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Liu P, Lin T, Fischer H, Feifel D, Ebner NC. Effects of four-week intranasal oxytocin administration on large-scale brain networks in older adults. Neuropharmacology 2024; 260:110130. [PMID: 39182569 DOI: 10.1016/j.neuropharm.2024.110130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 07/18/2024] [Accepted: 08/20/2024] [Indexed: 08/27/2024]
Abstract
Oxytocin (OT) is a crucial modulator of social cognition and behavior. Previous work primarily examined effects of acute intranasal oxytocin administration (IN-OT) in younger males on isolated brain regions. Not well understood are (i) chronic IN-OT effects, (ii) in older adults, (iii) on large-scale brain networks, representative of OT's wider-ranging brain mechanisms. To address these research gaps, 60 generally healthy older adults (mean age = 70.12 years, range = 55-83) were randomly assigned to self-administer either IN-OT or placebo twice daily via nasal spray over four weeks. Chronic IN-OT reduced resting-state functional connectivity (rs-FC) of both the right insula and the left middle cingulate cortex with the salience network but enhanced rs-FC of the left medial prefrontal cortex with the default mode network as well as the left thalamus with the basal ganglia-thalamus network. No significant chronic IN-OT effects were observed for between-network rs-FC. However, chronic IN-OT increased selective rs-FC of the basal ganglia-thalamus network with the salience network and the default mode network, indicative of more specialized, efficient communication between these networks. Directly comparing chronic vs. acute IN-OT, reduced rs-FC of the right insula with the salience network and between the default mode network and the basal ganglia-thalamus network, and greater selective rs-FC of the salience network with the default mode network and the basal ganglia-thalamus network, were more pronounced after chronic than acute IN-OT. Our results delineate the modulatory role of IN-OT on large-scale brain networks among older adults.
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Affiliation(s)
- Peiwei Liu
- Department of Psychology, University of Florida, Gainesville, FL, 32611, USA.
| | - Tian Lin
- Department of Psychology, University of Florida, Gainesville, FL, 32611, USA
| | - Håkan Fischer
- Department of Psychology, Stockholm University, Stockholm, SE-106 91, Sweden; Stockholm University Brain Imaging Centre (SUBIC), Stockholm University, Stockholm, SE-106 91, Sweden; Aging Research Centre, Karolinska Institute, Stockholm, SE-171 77, Stockholm, Sweden
| | - David Feifel
- Department of Psychiatry, University of California, San Diego, CA, 92093, USA
| | - Natalie C Ebner
- Department of Psychology, University of Florida, Gainesville, FL, 32611, USA; Institute on Aging, University of Florida, Gainesville, FL, 32611, USA; Center for Cognitive Aging and Memory, University of Florida, Gainesville, FL, 32610, USA.
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2
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Tuominen RK, Renko JM. Biomarkers of Parkinson's disease in perspective of early diagnosis and translation of neurotrophic therapies. Basic Clin Pharmacol Toxicol 2024; 135:271-284. [PMID: 38973499 DOI: 10.1111/bcpt.14042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 04/15/2024] [Accepted: 05/28/2024] [Indexed: 07/09/2024]
Abstract
Parkinson's disease (PD) is a common neurodegenerative disorder characterized by progressive loss of dopamine neurons and aberrant deposits of alpha-synuclein (α-syn) in the brain. The symptomatic treatment is started after the onset of motor manifestations in a late stage of the disease. Preclinical studies with neurotrophic factors (NTFs) show promising results of disease-modifying neuroprotective or even neurorestorative effects. Four NTFs have entered phase I-II clinical trials with inconclusive outcomes. This is not surprising because the preclinical evidence is from acute early-stage disease models, but the clinical trials included advanced PD patients. To conclude the value of NTF therapies, clinical studies should be performed in early-stage patients with prodromal symptoms, that is, before motor manifestations. In this review, we summarize currently available diagnostic and prognostic biomarkers that could help identify at-risk patients benefiting from NTF therapies. Focus is on biochemical and imaging biomarkers, but also other modalities are discussed. Neuroimaging is the most important diagnostic tool today, but α-syn imaging is not yet viable. Modern techniques allow measuring various forms of α-syn in cerebrospinal fluid, blood, saliva, and skin. Digital biomarkers and artificial intelligence offer new means for early diagnosis and longitudinal follow-up of degenerative brain diseases.
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Affiliation(s)
- Raimo K Tuominen
- Drug Research Program, Division of Pharmacology and Pharmacotherapy, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland
| | - Juho-Matti Renko
- Drug Research Program, Division of Pharmacology and Pharmacotherapy, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland
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3
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Williams D. Why so slow? Models of parkinsonian bradykinesia. Nat Rev Neurosci 2024; 25:573-586. [PMID: 38937655 DOI: 10.1038/s41583-024-00830-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/20/2024] [Indexed: 06/29/2024]
Abstract
Bradykinesia, or slowness of movement, is a defining feature of Parkinson disease (PD) and a major contributor to the negative effects on quality of life associated with this disorder and related conditions. A dominant pathophysiological model of bradykinesia in PD has existed for approximately 30 years and has been the basis for the development of several therapeutic interventions, but accumulating evidence has made this model increasingly untenable. Although more recent models have been proposed, they also appear to be flawed. In this Perspective, I consider the leading prior models of bradykinesia in PD and argue that a more functionally related model is required, one that considers changes that disrupt the fundamental process of accurate information transmission. In doing so, I review emerging evidence of network level functional connectivity changes, information transfer dysfunction and potential motor code transmission error and present a novel model of bradykinesia in PD that incorporates this evidence. I hope that this model may reconcile inconsistencies in its predecessors and encourage further development of therapeutic interventions.
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Affiliation(s)
- David Williams
- Department of Internal Medicine, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.
- Department of Neurology, Whipps Cross University Hospital, Barts Health NHS Trust, London, UK.
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4
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Churchill L, Chen YC, Lewis SJG, Matar E. Understanding REM Sleep Behavior Disorder through Functional MRI: A Systematic Review. Mov Disord 2024. [PMID: 38934216 DOI: 10.1002/mds.29898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 05/08/2024] [Accepted: 06/03/2024] [Indexed: 06/28/2024] Open
Abstract
Neuroimaging studies in rapid eye movement sleep behavior disorder (RBD) can inform fundamental questions about the pathogenesis of Parkinson's disease (PD). Across modalities, functional magnetic resonance imaging (fMRI) may be better suited to identify changes between neural networks in the earliest stages of Lewy body diseases when structural changes may be subtle or absent. This review synthesizes the findings from all fMRI studies of RBD to gain further insight into the pathophysiology and progression of Lewy body diseases. A total of 32 studies were identified using a systematic review conducted according to PRISMA guidelines between January 2000 to February 2024 for original fMRI studies in patients with either isolated RBD (iRBD) or RBD secondary to PD. Common functional alterations were detectable in iRBD patients compared with healthy controls across brainstem nuclei, basal ganglia, frontal and occipital lobes, and whole brain network measures. Patients with established PD and RBD demonstrated decreased functional connectivity across the whole brain and brainstem nuclei, but increased functional connectivity in the cerebellum and frontal lobe compared with those PD patients without RBD. Finally, longitudinal changes in resting state functional connectivity were found to track with disease progression. Currently, fMRI studies in RBD have demonstrated early signatures of neurodegeneration across both motor and non-motor pathways. Although more work is needed, such findings have the potential to inform our understanding of disease, help to distinguish between prodromal PD and prodromal dementia with Lewy bodies, and support the development of fMRI-based outcome measures of phenoconversion and progression in future disease modifying trials. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Lachlan Churchill
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Yu-Chi Chen
- Brain Dynamic Centre, Westmead Institute for Medical Research, Westmead, New South Wales, Australia
| | - Simon J G Lewis
- Macquarie Medical School and Macquarie University Centre for Parkinson's Disease Research, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Elie Matar
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
- Centre for Integrated Research and Understanding of Sleep (CIRUS), Woolcock Institute of Medical Research, Sydney, New South Wales, Australia
- Department of Neurology, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia
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5
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Pan Y, Bi C, Kochunov P, Shardell M, Smith JC, McCoy RG, Ye Z, Yu J, Lu T, Yang Y, Lee H, Liu S, Gao S, Ma Y, Li Y, Chen C, Ma T, Wang Z, Nichols T, Hong LE, Chen S. Brain-wide functional connectome analysis of 40,000 individuals reveals brain networks that show aging effects in older adults. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.17.594743. [PMID: 38798606 PMCID: PMC11118564 DOI: 10.1101/2024.05.17.594743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
The functional connectome changes with aging. We systematically evaluated aging related alterations in the functional connectome using a whole-brain connectome network analysis in 39,675 participants in UK Biobank project. We used adaptive dense network discovery tools to identify networks directly associated with aging from resting-state fMRI data. We replicated our findings in 499 participants from the Lifespan Human Connectome Project in Aging study. The results consistently revealed two motor-related subnetworks (both permutation test p-values <0.001) that showed a decline in resting-state functional connectivity (rsFC) with increasing age. The first network primarily comprises sensorimotor and dorsal/ventral attention regions from precentral gyrus, postcentral gyrus, superior temporal gyrus, and insular gyrus, while the second network is exclusively composed of basal ganglia regions, namely the caudate, putamen, and globus pallidus. Path analysis indicates that white matter fractional anisotropy mediates 19.6% (p<0.001, 95% CI [7.6% 36.0%]) and 11.5% (p<0.001, 95% CI [6.3% 17.0%]) of the age-related decrease in both networks, respectively. The total volume of white matter hyperintensity mediates 32.1% (p<0.001, 95% CI [16.8% 53.0%]) of the aging-related effect on rsFC in the first subnetwork.
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Affiliation(s)
- Yezhi Pan
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Chuan Bi
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Peter Kochunov
- Department of Psychiatry and Behavioral Science, University of Texas Health Science Center Houston, Houston, United States of America
| | - Michelle Shardell
- Department of Epidemiology and Public Health and Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - J. Carson Smith
- Department of Kinesiology, University of Maryland, College Park, Maryland, United States of America
| | - Rozalina G. McCoy
- Division of Endocrinology, Diabetes, & Nutrition, Department of Medicine, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Zhenyao Ye
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Jiaao Yu
- Department of Mathematics, University of Maryland, College Park, Maryland, United States of America
| | - Tong Lu
- Department of Mathematics, University of Maryland, College Park, Maryland, United States of America
| | - Yifan Yang
- Department of Mathematics, University of Maryland, College Park, Maryland, United States of America
| | - Hwiyoung Lee
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Song Liu
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
| | - Si Gao
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Yizhou Ma
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Yiran Li
- Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Chixiang Chen
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America
| | - Ze Wang
- Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Thomas Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - L. Elliot Hong
- Department of Psychiatry and Behavioral Science, University of Texas Health Science Center Houston, Houston, United States of America
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
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Ng MG, Chan BJL, Koh RY, Ng KY, Chye SM. Prevention of Parkinson's Disease: From Risk Factors to Early Interventions. CNS & NEUROLOGICAL DISORDERS DRUG TARGETS 2024; 23:746-760. [PMID: 37326115 DOI: 10.2174/1871527322666230616092054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 05/15/2023] [Accepted: 05/19/2023] [Indexed: 06/17/2023]
Abstract
Parkinson's disease (PD) is a debilitating neurological disorder characterized by progressively worsening motor dysfunction. Currently, available therapies merely alleviate symptoms, and there are no cures. Consequently, some researchers have now shifted their attention to identifying the modifiable risk factors of PD, with the intention of possibly implementing early interventions to prevent the development of PD. Four primary risk factors for PD are discussed including environmental factors (pesticides and heavy metals), lifestyle (physical activity and dietary intake), drug abuse, and individual comorbidities. Additionally, clinical biomarkers, neuroimaging, biochemical biomarkers, and genetic biomarkers could also help to detect prodromal PD. This review compiled available evidence that illustrates the relationship between modifiable risk factors, biomarkers, and PD. In summary, we raise the distinct possibility of preventing PD via early interventions of the modifiable risk factors and early diagnosis.
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Affiliation(s)
- Ming Guan Ng
- School of Health Science, International Medical University, 57000 Kuala Lumpur, Malaysia
| | - Brendan Jun Lam Chan
- School of Health Science, International Medical University, 57000 Kuala Lumpur, Malaysia
| | - Rhun Yian Koh
- Division of Applied Biomedical Science and Biotechnology, School of Health Science, International Medical University, Kuala Lumpur, Malaysia
| | - Khuen Yen Ng
- School of Pharmacy, Monash University, 47500 Selangor, Malaysia
| | - Soi Moi Chye
- Division of Applied Biomedical Science and Biotechnology, School of Health Science, International Medical University, Kuala Lumpur, Malaysia
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7
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Vijiaratnam N, Foltynie T. How should we be using biomarkers in trials of disease modification in Parkinson's disease? Brain 2023; 146:4845-4869. [PMID: 37536279 PMCID: PMC10690028 DOI: 10.1093/brain/awad265] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/18/2023] [Accepted: 07/22/2023] [Indexed: 08/05/2023] Open
Abstract
The recent validation of the α-synuclein seed amplification assay as a biomarker with high sensitivity and specificity for the diagnosis of Parkinson's disease has formed the backbone for a proposed staging system for incorporation in Parkinson's disease clinical studies and trials. The routine use of this biomarker should greatly aid in the accuracy of diagnosis during recruitment of Parkinson's disease patients into trials (as distinct from patients with non-Parkinson's disease parkinsonism or non-Parkinson's disease tremors). There remain, however, further challenges in the pursuit of biomarkers for clinical trials of disease modifying agents in Parkinson's disease, namely: optimizing the distinction between different α-synucleinopathies; the selection of subgroups most likely to benefit from a candidate disease modifying agent; a sensitive means of confirming target engagement; and the early prediction of longer-term clinical benefit. For example, levels of CSF proteins such as the lysosomal enzyme β-glucocerebrosidase may assist in prognostication or allow enrichment of appropriate patients into disease modifying trials of agents with this enzyme as the target; the presence of coexisting Alzheimer's disease-like pathology (detectable through CSF levels of amyloid-β42 and tau) can predict subsequent cognitive decline; imaging techniques such as free-water or neuromelanin MRI may objectively track decline in Parkinson's disease even in its later stages. The exploitation of additional biomarkers to the α-synuclein seed amplification assay will, therefore, greatly add to our ability to plan trials and assess the disease modifying properties of interventions. The choice of which biomarker(s) to use in the context of disease modifying clinical trials will depend on the intervention, the stage (at risk, premotor, motor, complex) of the population recruited and the aims of the trial. The progress already made lends hope that panels of fluid biomarkers in tandem with structural or functional imaging may provide sensitive and objective methods of confirming that an intervention is modifying a key pathophysiological process of Parkinson's disease. However, correlation with clinical progression does not necessarily equate to causation, and the ongoing validation of quantitative biomarkers will depend on insightful clinical-genetic-pathophysiological comparisons incorporating longitudinal biomarker changes from those at genetic risk with evidence of onset of the pathophysiology and those at each stage of manifest clinical Parkinson's disease.
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Affiliation(s)
- Nirosen Vijiaratnam
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Thomas Foltynie
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
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8
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Beccano-Kelly DA, Cherubini M, Mousba Y, Cramb KM, Giussani S, Caiazza MC, Rai P, Vingill S, Bengoa-Vergniory N, Ng B, Corda G, Banerjee A, Vowles J, Cowley S, Wade-Martins R. Calcium dysregulation combined with mitochondrial failure and electrophysiological maturity converge in Parkinson's iPSC-dopamine neurons. iScience 2023; 26:107044. [PMID: 37426342 PMCID: PMC10329047 DOI: 10.1016/j.isci.2023.107044] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 11/30/2022] [Accepted: 06/01/2023] [Indexed: 07/11/2023] Open
Abstract
Parkinson's disease (PD) is characterized by a progressive deterioration of motor and cognitive functions. Although death of dopamine neurons is the hallmark pathology of PD, this is a late-stage disease process preceded by neuronal dysfunction. Here we describe early physiological perturbations in patient-derived induced pluripotent stem cell (iPSC)-dopamine neurons carrying the GBA-N370S mutation, a strong genetic risk factor for PD. GBA-N370S iPSC-dopamine neurons show an early and persistent calcium dysregulation notably at the mitochondria, followed by reduced mitochondrial membrane potential and oxygen consumption rate, indicating mitochondrial failure. With increased neuronal maturity, we observed decreased synaptic function in PD iPSC-dopamine neurons, consistent with the requirement for ATP and calcium to support the increase in electrophysiological activity over time. Our work demonstrates that calcium dyshomeostasis and mitochondrial failure impair the higher electrophysiological activity of mature neurons and may underlie the vulnerability of dopamine neurons in PD.
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Affiliation(s)
- Dayne A. Beccano-Kelly
- Oxford Parkinson’s Disease Centre, University of Oxford, Oxford, United Kingdom
- Department of Physiology, Anatomy and Genetics, University of Oxford, South Parks Road, Oxford OX3 7BN, UK
| | - Marta Cherubini
- Oxford Parkinson’s Disease Centre, University of Oxford, Oxford, United Kingdom
- Department of Physiology, Anatomy and Genetics, University of Oxford, South Parks Road, Oxford OX3 7BN, UK
| | - Yassine Mousba
- Oxford Parkinson’s Disease Centre, University of Oxford, Oxford, United Kingdom
- Department of Physiology, Anatomy and Genetics, University of Oxford, South Parks Road, Oxford OX3 7BN, UK
| | - Kaitlyn M.L. Cramb
- Oxford Parkinson’s Disease Centre, University of Oxford, Oxford, United Kingdom
- Department of Physiology, Anatomy and Genetics, University of Oxford, South Parks Road, Oxford OX3 7BN, UK
- Kavli Institute for Nanoscience Discovery, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford OX1 3QU, UK
| | - Stefania Giussani
- Oxford Parkinson’s Disease Centre, University of Oxford, Oxford, United Kingdom
- Department of Physiology, Anatomy and Genetics, University of Oxford, South Parks Road, Oxford OX3 7BN, UK
| | - Maria Claudia Caiazza
- Oxford Parkinson’s Disease Centre, University of Oxford, Oxford, United Kingdom
- Department of Physiology, Anatomy and Genetics, University of Oxford, South Parks Road, Oxford OX3 7BN, UK
- Kavli Institute for Nanoscience Discovery, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford OX1 3QU, UK
| | - Pavandeep Rai
- Oxford Parkinson’s Disease Centre, University of Oxford, Oxford, United Kingdom
- Department of Physiology, Anatomy and Genetics, University of Oxford, South Parks Road, Oxford OX3 7BN, UK
| | - Siv Vingill
- Oxford Parkinson’s Disease Centre, University of Oxford, Oxford, United Kingdom
- Department of Physiology, Anatomy and Genetics, University of Oxford, South Parks Road, Oxford OX3 7BN, UK
| | - Nora Bengoa-Vergniory
- Oxford Parkinson’s Disease Centre, University of Oxford, Oxford, United Kingdom
- Department of Physiology, Anatomy and Genetics, University of Oxford, South Parks Road, Oxford OX3 7BN, UK
- Kavli Institute for Nanoscience Discovery, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford OX1 3QU, UK
| | - Bryan Ng
- Department of Physiology, Anatomy and Genetics, University of Oxford, South Parks Road, Oxford OX3 7BN, UK
- Kavli Institute for Nanoscience Discovery, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford OX1 3QU, UK
| | - Gabriele Corda
- Department of Physiology, Anatomy and Genetics, University of Oxford, South Parks Road, Oxford OX3 7BN, UK
| | - Abhirup Banerjee
- Radcliffe Department of Medicine, Division of Cardiovascular Medicine, University of Oxford, Oxford OX3 9DU, UK
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford OX3 7DQ, UK
| | - Jane Vowles
- Oxford Parkinson’s Disease Centre, University of Oxford, Oxford, United Kingdom
- The James Martin Stem Cell Facility, Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford OX1 3RE, UK
| | - Sally Cowley
- Oxford Parkinson’s Disease Centre, University of Oxford, Oxford, United Kingdom
- The James Martin Stem Cell Facility, Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford OX1 3RE, UK
| | - Richard Wade-Martins
- Oxford Parkinson’s Disease Centre, University of Oxford, Oxford, United Kingdom
- Department of Physiology, Anatomy and Genetics, University of Oxford, South Parks Road, Oxford OX3 7BN, UK
- Kavli Institute for Nanoscience Discovery, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford OX1 3QU, UK
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9
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Meulenberg CJW, Rehfeld K, Jovanović S, Marusic U. Unleashing the potential of dance: a neuroplasticity-based approach bridging from older adults to Parkinson's disease patients. Front Aging Neurosci 2023; 15:1188855. [PMID: 37434737 PMCID: PMC10331838 DOI: 10.3389/fnagi.2023.1188855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 06/05/2023] [Indexed: 07/13/2023] Open
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder that affects >1% of individuals worldwide and is manifested by motor symptoms such as tremor, rigidity, and bradykinesia, as well as non-motor symptoms such as cognitive impairment and depression. Non-pharmacological interventions such as dance therapy are becoming increasingly popular as complementary therapies for PD, in addition to pharmacological treatments that are currently widely available. Dance as a sensorimotor activity stimulates multiple layers of the neural system, including those involved in motor planning and execution, sensory integration, and cognitive processing. Dance interventions in healthy older people have been associated with increased activation of the prefrontal cortex, as well as enhanced functional connectivity between the basal ganglia, cerebellum, and prefrontal cortex. Overall, the evidence suggests that dance interventions can induce neuroplastic changes in healthy older participants, leading to improvements in both motor and cognitive functions. Dance interventions involving patients with PD show better quality of life and improved mobility, whereas the literature on dance-induced neuroplasticity in PD is sparse. Nevertheless, this review argues that similar neuroplastic mechanisms may be at work in patients with PD, provides insight into the potential mechanisms underlying dance efficacy, and highlights the potential of dance therapy as a non-pharmacological intervention in PD. Further research is warranted to determine the optimal dance style, intensity, and duration for maximum therapeutic benefit and to determine the long-term effects of dance intervention on PD progression.
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Affiliation(s)
| | - Kathrin Rehfeld
- Institute for Sport Science, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Saša Jovanović
- Faculty of Physical Education and Sport, University of Banja Luka, Banja Luka, Bosnia and Herzegovina
| | - Uros Marusic
- Institute for Kinesiology Research, Science and Research Centre Koper, Koper, Slovenia
- Department of Health Sciences, Alma Mater Europaea–ECM, Maribor, Slovenia
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10
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Boon LI, Hillebrand A, Schoonheim MM, Twisk JW, Stam CJ, Berendse HW. Cortical and Subcortical Changes in MEG Activity Reflect Parkinson's Progression over a Period of 7 Years. Brain Topogr 2023:10.1007/s10548-023-00965-w. [PMID: 37154884 DOI: 10.1007/s10548-023-00965-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 04/15/2023] [Indexed: 05/10/2023]
Abstract
In this study of early functional changes in Parkinson's disease (PD), we aimed to provide a comprehensive assessment of the development of changes in both cortical and subcortical neurophysiological brain activity, including their association with clinical measures of disease severity. Repeated resting-state MEG recordings and clinical assessments were obtained in the context of a unique longitudinal cohort study over a seven-year period using a multiple longitudinal design. We used linear mixed-models to analyze the relationship between neurophysiological (spectral power and functional connectivity) and clinical data. At baseline, early-stage (drug-naïve) PD patients demonstrated spectral slowing compared to healthy controls in both subcortical and cortical brain regions, most outspoken in the latter. Over time, spectral slowing progressed in strong association with clinical measures of disease progression (cognitive and motor). Global functional connectivity was not different between groups at baseline and hardly changed over time. Therefore, investigation of associations with clinical measures of disease progression were not deemed useful. An analysis of individual connections demonstrated differences between groups at baseline (higher frontal theta, lower parieto-occipital alpha2 band functional connectivity) and over time in PD patients (increase in frontal delta and theta band functional connectivity). Our results suggest that spectral measures are promising candidates in the search for non-invasive markers of both early-stage PD and of the ongoing disease process.
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Affiliation(s)
- Lennard I Boon
- Department of Neurology, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
- Department of Clinical Neurophysiology and Magnetoencephalography Center, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
| | - Arjan Hillebrand
- Department of Neurology, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
- Department of Clinical Neurophysiology and Magnetoencephalography Center, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Jos W Twisk
- Department of Epidemiology and Biostatistics, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Department of Neurology, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
- Department of Clinical Neurophysiology and Magnetoencephalography Center, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Henk W Berendse
- Department of Neurology, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
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11
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Chen B, Cui W, Wang S, Sun A, Yu H, Liu Y, He J, Fan G. Functional connectome automatically differentiates multiple system atrophy (parkinsonian type) from idiopathic Parkinson's disease at early stages. Hum Brain Mapp 2023; 44:2176-2190. [PMID: 36661217 PMCID: PMC10028675 DOI: 10.1002/hbm.26201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 12/08/2022] [Accepted: 12/30/2022] [Indexed: 01/21/2023] Open
Abstract
Differentiating the parkinsonian variant of multiple system atrophy (MSA-P) from idiopathic Parkinson's disease (IPD) is challenging, especially in the early stages. This study aimed to investigate differences and similarities in the brain functional connectomes of IPD and MSA-P patients and use machine learning methods to explore the diagnostic utility of these features. Resting-state fMRI data were acquired from 88 healthy controls, 76 MSA-P patients, and 53 IPD patients using a 3.0 T scanner. The whole-brain functional connectome was constructed by thresholding the Pearson correlation matrices of 116 regions, and topological properties were evaluated through graph theory approaches. Connectome measurements were used as features in machine learning models (random forest [RF]/logistic regression [LR]/support vector machine) to distinguish IPD and MSA-P patients. Regarding graph metrics, early IPD and MSA-P patients shared network topological properties. Both patient groups showed functional connectivity disruptions within the cerebellum-basal ganglia-cortical network, but these disconnections were mainly in the cortico-thalamo-cerebellar circuits in MSA-P patients and the basal ganglia-thalamo-cortical circuits in IPD patients. Among the connectome parameters, t tests combined with the RF method identified 15 features, from which the LR classifier achieved the best diagnostic performance on the validation set (accuracy = 92.31%, sensitivity = 90.91%, specificity = 93.33%, area under the receiver operating characteristic curve = 0.89). MSA-P and IPD patients show similar whole-brain network topological alterations. MSA-P primarily affects cerebellar nodes, and IPD primarily affects basal ganglia nodes; both conditions disrupt the cerebellum-basal ganglia-cortical network. Moreover, functional connectome parameters showed outstanding value in the differential diagnosis of early MSA-P and IPD.
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Affiliation(s)
- Boyu Chen
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Wenzhuo Cui
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Shanshan Wang
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Anlan Sun
- Yizhun Medical AI Co. Ltd, Beijing, People's Republic of China
| | - Hongmei Yu
- Department of Neurology, The First Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Yu Liu
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Jiachuan He
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Guoguang Fan
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
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12
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Inguanzo A, Poulakis K, Mohanty R, Schwarz CG, Przybelski SA, Diaz-Galvan P, Lowe VJ, Boeve BF, Lemstra AW, van de Beek M, van der Flier W, Barkhof F, Blanc F, Loureiro de Sousa P, Philippi N, Cretin B, Demuynck C, Nedelska Z, Hort J, Segura B, Junque C, Oppedal K, Aarsland D, Westman E, Kantarci K, Ferreira D. MRI data-driven clustering reveals different subtypes of Dementia with Lewy bodies. NPJ Parkinsons Dis 2023; 9:5. [PMID: 36670121 PMCID: PMC9859778 DOI: 10.1038/s41531-023-00448-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 01/05/2023] [Indexed: 01/21/2023] Open
Abstract
Dementia with Lewy bodies (DLB) is a neurodegenerative disorder with a wide heterogeneity of symptoms, which suggests the existence of different subtypes. We used data-driven analysis of magnetic resonance imaging (MRI) data to investigate DLB subtypes. We included 165 DLB from the Mayo Clinic and 3 centers from the European DLB consortium and performed a hierarchical cluster analysis to identify subtypes based on gray matter (GM) volumes. To characterize the subtypes, we used demographic and clinical data, as well as β-amyloid, tau, and cerebrovascular biomarkers at baseline, and cognitive decline over three years. We identified 3 subtypes: an older subtype with reduced cortical GM volumes, worse cognition, and faster cognitive decline (n = 49, 30%); a subtype with low GM volumes in fronto-occipital regions (n = 76, 46%); and a subtype of younger patients with the highest cortical GM volumes, proportionally lower GM volumes in basal ganglia and the highest frequency of cognitive fluctuations (n = 40, 24%). This study shows the existence of MRI subtypes in DLB, which may have implications for clinical workout, research, and therapeutic decisions.
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Grants
- R01 AG041851 NIA NIH HHS
- C06 RR018898 NCRR NIH HHS
- P50 AG016574 NIA NIH HHS
- R01 AG040042 NIA NIH HHS
- R01 NS080820 NINDS NIH HHS
- R37 AG011378 NIA NIH HHS
- U01 NS100620 NINDS NIH HHS
- U01 AG006786 NIA NIH HHS
- Alzheimerfonden
- Center for Innovative Medicine (CIMED) Swedish Brain funding (Hjärnfonden) ALF Medicine Swedish Dementia funding (Demensförbundet) Foundation for Geriatric Diseases at Karolinska Institutet Karolinska Institutet travel grants
- Little Family Foundation
- National Institutes of Health (U01-NS100620, P50-AG016574, U01-AG006786, R37-AG011378, R01-AG041851, R01-AG040042, C06-RR018898 and R01-NS080820), Foundation Dr. Corinne Schuler, the Mangurian Foundation for Lewy Body Research, the Elsie and Marvin Dekelboum Family Foundation, the Robert H. and Clarice Smith and Abigail Van Buren Alzheimer’s Disease Research Program
- Projet Hospitalier de Recherche Clinique (PHRC, IDCRB 2012-A00992-41) and Fondation Université de Strasbourg
- The Grant Agency of Charles University (grant PRIMUS 22/MED/011).
- Western Norway Regional Health Authority, the Swedish Foundation for Strategic Research (SSF), the Swedish Research Council (VR)Center for Innovative Medicine (CIMED), the Swedish Brain funding (Hjärnfonden), ALF Medicine.
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Affiliation(s)
- Anna Inguanzo
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden
- Medical Psychology Unit, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
- Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Konstantinos Poulakis
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden
| | - Rosaleena Mohanty
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden
| | | | | | - Patricia Diaz-Galvan
- 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, US
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN, US
| | | | - Afina W Lemstra
- Department of Neurology and Alzheimer Center, VU University Medical Center, Amsterdam, Netherlands
| | - Marleen van de Beek
- Department of Neurology and Alzheimer Center, VU University Medical Center, Amsterdam, Netherlands
| | - Wiesje van der Flier
- Department of Neurology and Alzheimer Center, VU University Medical Center, Amsterdam, Netherlands
| | - Frederik Barkhof
- Department of Neurology and Alzheimer Center, VU University Medical Center, Amsterdam, Netherlands
- UCL institutes of neurology and center for medical image computing, London, UK
| | - Frederic Blanc
- Day Hospital of Geriatrics, Memory Resource and Research Center (CM2R) of Strasbourg, Department of Geriatrics, Hopitaux Universitaires de Strasbourg, Strasbourg, France
- University of Strasbourg and French National Center for Scientific Research (CNRS), ICube Laboratory and Federation de Medecine Translationnelle de Strasbourg (FMTS), Team Imagerie Multimodale Integrative en Sante (IMIS)/ICONE, Strasbourg, France
| | - Paulo Loureiro de Sousa
- Day Hospital of Geriatrics, Memory Resource and Research Center (CM2R) of Strasbourg, Department of Geriatrics, Hopitaux Universitaires de Strasbourg, Strasbourg, France
- University of Strasbourg and French National Center for Scientific Research (CNRS), ICube Laboratory and Federation de Medecine Translationnelle de Strasbourg (FMTS), Team Imagerie Multimodale Integrative en Sante (IMIS)/ICONE, Strasbourg, France
| | - Nathalie Philippi
- Day Hospital of Geriatrics, Memory Resource and Research Center (CM2R) of Strasbourg, Department of Geriatrics, Hopitaux Universitaires de Strasbourg, Strasbourg, France
- University of Strasbourg and French National Center for Scientific Research (CNRS), ICube Laboratory and Federation de Medecine Translationnelle de Strasbourg (FMTS), Team Imagerie Multimodale Integrative en Sante (IMIS)/ICONE, Strasbourg, France
| | - Benjamin Cretin
- Day Hospital of Geriatrics, Memory Resource and Research Center (CM2R) of Strasbourg, Department of Geriatrics, Hopitaux Universitaires de Strasbourg, Strasbourg, France
- University of Strasbourg and French National Center for Scientific Research (CNRS), ICube Laboratory and Federation de Medecine Translationnelle de Strasbourg (FMTS), Team Imagerie Multimodale Integrative en Sante (IMIS)/ICONE, Strasbourg, France
| | - Catherine Demuynck
- Day Hospital of Geriatrics, Memory Resource and Research Center (CM2R) of Strasbourg, Department of Geriatrics, Hopitaux Universitaires de Strasbourg, Strasbourg, France
- University of Strasbourg and French National Center for Scientific Research (CNRS), ICube Laboratory and Federation de Medecine Translationnelle de Strasbourg (FMTS), Team Imagerie Multimodale Integrative en Sante (IMIS)/ICONE, Strasbourg, France
| | - Zuzana Nedelska
- Department of Radiology, Mayo Clinic, Rochester, MN, US
- Department of Neurology, Charles University, 2nd Faculty of Medicine, Motol University Hospital, Prague, Czech Republic
- International Clinical Research Center, St. Annes University Hospital Brno, Brno, Czech Republic
| | - Jakub Hort
- Department of Neurology, Charles University, 2nd Faculty of Medicine, Motol University Hospital, Prague, Czech Republic
- International Clinical Research Center, St. Annes University Hospital Brno, Brno, Czech Republic
| | - Barbara Segura
- Medical Psychology Unit, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
- Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Carme Junque
- Medical Psychology Unit, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
- Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Ketil Oppedal
- Center for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
- Stavanger Medical Imaging Laboratory (SMIL), Department of Radiology, Stavanger University Hospital, Stavanger, Norway
- Department of Electrical Engineering and Computer Science, University of Stavanger, Stavanger, Norway
| | - Dag Aarsland
- Department of Neuroimaging, Center for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK
| | - Eric Westman
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden
- Department of Neuroimaging, Center for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK
| | | | - Daniel Ferreira
- 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, US.
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13
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Williams D. Basal ganglia functional connectivity network analysis does not support the 'noisy signal' hypothesis of Parkinson's disease. Brain Commun 2023; 5:fcad123. [PMID: 37124947 PMCID: PMC10139445 DOI: 10.1093/braincomms/fcad123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 02/23/2023] [Accepted: 04/12/2023] [Indexed: 05/02/2023] Open
Abstract
The 'noisy signal' hypothesis of basal ganglia dysfunction in Parkinson's disease (PD) suggests that major motor symptoms of the disorder are caused by the development of abnormal basal ganglia activity patterns resulting in the propagation of 'noisy' signals to target systems. While such abnormal activity patterns might be useful biomarkers for the development of therapeutic interventions, correlation between specific changes in activity and PD symptoms has been inconsistently demonstrated, and raises questions concerning the accuracy of the hypothesis. Here, we tested this hypothesis by considering three nodes of the basal ganglia network, the subthalamus, globus pallidus interna, and cortex during self-paced and cued movements in patients with PD. Interactions between these regions were analyzed using measures that assess both linear and non-linear relationships. Marked changes in the network are observed with dopamine state. Specifically, we detected functional disconnection of the basal ganglia from the cortex and higher network variability in untreated PD, but various patterns of directed functional connectivity with lower network variability in treated PD. When we examine the system output, significant correlation is observed between variability in the cortico-basal ganglia network and muscle activity variability but only in the treated state. Rather than supporting a role of the basal ganglia in the transmission of noisy signals in patients with PD, these findings suggest that cortico-basal ganglia network interactions by fault or design, in the treated Parkinsonian state, are actually associated with improved cortical network output variability.
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Affiliation(s)
- David Williams
- Correspondence to: Dr David Williams. Department of Internal Medicine, College of Medicine and Health Sciences, United Arab Emirates University, Khalifa Bin Zayed Street, Tawam, Next to Tawam Hospital, Al Ain, PO Box 15551, United Arab Emirates. E-mail:
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14
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Anticevic A, Halassa MM. The thalamus in psychosis spectrum disorder. Front Neurosci 2023; 17:1163600. [PMID: 37123374 PMCID: PMC10133512 DOI: 10.3389/fnins.2023.1163600] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 03/21/2023] [Indexed: 05/02/2023] Open
Abstract
Psychosis spectrum disorder (PSD) affects 1% of the world population and results in a lifetime of chronic disability, causing devastating personal and economic consequences. Developing new treatments for PSD remains a challenge, particularly those that target its core cognitive deficits. A key barrier to progress is the tenuous link between the basic neurobiological understanding of PSD and its clinical phenomenology. In this perspective, we focus on a key opportunity that combines innovations in non-invasive human neuroimaging with basic insights into thalamic regulation of functional cortical connectivity. The thalamus is an evolutionary conserved region that forms forebrain-wide functional loops critical for the transmission of external inputs as well as the construction and update of internal models. We discuss our perspective across four lines of evidence: First, we articulate how PSD symptomatology may arise from a faulty network organization at the macroscopic circuit level with the thalamus playing a central coordinating role. Second, we discuss how recent animal work has mechanistically clarified the properties of thalamic circuits relevant to regulating cortical dynamics and cognitive function more generally. Third, we present human neuroimaging evidence in support of thalamic alterations in PSD, and propose that a similar "thalamocortical dysconnectivity" seen in pharmacological imaging (under ketamine, LSD and THC) in healthy individuals may link this circuit phenotype to the common set of symptoms in idiopathic and drug-induced psychosis. Lastly, we synthesize animal and human work, and lay out a translational path for biomarker and therapeutic development.
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Affiliation(s)
- Alan Anticevic
- School of Medicine, Yale University, New Haven, CT, United States
- *Correspondence: Alan Anticevic,
| | - Michael M. Halassa
- Department of Neuroscience, Tufts University School of Medicine, Boston, MA, United States
- Michael M. Halassa,
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15
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Togo H, Nakamura T, Wakasugi N, Takahashi Y, Hanakawa T. Interactions across emotional, cognitive and subcortical motor networks underlying freezing of gait. Neuroimage Clin 2023; 37:103342. [PMID: 36739790 PMCID: PMC9932566 DOI: 10.1016/j.nicl.2023.103342] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 01/23/2023] [Accepted: 01/29/2023] [Indexed: 02/05/2023]
Abstract
Freezing of gait (FOG) is a gait disorder affecting patients with Parkinson's disease (PD) and related disorders. The pathophysiology of FOG is unclear because of its phenomenological complexity involving motor, cognitive, and emotional aspects of behavior. Here we used resting-state functional MRI to retrieve functional connectivity (FC) correlated with the New FOG questionnaire (NFOGQ) reflecting severity of FOG in 67 patients with PD. NFOGQ scores were correlated with FCs in the extended basal ganglia network (BGN) involving the striatum and amygdala, and in the extra-cerebellum network (CBLN) involving the frontoparietal network (FPN). These FCs represented interactions across the emotional (amygdala), subcortical motor (BGN and CBLN), and cognitive networks (FPN). Using these FCs as features, we constructed statistical models that explained 40% of the inter-individual variances of FOG severity and that discriminated between PD patients with and without FOG. The amygdala, which connects to the subcortical motor (BGN and CBLN) and cognitive (FPN) networks, may have a pivotal role in interactions across the emotional, cognitive, and subcortical motor networks. Future refinement of the machine learning-based classifier using FCs may clarify the complex pathophysiology of FOG further and help diagnose and evaluate FOG in clinical settings.
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Affiliation(s)
- Hiroki Togo
- Department of Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto, Yoshida-Konoe, Sakyo-ku, Kyoto 606-8501, Japan; Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry (NCNP), 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan
| | - Tatsuhiro Nakamura
- Department of Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto, Yoshida-Konoe, Sakyo-ku, Kyoto 606-8501, Japan; Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry (NCNP), 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan
| | - Noritaka Wakasugi
- Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry (NCNP), 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan
| | - Yuji Takahashi
- Department of Neurology, National Center Hospital, National Center of Neurology and Psychiatry (NCNP), Tokyo, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan
| | - Takashi Hanakawa
- Department of Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto, Yoshida-Konoe, Sakyo-ku, Kyoto 606-8501, Japan; Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry (NCNP), 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan.
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16
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Wen J, Guo T, Wu J, Bai X, Zhou C, Wu H, Liu X, Chen J, Cao Z, Gu L, Pu J, Zhang B, Zhang M, Guan X, Xu X. Nigral Iron Deposition Influences Disease Severity by Modulating the Effect of Parkinson's Disease on Brain Networks. JOURNAL OF PARKINSON'S DISEASE 2022; 12:2479-2492. [PMID: 36336939 PMCID: PMC9837680 DOI: 10.3233/jpd-223372] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND In Parkinson's disease (PD), excessive iron deposition in the substantia nigra may exacerbate α-synuclein aggregation, facilitating the degeneration of dopaminergic neurons and their neural projection. OBJECTIVE To investigate the interaction effect between nigral iron deposition and PD status on brain networks. METHODS Eighty-five PD patients and 140 normal controls (NC) were included. Network function and nigral iron were measured using multi-modality magnetic resonance imaging. According to the median of nigral magnetic susceptibility of NC (0.095 ppm), PD and NC were respectively divided into high and low nigral iron group. The main and interaction effects were investigated by mixed effect analysis. RESULTS The main effect of disease was observed in basal ganglia network (BGN) and visual network (VN). The interaction effect between nigral iron and PD status was observed in left inferior frontal gyrus and left insular lobe in BGN, as well as right middle occipital gyrus, right superior temporal gyrus, and bilateral cuneus in VN. Furthermore, multiple mediation analysis revealed that the functional connectivity of interaction effect clusters in BGN and medial VN partially mediated the relationship between nigral iron and Unified Parkinson's Disease Rating Scale II score. CONCLUSION Our study demonstrates an interaction of nigral iron deposition and PD status on brain networks, that is, nigral iron deposition is associated with the change of brain network configuration exclusively when in PD. We identified a potential causal mediation pathway for iron to affect disease severity that was mediated by both BGN dysfunction and VN hyperfunction in PD.
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Affiliation(s)
- Jiaqi Wen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tao Guo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingjing Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xueqin Bai
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Cheng Zhou
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Haoting Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingwen Chen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhengye Cao
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Luyan Gu
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiali Pu
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China,Correspondence to: Xiaojun Xu, MD, Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 310009, China. Tel.: +86 0571 87315255; Fax: +86 0571 87315255; E-mail: and Xiaojun Guan, PhD, Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No. 88 Jiefang Road, Shangcheng District, Hangzhou 310009, China. Tel.: +86 0571 87315255; Fax: +86 0571 87315255;
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China,Correspondence to: Xiaojun Xu, MD, Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 310009, China. Tel.: +86 0571 87315255; Fax: +86 0571 87315255; E-mail: and Xiaojun Guan, PhD, Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No. 88 Jiefang Road, Shangcheng District, Hangzhou 310009, China. Tel.: +86 0571 87315255; Fax: +86 0571 87315255;
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17
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Brak IV, Filimonova E, Zakhariya O, Khasanov R, Stepanyan I. Transcranial Current Stimulation as a Tool of Neuromodulation of Cognitive Functions in Parkinson’s Disease. Front Neurosci 2022; 16:781488. [PMID: 35903808 PMCID: PMC9314857 DOI: 10.3389/fnins.2022.781488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 04/28/2022] [Indexed: 11/13/2022] Open
Abstract
Decrease in cognitive function is one of the most common causes of poor life quality and early disability in patients with Parkinson’s disease (PD). Existing methods of treatment are aimed at both correction of motor and non-motor symptoms. Methods of adjuvant therapy (or complementary therapy) for maintaining cognitive functions in patients with PD are of interest. A promising subject of research in this regard is the method of transcranial electric current stimulation (tES). Here we reviewed the current understanding of the pathogenesis of cognitive impairment in PD and of the effects of transcranial direct current stimulation and transcranial alternating current stimulation on the cognitive function of patients with PD-MCI (Parkinson’s Disease–Mild Cognitive Impairment).
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Affiliation(s)
- Ivan V. Brak
- Laboratory of Comprehensive Problems of Risk Assessment to Population and Workers’ Health, Federal State Budgetary Scientific Institution “Izmerov Research Institute of Occupational Health”, Moscow, Russia
- “Engiwiki” Scientific and Engineering Projects Laboratory, Department of Information Technologies, Novosibirsk State University, Novosibirsk, Russia
- *Correspondence: Ivan V. Brak,
| | | | - Oleg Zakhariya
- Faculty of Philosophy, Lomonosov Moscow State University, Moscow, Russia
| | - Rustam Khasanov
- Faculty of Philosophy, Lomonosov Moscow State University, Moscow, Russia
- Independent Researcher, Novosibirsk, Russia
| | - Ivan Stepanyan
- Peoples’ Friendship University of Russia (RUDN University), Moscow, Russia
- Mechanical Engineering Research Institute of the Russian Academy of Sciences, Moscow, Russia
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18
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Kulkarni AS, Burns MR, Brundin P, Wesson DW. Linking α-synuclein-induced synaptopathy and neural network dysfunction in early Parkinson's disease. Brain Commun 2022; 4:fcac165. [PMID: 35822101 PMCID: PMC9272065 DOI: 10.1093/braincomms/fcac165] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/11/2022] [Accepted: 06/20/2022] [Indexed: 01/18/2023] Open
Abstract
The prodromal phase of Parkinson's disease is characterized by aggregation of the misfolded pathogenic protein α-synuclein in select neural centres, co-occurring with non-motor symptoms including sensory and cognitive loss, and emotional disturbances. It is unclear whether neuronal loss is significant during the prodrome. Underlying these symptoms are synaptic impairments and aberrant neural network activity. However, the relationships between synaptic defects and network-level perturbations are not established. In experimental models, pathological α-synuclein not only impacts neurotransmission at the synaptic level, but also leads to changes in brain network-level oscillatory dynamics-both of which likely contribute to non-motor deficits observed in Parkinson's disease. Here we draw upon research from both human subjects and experimental models to propose a 'synapse to network prodrome cascade' wherein before overt cell death, pathological α-synuclein induces synaptic loss and contributes to aberrant network activity, which then gives rise to prodromal symptomology. As the disease progresses, abnormal patterns of neural activity ultimately lead to neuronal loss and clinical progression of disease. Finally, we outline goals and research needed to unravel the basis of functional impairments in Parkinson's disease and other α-synucleinopathies.
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Affiliation(s)
- Aishwarya S Kulkarni
- Department of Pharmacology & Therapeutics, University of Florida, 1200 Newell Dr, Gainesville, FL 32610, USA
| | - Matthew R Burns
- Department of Neurology, University of Florida, 1200 Newell Dr, Gainesville, FL 32610, USA
- Norman Fixel Institute for Neurological Disorders, University of Florida, 1200 Newell Dr, Gainesville, FL 32610, USA
| | - Patrik Brundin
- Pharma Research and Early Development (pRED), F. Hoffman-La Roche, Little Falls, NJ, USA
| | - Daniel W Wesson
- Department of Pharmacology & Therapeutics, University of Florida, 1200 Newell Dr, Gainesville, FL 32610, USA
- Norman Fixel Institute for Neurological Disorders, University of Florida, 1200 Newell Dr, Gainesville, FL 32610, USA
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19
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Chen L, Huang T, Ma D, Chen YC. Altered Default Mode Network Functional Connectivity in Parkinson’s Disease: A Resting-State Functional Magnetic Resonance Imaging Study. Front Neurosci 2022; 16:905121. [PMID: 35720728 PMCID: PMC9204219 DOI: 10.3389/fnins.2022.905121] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 05/10/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeWhether the intrinsic functional connectivity pattern of the default mode network (DMN) is involved in the progression of cognitive decline in Parkinson’s disease (PD) remains unclear. This study aimed to investigate the intrinsic functional connectivity (FC) pattern of the DMN anchored on the posterior cingulate cortex (PCC) in patients with PD by resting-state functional magnetic resonance imaging (fMRI).MethodsFifty patients with PD and 50 healthy controls (HCs) were included for resting-state fMRI scanning. A seed-based FC method was used to reveal FC patterns in the DMN with region of interest (ROI) in the PCC. Relationships between FC patterns and disease severity (UPDRS-III) were detected.ResultsCompared with the HCs, the patients with PD showed increased FC between the PCC and the right precuneus, left cuneus, and right angular gyrus. In the PD group, the increased FC values in the right precuneus were significantly and positively correlated with motor severity as assessed with UPDRS-III scores (rho = 0.337, p = 0.02).ConclusionOur result highlights that the patients with PD showed increased FC between the PCC and the right precuneus, left cuneus, and right angular gyrus in the DMN. The altered connectivity pattern in the DMN may play a crucial role in the neurophysiological mechanism of cognitive decline in patients with PD. These findings might provide new insights into neural mechanisms of cognitive decline in PD.
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Affiliation(s)
- Lu Chen
- Department of Radiology, Nanjing Integrated Traditional Chinese and Western Medicine Hospital Affiliated With Nanjing University of Chinese Medicine, Nanjing, China
| | - Ting Huang
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Di Ma
- College of Information Science and Technology, Nanjing Forestry University, Nanjing, China
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
- *Correspondence: Yu-Chen Chen,
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20
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Wang X, Yoo K, Chen H, Zou T, Wang H, Gao Q, Meng L, Hu X, Li R. Antagonistic network signature of motor function in Parkinson's disease revealed by connectome-based predictive modeling. NPJ Parkinsons Dis 2022; 8:49. [PMID: 35459232 PMCID: PMC9033778 DOI: 10.1038/s41531-022-00315-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 03/31/2022] [Indexed: 12/12/2022] Open
Abstract
Motor impairment is a core clinical feature of Parkinson’s disease (PD). Although the decoupled brain connectivity has been widely reported in previous neuroimaging studies, how the functional connectome is involved in motor dysfunction has not been well elucidated in PD patients. Here we developed a distributed brain signature by predicting clinical motor scores of PD patients across multicenter datasets (total n = 236). We decomposed the Pearson’s correlation into accordance and discordance via a temporal discrete procedure, which can capture coupling and anti-coupling respectively. Using different profiles of functional connectivity, we trained candidate predictive models and tested them on independent and heterogeneous PD samples. We showed that the antagonistic model measured by discordance had the best sensitivity and generalizability in all validations and it was dubbed as Parkinson’s antagonistic motor signature (PAMS). The PAMS was dominated by the subcortical, somatomotor, visual, cerebellum, default-mode, and frontoparietal networks, and the motor-visual stream accounted for the most part of predictive weights among network pairs. Additional stage-specific analysis showed that the predicted scores generated from the antagonistic model tended to be higher than the observed scores in the early course of PD, indicating that the functional signature may vary more sensitively with the neurodegenerative process than clinical behaviors. Together, these findings suggest that motor dysfunction of PD is represented as antagonistic interactions within multi-level brain systems. The signature shows great potential in the early motor evaluation and developing new therapeutic approaches for PD in the clinical realm.
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Affiliation(s)
- Xuyang Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Kwangsun Yoo
- Department of Psychology, Yale University, New Haven, CT, 06520, USA
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
| | - Ting Zou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Hongyu Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Qing Gao
- School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Li Meng
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, People's Republic of China.
| | - Xiaofei Hu
- Department of Radiology, Southwest Hospital, Third Military Medical University, Chongqing, 610054, People's Republic of China.
| | - Rong Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
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21
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Wapstra NJ, Ketola M, Thompson S, Lee A, Madhyastha T, Grabowski TJ, Stocco A. Increased Basal Ganglia Modulatory Effective Connectivity Observed in Resting-State fMRI in Individuals With Parkinson's Disease. Front Aging Neurosci 2022; 14:719089. [PMID: 35350633 PMCID: PMC8957976 DOI: 10.3389/fnagi.2022.719089] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 02/16/2022] [Indexed: 01/17/2023] Open
Abstract
Alterations to interactions between networked brain regions underlie cognitive impairment in many neurodegenerative diseases, providing an important physiological link between brain structure and cognitive function. Previous attempts to characterize the effects of Parkinson's disease (PD) on network functioning using resting-state functional magnetic resonance imaging (rs-fMRI), however, have yielded inconsistent and contradictory results. Potential problems with prior work arise in the specifics of how the area targeted by the diseases (the basal ganglia) interacts with other brain regions. Specifically, current computational models point to the fact that the basal ganglia contributions should be captured with modulatory (i.e., second-order) rather than direct (i.e., first-order) functional connectivity measures. Following this hypothesis, a principled but manageable large-scale brain architecture, the Common Model of Cognition, was used to identify differences in basal ganglia connectivity in PD by analyzing resting-state fMRI data from 111 participants (70 patients with PD; 41 healthy controls) using Dynamic Causal Modeling (DCM). Specifically, the functional connectivity of the basal ganglia was modeled as two second-level, modulatory connections that control projections from sensory cortices to the prefrontal cortex, and from the hippocampus and medial temporal lobe to the prefrontal cortex. We then examined group differences between patients with PD and healthy controls in estimated modulatory effective connectivity in these connections. The Modulatory variant of the Common Model of Cognition outperformed the Direct model across all subjects. It was also found that these second-level modulatory connections had higher estimates of effective connectivity in the PD group compared to the control group, and that differences in effective connectivity were observed for all direct connections between the PD and control groups.We make the case that accounting for modulatory effective connectivity better captures the effects of PD on network functioning and influences the interpretation of the directionality of the between-group results. Limitations include that the PD group was scanned on dopaminergic medication, results were derived from a reasonable but small number of individuals and the ratio of PD to healthy control participants was relatively unbalanced. Future research will examine if the observed effect holds for individuals with PD scanned off their typical dopaminergic medications.
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Affiliation(s)
- Nicholas J. Wapstra
- Department of Radiology, University of Washington, Seattle, WA, United States
| | - Micah Ketola
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, United States
| | - Shelby Thompson
- Department of Kinesiology, University of Georgia, Athens, GA, United States
| | - Adel Lee
- Etosha Business and Research Consulting, Mount Berry, GA, United States
| | | | - Thomas J. Grabowski
- Department of Radiology, University of Washington, Seattle, WA, United States,Department of Neurology, University of Washington, Seattle, WA, United States
| | - Andrea Stocco
- Department of Psychology, University of Washington, Seattle, WA, United States,*Correspondence: Andrea Stocco
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22
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Droby A, Artzi M, Lerman H, Hutchison RM, Bashat DB, Omer N, Gurevich T, Orr-Urtreger A, Cohen B, Cedarbaum JM, Sapir EE, Giladi N, Mirelman A, Thaler A. Aberrant dopamine transporter and functional connectivity patterns in LRRK2 and GBA mutation carriers. NPJ Parkinsons Dis 2022; 8:20. [PMID: 35241697 PMCID: PMC8894349 DOI: 10.1038/s41531-022-00285-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 02/01/2022] [Indexed: 12/28/2022] Open
Abstract
Non-manifesting carriers (NMCs) of Parkinson’s disease (PD)-related mutations such as LRRK2 and GBA are at an increased risk for developing PD. Dopamine transporter (DaT)-spectral positron emission computed tomography is widely used for capturing functional nigrostriatal dopaminergic activity. However, it does not reflect other ongoing neuronal processes; especially in the prodromal stages of the disease. Resting-state fMRI (rs-fMRI) has been proposed as a mode for assessing functional alterations associated with PD, but its relation to dopaminergic deficiency remains unclear. We aimed to study the association between presynaptic striatal dopamine uptake and functional connectivity (FC) patterns among healthy first-degree relatives of PD patients with mutations in LRRK2 and GBA genes. N = 85 healthy first-degree subjects were enrolled and genotyped. All participants underwent DaT and rs-fMRI scans, as well as a comprehensive clinical assessment battery. Between-group differences in FC within striatal regions were investigated and compared with striatal binding ratios (SBR). N = 26 GBA-NMCs, N = 25 LRRK2-NMCs, and N = 34 age-matched nonmanifesting noncarriers (NM-NCs) were included in each study group based on genetic status. While genetically-defined groups were similar across clinical measures, LRRK2-NMCs demonstrated lower SBR in the right putamen compared with NM-NCs, and higher right putamen FC compared to GBA-NMCs. In this group, higher striatal FC was associated with increased risk for PD. The observed differential SBR and FC patterns among LRRK2-NMCs and GBA-NMCs indicate that DaTscan and FC assessments might offer a more sensitive prediction of the risk for PD in the pre-clinical stages of the disease.
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Affiliation(s)
- Amgad Droby
- Movement Disorders Unit, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel. .,Laboratory for Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel. .,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel. .,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
| | - Moran Artzi
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.,Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Hedva Lerman
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Department of Nuclear Medicine, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | | | - Dafna Ben Bashat
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.,Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Nurit Omer
- Movement Disorders Unit, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.,Laboratory for Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Tanya Gurevich
- Movement Disorders Unit, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Avi Orr-Urtreger
- Movement Disorders Unit, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Batsheva Cohen
- Movement Disorders Unit, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.,Laboratory for Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
| | | | - Einat Even Sapir
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Department of Nuclear Medicine, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Nir Giladi
- Movement Disorders Unit, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.,Laboratory for Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Anat Mirelman
- Movement Disorders Unit, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.,Laboratory for Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Avner Thaler
- Movement Disorders Unit, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.,Laboratory for Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
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23
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Shi D, Zhang H, Wang G, Wang S, Yao X, Li Y, Guo Q, Zheng S, Ren K. Machine Learning for Detecting Parkinson's Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis. Front Aging Neurosci 2022; 14:806828. [PMID: 35309885 PMCID: PMC8928361 DOI: 10.3389/fnagi.2022.806828] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 01/19/2022] [Indexed: 12/03/2022] Open
Abstract
Parkinson's disease (PD) is one of the most common progressive degenerative diseases, and its diagnosis is challenging on clinical grounds. Clinically, effective and quantifiable biomarkers to detect PD are urgently needed. In our study, we analyzed data from two centers, the primary set was used to train the model, and the independent external validation set was used to validate our model. We applied amplitude of low-frequency fluctuation (ALFF)-based radiomics method to extract radiomics features (including first- and high-order features). Subsequently, t-test and least absolute shrinkage and selection operator (LASSO) were harnessed for feature selection and data dimensionality reduction, and grid search method and nested 10-fold cross-validation were applied to determine the optimal hyper-parameter λ of LASSO and evaluate the performance of the model, in which a support vector machine was used to construct the classification model to classify patients with PD and healthy controls (HCs). We found that our model achieved good performance [accuracy = 81.45% and area under the curve (AUC) = 0.850] in the primary set and good generalization in the external validation set (accuracy = 67.44% and AUC = 0.667). Most of the discriminative features were high-order radiomics features, and the identified brain regions were mainly located in the sensorimotor network and lateral parietal cortex. Our study indicated that our proposed method can effectively classify patients with PD and HCs, ALFF-based radiomics features that might be potential biomarkers of PD, and provided further support for the pathological mechanism of PD, that is, PD may be related to abnormal brain activity in the sensorimotor network and lateral parietal cortex.
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Affiliation(s)
- Dafa Shi
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Haoran Zhang
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Guangsong Wang
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Siyuan Wang
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Xiang Yao
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Yanfei Li
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Qiu Guo
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Shuang Zheng
- School of Medicine, Xiamen University, Xiamen, China
| | - Ke Ren
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Xiamen Key Laboratory for Endocrine-Related Cancer Precision Medicine, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
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24
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Hengenius JB, Bohnen NI, Rosso A, Huppert TJ, Rosano C. Cortico-striatal functional connectivity and cerebral small vessel disease: Contribution to mild Parkinsonian signs. J Neuroimaging 2022; 32:352-362. [PMID: 34957653 PMCID: PMC9119198 DOI: 10.1111/jon.12949] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 11/01/2021] [Accepted: 11/01/2021] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND AND PURPOSE Mild Parkinsonian signs (MPS) are common in older adults. We hypothesized that MPS are associated with lower functional connectivity (FC) in dopamine-dependent cortico-striatal networks, and these associations vary with white matter hyperintensity (WMH), a risk factor for MPS. METHODS We examined resting-state functional MRI in 266 participants (mean age 83; 57% female; 41% African American) with data on MPS (Unified Parkinson's Disease Rating Scale), demographics, cognition, muscle-skeletal, and cardiometabolic health. FC between cortex and striatum was examined separately for sensorimotor, executive, and limbic functional subregions. Logistic regression tested the association of FC in each network with MPS, adjusted for covariates. Interactions of FC by WMH were tested; and analyses were repeated stratified by WMH above/below the median. RESULTS Compared to those without MPS, those with MPS had lower cortico-striatal FC in the left executive network (adjusted odds ratio [95% confidence interval], p-value: 0.188 [0.043, 0.824], .027). The interaction with WMH was p = .064; left executive FC was inversely associated with MPS for high WMH (0.077 [0.010, 0.599], .014) but not low WMH participants (1.245 [0.128, 12.132], .850). CONCLUSIONS MPS appear related to lower executive network FC, robust to adjustment for other risk factors, and stronger for those with higher burden of WMH. Future longitudinal studies should examine the interplay between cerebral small vessel disease and connectivity influencing MPS.
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Affiliation(s)
- James B. Hengenius
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Nicolaas I. Bohnen
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Andrea Rosso
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Theodore J. Huppert
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Caterina Rosano
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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25
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Jiang R, Chazot P, Pavese N, Crookes D, Bouridane A, Celebi ME. Private Facial Prediagnosis as an Edge Service for Parkinson's DBS Treatment Valuation. IEEE J Biomed Health Inform 2022; 26:2703-2713. [PMID: 35085096 DOI: 10.1109/jbhi.2022.3146369] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Facial phenotyping for medical prediagnosis has recently been successfully exploited as a novel way for the preclinical assessment of a range of rare genetic diseases, where facial biometrics is revealed to have rich links to underlying genetic or medical causes. In this paper, we aim to extend this facial prediagnosis technology for a more general dis-ease, Parkinson's Diseases (PD), and proposed an Artificial-Intelligence-of-Things (AIoT) edge-oriented privacy-preserving facial prediagnosis framework to analyze the treatment of Deep Brain Stimulation (DBS) on PD patients. In the proposed framework, a novel edge-based privacy-preserving framework is proposed to implement private deep facial diagnosis as a service over an AIoT-oriented information theoretically secure multi-party communication scheme, where partial homomorphic encryption (PHE) is leveraged to enable privacy-preserving deep facial diagnosis on encrypted facial patterns. In our experiments with a collected facial dataset from PD patients, for the first time, we proved that facial patterns could be used to evaluate the facial difference of PD patients undergoing DBS treatment. We further implemented a privacy-preserving information theoretical secure deep facial prediagnosis framework that can achieve the same accuracy as the non-encrypted one, showing the potential of our facial prediagnosis as a trust-worthy edge service for grading the severity of PD in patients.
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26
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Mining imaging and clinical data with machine learning approaches for the diagnosis and early detection of Parkinson's disease. NPJ Parkinsons Dis 2022; 8:13. [PMID: 35064123 PMCID: PMC8783003 DOI: 10.1038/s41531-021-00266-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 12/10/2021] [Indexed: 12/14/2022] Open
Abstract
Parkinson’s disease (PD) is a common, progressive, and currently incurable neurodegenerative movement disorder. The diagnosis of PD is challenging, especially in the differential diagnosis of parkinsonism and in early PD detection. Due to the advantages of machine learning such as learning complex data patterns and making inferences for individuals, machine-learning techniques have been increasingly applied to the diagnosis of PD, and have shown some promising results. Machine-learning-based imaging applications have made it possible to help differentiate parkinsonism and detect PD at early stages automatically in a number of neuroimaging studies. Comparative studies have shown that machine-learning-based SPECT image analysis applications in PD have outperformed conventional semi-quantitative analysis in detecting PD-associated dopaminergic degeneration, performed comparably well as experts’ visual inspection, and helped improve PD diagnostic accuracy of radiologists. Using combined multi-modal (imaging and clinical) data in these applications may further enhance PD diagnosis and early detection. To integrate machine-learning-based diagnostic applications into clinical systems, further validation and optimization of these applications are needed to make them accurate and reliable. It is anticipated that machine-learning techniques will further help improve differential diagnosis of parkinsonism and early detection of PD, which may reduce the error rate of PD diagnosis and help detect PD at pre-motor stage to make it possible for early treatments (e.g., neuroprotective treatment) to slow down PD progression, prevent severe motor symptoms from emerging, and relieve patients from suffering.
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27
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Wu C, Matias C, Foltynie T, Limousin P, Zrinzo L, Akram H. Dynamic Network Connectivity Reveals Markers of Response to Deep Brain Stimulation in Parkinson's Disease. Front Hum Neurosci 2021; 15:729677. [PMID: 34690721 PMCID: PMC8526554 DOI: 10.3389/fnhum.2021.729677] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 07/19/2021] [Indexed: 01/10/2023] Open
Abstract
Background: Neuronal loss in Parkinson's Disease (PD) leads to widespread neural network dysfunction. While graph theory allows for analysis of whole brain networks, patterns of functional connectivity (FC) associated with motor response to deep brain stimulation of the subthalamic nucleus (STN-DBS) have yet to be explored. Objective/Hypothesis: To investigate the distributed network properties associated with STN-DBS in patients with advanced PD. Methods: Eighteen patients underwent 3-Tesla resting state functional MRI (rs-fMRI) prior to STN-DBS. Improvement in UPDRS-III scores following STN-DBS were assessed 1 year after implantation. Independent component analysis (ICA) was applied to extract spatially independent components (ICs) from the rs-fMRI. FC between ICs was calculated across the entire time series and for dynamic brain states. Graph theory analysis was performed to investigate whole brain network topography in static and dynamic states. Results: Dynamic analysis identified two unique brain states: a relative hypoconnected state and a relative hyperconnected state. Time spent in a state, dwell time, and number of transitions were not correlated with DBS response. There were no significant FC findings, but graph theory analysis demonstrated significant relationships with STN-DBS response only during the hypoconnected state - STN-DBS was negatively correlated with network assortativity. Conclusion: Given the widespread effects of dopamine depletion in PD, analysis of whole brain networks is critical to our understanding of the pathophysiology of this disease. Only by leveraging graph theoretical analysis of dynamic FC were we able to isolate a hypoconnected brain state that contained distinct network properties associated with the clinical effects of STN-DBS.
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Affiliation(s)
- Chengyuan Wu
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, PA, United States
- Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Caio Matias
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, PA, United States
| | - Thomas Foltynie
- Unit of Functional Neurosurgery, UCL Institute of Neurology, London, United Kingdom
| | - Patricia Limousin
- Unit of Functional Neurosurgery, UCL Institute of Neurology, London, United Kingdom
| | - Ludvic Zrinzo
- Unit of Functional Neurosurgery, UCL Institute of Neurology, London, United Kingdom
- Victor Horsley Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Harith Akram
- Unit of Functional Neurosurgery, UCL Institute of Neurology, London, United Kingdom
- Victor Horsley Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, London, United Kingdom
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28
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Xia Z, Zhou T, Mamoon S, Lu J. Recognition of Dementia Biomarkers With Deep Finer-DBN. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1926-1935. [PMID: 34506288 DOI: 10.1109/tnsre.2021.3111989] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The treatment of neurodegenerative diseases is expensive, and long-term treatment makes families bear a heavy burden. Accumulating evidence suggests that the high conversion rate can possibly be reduced if clinical interventions are applied at the early stage of brain diseases. Thus, a variety of deep learning methods are utilized to recognize the early stages of neurodegenerative diseases for clinical intervention and treatment. However, most existing methods have ignored the issue of sample imbalance, which often makes it difficult to train an effective model due to lack of a large number of negative samples. To address this problem, we propose a two-stage method, which is used to learn the compression and recover rules of normal subjects so that potential negative samples can be detected. The experimental results show that the proposed method can not only obtain a superb recognition result, but also give an explanation that conforms to the physiological mechanism. Most importantly, the deep learning model does not need to be retrained for each type of disease, which can be widely applied to the diagnosis of various brain diseases. Furthermore, this research could have great potential in understanding regional dysfunction of various brain diseases.
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29
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Baglio F, Pirastru A, Bergsland N, Cazzoli M, Tavazzi E. Neuroplasticity mediated by motor rehabilitation in Parkinson's disease: a systematic review on structural and functional MRI markers. Rev Neurosci 2021; 33:213-226. [PMID: 34461010 DOI: 10.1515/revneuro-2021-0064] [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: 05/07/2021] [Accepted: 07/30/2021] [Indexed: 01/06/2023]
Abstract
Parkinson's disease (PD) is the second most common neurological disease affecting the elderly population. Pharmacological and surgical interventions usually employed for PD treatment show transient effectiveness and are associated with the insurgence of side effects. Therefore, motor rehabilitation has been proposed as a promising supplement in the treatment of PD, reducing the global burden of the disease and improving patients quality of life. The present systematic review aimed to critically analyse the literature concerning MRI markers of brain functional and structural response to motor rehabilitation in PD. Fourteen out of 1313 studies were selected according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses criteria. Despite the limited number of retrieved studies coupled with their heterogeneity prevent ultimate conclusions from being drawn, motor rehabilitation seems to have beneficial effects on PD as measured both with clinical outcomes and MRI derived indices. Interestingly, consistent results seem to indicate that motor rehabilitation acts via a dual mechanism of strengthening cortico-subcortical pathways, restoring movements automaticity, or activating compensatory networks such as the fronto-parietal one. The employment of more advanced and quantitative MRI methods is warranted to establish and validate standardized metrics capable of reliably determining the changes induced by rehabilitative intervention.
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Affiliation(s)
- Francesca Baglio
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, via Capecelatro 66, 20148Milan, Italy
| | - Alice Pirastru
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, via Capecelatro 66, 20148Milan, Italy
| | - Niels Bergsland
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, via Capecelatro 66, 20148Milan, Italy.,Department of Neurology, Buffalo Neuroimaging Analysis Center, School of Medicine and Biomedical Sciences, State University of New York at Buffalo, 100 High Street, Buffalo, NY14203, USA
| | - Marta Cazzoli
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, via Capecelatro 66, 20148Milan, Italy
| | - Eleonora Tavazzi
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, via Capecelatro 66, 20148Milan, Italy.,Department of Neurology, Buffalo Neuroimaging Analysis Center, School of Medicine and Biomedical Sciences, State University of New York at Buffalo, 100 High Street, Buffalo, NY14203, USA
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Cai J, Kim JL, Baumeister TR, Zhu M, Wang Y, Liu A, Lee S, McKeown MJ. A Multi-sequence MRI Study in Parkinson's Disease: Association Between Rigidity and Myelin. J Magn Reson Imaging 2021; 55:451-462. [PMID: 34374158 DOI: 10.1002/jmri.27853] [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: 04/26/2021] [Revised: 07/08/2021] [Accepted: 07/09/2021] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND The pathophysiology of rigidity in Parkinson's disease (PD) is poorly understood. Multi-sequence functional and structural brain MRI may further clarify the origin of this clinical characteristic. PURPOSE To examine both joint and unique relationships of MRI-based functional and structural imaging modalities to rigidity and other clinical features of PD. STUDY TYPE Retrospective cross-sectional study. POPULATION 31 PD subjects (aged 68.0 ± 5.9 years, 21 males) with average disease duration 9.3 ± 5.4 years. FIELD STRENGTH/SEQUENCE Multi-echo GRASE, diffusion-weighted echo planar imaging (EPI), and blood oxygen level dependent contrast EPI T2*-weighted sequences on a 3T scanner. ASSESSMENT Myelin water fraction (MWF) and fractional anisotropy (FA) of 20 white-matter regions of interest (ROIs), and functional connectivity derived from resting-state fMRI among 56 ROIs were assessed. The Unified Parkinson's Disease Rating Scale-Part III, Montreal Cognitive Assessment, Beck Depression Index, and Apathy Rating Scales were used to assess motor and non-motor symptoms. STATISTICAL TESTS Multiset canonical correlation analysis (MCCA) and canonical correlation analysis (CCA) were utilized to examine the joint and unique relationships of multiple imaging measures with clinical symptoms of PD. A permutation test was used to determine statistical significance (P < 0.05). RESULTS MCCA revealed a single significant component jointly linking MWF, FA, and functional connectivity to age, bradykinesia, and leg agility, non-motor symptoms of cognition, depression, and apathy, but not rigidity (P = 0.77), tremor (P = 0.50 and 0.67 on the left and right side), or sex (P = 0.54). After controlling for this joint component, CCA found a unique significant association between MWF and rigidity, but no other associations were detected, including with FA (P = 0.87). DATA CONCLUSION MWF, FA, and functional connectivity can serve as multi-sequence imaging markers to characterize many PD symptoms. However, rigidity in PD is additionally associated with widespread myelin changes. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Jiayue Cai
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Department of Medicine, Division of Neurology, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Jowon L Kim
- Department of Medicine, Division of Neurology, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Tobias R Baumeister
- School of Biomedical Engineering, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Maria Zhu
- Department of Medicine, Division of Neurology, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Yuheng Wang
- School of Biomedical Engineering, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Aiping Liu
- Department of Electronic Science and Technology, University of Science and Technology of China, Hefei, China
| | - Soojin Lee
- School of Biomedical Engineering, The University of British Columbia, Vancouver, British Columbia, Canada.,Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Martin J McKeown
- Department of Medicine, Division of Neurology, The University of British Columbia, Vancouver, British Columbia, Canada
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Owens-Walton C, Jakabek D, Power BD, Walterfang M, Hall S, van Westen D, Looi JCL, Shaw M, Hansson O. Structural and functional neuroimaging changes associated with cognitive impairment and dementia in Parkinson's disease. Psychiatry Res Neuroimaging 2021; 312:111273. [PMID: 33892387 DOI: 10.1016/j.pscychresns.2021.111273] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Revised: 12/09/2020] [Accepted: 01/12/2021] [Indexed: 12/29/2022]
Abstract
This study seeks a better understanding of possible pathophysiological mechanisms associated with cognitive impairment and dementia in Parkinson's disease using structural and functional MRI. We investigated resting-state functional connectivity of important subdivisions of the caudate nucleus, putamen and thalamus, and also how the morphology of these structures are impacted in the disorder. We found cognitively unimpaired Parkinson's disease subjects (n = 33), compared to controls (n = 26), display increased functional connectivity of the dorsal caudate, anterior putamen and mediodorsal thalamic subdivisions with areas across the frontal lobe, as well as reduced functional connectivity of the dorsal caudate with posterior cortical and cerebellar regions. Compared to cognitively unimpaired subjects, those with mild cognitive impairment (n = 22) demonstrated reduced functional connectivity of the mediodorsal thalamus with the paracingulate cortex, while also demonstrating increased functional connectivity of the mediodorsal thalamus with the posterior cingulate cortex, compared to subjects with dementia (n = 17). Extensive volumetric and surface-based deflation was found in subjects with dementia compared to cognitively unimpaired Parkinson's disease participants and controls. Our research suggests that structures within basal ganglia-thalamocortical circuits are implicated in cognitive impairment and dementia in Parkinson's disease, with cognitive impairment and dementia associated with a breakdown in functional connectivity of the mediodorsal thalamus with para- and posterior cingulate regions of the brain respectively.
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Affiliation(s)
- Conor Owens-Walton
- Research Centre for the Neurosciences of Ageing, Academic Unit of Psychiatry and Addiction Medicine, School of Clinical Medicine, Medical School, Australian National University, Canberra, Australia.
| | - David Jakabek
- Graduate School of Medicine, University of Wollongong, Wollongong, Australia
| | - Brian D Power
- School of Medicine, The University of Notre Dame, Fremantle, Australia; Clinical Research Centre, North Metropolitan Health Service - Mental Health, Perth, Australia
| | - Mark Walterfang
- Neuropsychiatry Unit, Royal Melbourne Hospital, Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia; Florey Institute of Neurosciences and Mental Health, University of Melbourne, Melbourne, Australia
| | - Sara Hall
- Memory Clinic, Skåne University Hospital, Malmö, Sweden; Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Danielle van Westen
- Centre for Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden; Diagnostic Radiology, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Jeffrey C L Looi
- Research Centre for the Neurosciences of Ageing, Academic Unit of Psychiatry and Addiction Medicine, School of Clinical Medicine, Medical School, Australian National University, Canberra, Australia; Neuropsychiatry Unit, Royal Melbourne Hospital, Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia
| | - Marnie Shaw
- College of Engineering and Computer Science, The Australian National University, Canberra, Australia
| | - Oskar Hansson
- Memory Clinic, Skåne University Hospital, Malmö, Sweden; Department of Clinical Sciences, Lund University, Malmö, Sweden
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Shi D, Zhang H, Wang S, Wang G, Ren K. Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson's Disease: A Histogram Analysis. Front Aging Neurosci 2021; 13:624731. [PMID: 34045953 PMCID: PMC8144304 DOI: 10.3389/fnagi.2021.624731] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Accepted: 03/22/2021] [Indexed: 01/08/2023] Open
Abstract
This study aimed to investigate the value of amplitude of low-frequency fluctuation (ALFF)-based histogram analysis in the diagnosis of Parkinson's disease (PD) and to investigate the regions of the most important discriminative features and their contribution to classification discrimination. Patients with PD (n = 59) and healthy controls (HCs; n = 41) were identified and divided into a primary set (80 cases, including 48 patients with PD and 32 HCs) and a validation set (20 cases, including 11 patients with PD and nine HCs). The Automated Anatomical Labeling (AAL) 116 atlas was used to extract the histogram features of the regions of interest in the brain. Machine learning methods were used in the primary set for data dimensionality reduction, feature selection, model construction, and model performance evaluation. The model performance was further validated in the validation set. After feature data dimension reduction and feature selection, 23 of a total of 1,276 features were entered in the model. The brain regions of the selected features included the frontal, temporal, parietal, occipital, and limbic lobes, as well as the cerebellum and the thalamus. In the primary set, the area under the curve (AUC) of the model was 0.974, the sensitivity was 93.8%, the specificity was 90.6%, and the accuracy was 93.8%. In the validation set, the AUC, sensitivity, specificity, and accuracy were 0.980, 90.9%, 88.9%, and 90.0%, respectively. ALFF-based histogram analysis can be used to classify patients with PD and HCs and to effectively identify abnormal brain function regions in PD patients.
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Affiliation(s)
| | | | | | | | - Ke Ren
- Department of Radiology, Xiang’an Hospital of Xia Men University, Xiamen, China
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Schumacher J, Taylor JP, Hamilton CA, Firbank M, Donaghy PC, Roberts G, Allan L, Durcan R, Barnett N, O'Brien JT, Thomas AJ. Functional connectivity in mild cognitive impairment with Lewy bodies. J Neurol 2021; 268:4707-4720. [PMID: 33928432 PMCID: PMC8563567 DOI: 10.1007/s00415-021-10580-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/23/2021] [Accepted: 04/24/2021] [Indexed: 12/13/2022]
Abstract
Previous resting-state fMRI studies in dementia with Lewy bodies have described changes in functional connectivity in networks related to cognition, motor function, and attention as well as alterations in connectivity dynamics. However, whether these changes occur early in the course of the disease and are already evident at the stage of mild cognitive impairment is not clear. We studied resting-state fMRI data from 31 patients with mild cognitive impairment with Lewy bodies compared to 28 patients with mild cognitive impairment due to Alzheimer’s disease and 24 age-matched controls. We compared the groups with respect to within- and between-network functional connectivity. Additionally, we applied two different approaches to study dynamic functional connectivity (sliding-window analysis and leading eigenvector dynamic analysis). We did not find any significant changes in the mild cognitive impairment groups compared to controls and no differences between the two mild cognitive impairment groups, using static as well as dynamic connectivity measures. While patients with mild cognitive impairment with Lewy bodies already show clear functional abnormalities on EEG measures, the fMRI analyses presented here do not appear to be sensitive enough to detect such early and subtle changes in brain function in these patients.
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Affiliation(s)
- Julia Schumacher
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK.
| | - John-Paul Taylor
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK
| | - Calum A Hamilton
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK
| | - Michael Firbank
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK
| | - Paul C Donaghy
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK
| | - Gemma Roberts
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK
| | - Louise Allan
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK.,Institute of Health Research, University of Exeter, Exeter, UK
| | - Rory Durcan
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK
| | - Nicola Barnett
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK
| | - John T O'Brien
- Department of Psychiatry, University of Cambridge School of Medicine, Cambridge, CB2 0SP, UK
| | - Alan J Thomas
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK
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Brooks DJ. Imaging Familial and Sporadic Neurodegenerative Disorders Associated with Parkinsonism. Neurotherapeutics 2021; 18:753-771. [PMID: 33432494 PMCID: PMC8423977 DOI: 10.1007/s13311-020-00994-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/16/2020] [Indexed: 11/24/2022] Open
Abstract
In this paper, the structural and functional imaging changes associated with sporadic and genetic Parkinson's disease and atypical Parkinsonian variants are reviewed. The role of imaging for supporting diagnosis and detecting subclinical disease is discussed, and the potential use and drawbacks of using imaging biomarkers for monitoring disease progression is debated. Imaging changes associated with nonmotor complications of PD are presented. The similarities and differences in imaging findings in Lewy body dementia, Parkinson's disease dementia, and Alzheimer's disease are discussed.
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Affiliation(s)
- David J Brooks
- Department of Nuclear Medicine, Aarhus University, Aarhus N, 8200, Denmark.
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK.
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35
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Arroyo A, Periáñez JA, Ríos-Lago M, Lubrini G, Andreo J, Benito-León J, Louis ED, Romero JP. Components determining the slowness of information processing in parkinson's disease. Brain Behav 2021; 11:e02031. [PMID: 33452724 PMCID: PMC7994698 DOI: 10.1002/brb3.2031] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 12/16/2020] [Accepted: 12/25/2020] [Indexed: 01/19/2023] Open
Abstract
INTRODUCTION Bradyphrenia is a key cognitive feature in Parkinson's disease (PD). There is no consensus on whether information processing speed is impaired or not beyond motor performance. OBJECTIVE This study aims to explore which perceptual, motor, or cognitive components of information processing are involved in the slowdown affecting cognitive performance. METHODS The study included 48 patients with PD (age: 63, 3 ± 8, 18; HY I-III; UPDRS 15,46 ± 7,76) and 53 healthy controls (age: 60,09 ± 12,83). Five reaction time (RT) tasks were administered to all participants. The average RT in each of the tasks and the percentage of correct answers were measured. Patients with PD were in "ON state" at the time of the evaluation. Perceptual, motor, and cognitive components were isolated by means of a series of ANCOVAs. RESULTS As expected, the motor component was slowed down in patients with PD. Moreover, while patients with PD showed slower RT than controls in all tasks, differences between groups did not exponentially increase with the increasing task complexity. ANCOVA analyses also revealed that the perceptual and sustained alert component resulted to be slowed down, with no differences being found in any of the remaining isolated cognitive components (i.e., response strategy-inhibition, decisional, visual search, or interference control). CONCLUSIONS The results revealed that slowness of information processing in PD was mainly associated with an impaired processing speed of the motor and perceptual-alertness components analyzed. The results may help designing new neurorehabilitation strategies, focusing on the improvement of perceptual and alertness mechanisms.
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Affiliation(s)
- Aida Arroyo
- Facultad de Ciencias Experimentales, Universidad Francisco de Vitoria, Madrid, Spain
| | - José A Periáñez
- Experimental Psychology Department, Universidad Complutense de Madrid, Madrid, Spain
| | - Marcos Ríos-Lago
- Basic Psychology II Department, UNED, Madrid 28040, Spain; Brain Damage Unit, Hospital Beata María Ana, Madrid, Spain
| | - Genny Lubrini
- Experimental Psychology Department, Universidad Complutense de Madrid, Madrid, Spain
| | - Jorge Andreo
- Facultad de Ciencias Experimentales, Universidad Francisco de Vitoria, Madrid, Spain
| | - Julián Benito-León
- Department of Neurology, University Hospital "12 de Octubre", Madrid, Spain; Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Spain; Department of Medicine, Complutense University, Madrid, Spain
| | - Elan D Louis
- Department of Neurology and Neurotherapeutics at UT Southwestern Medical Center
| | - Juan Pablo Romero
- Facultad de Ciencias Experimentales, Universidad Francisco de Vitoria, Madrid 28223, Spain; Brain Damage Unit, Hospital Beata María Ana, Madrid, Spain
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Zitser J, Casaletto KB, Staffaroni AM, Sexton C, Weiner-Light S, Wolf A, Brown JA, Miller BL, Kramer JH. Mild Motor Signs Matter in Typical Brain Aging: The Value of the UPDRS Score Within a Functionally Intact Cohort of Older Adults. Front Aging Neurosci 2021; 13:594637. [PMID: 33643020 PMCID: PMC7904682 DOI: 10.3389/fnagi.2021.594637] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 01/11/2021] [Indexed: 11/20/2022] Open
Abstract
Objectives: To characterize the clinical correlates of subclinical Parkinsonian signs, including longitudinal cognitive and neural (via functional connectivity) outcomes, among functionally normal older adults. Methods: Participants included 737 functionally intact community-dwelling older adults who performed prospective comprehensive evaluations at ~15-months intervals for an average of 4.8 years (standard deviation 3.2 years). As part of these evaluations, participants completed the Unified Parkinson's Disease Rating Scale (UPDRS) longitudinally and measures of processing speed, executive functioning and verbal episodic memory. T1-weighted structural scans and task-free functional MRI scans were acquired on 330 participants. We conducted linear mixed-effects models to determine the relationship between changes in UPDRS with cognitive and neural changes, using age, sex, and education as covariates. Results: Cognitive outcomes were processing speed, executive functioning, and episodic memory. Greater within-person increases in UPDRS were associated with more cognitive slowing over time. Although higher average UPDRS scores were significantly associated with overall poorer executive functions, there was no association between UPDRS and executive functioning longitudinally. UPDRS scores did not significantly relate to longitudinal memory performances. Regarding neural correlates, greater increases in UPDRS scores were associated with reduced intra-subcortical network connectivity over time. There were no relationships with intra-frontoparietal or inter-subcortical-frontoparietal connectivity. Conclusions: Our findings add to the aging literature by indicating that mild motor changes are negatively associated with cognition and network connectivity in functionally intact adults.
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Affiliation(s)
- Jennifer Zitser
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA, United States.,Global Brain Health Institute, University of California, San Francisco, San Francisco, CA, United States.,Movement Disorders Unit, Department of Neurology, Tel Aviv Sourasky Medical Center, Affiliated to the Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv-Yafo, Israel
| | - Kaitlin B Casaletto
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA, United States
| | - Adam M Staffaroni
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA, United States
| | - Claire Sexton
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA, United States.,Global Brain Health Institute, University of California, San Francisco, San Francisco, CA, United States
| | - Sophia Weiner-Light
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA, United States
| | - Amy Wolf
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA, United States
| | - Jesse A Brown
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA, United States
| | - Bruce L Miller
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA, United States.,Global Brain Health Institute, University of California, San Francisco, San Francisco, CA, United States
| | - Joel H Kramer
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA, United States.,Global Brain Health Institute, University of California, San Francisco, San Francisco, CA, United States
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Functional connectivity between resting-state networks reflects decline in executive function in Parkinson's disease: A longitudinal fMRI study. NEUROIMAGE-CLINICAL 2021; 28:102468. [PMID: 33383608 PMCID: PMC7581965 DOI: 10.1016/j.nicl.2020.102468] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 09/30/2020] [Accepted: 10/11/2020] [Indexed: 11/22/2022]
Abstract
Over time, Parkinson’s disease (PD) patients declined on multiple cognitive domains. Executive dysfunction was related to interactions between specific resting-state networks. These interactions involved deep grey matter, frontoparietal, and attentional networks. Destabilization of functional network interactions may influence PD progression.
Deficits in cognitive functioning are a common yet poorly understood symptom in Parkinson’s disease (PD). Recent studies have highlighted the importance of (dynamic) interactions between resting-state networks for cognition, which remains understudied in PD. We investigated how altered (dynamic) functional interactions between brain networks relate to cognitive dysfunction in PD patients. In this fMRI study, 50 PD patients (mean age 65.5 years ± 6.27) on dopaminergic medication were studied cross-sectionally, and of this cohort 31 PD patients were studied longitudinally. MRI imaging and neuropsychological testing was performed at two time points, with a follow-up duration of approximately three years. Functional connectivity within and between seven resting-state networks was calculated (both statically and dynamically) and correlated with four neuropsychological test scores; a combined score of (four) executive tasks, a motor perseveration, memory, and category fluency task. Cognitive dysfunction was determined based on a longitudinal sample of age-matched healthy controls (n = 13). PD patients showed dysfunction on six out of seven cognitive tasks when compared to healthy controls. Severity of executive dysfunction was correlated with higher static and lower dynamic functional connectivity between deep gray matter regions and the frontoparietal network (DGM-FPN). Over time, declining executive function was related to increasing static DGM-FPN connectivity, together with changes of connectivity involving the dorsal attention network (amongst others with the ventral attention network). Static functional connectivity between the ventral and dorsal attention network correlated with motor perseveration. Our findings demonstrate that in PD patients, dysfunctional communication between (i) subcortical, fronto-parietal and attention networks mostly underlies worsening of executive functioning, (ii) attention networks are involved in motor perseveration.
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Qi CX, Huang X, Tong Y, Shen Y. Altered Functional Connectivity Strength of Primary Visual Cortex in Subjects with Diabetic Retinopathy. Diabetes Metab Syndr Obes 2021; 14:3209-3219. [PMID: 34285528 PMCID: PMC8286104 DOI: 10.2147/dmso.s311009] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Accepted: 06/18/2021] [Indexed: 12/18/2022] Open
Abstract
OBJECTIVE The purpose of the study was to find the differences in intrinsic functional connectivity (FC) patterns of the primary visual area (V1) among diabetic retinopathy (DR), diabetes mellitus (DM), and healthy controls (HCs) applying resting-state functional magnetic resonance imaging (rs-fMRI). PATIENTS AND METHODS Thirty-five subjects with DR (18 males and 17 females), 22 DM (10 males and 12 females) and 38 HCs (16 males and 22 females) matched for sex, age, and education underwent rs-fMRI scanning. Seed-based FC analysis was performed to find the alterations in the intrinsic FC patterns of V1 in DR compared with DM and HCs. RESULTS The study found that DR patients had a significant lower FC between the bilateral calcarine (CAL)/left lingual gyrus (LING) (BA 17/18) and the left V1, and between the bilateral CAL/left LING (BA 17/18) and the right V1 compared with the HCs. Meanwhile, patients with DR exhibited higher FC strength between the left V1 and the bilateral Caudate/Olfactory/Orbital superior frontal gyrus (OSFG), and between the bilateral Caudate/Olfactory/OSFG (BA 3/4/6) and the right V1. Compared with DM group, patients with DR showed increased FC strength between the right CAL (BA 17/18) and the right V1. DM group exhibited lower FC strength between the left fusiform and the left V1, and between the bilateral CAL and the right V1 when compared with HCs. Moreover, DM group was observed to have higher FC strength between the left superior frontal gyrus and the left V1. CONCLUSION Our findings indicated that DR patients exhibited FC disruptions between V1 and higher visual regions at rest, which may reflect the aberrant information communication in the V1 area of DR individuals. The findings offer important insights into the neuromechanism of vision disorder in DR patients.
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Affiliation(s)
- Chen-xing Qi
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, People’s Republic of China
| | - Xin Huang
- Department of Ophthalmology, Jiangxi Provincial People’s Hospital, Nanchang330006, People’s Republic of China
| | - Yan Tong
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, People’s Republic of China
| | - Yin Shen
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, People’s Republic of China
- Medical Research Institute, Wuhan University, Wuhan, 430071, Hubei, People’s Republic of China
- Correspondence: Yin Shen Eye Center, Renmin Hospital of Wuhan University, No. 238, Jie Fang Road, Wu Chang District, Wuhan, 430060, Hubei, People’s Republic of ChinaTel +86 13871550513 Email
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Wang S, Zhang Y, Lei J, Guo S. Investigation of sensorimotor dysfunction in Parkinson disease by resting-state fMRI. Neurosci Lett 2020; 742:135512. [PMID: 33221477 DOI: 10.1016/j.neulet.2020.135512] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 11/10/2020] [Accepted: 11/15/2020] [Indexed: 12/14/2022]
Abstract
PURPOSE Functional MRI has played a fundamental role in Parkinson's disease(PD) study. In this paper, we performed an independent component analysis (ICA) based on functional networks to reveal the intricate variations on the morphology and functional properties of brain. Our analysis aims at discovering the differences between PD patients with sensorimotor function impairment and normal controls(NC), thus helping to understand the coordination neurological function degeneration in PD objectively. METHOD We investigated the blood oxygen level dependent(BOLD) functional MRI obtained at a 3.0 T MRI scanner. 30 PD patients and 28 NC subjects underwent the scan in resting state. The signals of sensory and motor coordinative control areas in the sensorimotor, insula and cerebellum networks acquired by ICA(Independent Component Analysis)were applied to analyze the functional alterations. Specifically, intra-network analysis was performed with signals in local networks, and inter-network analysis was conducted by functional network connectivity (FNC) with signals across different networks. Two sample T test was carried out to detect the significant (p < 0.05, FDR p < 0.05) functional abnormality in PD patients. CONCLUSION We identified an obvious increase in bilateral posterior insula, but decrease in bilateral cerebellum hemisphere, supplementary motor area(SMA) and precentral gyrus paracentral lobule of left postcentral gyrus. Besides, we found a significantly increased connection between independent component (IC) 13 which was located in right postcentral gyrus and cerebellum. Decreased connections were detected between sensory and motor cortex in sensorimotor network and between cerebellum and insula network by FNC analysis in PD patients as well. DISCUSSION Parkinson's disease derives from the degeneration of the dopaminergic neurons in substantia nigra, and results in decreased secretion of inhibitory neurotransmitter. The significant differences between PD and NC groups in our research maybe explain the clinical manifestations of prominent bradykinesia and multiple extrapyramidal symptoms.
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Affiliation(s)
- Shuaiwen Wang
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, 730000, China; Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou, Gansu, 730000, China; Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, Lanzhou, Gansu, 730000, China.
| | - Yanli Zhang
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, 730000, China; Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou, Gansu, 730000, China; Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, Lanzhou, Gansu, 730000, China
| | - Junqiang Lei
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, 730000, China; Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou, Gansu, 730000, China; Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, Lanzhou, Gansu, 730000, China
| | - Shunlin Guo
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, 730000, China; Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou, Gansu, 730000, China; Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, Lanzhou, Gansu, 730000, China
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40
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Li MG, Bian XB, Zhang J, Wang ZF, Ma L. Aberrant voxel-based degree centrality in Parkinson's disease patients with mild cognitive impairment. Neurosci Lett 2020; 741:135507. [PMID: 33217504 DOI: 10.1016/j.neulet.2020.135507] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 11/09/2020] [Accepted: 11/11/2020] [Indexed: 01/21/2023]
Abstract
The purpose was to explore the intrinsic dysconnectivity pattern of whole-brain functional networks in Parkinson's disease patients with mild cognitive impairment (PD-MCI) using a voxel-wise degree centrality (DC) analysis approach. The resting-state functional magnetic resonance imaging (rs-fMRI) scanning was performed in all subjects including PD-MCI, PD patients with no cognitive impairment (PD-NCI), and healthy controls (HCs). DC mapping was used to identify functional connectivity (FC) alterations among these groups. Correlation between abnormal DC and clinical features was performed. Secondary seed-based FC analyses and voxel-based morphometry (VBM) analyses were also conducted. Compared with HCs, PD-MCI and PD-NCI showed DC abnormalities mainly in the right temporal lobe, thalamus, left cuneus, middle frontal gyrus, and corpus callosum. Compared with PD-NCI, PD-MCI showed abnormal DC in the left fusiform gyrus (FFG) and left cerebellum lobule VI, left cuneus, right hippocampus, and bilateral precuneus. In PD-MCI patients, correlation analyses revealed that DC in the left FFG was positively correlated with the Montreal Cognitive Assessment (MoCA) scores, and DC in the left precuneus was negatively correlated with the MoCA scores. Secondary seed-based FC analysis further revealed FC changes mainly in the default mode network, right middle cingulum, right supramarginal gyrus, and right postcentral/precentral gyrus. However, no significant difference was found in the secondary VBM analysis. The findings suggest that dysfunction in extensive brain areas is involved in PD-MCI. Among these regions, the left precuneus, FFG, and cerebellum VI may be the key hubs in the pathogenesis of PD-MCI.
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Affiliation(s)
- Ming-Ge Li
- School of Medicine, Nankai University, Tianjin, China; Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Xiang-Bing Bian
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Jun Zhang
- Department of Radiology, Chinese PLA General Hospital, Beijing, China; Department of Radiology, The Six Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Zhen-Fu Wang
- Department of Neurology, Chinese PLA General Hospital, Beijing, China
| | - Lin Ma
- School of Medicine, Nankai University, Tianjin, China; Department of Radiology, Chinese PLA General Hospital, Beijing, China.
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41
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Saeed U, Lang AE, Masellis M. Neuroimaging Advances in Parkinson's Disease and Atypical Parkinsonian Syndromes. Front Neurol 2020; 11:572976. [PMID: 33178113 PMCID: PMC7593544 DOI: 10.3389/fneur.2020.572976] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 09/02/2020] [Indexed: 12/11/2022] Open
Abstract
Parkinson's disease (PD) and atypical Parkinsonian syndromes are progressive heterogeneous neurodegenerative diseases that share clinical characteristic of parkinsonism as a common feature, but are considered distinct clinicopathological disorders. Based on the predominant protein aggregates observed within the brain, these disorders are categorized as, (1) α-synucleinopathies, which include PD and other Lewy body spectrum disorders as well as multiple system atrophy, and (2) tauopathies, which comprise progressive supranuclear palsy and corticobasal degeneration. Although, great strides have been made in neurodegenerative disease research since the first medical description of PD in 1817 by James Parkinson, these disorders remain a major diagnostic and treatment challenge. A valid diagnosis at early disease stages is of paramount importance, as it can help accommodate differential prognostic and disease management approaches, enable the elucidation of reliable clinicopathological relationships ideally at prodromal stages, as well as facilitate the evaluation of novel therapeutics in clinical trials. However, the pursuit for early diagnosis in PD and atypical Parkinsonian syndromes is hindered by substantial clinical and pathological heterogeneity, which can influence disease presentation and progression. Therefore, reliable neuroimaging biomarkers are required in order to enhance diagnostic certainty and ensure more informed diagnostic decisions. In this article, an updated presentation of well-established and emerging neuroimaging biomarkers are reviewed from the following modalities: (1) structural magnetic resonance imaging (MRI), (2) diffusion-weighted and diffusion tensor MRI, (3) resting-state and task-based functional MRI, (4) proton magnetic resonance spectroscopy, (5) transcranial B-mode sonography for measuring substantia nigra and lentiform nucleus echogenicity, (6) single photon emission computed tomography for assessing the dopaminergic system and cerebral perfusion, and (7) positron emission tomography for quantifying nigrostriatal functions, glucose metabolism, amyloid, tau and α-synuclein molecular imaging, as well as neuroinflammation. Multiple biomarkers obtained from different neuroimaging modalities can provide distinct yet corroborative information on the underlying neurodegenerative processes. This integrative "multimodal approach" may prove superior to single modality-based methods. Indeed, owing to the international, multi-centered, collaborative research initiatives as well as refinements in neuroimaging technology that are currently underway, the upcoming decades will mark a pivotal and exciting era of further advancements in this field of neuroscience.
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Affiliation(s)
- Usman Saeed
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Anthony E Lang
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON, Canada.,Edmond J Safra Program in Parkinson's Disease and the Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Mario Masellis
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON, Canada.,L.C. Campbell Cognitive Neurology Research Unit, Sunnybrook Health Sciences Center, Toronto, ON, Canada.,Cognitive and Movement Disorders Clinic, Sunnybrook Health Sciences Center, Toronto, ON, Canada
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42
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Gooßes M, Saliger J, Folkerts AK, Nielsen J, Zierer J, Schmoll P, Niepold A, Colbach L, Leemhuis J, Engels L, van Krüchten M, Ophey A, Allert N, Karbe H, Kalbe E. Feasibility of Music-Assisted Treadmill Training in Parkinson's Disease Patients With and Without Deep Brain Stimulation: Insights From an Ongoing Pilot Randomized Controlled Trial. Front Neurol 2020; 11:790. [PMID: 33013612 PMCID: PMC7498575 DOI: 10.3389/fneur.2020.00790] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 06/25/2020] [Indexed: 01/07/2023] Open
Abstract
Background: Music-assisted treadmill training (MATT) is a new therapeutic approach for Parkinson's disease (PD) patients, combining treadmill training with rhythmic auditory cueing and visual feedback. PD studies have shown larger positive effects on motor outcomes than usual treadmill training. However, effects on cognition, in contrast, are less clear. Existing studies provided intensive training protocols and included only stable medicated patients. Thus, a pilot randomized controlled trial was designed to analyze the feasibility of a shorter training protocol as well as preliminary effects on cognition, motor function, and patient-centered outcomes in a rehabilitation setting where PD patients with and without deep brain stimulation (DBS) undergo adaptation of medication and DBS settings. Here, we present the results from the feasibility analysis of the still ongoing trial. Methods: Non-demented PD patients with and without DBS were recruited during their inpatient rehabilitation and randomized to an experimental group (EG; 20 min MATT) or an active control group (CG; 20 min bike ergometer training). The trainings took place for 8 consecutive days and were added to the usual rehabilitation. Feasibility was assessed with the following parameters: patients' study protocol acceptance, study protocol transferability into clinical routine, training-induced adverse events, and patients' training perception. Results: Thirty-two patients (EG: n = 15; CG: n = 17; 72% DBS) were included. The study protocol was well-accepted (inclusion rate: 84%). It was transferable into clinical routines; dropout rates of 40% (EG) and 18% (CG) were observed. However, an in-depth analysis of the dropout cohort did not reveal intervention-related dropout reasons. The MATT and the standard ergometer training showed no adverse events and were positively perceived by PD patients with and without DBS. Conclusion: MATT was shown to be a feasible, safe, and enjoyable treatment option in PD patients with and without DBS. Furthermore, the dropout cohort analysis revealed some exciting first insights into possible dropout reasons that go beyond the form of intervention. Therefore, research would benefit from a common practice of dropout analyses, as this would enhance our understanding of patients' therapy adherence and expectations.
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Affiliation(s)
- Mareike Gooßes
- Medical Psychology
- Neuropsychology and Gender Studies, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Neurological Rehabilitation Center Godeshoehe, Bonn, Germany
| | - Jochen Saliger
- Neurological Rehabilitation Center Godeshoehe, Bonn, Germany
| | - Ann-Kristin Folkerts
- Medical Psychology
- Neuropsychology and Gender Studies, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Jörn Nielsen
- Neurological Rehabilitation Center Godeshoehe, Bonn, Germany
| | - Jürgen Zierer
- Neurological Rehabilitation Center Godeshoehe, Bonn, Germany
| | - Paula Schmoll
- Medical Psychology
- Neuropsychology and Gender Studies, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Neurological Rehabilitation Center Godeshoehe, Bonn, Germany
| | - Annika Niepold
- Medical Psychology
- Neuropsychology and Gender Studies, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Liz Colbach
- Medical Psychology
- Neuropsychology and Gender Studies, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Neurological Rehabilitation Center Godeshoehe, Bonn, Germany
| | - Janna Leemhuis
- Medical Psychology
- Neuropsychology and Gender Studies, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Lea Engels
- Medical Psychology
- Neuropsychology and Gender Studies, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Maria van Krüchten
- Medical Psychology
- Neuropsychology and Gender Studies, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Neurological Rehabilitation Center Godeshoehe, Bonn, Germany
| | - Anja Ophey
- Medical Psychology
- Neuropsychology and Gender Studies, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Niels Allert
- Neurological Rehabilitation Center Godeshoehe, Bonn, Germany
| | - Hans Karbe
- Neurological Rehabilitation Center Godeshoehe, Bonn, Germany
| | - Elke Kalbe
- Medical Psychology
- Neuropsychology and Gender Studies, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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43
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Li W, Lao-Kaim NP, Roussakis AA, Martín-Bastida A, Valle-Guzman N, Paul G, Soreq E, Daws RE, Foltynie T, Barker RA, Hampshire A, Piccini P. Longitudinal functional connectivity changes related to dopaminergic decline in Parkinson's disease. Neuroimage Clin 2020; 28:102409. [PMID: 32916466 PMCID: PMC7490914 DOI: 10.1016/j.nicl.2020.102409] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 08/24/2020] [Accepted: 08/30/2020] [Indexed: 01/02/2023]
Abstract
BACKGROUND Resting-state functional magnetic resonance imaging (fMRI) studies have demonstrated that basal ganglia functional connectivity is altered in Parkinson's disease (PD) as compared to healthy controls. However, such functional connectivity alterations have not been related to the dopaminergic deficits that occurs in PD over time. OBJECTIVES To examine whether functional connectivity impairments are correlated with dopaminergic deficits across basal ganglia subdivisions in patients with PD both cross-sectionally and longitudinally. METHODS We assessed resting-state functional connectivity of basal ganglia subdivisions and dopamine transporter density using 11C-PE2I PET in thirty-four PD patients at baseline. Of these, twenty PD patients were rescanned after 19.9 ± 3.8 months. A seed-based approach was used to analyze resting-state fMRI data. 11C-PE2I binding potential (BPND) was calculated for each participant. PD patients were assessed for disease severity. RESULTS At baseline, PD patients with greater dopaminergic deficits, as measured with 11C-PE2I PET, showed larger decreases in posterior putamen functional connectivity with the midbrain and pallidum. Reduced functional connectivity of the posterior putamen with the thalamus, midbrain, supplementary motor area and sensorimotor cortex over time were significantly associated with changes in DAT density over the same period. Furthermore, increased motor disability was associated with lower intraregional functional connectivity of the posterior putamen. CONCLUSIONS Our findings suggest that basal ganglia functional connectivity is related to integrity of dopaminergic system in patients with PD. Application of resting-state fMRI in a large cohort and longitudinal scanning may be a powerful tool for assessing underlying PD pathology and its progression.
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Affiliation(s)
- Weihua Li
- Centre for Neurodegeneration and Neuroinflammation, Division of Brain Sciences, Imperial College London, London W12 0NN, United Kingdom; Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China.
| | - Nick P Lao-Kaim
- Centre for Neurodegeneration and Neuroinflammation, Division of Brain Sciences, Imperial College London, London W12 0NN, United Kingdom
| | - Andreas-Antonios Roussakis
- Centre for Neurodegeneration and Neuroinflammation, Division of Brain Sciences, Imperial College London, London W12 0NN, United Kingdom
| | - Antonio Martín-Bastida
- Centre for Neurodegeneration and Neuroinflammation, Division of Brain Sciences, Imperial College London, London W12 0NN, United Kingdom; Department of Neurology and Neurosciences, Clínica universidad de Navarra, Pamplona-Madrid, Spain
| | - Natalie Valle-Guzman
- John Van Geest Centre for Brain Repair, University of Cambridge, Cambridge CB2 0PY, United Kingdom
| | - Gesine Paul
- Translational Neurology Group, Department of Clinical Sciences, Wallenberg Neuroscience Centre, Lund University, Lund 221 84, Sweden; Division of Neurology, Department of Clinical Sciences, Lund University, Skåne University Hospital, Lund 22185, Sweden
| | - Eyal Soreq
- Imperial College London, Division of Brain Sciences, Computational Cognitive & Clinical Neuroimaging Lab (C(3)NL), London W12 0NN, United Kingdom
| | - Richard E Daws
- Imperial College London, Division of Brain Sciences, Computational Cognitive & Clinical Neuroimaging Lab (C(3)NL), London W12 0NN, United Kingdom
| | - Tom Foltynie
- Sobell Department of Motor Neuroscience, UCL Institute of Neurology, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, United Kingdom
| | - Roger A Barker
- John Van Geest Centre for Brain Repair, University of Cambridge, Cambridge CB2 0PY, United Kingdom
| | - Adam Hampshire
- Imperial College London, Division of Brain Sciences, Computational Cognitive & Clinical Neuroimaging Lab (C(3)NL), London W12 0NN, United Kingdom
| | - Paola Piccini
- Centre for Neurodegeneration and Neuroinflammation, Division of Brain Sciences, Imperial College London, London W12 0NN, United Kingdom
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Griffanti L, Klein JC, Szewczyk-Krolikowski K, Menke RAL, Rolinski M, Barber TR, Lawton M, Evetts SG, Begeti F, Crabbe M, Rumbold J, Wade-Martins R, Hu MT, Mackay C. Cohort profile: the Oxford Parkinson's Disease Centre Discovery Cohort MRI substudy (OPDC-MRI). BMJ Open 2020; 10:e034110. [PMID: 32792423 PMCID: PMC7430482 DOI: 10.1136/bmjopen-2019-034110] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
PURPOSE The Oxford Parkinson's Disease Centre (OPDC) Discovery Cohort MRI substudy (OPDC-MRI) collects high-quality multimodal brain MRI together with deep longitudinal clinical phenotyping in patients with Parkinson's, at-risk individuals and healthy elderly participants. The primary aim is to detect pathological changes in brain structure and function, and develop, together with the clinical data, biomarkers to stratify, predict and chart progression in early-stage Parkinson's and at-risk individuals. PARTICIPANTS Participants are recruited from the OPDC Discovery Cohort, a prospective, longitudinal study. Baseline MRI data are currently available for 290 participants: 119 patients with early idiopathic Parkinson's, 15 Parkinson's patients with pathogenic mutations of the leucine-rich repeat kinase 2 or glucocerebrosidase (GBA) genes, 68 healthy controls and 87 individuals at risk of Parkinson's (asymptomatic carriers of GBA mutation and patients with idiopathic rapid eye movement sleep behaviour disorder-RBD). FINDINGS TO DATE Differences in brain structure in early Parkinson's were found to be subtle, with small changes in the shape of the globus pallidus and evidence of alterations in microstructural integrity in the prefrontal cortex that correlated with performance on executive function tests. Brain function, as assayed with resting fMRI yielded more substantial differences, with basal ganglia connectivity reduced in early Parkinson'sand RBD. Imaging of the substantia nigra with the more recent adoption of sequences sensitive to iron and neuromelanin content shows promising results in identifying early signs of Parkinsonian disease. FUTURE PLANS Ongoing studies include the integration of multimodal MRI measures to improve discrimination power. Follow-up clinical data are now accumulating and will allow us to correlate baseline imaging measures to clinical disease progression. Follow-up MRI scanning started in 2015 and is currently ongoing, providing the opportunity for future longitudinal imaging analyses with parallel clinical phenotyping.
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Affiliation(s)
- Ludovica Griffanti
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, Oxfordshire, UK
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
| | - Johannes C Klein
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Konrad Szewczyk-Krolikowski
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Ricarda A L Menke
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Michal Rolinski
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
- Institute of Clinical Neurosciences, University of Bristol, Bristol, UK
| | - Thomas R Barber
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, Oxfordshire, UK
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Michael Lawton
- Population Health Sciences, University of Bristol, Bristol, UK
| | - Samuel G Evetts
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Faye Begeti
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Marie Crabbe
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Jane Rumbold
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Richard Wade-Martins
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, Oxfordshire, UK
| | - Michele T Hu
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Clare Mackay
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, Oxfordshire, UK
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Oxford Health, NHS Foundation Trust, Oxford, Oxfordshire, UK
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Tessitore A, Cirillo M, De Micco R. Functional Connectivity Signatures of Parkinson's Disease. JOURNAL OF PARKINSONS DISEASE 2020; 9:637-652. [PMID: 31450512 PMCID: PMC6839494 DOI: 10.3233/jpd-191592] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Resting-state functional magnetic resonance imaging (RS-fMRI) studies have been extensively applied to analyze the pathophysiology of neurodegenerative disorders such as Parkinson’s disease (PD). In the present narrative review, we attempt to summarize the most recent RS-fMRI findings highlighting the role of brain networks re-organization and adaptation in the course of PD. We also discuss limitations and potential definition of early functional connectivity signatures to track and predict future PD progression. Understanding the neural correlates and potential predisposing factors of clinical progression and complication will be crucial to guide novel clinical trials and to foster preventive strategies.
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Affiliation(s)
- Alessandro Tessitore
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy.,MRI Research Center SUN-FISM, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Mario Cirillo
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy.,MRI Research Center SUN-FISM, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Rosa De Micco
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy.,MRI Research Center SUN-FISM, University of Campania "Luigi Vanvitelli", Naples, Italy
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46
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Increased large-scale inter-network connectivity in relation to impulsivity in Parkinson's disease. Sci Rep 2020; 10:11418. [PMID: 32651411 PMCID: PMC7351767 DOI: 10.1038/s41598-020-68266-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 06/11/2020] [Indexed: 12/21/2022] Open
Abstract
Impulsivity is a neuropsychiatric feature of Parkinson’s disease (PD). We investigated the pathophysiology of impulsivity in PD using resting-state functional magnetic resonance imaging (rs-fMRI). We investigated 45 patients with idiopathic PD and 21 healthy controls. Based on Barratt Impulsiveness Scale (BIS-11) score, PD patients were classified as higher (PD-HI) or lower impulsivity (PD-LI). Functional connectivity (FC) between various large-scale brain networks were analysed using the CONN toolbox. FC between the right frontoparietal network (FPN) and medial visual network (MVN) was significantly higher in PD-HI patients than PD-LI patients (false discovery rate [FDR]-adjusted p = 0.0315). FC between the right FPN and MVN had a significant positive correlation with total BIS-11 score (FDR-adjusted p = 0.010) and the attentional impulsivity (FDR-adjusted p = 0.046) and non-planning impulsivity subscale scores (FDR-adjusted p = 0.018). On the other hand, motor impulsivity subscale score had a significant negative correlation with the FC between the default-mode and salience networks (right supramarginal gyrus, FDR-adjusted p = 0.018; anterior cingulate cortex, FDR-adjusted p = 0.027); this trend was observed in healthy controls. The attentional and non-planning impulsivity, regarded as ‘cognitive’ impulsivity, may be associated with dysfunction in integration of perceptual information and flexible cognitive control in PD.
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47
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Li MG, He JF, Liu XY, Wang ZF, Lou X, Ma L. Structural and Functional Thalamic Changes in Parkinson's Disease With Mild Cognitive Impairment. J Magn Reson Imaging 2020; 52:1207-1215. [PMID: 32557988 DOI: 10.1002/jmri.27195] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 04/30/2020] [Accepted: 05/01/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND The thalamus is a key node of deep gray matter and previous studies have demonstrated that it is involved in the modulation of cognition. PURPOSE To investigate the volume changes of the thalamus and its subregions and altered thalamus functional connectivity patterns in Parkinson's disease (PD) patients with and without mild cognitive impairment (MCI). STUDY TYPE Prospective. POPULATION Thirty-three patients with MCI (PD-MCI), 36 PD patients having no cognitive impairment (PD-NCI), 21 healthy controls (HCs). SEQUENCE 3.0T MRI scanner; 3D T1 -weighted fast spoiled gradient recalled echo (3D T1 -FSPGR); resting-state fMRI ASSESSMENT: Voxel-based morphometry (VBM) was performed to calculate the volume of the thalamus and its subregions. The left and right total thalamus were considered seeds and seed-based functional connectivity (FC) was analyzed. Additionally, correlations between volumes and cognitive performance and between FC values and cognitive performance were examined separately. STATISTICAL TEST Analysis of covariance (ANCOVA); two-sample t-tests; partial correlation analysis. RESULTS The volumes of the total thalamus (PD-MCI vs. PD-NCI vs. HCs: 18.39 ± 1.67 vs. 19.63 ± 1.79 vs. 19.47 ± 1.35) and its subregions were significantly reduced in PD-MCI as compared to PD-NCI (total thalamus: P = 0.002) and HCs (total thalamus: P = 0.012). Compared with PD-NCI, PD-MCI showed increased FC between the thalamus and bilateral middle cingulate cortex and left posterior cingulate cortex, and decreased FC between thalamus and the left superior occipital gyrus, left cuneus, left precuneus, and left middle occipital gyrus. Volumes of thalamus and the subregions, as well as the FC of thalamus with the identified regions, were significantly correlated (P < 0.05, FDR-corrected) with neuropsychological scores in PD patients. DATA CONCLUSION We noted volume loss and altered FC of thalamus in PD-MCI patients, and these changes were correlated with global cognitive performance. LEVEL OF EVIDENCE 2 TECHNICAL EFFICIENCY: Stage 2 J. Magn. Reson. Imaging 2020;52:1207-1215.
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Affiliation(s)
- Ming-Ge Li
- School of Medicine, Nankai University, Tianjin, China.,Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Jian-Feng He
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Xin-Yun Liu
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Zhen-Fu Wang
- Department of Neurology, Chinese PLA General Hospital, Beijing, China
| | - Xin Lou
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Lin Ma
- School of Medicine, Nankai University, Tianjin, China.,Department of Radiology, Chinese PLA General Hospital, Beijing, China
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Lang S, Ismail Z, Kibreab M, Kathol I, Sarna J, Monchi O. Common and unique connectivity at the interface of motor, neuropsychiatric, and cognitive symptoms in Parkinson's disease: A commonality analysis. Hum Brain Mapp 2020; 41:3749-3764. [PMID: 32476230 PMCID: PMC7416059 DOI: 10.1002/hbm.25084] [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: 02/11/2020] [Revised: 05/13/2020] [Accepted: 05/18/2020] [Indexed: 01/13/2023] Open
Abstract
Parkinson's disease (PD) is characterized by overlapping motor, neuropsychiatric, and cognitive symptoms. Worse performance in one domain is associated with worse performance in the other domains. Commonality analysis (CA) is a method of variance partitioning in multiple regression, used to separate the specific and common influence of collinear predictors. We apply, for the first time, CA to the functional connectome to investigate the unique and common neural connectivity underlying the interface of the symptom domains in 74 non-demented PD subjects. Edges were modeled as a function of global motor, cognitive, and neuropsychiatric scores. CA was performed, yielding measures of the unique and common contribution of the symptom domains. Bootstrap confidence intervals were used to determine the precision of the estimates and to directly compare each commonality coefficient. The overall model identified a network with the caudate nucleus as a hub. Neuropsychiatric impairment accounted for connectivity in the caudate-dorsal anterior cingulate and caudate-right dorsolateral prefrontal-right inferior parietal circuits, while caudate-medial prefrontal connectivity reflected a unique effect of both neuropsychiatric and cognitive impairment. Caudate-precuneus connectivity was explained by both unique and shared influence of neuropsychiatric and cognitive symptoms. Lastly, posterior cortical connectivity reflected an interplay of the unique and common effects of each symptom domain. We show that CA can determine the amount of variance in the connectome that is unique and shared amongst motor, neuropsychiatric, and cognitive symptoms in PD, thereby improving our ability to interpret the data while gaining novel insight into networks at the interface of these symptom domains.
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Affiliation(s)
- Stefan Lang
- Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.,Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Zahinoor Ismail
- Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.,Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.,Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada.,Mathison Center for Brain and Mental Health Research, University of Calgary, Calgary, Alberta, Canada
| | - Mekale Kibreab
- Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.,Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Iris Kathol
- Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.,Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Justyna Sarna
- Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.,Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Oury Monchi
- Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.,Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.,Department of Radiology, University of Calgary, Calgary, Alberta, Canada
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49
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An analytical workflow for seed-based correlation and independent component analysis in interventional resting-state fMRI studies. Neurosci Res 2020; 165:26-37. [PMID: 32464181 DOI: 10.1016/j.neures.2020.05.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 05/08/2020] [Accepted: 05/18/2020] [Indexed: 12/12/2022]
Abstract
Resting-state functional MRI (rs-fMRI) is a task-free method of detecting spatially distinct brain regions with correlated activity, which form organised networks known as resting-state networks (RSNs). The two most widely used methods for analysing RSN connectivity are seed-based correlation analysis (SCA) and independent component analysis (ICA) but there is no established workflow of the optimal combination of analytical steps and how to execute them. Rodent rs-fMRI data from our previous longitudinal brain stimulation studies were used to investigate these two methods using FSL. Specifically, we examined: (1) RSN identification and group comparisons in ICA, (2) ICA-based denoising compared to nuisance signal regression in SCA, and (3) seed selection in SCA. In ICA, using a baseline-only template resulted in greater functional connectivity within RSNs and more sensitive detection of group differences than when an average pre/post stimulation template was used. In SCA, the use of an ICA-based denoising method in the preprocessing of rs-fMRI data and the use of seeds from individual functional connectivity maps in running group comparisons increased the sensitivity of detecting group differences by preventing the reduction in signals of interest. Accordingly, when analysing animal and human rs-fMRI data, we infer that the use of baseline-only templates in ICA and ICA-based denoising and individualised seeds in SCA will improve the reliability of results and comparability across rs-fMRI studies.
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Liu S, Zhao W, Li Y, Li X, Li J, Cao H, Yang Z, Xu Y. Improve cognition of depressive patients through the regulation of basal ganglia connectivity: Combined medication using Shuganjieyu capsule. J Psychiatr Res 2020; 123:39-47. [PMID: 32035307 DOI: 10.1016/j.jpsychires.2020.01.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 01/24/2020] [Accepted: 01/25/2020] [Indexed: 01/25/2023]
Abstract
Shuganjieyu capsule (Shugan) is a combined extract of Hypericum perforatum (HP) and Eleutherococcus senticosus (ES). Both HP and ES have been proven effective in the treatment of depression and impaired cognition. However, for mild to moderate depression (MMD), the treatment effect and underlying mechanism by combining both HP and ES are largely unknown. Here, we aim to evaluate the therapeutic effects on impaired cognition using Shugan, a combined medication of HP and ES. Resting-state magnetic resonance imaging (MRI) data and cognitive assessment have been collected from 54 healthy controls and 55 MMD patients that have been undergoing 8-week Shugan-treatment. The functional connectivity (FC) and brain region volume changes of the basal ganglia seeded circuit have been measured, and their relation with the cognitive assessment score was calculated. After that, a literature-based pathway analysis has been conducted to explore the biological relations among Shugan, brain regions, and depression. Compared to healthy controls, MMD patients demonstrated a significantly higher FC (P= 0.0025) between right ventral caudate (vCa) and left orbitofrontal cortex (OFC), which was decreased after the treatment (P < 0.001). A volume of the right caudate, which is increased in MMD, has also been reduced by Shugan treatment (P= 0.017). Importantly, the cognitive scores were strongly correlated with both Shugan treatment and the FC between vCa and OFC (r= 0.321, P= 0.02). Besides, we identified multiple signaling pathways, through which Shugan might improve the cognition of MMD patients. Our results support the therapeutic effects of Shugan on cognition in MMD, which may be realized partly through the regulation within two brain regions, vCa and OFC.
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Affiliation(s)
- Sha Liu
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China; Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Wentao Zhao
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China; Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Yaojun Li
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China; Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Xinrong Li
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China; Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Jing Li
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China; Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Hongbao Cao
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China; Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China; School of Systems Biology, George Mason University (GMU), Fairfax, VA, USA
| | - Zhi Yang
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University Medical School, Shanghai, China
| | - Yong Xu
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China; Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China.
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