neuroscience Institute, University of California, Berkeley, USA
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Stephen F Carter
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Wolfson Molecular Imaging Centre, Division of Neuroscience and Experimental Psychology, MAHSC, University of Manchester, Manchester, UK
| | - Ross W Paterson
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK
| | - Michael Schöll
- Wallenberg Centre for Molecular and Translational Medicine and the Department of Psychiatry and Neurochemistry, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden.
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK.
- Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
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20
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Altered structural brain network topology in chronic migraine. Brain Struct Funct 2019; 225:161-172. [DOI: 10.1007/s00429-019-01994-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 11/23/2019] [Indexed: 02/05/2023]
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21
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Li J, Jin D, Li A, Liu B, Song C, Wang P, Wang D, Xu K, Yang H, Yao H, Zhou B, Bejanin A, Chetelat G, Han T, Lu J, Wang Q, Yu C, Zhang X, Zhou Y, Zhang X, Jiang T, Liu Y, Han Y. ASAF: altered spontaneous activity fingerprinting in Alzheimer's disease based on multisite fMRI. Sci Bull (Beijing) 2019; 64:998-1010. [PMID: 36659811 DOI: 10.1016/j.scib.2019.04.034] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 03/22/2019] [Accepted: 03/25/2019] [Indexed: 01/21/2023]
Abstract
Several monocentric studies have noted alterations in spontaneous brain activity in Alzheimer's disease (AD), although there is no consensus on the altered amplitude of low-frequency fluctuations in AD patients. The main aim of the present study was to identify a reliable and reproducible abnormal brain activity pattern in AD. The amplitude of local brain activity (AM), which can provide fast mapping of spontaneous brain activity across the whole brain, was evaluated based on multisite rs-fMRI data for 688 subjects (215 normal controls (NCs), 221 amnestic mild cognitive impairment (aMCI) 252 AD). Two-sample t-tests were used to detect group differences between AD patients and NCs from the same site. Differences in the AM maps were statistically analyzed via the Stouffer's meta-analysis. Consistent regions of lower spontaneous brain activity in the default mode network and increased activity in the bilateral hippocampus/parahippocampus, thalamus, caudate nucleus, orbital part of the middle frontal gyrus and left fusiform were observed in the AD patients compared with those in NCs. Significant correlations (P < 0.05, Bonferroni corrected) between the normalized amplitude index and Mini-Mental State Examination scores were found in the identified brain regions, which indicates that the altered brain activity was associated with cognitive decline in the patients. Multivariate analysis and leave-one-site-out cross-validation led to a 78.49% prediction accuracy for single-patient classification. The altered activity patterns of the identified brain regions were largely correlated with the FDG-PET results from another independent study. These results emphasized the impaired brain activity to provide a robust and reproducible imaging signature of AD.
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Affiliation(s)
- Jiachen Li
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
| | - Dan Jin
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ang Li
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Bing Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Chengyuan Song
- Department of Neurology, Qilu Hospital of Shandong University, Ji'nan 250012, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin 300350, China; Institute of Geriatrics and Gerontology, Chinese PLA General Hospital, Beijing 100853, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital, Ji'nan 250012, China
| | - Kaibin Xu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Hongwei Yang
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
| | - Hongxiang Yao
- Department of Radiology, Chinese PLA General Hospital, Beijing 100853, China
| | - Bo Zhou
- Institute of Geriatrics and Gerontology, Chinese PLA General Hospital, Beijing 100853, China
| | - Alexandre Bejanin
- Université Normandie, Inserm, Université de Caen-Normandie, Inserm UMR-S U1237, GIP Cyceron, Caen 14000, France
| | - Gael Chetelat
- Université Normandie, Inserm, Université de Caen-Normandie, Inserm UMR-S U1237, GIP Cyceron, Caen 14000, France
| | - Tong Han
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin 300350, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
| | - Qing Wang
- Department of Radiology, Qilu Hospital, Ji'nan 250012, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Xinqing Zhang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
| | - Yuying Zhou
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin 300350, China
| | - Xi Zhang
- Institute of Geriatrics and Gerontology, Chinese PLA General Hospital, Beijing 100853, China
| | - Tianzi Jiang
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China; Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing 100053, China; Beijing Institute of Geriatrics, Beijing 100053, China; National Clinical Research Center for Geriatric Disorders, Beijing 100053, China.
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22
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Sala A, Perani D. Brain Molecular Connectivity in Neurodegenerative Diseases: Recent Advances and New Perspectives Using Positron Emission Tomography. Front Neurosci 2019; 13:617. [PMID: 31258466 PMCID: PMC6587303 DOI: 10.3389/fnins.2019.00617] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 05/29/2019] [Indexed: 12/12/2022] Open
Abstract
Positron emission tomography (PET) represents a unique molecular tool to get in vivo access to a wide spectrum of biological and neuropathological processes, of crucial relevance for neurodegenerative conditions. Although most PET findings are based on massive univariate approaches, in the last decade the increasing interest in multivariate methods has paved the way to the assessment of unexplored cerebral features, spanning from resting state brain networks to whole-brain connectome properties. Currently, the combination of molecular neuroimaging techniques with multivariate connectivity methods represents one of the most powerful, yet still emerging, approach to achieve novel insights into the pathophysiology of neurodegenerative diseases. In this review, we will summarize the available evidence in the field of PET molecular connectivity, with the aim to provide an overview of how these studies may increase the understanding of the pathogenesis of neurodegenerative diseases, over and above "traditional" structural/functional connectivity studies. Considering the available evidence, a major focus will be represented by molecular connectivity studies using [18F]FDG-PET, today applied in the major neuropathological spectra, from amyloidopathies and tauopathies to synucleinopathies and beyond. Pioneering studies using PET tracers targeting brain neuropathology and neurotransmission systems for connectivity studies will be discussed, their strengths and limitations highlighted with reference to both applied methodology and results interpretation. The most common methods for molecular connectivity assessment will be reviewed, with particular emphasis on the available strategies to investigate molecular connectivity at the single-subject level, of potential relevance for not only research but also diagnostic purposes. Finally, we will highlight possible future perspectives in the field, with reference in particular to newly available PET tracers, which will expand the application of molecular connectivity to new, exciting, unforeseen possibilities.
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Affiliation(s)
- Arianna Sala
- Faculty of Medicine and Surgery, Vita-Salute San Raffaele University, Milan, Italy.,Division of Neuroscience, Faculty of Psychology, San Raffaele Scientific Institute (IRCCS), Milan, Italy
| | - Daniela Perani
- Division of Neuroscience, Faculty of Psychology, San Raffaele Scientific Institute (IRCCS), Milan, Italy.,Faculty of Psychology, Vita-Salute San Raffaele University, Milan, Italy.,Nuclear Medicine Unit, Faculty of Psychology, San Raffaele Hospital (IRCCS), Milan, Italy
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23
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Ferreira D, Pereira JB, Volpe G, Westman E. Subtypes of Alzheimer's Disease Display Distinct Network Abnormalities Extending Beyond Their Pattern of Brain Atrophy. Front Neurol 2019; 10:524. [PMID: 31191430 PMCID: PMC6547836 DOI: 10.3389/fneur.2019.00524] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2019] [Accepted: 05/01/2019] [Indexed: 01/08/2023] Open
Abstract
Different subtypes of Alzheimer's disease (AD) with characteristic distributions of neurofibrillary tangles and corresponding brain atrophy patterns have been identified using structural magnetic resonance imaging (MRI). However, the underlying biological mechanisms that determine this differential expression of neurofibrillary tangles are still unknown. Here, we applied graph theoretical analysis to structural MRI data to test the hypothesis that differential network disruption is at the basis of the emergence of these AD subtypes. We studied a total of 175 AD patients and 81 controls. Subtyping was done using the Scheltens' scale for medial temporal lobe atrophy, the Koedam's scale for posterior atrophy, and the Pasquier's global cortical atrophy scale for frontal atrophy. A total of 89 AD patients showed a brain atrophy pattern consistent with typical AD; 30 patients showed a limbic-predominant pattern; 29 patients showed a hippocampal-sparing pattern; and 27 showed minimal atrophy. We built brain structural networks from 68 cortical regions and 14 subcortical gray matter structures for each AD subtype and for the controls, and we compared between-group measures of integration, segregation, and modular organization. At the global level, modularity was increased and differential modular reorganization was detected in the four subtypes. We also found a decrease of transitivity in the typical and hippocampal-sparing subtypes, as well as an increase of average local efficiency in the minimal atrophy and hippocampal-sparing subtypes. We conclude that the AD subtypes have a distinct signature of network disruption associated with their atrophy patterns and further extending to other brain regions, presumably reflecting the differential spread of neurofibrillary tangles. We discuss the hypothetical emergence of these subtypes and possible clinical implications.
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Affiliation(s)
- Daniel Ferreira
- Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden
| | - Joana B Pereira
- Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden
| | - Giovanni Volpe
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Eric Westman
- Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
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24
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Kim J, Ghadery C, Cho SS, Mihaescu A, Christopher L, Valli M, Houle S, Strafella AP. Network Patterns of Beta-Amyloid Deposition in Parkinson's Disease. Mol Neurobiol 2019; 56:7731-7740. [PMID: 31111400 DOI: 10.1007/s12035-019-1625-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 04/24/2019] [Indexed: 01/07/2023]
Abstract
Beta-amyloid (Aβ) in the brain is a key pathological feature of certain neurodegenerative diseases. Recent studies using graph theory have shown that Aβ brain networks are of pathological significance in Alzheimer's disease (AD). However, the characteristics of Aβ brain networks in Parkinson's disease (PD) are unknown. In the present study using positron emission tomography (PET) with [11C]-Pittsburgh compound B (PiB), we applied a graph theory-based analysis to assess the topological properties of Aβ brain network in PD patients with and without Aβ burden (PiB-positive and PiB-negative, respectively) and healthy controls with Aβ burden. We found that the PD PiB-positive group demonstrated significantly lower value in global efficiency and modularity compared with PD PiB-negative group. The less robust modular structure indicates the tendency of having increased inter-modular connections than intra-modular connectivity (i.e., reduced segregation). Results of hub organization showed that relative to PD PiB-negative group, different hubs were identified in the PiB-positive group, which were located mainly within the default mode network. Overall, our findings suggest disturbances in Aβ topological organization characterized by abnormal network integration and segregation in PD patients with Aβ burden. The stronger inter-modular connectivity observed in the PD PiB-positive group may suggest the spreading pattern of Aβ between modules in those PD patients with elevated PiB burden, thus providing insight into the beta-amyloidopathy of PD.
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Affiliation(s)
- Jinhee Kim
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada. .,Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Toronto, Ontario, Canada.
| | - Christine Ghadery
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada.,Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Sang Soo Cho
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada.,Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Alexander Mihaescu
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada.,Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Toronto, Ontario, Canada.,Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Leigh Christopher
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada.,Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Mikaeel Valli
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada.,Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Toronto, Ontario, Canada.,Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Sylvain Houle
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada
| | - Antonio P Strafella
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada.,Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Toronto, Ontario, Canada.,Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada.,Morton and Gloria Shulman Movement Disorder Unit and E.J. Safra Parkinson Disease Program, Neurology Division, Department of Medicine, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
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25
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Hahn A, Lanzenberger R, Kasper S. Making Sense of Connectivity. Int J Neuropsychopharmacol 2019; 22:194-207. [PMID: 30544240 PMCID: PMC6403091 DOI: 10.1093/ijnp/pyy100] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 11/07/2018] [Accepted: 12/11/2018] [Indexed: 02/07/2023] Open
Abstract
In addition to the assessment of local alterations of specific brain regions, the investigation of entire networks with in vivo neuroimaging techniques has gained increasing attention. In general, connectivity analysis refers to the investigation of links between brain regions, with the aim to characterize their interactions and information transfer. These may represent or relate to different physiological characteristics (structural, functional, or metabolic information) and can be calculated across different levels of granularity (2 regions vs whole brain). In this article, we provide an overview of different connectivity analysis approaches with interpretations and limitations as well as examples in pharmacological imaging and clinical applications. Structural connectivity obtained from diffusion MRI enables the reconstruction of neuronal fiber tracts. These physical links represent major constraints of functional connections, which are in turn defined as correlations between signal time courses. In addition, molecular connectivity approaches based on PET imaging enable the assessment of interregional associations of metabolic demands and neurotransmitter systems. Application of these approaches in clinical investigations has demonstrated novel alterations in various neurological and psychiatric disorders on a network level. Future work should aim for the combined assessment of multiple imaging modalities and to establish robust biomarkers for clinical use. These advancements will further improve the biological interpretation of connectivity metrics and networks of the human brain.
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Affiliation(s)
- Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Siegfried Kasper
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
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26
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Molinuevo JL, Ayton S, Batrla R, Bednar MM, Bittner T, Cummings J, Fagan AM, Hampel H, Mielke MM, Mikulskis A, O'Bryant S, Scheltens P, Sevigny J, Shaw LM, Soares HD, Tong G, Trojanowski JQ, Zetterberg H, Blennow K. Current state of Alzheimer's fluid biomarkers. Acta Neuropathol 2018; 136:821-853. [PMID: 30488277 PMCID: PMC6280827 DOI: 10.1007/s00401-018-1932-x] [Citation(s) in RCA: 337] [Impact Index Per Article: 56.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Revised: 11/05/2018] [Accepted: 11/07/2018] [Indexed: 12/12/2022]
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disease with a complex and heterogeneous pathophysiology. The number of people living with AD is predicted to increase; however, there are no disease-modifying therapies currently available and none have been successful in late-stage clinical trials. Fluid biomarkers measured in cerebrospinal fluid (CSF) or blood hold promise for enabling more effective drug development and establishing a more personalized medicine approach for AD diagnosis and treatment. Biomarkers used in drug development programmes should be qualified for a specific context of use (COU). These COUs include, but are not limited to, subject/patient selection, assessment of disease state and/or prognosis, assessment of mechanism of action, dose optimization, drug response monitoring, efficacy maximization, and toxicity/adverse reactions identification and minimization. The core AD CSF biomarkers Aβ42, t-tau, and p-tau are recognized by research guidelines for their diagnostic utility and are being considered for qualification for subject selection in clinical trials. However, there is a need to better understand their potential for other COUs, as well as identify additional fluid biomarkers reflecting other aspects of AD pathophysiology. Several novel fluid biomarkers have been proposed, but their role in AD pathology and their use as AD biomarkers have yet to be validated. In this review, we summarize some of the pathological mechanisms implicated in the sporadic AD and highlight the data for several established and novel fluid biomarkers (including BACE1, TREM2, YKL-40, IP-10, neurogranin, SNAP-25, synaptotagmin, α-synuclein, TDP-43, ferritin, VILIP-1, and NF-L) associated with each mechanism. We discuss the potential COUs for each biomarker.
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Affiliation(s)
- José Luis Molinuevo
- BarcelonaBeta Brain Research Center, Fundació Pasqual Maragall, Universitat Pompeu Fabra, Barcelona, Spain
- Unidad de Alzheimer y otros trastornos cognitivos, Hospital Clinic-IDIBAPS, Barcelona, Spain
| | - Scott Ayton
- Melbourne Dementia Research Centre, Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, VIC, Australia
| | - Richard Batrla
- Roche Centralised and Point of Care Solutions, Roche Diagnostics International, Rotkreuz, Switzerland
| | - Martin M Bednar
- Neuroscience Therapeutic Area Unit, Takeda Development Centre Americas Ltd, Cambridge, MA, USA
| | - Tobias Bittner
- Genentech, A Member of the Roche Group, Basel, Switzerland
| | - Jeffrey Cummings
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
| | - Anne M Fagan
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Harald Hampel
- AXA Research Fund and Sorbonne University Chair, Paris, France
- Sorbonne University, GRC No 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France
- Brain and Spine Institute (ICM), INSERM U 1127, CNRS UMR 7225, Paris, France
- Department of Neurology, Institute of Memory and Alzheimer's Disease (IM2A), Pitié-Salpêtrière Hospital, AP-HP, Paris, France
| | - Michelle M Mielke
- Departments of Epidemiology and Neurology, Mayo Clinic, Rochester, MN, USA
| | | | - Sid O'Bryant
- Department of Pharmacology and Neuroscience; Institute for Healthy Aging, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Philip Scheltens
- Department of Neurology and Alzheimer Center, VU University Medical Center, Amsterdam, The Netherlands
| | - Jeffrey Sevigny
- Roche Innovation Center Basel, F. Hoffmann-La Roche, Basel, Switzerland
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, and Center for Neurodegenerative Disease Research, University of Pennsylvania, Philadelphia, PA, USA
| | - Holly D Soares
- Clinical Development Neurology, AbbVie, North Chicago, IL, USA
| | | | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Disease Research, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal Campus, Sahlgrenska University Hospital, 431 80, Mölndal, Sweden
- Department of Molecular Neuroscience, UCL Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute at UCL, London, UK
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.
- Clinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal Campus, Sahlgrenska University Hospital, 431 80, Mölndal, Sweden.
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