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Quattrini G, Pini L, Boscolo Galazzo I, Jelescu IO, Jovicich J, Manenti R, Frisoni GB, Marizzoni M, Pizzini FB, Pievani M. Microstructural alterations in the locus coeruleus-entorhinal cortex pathway in Alzheimer's disease and frontotemporal dementia. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12513. [PMID: 38213948 PMCID: PMC10781651 DOI: 10.1002/dad2.12513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 11/04/2023] [Accepted: 11/20/2023] [Indexed: 01/13/2024]
Abstract
INTRODUCTION We investigated in vivo the microstructural integrity of the pathway connecting the locus coeruleus to the transentorhinal cortex (LC-TEC) in patients with Alzheimer's disease (AD) and frontotemporal dementia (FTD). METHODS Diffusion-weighted MRI scans were collected for 21 AD, 20 behavioral variants of FTD (bvFTD), and 20 controls. Fractional anisotropy (FA), mean, axial, and radial diffusivities (MD, AxD, RD) were computed in the LC-TEC pathway using a normative atlas. Atrophy was assessed using cortical thickness and correlated with microstructural measures. RESULTS We found (i) higher RD in AD than controls; (ii) higher MD, RD, and AxD, and lower FA in bvFTD than controls and AD; and (iii) a negative association between LC-TEC MD, RD, and AxD, and entorhinal cortex (EC) thickness in bvFTD (all p < 0.050). DISCUSSION LC-TEC microstructural alterations are more pronounced in bvFTD than AD, possibly reflecting neurodegeneration secondary to EC atrophy. Highlights Microstructural integrity of LC-TEC pathway is understudied in AD and bvFTD.LC-TEC microstructural alterations are present in both AD and bvFTD.Greater LC-TEC microstructural alterations in bvFTD than AD.LC-TEC microstructural alterations in bvFTD are associated to EC neurodegeneration.
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Affiliation(s)
- Giulia Quattrini
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE)IRCCS Istituto Centro San Giovanni di Dio FatebenefratelliBresciaItaly
- Department of Molecular and Translational MedicineUniversity of BresciaBresciaItaly
| | - Lorenzo Pini
- Padova Neuroscience CenterUniversity of PadovaPadovaItaly
| | | | - Ileana O. Jelescu
- Department of RadiologyLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Jorge Jovicich
- Center of Mind/Brain SciencesUniversity of TrentoRoveretoItaly
| | - Rosa Manenti
- Neuropsychology UnitIRCCS Istituto Centro San Giovanni di Dio FatebenefratelliBresciaItaly
| | - Giovanni B. Frisoni
- Memory Center and LANVIE ‐ Laboratory of Neuroimaging of AgingUniversity Hospitals and University of GenevaGenevaSwitzerland
| | - Moira Marizzoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE)IRCCS Istituto Centro San Giovanni di Dio FatebenefratelliBresciaItaly
- Laboratory of Biological PsychiatryIRCCS Istituto Centro San Giovanni di Dio FatebenefratelliBresciaItaly
| | - Francesca B. Pizzini
- Department of Engineering for Innovation MedicineUniversity of VeronaVeronaItaly
| | - Michela Pievani
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE)IRCCS Istituto Centro San Giovanni di Dio FatebenefratelliBresciaItaly
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Filippi M, Spinelli EG, Cividini C, Ghirelli A, Basaia S, Agosta F. The human functional connectome in neurodegenerative diseases: relationship to pathology and clinical progression. Expert Rev Neurother 2023; 23:59-73. [PMID: 36710600 DOI: 10.1080/14737175.2023.2174016] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
INTRODUCTION Neurodegenerative diseases can be considered as 'disconnection syndromes,' in which a communication breakdown prompts cognitive or motor dysfunction. Mathematical models applied to functional resting-state MRI allow for the organization of the brain into nodes and edges, which interact to form the functional brain connectome. AREAS COVERED The authors discuss the recent applications of functional connectomics to neurodegenerative diseases, from preclinical diagnosis, to follow up along with the progressive changes in network organization, to the prediction of the progressive spread of neurodegeneration, to stratification of patients into prognostic groups, and to record responses to treatment. The authors searched PubMed using the terms 'neurodegenerative diseases' AND 'fMRI' AND 'functional connectome' OR 'functional connectivity' AND 'connectomics' OR 'graph metrics' OR 'graph analysis.' The time range covered the past 20 years. EXPERT OPINION Considering the great pathological and phenotypical heterogeneity of neurodegenerative diseases, identifying a common framework to diagnose, monitor and elaborate prognostic models is challenging. Graph analysis can describe the complexity of brain architectural rearrangements supporting the network-based hypothesis as unifying pathogenetic mechanism. Although a multidisciplinary team is needed to overcome the limit of methodologic complexity in clinical application, advanced methodologies are valuable tools to better characterize functional disconnection in neurodegeneration.
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Affiliation(s)
- Massimo Filippi
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy.,Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Edoardo Gioele Spinelli
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Camilla Cividini
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Alma Ghirelli
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Silvia Basaia
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Federica Agosta
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
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Kazemi-Harikandei SZ, Shobeiri P, Salmani Jelodar MR, Tavangar SM. Effective connectivity in individuals with Alzheimer's disease and mild cognitive impairment: A systematic review. NEUROSCIENCE INFORMATICS 2022; 2:100104. [DOI: 10.1016/j.neuri.2022.100104] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/27/2023]
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Zhang X, Guan Q, Li Y, Zhang J, Zhu W, Luo Y, Zhang H. Aberrant Cross-Tissue Functional Connectivity in Alzheimer’s Disease: Static, Dynamic, and Directional Properties. J Alzheimers Dis 2022; 88:273-290. [DOI: 10.3233/jad-215649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: BOLD signals in the gray matter (GM) and white matter (WM) are tightly coupled. However, our understanding of the cross-tissue functional network in Alzheimer’s disease (AD) is limited. Objective: We investigated the changes of cross-tissue functional connectivity (FC) metrics for the GM regions susceptible to AD damage. Methods: For each GM region in the default mode (DMN) and limbic networks, we obtained its low-order static FC with any WM region, and the high-order static FC between any two WM regions based on their FC pattern similarity with multiple GM regions. The dynamic and directional properties of cross-tissue FC were then acquired, specifically for the regional pairs whose low- or high-order static FCs showed significant differences between AD and normal control (NC). Moreover, these cross-tissue FC metrics were correlated with voxel-based GM volumes and MMSE in all participants. Results: Compared to NC, AD patients showed decreased low-order static FCs between the intra-hemispheric GM-WM pairs (right ITG-right fornix; left MoFG-left posterior corona radiata), and increased low-order static, dynamic, and directional FCs between the inter-hemispheric GM-WM pairs (right MTG-left superior/posterior corona radiata). The high-order static and directional FCs between the left cingulate bundle-left tapetum were increased in AD, based on their FCs with the GMs of DMN. Those decreased and increased cross-tissue FC metrics in AD had opposite correlations with memory-related GM volumes and MMSE (positive for the decreased and negative for the increased). Conclusion: Cross-tissue FC metrics showed opposite changes in AD, possibly as useful neuroimaging biomarkers to reflect neurodegenerative and compensatory mechanisms.
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Affiliation(s)
- Xingxing Zhang
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Shenzhen University, Shenzhen, China
| | - Qing Guan
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Shenzhen University, Shenzhen, China
- Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, China
- Center for Neuroimaging, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Yingjia Li
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Shenzhen University, Shenzhen, China
| | - Jianfeng Zhang
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Shenzhen University, Shenzhen, China
| | - Wanlin Zhu
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yuejia Luo
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Shenzhen University, Shenzhen, China
| | - Haobo Zhang
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Shenzhen University, Shenzhen, China
- Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, China
- Center for Neuroimaging, Shenzhen Institute of Neuroscience, Shenzhen, China
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David M, Malhotra PA. New approaches for the quantification and targeting of noradrenergic dysfunction in Alzheimer's disease. Ann Clin Transl Neurol 2022; 9:582-596. [PMID: 35293158 PMCID: PMC8994981 DOI: 10.1002/acn3.51539] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 02/23/2022] [Accepted: 02/25/2022] [Indexed: 12/14/2022] Open
Abstract
There is clear, early noradrenergic dysfunction in Alzheimer's disease. This is likely secondary to pathological tau deposition in the locus coeruleus, the pontine nucleus that produces and releases noradrenaline, prior to involvement of cortical brain regions. Disruption of noradrenergic pathways affects cognition, especially attention, impacting memory and broader functioning. Additionally, it leads to autonomic and neuropsychiatric symptoms. Despite the strong evidence of noradrenergic involvement in Alzheimer's, there are no clear trial data supporting the clinical use of any noradrenergic treatments. Several approaches have been tried, including proof-of-principle studies and (mostly small scale) randomised controlled trials. Treatments have included pharmacotherapies as well as stimulation. The lack of clear positive findings is likely secondary to limitations in gauging locus coeruleus integrity and dysfunction at an individual level. However, the recent development of several novel biomarkers holds potential and should allow quantification of dysfunction. This may then inform inclusion criteria and stratification for future trials. Imaging approaches have improved greatly following the development of neuromelanin-sensitive sequences, enabling the use of structural MRI to estimate locus coeruleus integrity. Additionally, functional MRI scanning has the potential to quantify network dysfunction. As well as neuroimaging, EEG, fluid biomarkers and pupillometry techniques may prove useful in assessing noradrenergic tone. Here, we review the development of these biomarkers and how they might augment clinical studies, particularly randomised trials, through identification of patients most likely to benefit from treatment. We outline the biomarkers with most potential, and how they may transform symptomatic therapy for people living with Alzheimer's disease.
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Affiliation(s)
- Michael David
- Imperial College London and the University of SurreyUK Dementia Research Institute Care Research and Technology CentreSir Michael Uren Hub, 86 Wood LaneLondonW12 0BZUK
- Imperial College London, Brain SciencesSouth KensingtonLondonSW7 2AZUK
- Imperial College Healthcare NHS Trust, Clinical NeurosciencesCharing Cross HospitalLondonW2 1NYUK
| | - Paresh A. Malhotra
- Imperial College London and the University of SurreyUK Dementia Research Institute Care Research and Technology CentreSir Michael Uren Hub, 86 Wood LaneLondonW12 0BZUK
- Imperial College London, Brain SciencesSouth KensingtonLondonSW7 2AZUK
- Imperial College Healthcare NHS Trust, Clinical NeurosciencesCharing Cross HospitalLondonW2 1NYUK
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Kelberman MA, Anderson CR, Chlan E, Rorabaugh JM, McCann KE, Weinshenker D. Consequences of Hyperphosphorylated Tau in the Locus Coeruleus on Behavior and Cognition in a Rat Model of Alzheimer's Disease. J Alzheimers Dis 2022; 86:1037-1059. [PMID: 35147547 PMCID: PMC9007891 DOI: 10.3233/jad-215546] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND The locus coeruleus (LC) is one of the earliest brain regions to accumulate hyperphosphorylated tau, but a lack of animal models that recapitulate this pathology has hampered our understanding of its contributions to Alzheimer's disease (AD) pathophysiology. OBJECTIVE We previously reported that TgF344-AD rats, which overexpress mutant human amyloid precursor protein and presenilin-1, accumulate early endogenous hyperphosphorylated tau in the LC. Here, we used TgF344-AD rats and a wild-type (WT) human tau virus to interrogate the effects of endogenous hyperphosphorylated rat tau and human tau in the LC on AD-related neuropathology and behavior. METHODS Two-month-old TgF344-AD and WT rats received bilateral LC infusions of full-length WT human tau or mCherry control virus driven by the noradrenergic-specific PRSx8 promoter. Rats were subsequently assessed at 6 and 12 months for arousal (sleep latency), anxiety-like behavior (open field, elevated plus maze, novelty-suppressed feeding), passive coping (forced swim task), and learning and memory (Morris water maze and fear conditioning). Hippocampal microglia, astrocyte, and AD pathology were evaluated using immunohistochemistry. RESULTS In general, the effects of age were more pronounced than genotype or treatment; older rats displayed greater hippocampal pathology, took longer to fall asleep, had reduced locomotor activity, floated more, and had impaired cognition compared to younger animals. TgF344-AD rats showed increased anxiety-like behavior and impaired learning and memory. The tau virus had negligible influence on most measures. CONCLUSION Effects of hyperphosphorylated tau on AD-like neuropathology and behavioral symptoms were subtle. Further investigation of different forms of tau is warranted.
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Affiliation(s)
- Michael A Kelberman
- Department of Human Genetics, Emory University, Atlanta, GA, USA.,Neuroscience Program, Laney Graduate School, Emory University, Atlanta, GA, USA
| | | | - Eli Chlan
- Neuroscience Program, Laney Graduate School, Emory University, Atlanta, GA, USA.,Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
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Ma ZZ, Lu YC, Wu JJ, Hua XY, Li SS, Ding W, Xu JG. Effective connectivity decreases in specific brain networks with postparalysis facial synkinesis: a dynamic causal modeling study. Brain Imaging Behav 2021; 16:748-760. [PMID: 34550534 DOI: 10.1007/s11682-021-00547-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 08/23/2021] [Indexed: 12/31/2022]
Abstract
Currently, the treatments for postparalysis facial synkinesis are still inadequate. However, neuroimaging mechanistic studies are very limited and blurred. Instead of mapping activation regions, we were devoted to characterizing the organizational features of brain regions to develop new targets for therapeutic intervention. Eighteen patients with unilateral facial synkinesis and 19 healthy controls were enrolled. They were instructed to perform task functional magnetic resonance imaging (eye blinking and lip pursing) examinations and resting-state scans. Then, we characterized group differences in task-state fMRI to identify three foci, including the contralateral precentral gyrus (PreCG), supramarginal gyrus (SMG), and superior parietal gyrus (SPG). Next, we employed a novel approach (using dynamic causal modeling) to identify directed connectivity differences between groups in different modes. Significant patterns in multiple regions in terms of regionally specific actions following synkinetic movements were demonstrated, although the resting state was not significant. The couplings from the SMG to the PreCG (p = 0.03) was significant in the task of left blinking, whereas the coupling from the SMG to the SPG (p = 0.04) was significant in the task of left smiling. We speculated that facial synkinesis affects disruption among the brain networks, and specific couplings that are modulated simultaneously can compensate for motor deficits. Therefore, behavioral or brain stimulation technique treatment could be applied to alter reorganization within specific couplings in the rehabilitation of facial function.
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Affiliation(s)
- Zhen-Zhen Ma
- Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ye-Chen Lu
- Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jia-Jia Wu
- Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xu-Yun Hua
- Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Department of Trauma and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Si-Si Li
- Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Wei Ding
- Department of Plastic and Reconstructive Surgery, Shanghai Ninth People Hospital, Shanghai Jiaotong University School of Medicine, No. 639, Zhizaoju Road, Shanghai, China.
| | - Jian-Guang Xu
- Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China. .,School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China. .,Department of Hand Surgery, Huashan Hospital, Fudan University, No.1200 Cailun Road, Shanghai, China.
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8
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Sun M, Xie H, Tang Y. Directed Network Defects in Alzheimer's Disease Using Granger Causality and Graph Theory. Curr Alzheimer Res 2020; 17:939-947. [PMID: 33327911 DOI: 10.2174/1567205017666201215140625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 09/19/2020] [Accepted: 11/17/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Few works studied the directed whole-brain interaction between different brain regions of Alzheimer's disease (AD). Here, we investigated the whole-brain effective connectivity and studied the graph metrics associated with AD. METHODS Large-scale Granger causality analysis was conducted to explore abnormal whole-brain effective connectivity of patients with AD. Moreover, graph-theoretical metrics including smallworldness, assortativity, and hierarchy, were computed from the effective connectivity network. Statistical analysis identified the aberrant network properties of AD subjects when compared against healthy controls. RESULTS Decreased small-worldness, and increased characteristic path length, disassortativity, and hierarchy were found in AD subjects. CONCLUSION This work sheds insight into the underlying neuropathological mechanism of the brain network of AD individuals such as less efficient information transmission and reduced resilience to a random or targeted attack.
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Affiliation(s)
- Man Sun
- School of Computer Science and Engineering, Central South University, Changsha, 410008 Hunan, China
| | - Hua Xie
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305, United States
| | - Yan Tang
- School of Computer Science and Engineering, Central South University, Changsha, 410008 Hunan, China
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Kelberman M, Keilholz S, Weinshenker D. What's That (Blue) Spot on my MRI? Multimodal Neuroimaging of the Locus Coeruleus in Neurodegenerative Disease. Front Neurosci 2020; 14:583421. [PMID: 33122996 PMCID: PMC7573566 DOI: 10.3389/fnins.2020.583421] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 09/16/2020] [Indexed: 01/04/2023] Open
Abstract
The locus coeruleus (LC) has long been underappreciated for its role in the pathophysiology of Alzheimer’s disease (AD), Parkinson’s disease (PD), and other neurodegenerative disorders. While AD and PD are distinct in clinical presentation, both are characterized by prodromal protein aggregation in the LC, late-stage degeneration of the LC, and comorbid conditions indicative of LC dysfunction. Many of these early studies were limited to post-mortem histological techniques due to the LC’s small size and location deep in the brainstem. Thus, there is a growing interest in utilizing in vivo imaging of the LC as a predictor of preclinical neurodegenerative processes and biomarker of disease progression. Simultaneously, neuroimaging in animal models of neurodegenerative disease holds promise for identifying early alterations to LC circuits, but has thus far been underutilized. While still in its infancy, a handful of studies have reported effects of single gene mutations and pathology on LC function in disease using various neuroimaging techniques. Furthermore, combining imaging and optogenetics or chemogenetics allows for interrogation of network connectivity in response to changes in LC activity. The purpose of this article is twofold: (1) to review what magnetic resonance imaging (MRI) and positron emission tomography (PET) have revealed about LC dysfunction in neurodegenerative disease and its potential as a biomarker in humans, and (2) to explore how animal models can be used to test hypotheses derived from clinical data and establish a mechanistic framework to inform LC-focused therapeutic interventions to alleviate symptoms and impede disease progression.
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Affiliation(s)
- Michael Kelberman
- Department of Human Genetics, Emory University, Atlanta, GA, United States
| | - Shella Keilholz
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - David Weinshenker
- Department of Human Genetics, Emory University, Atlanta, GA, United States
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Deshpande G, Jia H. Multi-Level Clustering of Dynamic Directional Brain Network Patterns and Their Behavioral Relevance. Front Neurosci 2020; 13:1448. [PMID: 32116487 PMCID: PMC7017718 DOI: 10.3389/fnins.2019.01448] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Accepted: 12/27/2019] [Indexed: 11/18/2022] Open
Abstract
Dynamic functional connectivity (DFC) obtained from resting state functional magnetic resonance imaging (fMRI) data has been shown to provide novel insights into brain function which may be obscured by static functional connectivity (SFC). Further, DFC, and by implication how different brain regions may engage or disengage with each other over time, has been shown to be behaviorally relevant and more predictive than SFC of behavioral performance and/or diagnostic status. DFC is not a directional entity and may capture neural synchronization. However, directional interactions between different brain regions is another putative mechanism by which neural populations communicate. Accordingly, static effective connectivity (SEC) has been explored as a means of characterizing such directional interactions. But investigation of its dynamic counterpart, i.e., dynamic effective connectivity (DEC), is still in its infancy. Of particular note are methodological insufficiencies in identifying DEC configurations that are reproducible across time and subjects as well as a lack of understanding of the behavioral relevance of DEC obtained from resting state fMRI. In order to address these issues, we employed a dynamic multivariate autoregressive (MVAR) model to estimate DEC. The method was first validated using simulations and then applied to resting state fMRI data obtained in-house (N = 21), wherein we performed dynamic clustering of DEC matrices across multiple levels [using adaptive evolutionary clustering (AEC)] – spatial location, time, and subjects. We observed a small number of directional brain network configurations alternating between each other over time in a quasi-stable manner akin to brain microstates. The dominant and consistent DEC network patterns involved several regions including inferior and mid temporal cortex, motor and parietal cortex, occipital cortex, as well as part of frontal cortex. The functional relevance of these DEC states were determined using meta-analyses and pertained mainly to memory and emotion, but also involved execution and language. Finally, a larger cohort of resting-state fMRI and behavioral data from the Human Connectome Project (HCP) (N = 232, Q1–Q3 release) was used to demonstrate that metrics derived from DEC can explain larger variance in 70 behaviors across different domains (alertness, cognition, emotion, and personality traits) compared to SEC in healthy individuals.
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Affiliation(s)
- Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Department of Psychology, Auburn University, Auburn, AL, United States.,Center for Neuroscience, Auburn University, Auburn, AL, United States.,Center for Health Ecology and Equity Research, Auburn, AL, United States.,Alabama Advanced Imaging Consortium, Birmingham, AL, United States.,Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India.,School of Psychology, Capital Normal University, Beijing, China.,Key Laboratory for Learning and Cognition, Capital Normal University, Beijing, China
| | - Hao Jia
- Department of Automation, College of Information Engineering, Taiyuan University of Technology, Taiyuan, China
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Lanka P, Rangaprakash D, Gotoor SSR, Dretsch MN, Katz JS, Denney TS, Deshpande G. MALINI (Machine Learning in NeuroImaging): A MATLAB toolbox for aiding clinical diagnostics using resting-state fMRI data. Data Brief 2020; 29:105213. [PMID: 32090157 PMCID: PMC7025186 DOI: 10.1016/j.dib.2020.105213] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 01/20/2020] [Accepted: 01/23/2020] [Indexed: 12/26/2022] Open
Abstract
Resting-state functional Magnetic Resonance Imaging (rs-fMRI) has been extensively used for diagnostic classification because it does not require task compliance and is easier to pool data from multiple imaging sites, thereby increasing the sample size. A MATLAB-based toolbox called Machine Learning in NeuroImaging (MALINI) for feature extraction and disease classification is presented. The MALINI toolbox extracts functional and effective connectivity features from preprocessed rs-fMRI data and performs classification between healthy and disease groups using any of 18 popular and widely used machine learning algorithms that are based on diverse principles. A consensus classifier combining the power of multiple classifiers is also presented. The utility of the toolbox is illustrated by accompanying data consisting of resting-state functional connectivity features from healthy controls and subjects with various brain-based disorders: autism spectrum disorder from autism brain imaging data exchange (ABIDE), Alzheimer's disease and mild cognitive impairment from Alzheimer's disease neuroimaging initiative (ADNI), attention deficit hyperactivity disorder from ADHD-200, and post-traumatic stress disorder and post-concussion syndrome acquired in-house. Results of classification performed on the above datasets can be obtained from the main article titled “Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets” [1]. The data was divided into homogeneous and heterogeneous splits, such that 80% could be used for training, model building and cross-validation, while the remaining 20% of the data could be used as a hold-out independent test data for replication of the classification performance, to ensure the robustness of the classifiers to population variance in image acquisition site and age of the sample.
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Affiliation(s)
- Pradyumna Lanka
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychological Sciences, University of California Merced, Merced, CA, USA
| | - D Rangaprakash
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Division of Health Science and Technology, Massachusetts Institute of Technology, Boston, MA, USA
| | - Sai Sheshan Roy Gotoor
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
| | - Michael N Dretsch
- U.S. Army Aeromedical Research Laboratory, Fort Rucker, AL, USA.,US Army Medical Research Directorate-West, Walter Reed Army Institute for Research, Joint Base Lewis-McChord, WA, USA.,Department of Psychology, Auburn University, Auburn, AL, USA
| | - Jeffrey S Katz
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, AL, USA.,Alabama Advanced Imaging Consortium, Birmingham, AL, USA.,Center for Neuroscience, Auburn University, Auburn, AL, USA
| | - Thomas S Denney
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, AL, USA.,Alabama Advanced Imaging Consortium, Birmingham, AL, USA.,Center for Neuroscience, Auburn University, Auburn, AL, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, AL, USA.,Alabama Advanced Imaging Consortium, Birmingham, AL, USA.,Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, USA.,Center for Neuroscience, Auburn University, Auburn, AL, USA.,Department of Psychiatry, National Institute of Mental and Neurosciences, Bangalore, India.,School of Psychology, Capital Normal University, Beijing, China.,Key Laboratory for Learning and Cognition, Capital Normal University, Beijing, China
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12
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Li Y, Wang C, Wang J, Zhou Y, Ye F, Zhang Y, Cheng X, Huang Z, Liu K, Fei G, Zhong C, Zeng M, Jin L. Mild cognitive impairment in de novo Parkinson's disease: A neuromelanin MRI study in locus coeruleus. Mov Disord 2019; 34:884-892. [PMID: 30938892 DOI: 10.1002/mds.27682] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2018] [Revised: 02/15/2019] [Accepted: 02/18/2019] [Indexed: 12/11/2022] Open
Affiliation(s)
- Yuanfang Li
- Department of NeurologyZhongshan Hospital, Fudan University Shanghai China
| | - Changpeng Wang
- Department of NeurologyZhongshan Hospital, Fudan University Shanghai China
| | - Jian Wang
- Department of RadiologyZhongshan Hospital, Fudan University Shanghai China
- Shanghai Medical Imaging Institute Shanghai China
| | - Ying Zhou
- Department of NeurologyZhongshan Hospital, Fudan University Shanghai China
| | - Fang Ye
- Department of RadiologyZhongshan Hospital, Fudan University Shanghai China
- Shanghai Medical Imaging Institute Shanghai China
| | - Yong Zhang
- MR Research, GE Healthcare Shanghai China
| | - Xiaoqin Cheng
- Department of NeurologyZhongshan Hospital, Fudan University Shanghai China
| | - Zhen Huang
- Department of NeurologyZhongshan Hospital, Fudan University Shanghai China
| | - Kai Liu
- Department of RadiologyZhongshan Hospital, Fudan University Shanghai China
- Shanghai Medical Imaging Institute Shanghai China
| | - Guoqiang Fei
- Department of NeurologyZhongshan Hospital, Fudan University Shanghai China
| | - Chunjiu Zhong
- Department of NeurologyZhongshan Hospital, Fudan University Shanghai China
| | - Mengsu Zeng
- Department of RadiologyZhongshan Hospital, Fudan University Shanghai China
- Shanghai Medical Imaging Institute Shanghai China
| | - Lirong Jin
- Department of NeurologyZhongshan Hospital, Fudan University Shanghai China
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13
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McCormick M, Reyna VF, Ball K, Katz JS, Deshpande G. Neural Underpinnings of Financial Decision Bias in Older Adults: Putative Theoretical Models and a Way to Reconcile Them. Front Neurosci 2019; 13:184. [PMID: 30930732 PMCID: PMC6427068 DOI: 10.3389/fnins.2019.00184] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Accepted: 02/15/2019] [Indexed: 12/31/2022] Open
Affiliation(s)
- Michael McCormick
- Department of Psychology, Auburn University, Auburn, AL, United States
| | - Valerie F. Reyna
- Human Neuroscience Institute, Cornell University, Ithaca, NY, United States
- Department of Human Development, Cornell University, Ithaca, NY, United States
- Center for Behavioral Economics and Decision Research, Cornell University, Ithaca, NY, United States
- Magnetic Resonance Imaging Facility, Cornell University, Ithaca, NY, United States
| | - Karlene Ball
- Center for Research on Applied Gerontology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Jeffrey S. Katz
- Department of Psychology, Auburn University, Auburn, AL, United States
- Department of Electrical Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Center for Neuroscience, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Birmingham, AL, United States
| | - Gopikrishna Deshpande
- Department of Psychology, Auburn University, Auburn, AL, United States
- Department of Electrical Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Center for Neuroscience, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Birmingham, AL, United States
- Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, United States
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14
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Peterson AC, Li CSR. Noradrenergic Dysfunction in Alzheimer's and Parkinson's Diseases-An Overview of Imaging Studies. Front Aging Neurosci 2018; 10:127. [PMID: 29765316 PMCID: PMC5938376 DOI: 10.3389/fnagi.2018.00127] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Accepted: 04/16/2018] [Indexed: 12/31/2022] Open
Abstract
Noradrenergic dysfunction contributes to cognitive impairment in Alzheimer's Disease (AD) and Parkinson's Disease (PD). Conventional therapeutic strategies seek to enhance cholinergic and dopaminergic neurotransmission in AD and PD, respectively, and few studies have examined noradrenergic dysfunction as a target for medication development. We review the literature of noradrenergic dysfunction in AD and PD with a focus on human imaging studies that implicate the locus coeruleus (LC) circuit. The LC sends noradrenergic projections diffusely throughout the cerebral cortex and plays a critical role in attention, learning, working memory, and cognitive control. The LC undergoes considerable degeneration in both AD and PD. Advances in magnetic resonance imaging have facilitated greater understanding of how structural and functional alteration of the LC may contribute to cognitive decline in AD and PD. We discuss the potential roles of the noradrenergic system in the pathogenesis of AD and PD with an emphasis on postmortem anatomical studies, structural MRI studies, and functional MRI studies, where we highlight changes in LC connectivity with the default mode network (DMN). LC degeneration may accompany deficient capacity in suppressing DMN activity and increasing saliency and task control network activities to meet behavioral challenges. We finish by proposing potential and new directions of research to address noradrenergic dysfunction in AD and PD.
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Affiliation(s)
- Andrew C Peterson
- Frank H. Netter MD School of Medicine, Quinnipiac University, North Haven, CT, United States.,Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Chiang-Shan R Li
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States.,Department of Neuroscience, Yale University School of Medicine, New Haven, CT, United States.,Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, United States
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15
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Rangaprakash D, Wu GR, Marinazzo D, Hu X, Deshpande G. Parameterized hemodynamic response function data of healthy individuals obtained from resting-state functional MRI in a 7T MRI scanner. Data Brief 2018; 17:1175-1179. [PMID: 29876476 PMCID: PMC5988211 DOI: 10.1016/j.dib.2018.01.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Revised: 12/22/2017] [Accepted: 01/02/2018] [Indexed: 01/10/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI), being an indirect measure of brain activity, is mathematically defined as a convolution of the unmeasured latent neural signal and the hemodynamic response function (HRF). The HRF is known to vary across the brain and across individuals, and it is modulated by neural as well as non-neural factors. Three parameters characterize the shape of the HRF, which is obtained by performing deconvolution on resting-state fMRI data: response height, time-to-peak and full-width at half-max. The data provided here, obtained from 47 healthy adults, contains these three HRF parameters at every voxel in the brain, as well as HRF parameters from the default-mode network (DMN). In addition, we have provided functional connectivity (FC) data from the same DMN regions, obtained for two cases: data with deconvolution (HRF variability minimized) and data with no deconvolution (HRF variability corrupted). This would enable researchers to compare regional changes in HRF with corresponding FC differences, to assess the impact of HRF variability on FC. Importantly, the data was obtained in a 7T MRI scanner. While most fMRI studies are conducted at lower field strengths, like 3T, ours is the first study to report HRF data obtained at 7T. FMRI data at ultra-high fields contains larger contributions from small vessels, consequently HRF variability is lower for small vessels at higher field strengths. This implies that findings made from this data would be more conservative than from data acquired at lower fields, such as 3T. Results obtained with this data and further interpretations are available in our recent research study (Rangaprakash et al., in press) [1]. This is a valuable dataset for studying HRF variability in conjunction with FC, and for developing the HRF profile in healthy individuals, which would have direct implications for fMRI data analysis, especially resting-state connectivity modeling. This is the first public HRF data at 7T.
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Affiliation(s)
- D. Rangaprakash
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - Guo-Rong Wu
- Department of Data Analysis, University of Ghent, Ghent, Belgium
- Key Laboratory of Cognition and Personality, Southwest University, Chongqing, China
| | | | - Xiaoping Hu
- Department of Bioengineering, University of California Riverside, Riverside, CA, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
- Department of Psychology, Auburn University, Auburn, AL, USA
- Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, AL, USA
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16
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Rangaprakash D, Dretsch MN, Venkataraman A, Katz JS, Denney TS, Deshpande G. Identifying disease foci from static and dynamic effective connectivity networks: Illustration in soldiers with trauma. Hum Brain Mapp 2017; 39:264-287. [PMID: 29058357 DOI: 10.1002/hbm.23841] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Revised: 08/29/2017] [Accepted: 10/01/2017] [Indexed: 12/15/2022] Open
Abstract
Brain connectivity studies report group differences in pairwise connection strengths. While informative, such results are difficult to interpret since our understanding of the brain relies on region-based properties, rather than on connection information. Given that large disruptions in the brain are often caused by a few pivotal sources, we propose a novel framework to identify the sources of functional disruption from effective connectivity networks. Our approach integrates static and time-varying effective connectivity modeling in a probabilistic framework, to identify aberrant foci and the corresponding aberrant connectomics network. Using resting-state fMRI, we illustrate the utility of this novel approach in U.S. Army soldiers (N = 87) with posttraumatic stress disorder (PTSD), mild traumatic brain injury (mTBI) and combat controls. Additionally, we employed machine-learning classification to identify those significant connectivity features that possessed high predictive ability. We identified three disrupted foci (middle frontal gyrus [MFG], insula, hippocampus), and an aberrant prefrontal-subcortical-parietal network of information flow. We found the MFG to be the pivotal focus of network disruption, with aberrant strength and temporal-variability of effective connectivity to the insula, amygdala and hippocampus. These connectivities also possessed high predictive ability (giving a classification accuracy of 81%); and they exhibited significant associations with symptom severity and neurocognitive functioning. In summary, dysregulation originating in the MFG caused elevated and temporally less-variable connectivity in subcortical regions, followed by a similar effect on parietal memory-related regions. This mechanism likely contributes to the reduced control over traumatic memories leading to re-experiencing, hyperarousal and flashbacks observed in soldiers with PTSD and mTBI. Hum Brain Mapp 39:264-287, 2018. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- D Rangaprakash
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Michael N Dretsch
- U.S. Army Aeromedical Research Laboratory, Fort Rucker, Alabama.,Human Dimension Division, HQ TRADOC, Fort Eustis, Virgina
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Jeffrey S Katz
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, Alabama.,Alabama Advanced Imaging Consortium, USA
| | - Thomas S Denney
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, Alabama.,Alabama Advanced Imaging Consortium, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, Alabama.,Alabama Advanced Imaging Consortium, USA
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