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Delli Pizzi S, Tomaiuolo F, Sestieri C, Chiarelli AM, Gambi F, Ferretti A, Sensi SL. Modafinil alters the functional connectivity of distinct thalamic nuclei with the neocortex. Neuroimage 2025; 312:121242. [PMID: 40288703 DOI: 10.1016/j.neuroimage.2025.121242] [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: 07/24/2024] [Revised: 04/23/2025] [Accepted: 04/24/2025] [Indexed: 04/29/2025] Open
Abstract
Modafinil promotes wakefulness and enhances cognitive function through mechanisms and neural effects that are still partially unknown. Several studies have shown that the compound alters the functional cortical architecture. In contrast, its influence on subcortical regions and thalamocortical connections, which are crucial for modulating neocortical connectivity, remains unexplored. The acute modulation of thalamo-cortical connectivity was assessed in two groups of participants who received either a single 100 mg dose of modafinil (N = 25) or a placebo (N = 25). Magnetic Resonance Imaging (MRI) was used to parcel the thalamus into its constituent nuclei, which served as seeds for voxel-wise resting state functional connectivity analyses. Additionally, maps of nuclei-specific functional reorganization were compared to those of receptor/transporter expression to assess their spatial overlaps. Modafinil, but not placebo, altered the connectivity of three thalamic nuclei. Specifically, the medial pulvinar nuclei showed increased connectivity with cortical regions of the Sensorimotor and Salience/Ventral Attention (SVAN) Networks. These functional changes spatially overlapped with the distribution of the norepinephrine transporter (NET). Additionally, the anterior and inferior pulvinar complex exhibited enhanced connectivity with the insular and supramarginal regions of the SVAN and superior frontal area of the Default Mode Network (DMN). However, unlike the medial pulvinar, these effects were not spatially linked to the expression of any specific receptor or transporter. Finally, the ventro-lateral anterior complex exhibited increased connectivity with the posterior region of the DMN and the Fronto-Parietal Control Network, along with decreased connectivity to the premotor cortex. The topography of these functional modifications mainly overlaps with the distribution of glutamatergic and serotonergic receptors. In summary, our findings highlight modafinil's influence on thalamocortical circuits, emphasizing the role of higher-order pulvinar nuclei and ventro-lateral anterior complex.
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Affiliation(s)
- Stefano Delli Pizzi
- Department of Neuroscience, Imaging, and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara, Italy; Institute for Advanced Biomedical Technologies (ITAB), University "G. d'Annunzio"of Chieti-Pescara, Italy; Molecular Neurology Unit, Center for Advanced Studies and Technology (CAST), University "G. d'Annunzio" of Chieti-Pescara, Italy.
| | - Federica Tomaiuolo
- Institute for Advanced Biomedical Technologies (ITAB), University "G. d'Annunzio"of Chieti-Pescara, Italy; Department of Engineering and Geology, University "G. d'Annunzio" of Chieti Pescara, Italy
| | - Carlo Sestieri
- Department of Neuroscience, Imaging, and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara, Italy; Institute for Advanced Biomedical Technologies (ITAB), University "G. d'Annunzio"of Chieti-Pescara, Italy
| | - Antonio Maria Chiarelli
- Department of Neuroscience, Imaging, and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara, Italy; Institute for Advanced Biomedical Technologies (ITAB), University "G. d'Annunzio"of Chieti-Pescara, Italy
| | - Francesco Gambi
- Department of Neuroscience, Imaging, and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara, Italy; Molecular Neurology Unit, Center for Advanced Studies and Technology (CAST), University "G. d'Annunzio" of Chieti-Pescara, Italy
| | - Antonio Ferretti
- Department of Neuroscience, Imaging, and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara, Italy; Institute for Advanced Biomedical Technologies (ITAB), University "G. d'Annunzio"of Chieti-Pescara, Italy; UdA-TechLab, Research Center, University "G. d'Annunzio" of Chieti-Pescara, Italy
| | - Stefano L Sensi
- Department of Neuroscience, Imaging, and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara, Italy; Institute for Advanced Biomedical Technologies (ITAB), University "G. d'Annunzio"of Chieti-Pescara, Italy; Molecular Neurology Unit, Center for Advanced Studies and Technology (CAST), University "G. d'Annunzio" of Chieti-Pescara, Italy; Neurology Institute, SS Annunziata University Hospital, University "G. d'Annunzio" of Chieti-Pescara, Italy.
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Ignatavicius A, Matar E, Lewis SJG. Visual hallucinations in Parkinson's disease: spotlight on central cholinergic dysfunction. Brain 2025; 148:376-393. [PMID: 39252645 PMCID: PMC11788216 DOI: 10.1093/brain/awae289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 07/02/2024] [Accepted: 08/30/2024] [Indexed: 09/11/2024] Open
Abstract
Visual hallucinations are a common non-motor feature of Parkinson's disease and have been associated with accelerated cognitive decline, increased mortality and early institutionalization. Despite their prevalence and negative impact on patient outcomes, the repertoire of treatments aimed at addressing this troubling symptom is limited. Over the past two decades, significant contributions have been made in uncovering the pathological and functional mechanisms of visual hallucinations, bringing us closer to the development of a comprehensive neurobiological framework. Convergent evidence now suggests that degeneration within the central cholinergic system may play a significant role in the genesis and progression of visual hallucinations. Here, we outline how cholinergic dysfunction may serve as a potential unifying neurobiological substrate underlying the multifactorial and dynamic nature of visual hallucinations. Drawing upon previous theoretical models, we explore the impact that alterations in cholinergic neurotransmission has on the core cognitive processes pertinent to abnormal perceptual experiences. We conclude by highlighting that a deeper understanding of cholinergic neurobiology and individual pathophysiology may help to improve established and emerging treatment strategies for the management of visual hallucinations and psychotic symptoms in Parkinson's disease.
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Affiliation(s)
- Anna Ignatavicius
- Faculty of Medicine and Health, Central Clinical School, University of Sydney, Sydney, NSW 2050, Australia
| | - Elie Matar
- Faculty of Medicine and Health, Central Clinical School, University of Sydney, Sydney, NSW 2050, Australia
- Centre for Integrated Research and Understanding of Sleep (CIRUS), Woolcock Institute of Medical Research, Sydney, NSW 2113, Australia
- Department of Neurology, Royal Prince Alfred Hospital, Sydney, NSW 2050, Australia
| | - Simon J G Lewis
- Faculty of Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Sydney, NSW 2109, Australia
- Faculty of Medicine, Health and Human Sciences, Macquarie University Centre for Parkinson’s Disease Research, Macquarie University, Sydney, NSW 2109, Australia
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Wu J, Jin X, Xie W, Liu L, Wang F, Zhu L, Shen Y, Qiu L. Global research trends and hotspots in Parkinson's disease psychosis: a 25-year bibliometric and visual analysis. Front Aging Neurosci 2024; 16:1480234. [PMID: 39649718 PMCID: PMC11621064 DOI: 10.3389/fnagi.2024.1480234] [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: 08/13/2024] [Accepted: 11/07/2024] [Indexed: 12/11/2024] Open
Abstract
Background Parkinson's disease psychosis (PDP) is one of the most severe and disabling non-motor symptoms in the progression of Parkinson's disease (PD), significantly impacting the prognosis of PD patients. In recent years, there has been an increase in literature on PDP. However, bibliometrics has rarely been applied to PDP research. This study provides an overview of the current state of PDP research and predicts future trends in this field. Methods The literature search was conducted using the Web of Science Core Collection, with the search terms (Parkinson* AND (psychotic* OR hallucination* OR illusion* OR delusion* OR misperception* OR psychosis OR psychoses)). VOSviewer and CiteSpace software were employed to perform bibliometric analysis and visual representation of the search results. Results A total of 603 articles were effectively included. Since 2017, there has been a significant upward trend in publications related to PDP. The United States, the United Kingdom, and Canada were the top three contributing countries in terms of publication volume, with France also having a strong influence in this field. Movement Disorders and King's College London included and published the most articles on PDP. The paper titled "Hallucinations in Parkinson's Disease: Prevalence, Phenomenology, and Risk Factors" received the highest number of citations and average citations. Cluster analysis results identified brain, prevalence, connectivity, and atypical antipsychotics as key hotspots in this field. High-frequency keywords were grouped into three themes: neurobiology, therapeutic strategies, and symptom research. Among them, pimavanserin, risk, and functional connectivity have been the most studied areas in the past 7 years and are likely to remain key topics in future research. Conclusion Research on PDP has garnered increasing attention. This study visualizes PDP research over the past 25 years to analyze global hotspots and trends. It offers researchers a valuable perspective for identifying key topics and understanding research trajectories in this expanding field.
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Affiliation(s)
- Jianhong Wu
- Affiliated Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, Jiangsu, China
| | - Xin Jin
- Northern Jiangsu People’s Hospital, Yangzhou, Jiangsu, China
| | - Weiming Xie
- Affiliated Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, Jiangsu, China
| | - Liang Liu
- Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Fei Wang
- Affiliated Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, Jiangsu, China
| | - Ling Zhu
- Jiangyin People's Hospital, Wuxi, Jiangsu, China
| | - Yuan Shen
- Affiliated Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, Jiangsu, China
| | - Linghe Qiu
- Affiliated Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, Jiangsu, China
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Pisani S, Gunasekera B, Lu Y, Vignando M, Ffytche D, Aarsland D, Chaudhuri KR, Ballard C, Lee JY, Kim YK, Velayudhan L, Bhattacharyya S. Functional and connectivity correlates associated with Parkinson's disease psychosis: a systematic review. Brain Commun 2024; 6:fcae358. [PMID: 39507273 PMCID: PMC11538965 DOI: 10.1093/braincomms/fcae358] [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: 09/10/2023] [Revised: 07/24/2024] [Accepted: 11/03/2024] [Indexed: 11/08/2024] Open
Abstract
Neural underpinnings of Parkinson's disease psychosis remain unclear to this day with relatively few studies and reviews available. Using a systematic review approach, here, we aimed to qualitatively synthesize evidence from studies investigating Parkinson's psychosis-specific alterations in brain structure, function or chemistry using different neuroimaging modalities. PubMed, Web of Science and Embase databases were searched for functional MRI (task-based and resting state), diffusion tensor imaging, PET and single-photon emission computed tomography studies comparing Parkinson's disease psychosis patients with Parkinson's patients without psychosis. We report findings from 29 studies (514 Parkinson's psychosis patients, mean age ± SD = 67.92 ± 4.37 years; 51.36% males; 853 Parkinson's patients, mean age ± SD = 66.75 ± 4.19 years; 55.81% males). Qualitative synthesis revealed widespread patterns of altered brain function across task-based and resting-state functional MRI studies in Parkinson's psychosis patients compared with Parkinson's patients without psychosis. Similarly, white matter abnormalities were reported in parietal, temporal and occipital regions. Hypo-metabolism and reduced dopamine transporter binding were also reported whole brain and in sub-cortical areas. This suggests extensive alterations affecting regions involved in high-order visual processing and attentional networks.
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Affiliation(s)
- Sara Pisani
- Division of Academic Psychiatry, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Brandon Gunasekera
- Division of Academic Psychiatry, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Yining Lu
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Miriam Vignando
- Centre for Neuroimaging Science, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Dominic Ffytche
- Division of Academic Psychiatry, Department of Psychological Medicine, Centre for Healthy Brain Ageing, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Dag Aarsland
- Division of Academic Psychiatry, Department of Psychological Medicine, Centre for Healthy Brain Ageing, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
- Centre for Age-Related Medicine (SESAM), Stavanger University Hospital, Stavanger 4011, Norway
| | - K R Chaudhuri
- Department of Neurosciences, Institute of Psychiatry, Psychology and Neuroscience, and Parkinson’s Foundation Centre of Excellence, King’s College Hospital, London SE5 9RS, UK
| | - Clive Ballard
- Faculty of Health and Life Sciences, University of Exeter, Exeter EX1 2LU, UK
| | - Jee-Young Lee
- Department of Neurology, Seoul National University-Seoul Metropolitan Government, Boramae Medical Center, Seoul 07061, Republic of Korea
| | - Yu Kyeong Kim
- Department of Nuclear Medicine, Seoul National University-Seoul Metropolitan Government, Boramae Medical Center, Seoul 07061, Republic of Korea
| | - Latha Velayudhan
- Division of Academic Psychiatry, Department of Psychological Medicine, Centre for Healthy Brain Ageing, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Sagnik Bhattacharyya
- Division of Academic Psychiatry, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
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Khan AF, Iturria-Medina Y. Beyond the usual suspects: multi-factorial computational models in the search for neurodegenerative disease mechanisms. Transl Psychiatry 2024; 14:386. [PMID: 39313512 PMCID: PMC11420368 DOI: 10.1038/s41398-024-03073-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 08/20/2024] [Accepted: 08/27/2024] [Indexed: 09/25/2024] Open
Abstract
From Alzheimer's disease to amyotrophic lateral sclerosis, the molecular cascades underlying neurodegenerative disorders remain poorly understood. The clinical view of neurodegeneration is confounded by symptomatic heterogeneity and mixed pathology in almost every patient. While the underlying physiological alterations originate, proliferate, and propagate potentially decades before symptomatic onset, the complexity and inaccessibility of the living brain limit direct observation over a patient's lifespan. Consequently, there is a critical need for robust computational methods to support the search for causal mechanisms of neurodegeneration by distinguishing pathogenic processes from consequential alterations, and inter-individual variability from intra-individual progression. Recently, promising advances have been made by data-driven spatiotemporal modeling of the brain, based on in vivo neuroimaging and biospecimen markers. These methods include disease progression models comparing the temporal evolution of various biomarkers, causal models linking interacting biological processes, network propagation models reproducing the spatial spreading of pathology, and biophysical models spanning cellular- to network-scale phenomena. In this review, we discuss various computational approaches for integrating cross-sectional, longitudinal, and multi-modal data, primarily from large observational neuroimaging studies, to understand (i) the temporal ordering of physiological alterations, i(i) their spatial relationships to the brain's molecular and cellular architecture, (iii) mechanistic interactions between biological processes, and (iv) the macroscopic effects of microscopic factors. We consider the extents to which computational models can evaluate mechanistic hypotheses, explore applications such as improving treatment selection, and discuss how model-informed insights can lay the groundwork for a pathobiological redefinition of neurodegenerative disorders.
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Affiliation(s)
- Ahmed Faraz Khan
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, Canada
- Ludmer Centre for Neuroinformatics & Mental Health, Montreal, Canada
| | - Yasser Iturria-Medina
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, Canada.
- Ludmer Centre for Neuroinformatics & Mental Health, Montreal, Canada.
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Vignando M, Ffytche D, Mazibuko N, Palma G, Montagnese M, Dave S, Nutt DJ, Gabay AS, Tai YF, Batzu L, Leta V, Williams Gray CH, Chaudhuri KR, Mehta MA. Visual mismatch negativity in Parkinson's psychosis and potential for testing treatment mechanisms. Brain Commun 2024; 6:fcae291. [PMID: 39355002 PMCID: PMC11443450 DOI: 10.1093/braincomms/fcae291] [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: 12/05/2023] [Revised: 06/25/2024] [Accepted: 09/02/2024] [Indexed: 10/03/2024] Open
Abstract
Psychosis and visual hallucinations are a prevalent non-motor symptom of Parkinson's disease, negatively affecting patients' quality of life and constituting a greater risk for dementia. Understanding neural mechanisms associated to these symptoms is instrumental for treatment development. The mismatch negativity is an event-related potential evoked by a violation in a sequence of sensory events. It is widely considered an index of sensory change-detection. Reduced mismatch negativity response is one of the most replicated results in schizophrenia and has been suggested to be a superior psychosis marker. To understand whether this event-related potential component could be a similarly robust marker for Parkinson's psychosis, we used electroencephalography with a change-detection task to study the mismatch negativity in the visual modality in 20 participants with Parkinson's and visual hallucinations and 18 matched Parkinson's participants without hallucinations. We find that visual mismatch negativity is clearly present in participants with Parkinson's disease without hallucinations at both parieto-occipital and frontal sites, whereas participants with Parkinson's and visual hallucinations show reduced or no differences in the two waveforms, confirming the sensitivity of mismatch negativity to psychosis, even within the same diagnostic group. We also explored the relationship between hallucination severity and visual mismatch negativity amplitude, finding a negative correlation between visual hallucinations severity scores and visual mismatch negativity amplitude at a central frontal and a parieto-occipital electrodes, whereby the more severe or complex (illusions, formed visual hallucinations) the symptoms the smaller the amplitude. We have also tested the potential role of the serotonergic 5-HT2A cascade in visual hallucinations in Parkinson's with these symptoms, following the receptor trafficking hypothesis. We did so with a pilot study in healthy controls (N = 18) providing support for the role of the Gi/o-dependent pathway in the psychedelic effect and a case series in participants with Parkinson's and visual hallucinations (N = 5) using a double-blind crossover design. Positive results on psychosis scores and mismatch amplitude add further to the potential role of serotonergic modulation of visual hallucinations in Parkinson's disease.
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Affiliation(s)
- Miriam Vignando
- Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
| | - Dominic Ffytche
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
| | - Ndabezinhle Mazibuko
- Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
| | - Giulio Palma
- Department of Psychology, University of Southampton, Southampton, SO17 1PS, UK
| | - Marcella Montagnese
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 2PY, UK
| | - Sonali Dave
- Department of Optometry and Visual Sciences, City, University of London, London, EC1V 0HB, UK
| | - David J Nutt
- Imperial College London, Faculty of Medicine, Department of Brain Sciences, Burlington Danes, The Hammersmith Hospital, London W12 0NN, UK
| | | | - Yen F Tai
- Imperial College London, Faculty of Medicine, Department of Brain Sciences, Charing Cross Hospital, London W6 8RF, UK
| | - Lucia Batzu
- Parkinson Foundation Centre of Excellence, King's College Hospital NHS Foundation Trust, SW9 8R, London, UK
| | - Valentina Leta
- Parkinson Foundation Centre of Excellence, King's College Hospital NHS Foundation Trust, SW9 8R, London, UK
- Department of Clinical Neurosciences, Parkinson and Movement Disorders Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, 20133, Italy
| | - Caroline H Williams Gray
- John Van Geest Centre for Brain Repair, Department of Clinical Neurosciences, University of Cambridge/Cambridge University Hospitals NHS Foundation Trust, Cambridge, CB2 0PY, UK
| | - K Ray Chaudhuri
- Parkinson Foundation Centre of Excellence, King's College Hospital NHS Foundation Trust, SW9 8R, London, UK
| | - Mitul A Mehta
- Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
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Pagonabarraga J, Bejr-Kasem H, Martinez-Horta S, Kulisevsky J. Parkinson disease psychosis: from phenomenology to neurobiological mechanisms. Nat Rev Neurol 2024; 20:135-150. [PMID: 38225264 DOI: 10.1038/s41582-023-00918-8] [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: 12/13/2023] [Indexed: 01/17/2024]
Abstract
Parkinson disease (PD) psychosis (PDP) is a spectrum of illusions, hallucinations and delusions that are associated with PD throughout its disease course. Psychotic phenomena can manifest from the earliest stages of PD and might follow a continuum from minor hallucinations to structured hallucinations and delusions. Initially, PDP was considered to be a complication associated with dopaminergic drug use. However, subsequent research has provided evidence that PDP arises from the progression of brain alterations caused by PD itself, coupled with the use of dopaminergic drugs. The combined dysfunction of attentional control systems, sensory processing, limbic structures, the default mode network and thalamocortical connections provides a conceptual framework to explain how new incoming stimuli are incorrectly categorized, and how aberrant hierarchical predictive processing can produce false percepts that intrude into the stream of consciousness. The past decade has seen the publication of new data on the phenomenology and neurobiological basis of PDP from the initial stages of the disease, as well as the neurotransmitter systems involved in PDP initiation and progression. In this Review, we discuss the latest clinical, neuroimaging and neurochemical evidence that could aid early identification of psychotic phenomena in PD and inform the discovery of new therapeutic targets and strategies.
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Affiliation(s)
- Javier Pagonabarraga
- Movement Disorder Unit, Neurology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain.
- Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain.
- Sant Pau Biomedical Research Institute (IIB-Sant Pau), Barcelona, Spain.
- Centro de Investigación en Red - Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain.
| | - Helena Bejr-Kasem
- Movement Disorder Unit, Neurology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
- Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain
- Sant Pau Biomedical Research Institute (IIB-Sant Pau), Barcelona, Spain
- Centro de Investigación en Red - Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Saul Martinez-Horta
- Movement Disorder Unit, Neurology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
- Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain
- Sant Pau Biomedical Research Institute (IIB-Sant Pau), Barcelona, Spain
- Centro de Investigación en Red - Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Jaime Kulisevsky
- Movement Disorder Unit, Neurology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
- Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain
- Sant Pau Biomedical Research Institute (IIB-Sant Pau), Barcelona, Spain
- Centro de Investigación en Red - Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
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8
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Wei Y, Zhang C, Peng Y, Chen C, Han S, Wang W, Zhang Y, Lu H, Cheng J. MRI Assessment of Intrinsic Neural Timescale and Gray Matter Volume in Parkinson's Disease. J Magn Reson Imaging 2024; 59:987-995. [PMID: 37318377 DOI: 10.1002/jmri.28864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 05/28/2023] [Accepted: 05/30/2023] [Indexed: 06/16/2023] Open
Abstract
BACKGROUND Numerous studies have indicated altered temporal features of the brain function in Parkinson's disease (PD), and the autocorrelation magnitude of intrinsic neural signals, called intrinsic neural timescales, were often applied to estimate how long neural information stored in local brain areas. However, it is unclear whether PD patients at different disease stages exhibit abnormal timescales accompanied with abnormal gray matter volume (GMV). PURPOSE To assess the intrinsic timescale and GMV in PD. STUDY TYPE Prospective. POPULATION 74 idiopathic PD patients (44 early stage (PD-ES) and 30 late stage (PD-LS), as determined by the Hoehn and Yahr (HY) severity classification scale), and 73 healthy controls (HC). FIELD STRENGTH/SEQUENCE 3.0 T MRI scanner; magnetization prepared rapid acquisition gradient echo and echo planar imaging sequences. ASSESSMENT The timescales were estimated by using the autocorrelation magnitude of neural signals. Voxel-based morphometry was performed to calculate GMV in the whole brain. Severity of motor symptoms and cognitive impairments were assessed using the unified PD rating scale, the HY scale, the Montreal cognitive assessment, and the mini-mental state examination. STATISTICAL TEST Analysis of variance; two-sample t-test; Spearman rank correlation analysis; Mann-Whitney U test; Kruskal-Wallis' H test. A P value <0.05 was considered statistically significant. RESULTS The PD group had significantly abnormal intrinsic timescales in the sensorimotor, visual, and cognitive-related areas, which correlated with the symptom severity (ρ = -0.265, P = 0.022) and GMV (ρ = 0.254, P = 0.029). Compared to the HC group, the PD-ES group had significantly longer timescales in anterior cortical regions, whereas the PD-LS group had significantly shorter timescales in posterior cortical regions. CONCLUSION This study suggested that PD patients have abnormal timescales in multisystem and distinct patterns of timescales and GMV in cerebral cortex at different disease stages. This may provide new insights for the neural substrate of PD. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Yarui Wei
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Chunyan Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Yuanyuan Peng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Chen Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Weijian Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Hong Lu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
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9
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Wen J, Nasrallah IM, Abdulkadir A, Satterthwaite TD, Yang Z, Erus G, Robert-Fitzgerald T, Singh A, Sotiras A, Boquet-Pujadas A, Mamourian E, Doshi J, Cui Y, Srinivasan D, Skampardoni I, Chen J, Hwang G, Bergman M, Bao J, Veturi Y, Zhou Z, Yang S, Dazzan P, Kahn RS, Schnack HG, Zanetti MV, Meisenzahl E, Busatto GF, Crespo-Facorro B, Pantelis C, Wood SJ, Zhuo C, Shinohara RT, Gur RC, Gur RE, Koutsouleris N, Wolf DH, Saykin AJ, Ritchie MD, Shen L, Thompson PM, Colliot O, Wittfeld K, Grabe HJ, Tosun D, Bilgel M, An Y, Marcus DS, LaMontagne P, Heckbert SR, Austin TR, Launer LJ, Espeland M, Masters CL, Maruff P, Fripp J, Johnson SC, Morris JC, Albert MS, Bryan RN, Resnick SM, Fan Y, Habes M, Wolk D, Shou H, Davatzikos C. Genomic loci influence patterns of structural covariance in the human brain. Proc Natl Acad Sci U S A 2023; 120:e2300842120. [PMID: 38127979 PMCID: PMC10756284 DOI: 10.1073/pnas.2300842120] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 10/31/2023] [Indexed: 12/23/2023] Open
Abstract
Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science, Department of Neurology, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ilya M. Nasrallah
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Radiology, University of Pennsylvania, Philadelphia, PA19104
| | - Ahmed Abdulkadir
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Theodore D. Satterthwaite
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Zhijian Yang
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Guray Erus
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Timothy Robert-Fitzgerald
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ashish Singh
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine, St. Louis, MO63110
| | - Aleix Boquet-Pujadas
- Biomedical Imaging Group, Department of Biomedical Engineering, École Polytechnique Fédérale de Lausanne, Lausanne1015, Switzerland
| | - Elizabeth Mamourian
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Jimit Doshi
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Yuhan Cui
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Dhivya Srinivasan
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ioanna Skampardoni
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Jiong Chen
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Gyujoon Hwang
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Mark Bergman
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA19104
| | - Yogasudha Veturi
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Zhen Zhou
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Shu Yang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA19104
| | - Paola Dazzan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, LondonWC2R 2LS, United Kingdom
| | - Rene S. Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Hugo G. Schnack
- Department of Psychiatry, University Medical Center Utrecht, Utrecht 3584 CX Ut, Netherlands
| | - Marcus V. Zanetti
- Institute of Psychiatry, Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo05508-070, Brazil
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Heinrich Heine University, Düsseldorf40204, Germany
| | - Geraldo F. Busatto
- Institute of Psychiatry, Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo05508-070, Brazil
| | - Benedicto Crespo-Facorro
- Hospital Universitario Virgen del Rocio, School of Medicine, University of Sevilla,Sevilla41004, Spain
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Stephen J. Wood
- Orygen and the Centre for Youth Mental Health, Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Chuanjun Zhuo
- Key Laboratory of Real Tine Tracing of Brain Circuits in Psychiatry and Neurology, Department of Psychiatry, Tianjin Medical University, Tianjin300070, China
| | - Russell T. Shinohara
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ruben C. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Raquel E. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich 80539, Germany
| | - Daniel H. Wolf
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Andrew J. Saykin
- Indiana Alzheimer’s Disease Research Center, Department of Radiology, Indiana University School of Medicine, Indianapolis, IN46202-3082
| | - Marylyn D. Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA19104
| | - Paul M. Thompson
- Imaging Genetics Center, Department of Neurology, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
| | - Olivier Colliot
- Institut du Cerveau, Sorbonne Université, Paris75013, France
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, German Center for Neurodegenerative Diseases, University Medicine Greifswald, Greifswald17475, Germany
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, German Center for Neurodegenerative Diseases, University Medicine Greifswald, Greifswald17475, Germany
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94143
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore21224, MD
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore21224, MD
| | - Daniel S. Marcus
- Department of Radiology, Washington University School of Medicine, St. Louis, MO63110
| | - Pamela LaMontagne
- Department of Radiology, Washington University School of Medicine, St. Louis, MO63110
| | - Susan R. Heckbert
- Department of Epidemiology, University of Washington, Seattle, WA98195
| | - Thomas R. Austin
- Department of Epidemiology, University of Washington, Seattle, WA98195
| | - Lenore J. Launer
- Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Washington, MD20817
| | - Mark Espeland
- Sticht Center for Healthy Aging and Alzheimer’s Prevention, Divisions of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC27101
| | - Colin L. Masters
- Florey Institute of Neuroscience and Mental Health, Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, VIC3010, Australia
| | - Paul Maruff
- Florey Institute of Neuroscience and Mental Health, Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, VIC3010, Australia
| | - Jurgen Fripp
- Health and Biosecurity, Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD4029, Australia
| | - Sterling C. Johnson
- Wisconsin Alzheimer's Institute, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI53792
| | - John C. Morris
- Knight Alzheimer Disease Research Center, Department of Neurology, Washington University in St. Louis, St. Louis, MO63110
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD21205
| | - R. Nick Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA19104
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore21224, MD
| | - Yong Fan
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, TX78229
| | - David Wolk
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Neurology, University of Pennsylvania, Philadelphia, PA19104
| | - Haochang Shou
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Christos Davatzikos
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
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10
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Nieto-Escamez F, Obrero-Gaitán E, Cortés-Pérez I. Visual Dysfunction in Parkinson's Disease. Brain Sci 2023; 13:1173. [PMID: 37626529 PMCID: PMC10452537 DOI: 10.3390/brainsci13081173] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 07/11/2023] [Accepted: 08/03/2023] [Indexed: 08/27/2023] Open
Abstract
Non-motor symptoms in Parkinson's disease (PD) include ocular, visuoperceptive, and visuospatial impairments, which can occur as a result of the underlying neurodegenerative process. Ocular impairments can affect various aspects of vision and eye movement. Thus, patients can show dry eyes, blepharospasm, reduced blink rate, saccadic eye movement abnormalities, smooth pursuit deficits, and impaired voluntary and reflexive eye movements. Furthermore, visuoperceptive impairments affect the ability to perceive and recognize visual stimuli accurately, including impaired contrast sensitivity and reduced visual acuity, color discrimination, and object recognition. Visuospatial impairments are also remarkable, including difficulties perceiving and interpreting spatial relationships between objects and difficulties judging distances or navigating through the environment. Moreover, PD patients can present visuospatial attention problems, with difficulties attending to visual stimuli in a spatially organized manner. Moreover, PD patients also show perceptual disturbances affecting their ability to interpret and determine meaning from visual stimuli. And, for instance, visual hallucinations are common in PD patients. Nevertheless, the neurobiological bases of visual-related disorders in PD are complex and not fully understood. This review intends to provide a comprehensive description of visual disturbances in PD, from sensory to perceptual alterations, addressing their neuroanatomical, functional, and neurochemical correlates. Structural changes, particularly in posterior cortical regions, are described, as well as functional alterations, both in cortical and subcortical regions, which are shown in relation to specific neuropsychological results. Similarly, although the involvement of different neurotransmitter systems is controversial, data about neurochemical alterations related to visual impairments are presented, especially dopaminergic, cholinergic, and serotoninergic systems.
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Affiliation(s)
- Francisco Nieto-Escamez
- Department of Psychology, University of Almeria, 04120 Almeria, Spain
- Center for Neuropsychological Assessment and Rehabilitation (CERNEP), 04120 Almeria, Spain
| | - Esteban Obrero-Gaitán
- Department of Health Sciences, University of Jaen, Paraje Las Lagunillas s/n, 23071 Jaen, Spain;
| | - Irene Cortés-Pérez
- Department of Health Sciences, University of Jaen, Paraje Las Lagunillas s/n, 23071 Jaen, Spain;
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11
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Bhome R, Thomas GEC, Zarkali A, Weil RS. Structural and Functional Imaging Correlates of Visual Hallucinations in Parkinson's Disease. Curr Neurol Neurosci Rep 2023; 23:287-299. [PMID: 37126201 PMCID: PMC10257588 DOI: 10.1007/s11910-023-01267-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/12/2023] [Indexed: 05/02/2023]
Abstract
PURPOSE OF REVIEW To review recent structural and functional MRI studies of visual hallucinations in Parkinson's disease. RECENT FINDINGS Previously, neuroimaging had shown inconsistent findings in patients with Parkinson's hallucinations, especially in studies examining grey matter volume. However, recent advances in structural and functional MRI techniques allow better estimates of structural connections, as well as the direction of connectivity in functional MRI. These provide more sensitive measures of changes in structural connectivity and allow models of the changes in directional functional connectivity to be tested. We identified 27 relevant studies and found that grey matter imaging continues to show heterogeneous findings in Parkinson's patients with visual hallucinations. Newer approaches in diffusion imaging and functional MRI are consistent with emerging models of Parkinson's hallucinations, suggesting shifts in attentional networks. In particular, reduced bottom-up, incoming sensory information, and over-weighting of top-down signals appear to be important drivers of visual hallucinations in Parkinson's disease.
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Affiliation(s)
- Rohan Bhome
- Dementia Research Centre, University College London, 8-11 Queen Square, London, WC1N 3AR, UK.
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
| | | | - Angeliki Zarkali
- Dementia Research Centre, University College London, 8-11 Queen Square, London, WC1N 3AR, UK
| | - Rimona Sharon Weil
- Dementia Research Centre, University College London, 8-11 Queen Square, London, WC1N 3AR, UK
- Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, WC1N 3AR, UK
- Movement Disorders Centre, National Hospital for Neurology and Neurosurgery, Queen Square, London, WC1N 3AR, UK
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12
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Gibson LL, Grinberg LT, Ffytche D, Leite REP, Rodriguez RD, Ferretti-Rebustini REL, Pasqualucci CA, Nitrini R, Jacob-Filho W, Aarsland D, Suemoto CK. Neuropathological correlates of neuropsychiatric symptoms in dementia. Alzheimers Dement 2023; 19:1372-1382. [PMID: 36150075 PMCID: PMC10033459 DOI: 10.1002/alz.12765] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 06/23/2022] [Accepted: 07/08/2022] [Indexed: 02/05/2023]
Abstract
INTRODUCTION Neuropsychiatric symptoms (NPS) are common in Lewy body disease (LBD), but their etiology is poorly understood. METHODS In a population-based post mortem study neuropathological data was collected for Lewy body (LB) neuropathology, neurofibrillary tangles (NFT), amyloid beta burden, TDP-43, lacunar infarcts, cerebral amyloid angiopathy (CAA), and hyaline atherosclerosis. Post mortem interviews collected systematic information regarding NPS and cognitive status. A total of 1038 cases were included: no pathology (NP; n = 761), Alzheimer's disease (AD; n = 189), LBD (n = 60), and AD+LBD (n = 28). RESULTS Hallucinations were associated with higher LB Braak stages, while higher NFT Braak staging was associated with depression, agitation, and greater number of symptoms in the Neuropsychiatric Inventory. Cases with dual AD+LBD pathology had the highest risk of hallucinations, agitation, apathy, and total symptoms but a multiplicative interaction between these pathologies was not significant. DISCUSSION LB and AD pathology contribute differentially to NPS likely with an additive process contributing to the increased burden of NPS.
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Affiliation(s)
- Lucy L Gibson
- Old Age Psychiatry Department, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Lea T Grinberg
- Memory and Aging Center, Department of Neurology and Pathology, University of California San Francisco, San Francisco, California, USA
- University of São Paulo Medical School, São Paulo, Brazil
| | - Dominic Ffytche
- Old Age Psychiatry Department, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | | | | | | | | | | | | | - Dag Aarsland
- Old Age Psychiatry Department, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Centre for Age-Related Disease, Stavanger University Hospital, Stavanger, Norway
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13
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Deng JH, Zhang HW, Liu XL, Deng HZ, Lin F. Morphological changes in Parkinson's disease based on magnetic resonance imaging: A mini-review of subcortical structures segmentation and shape analysis. World J Psychiatry 2022; 12:1356-1366. [PMID: 36579355 PMCID: PMC9791612 DOI: 10.5498/wjp.v12.i12.1356] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 11/02/2022] [Accepted: 11/22/2022] [Indexed: 12/16/2022] Open
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder caused by the loss of dopaminergic neurons in the substantia nigra, resulting in clinical symptoms, including bradykinesia, resting tremor, rigidity, and postural instability. The pathophysiological changes in PD are inextricably linked to the subcortical structures. Shape analysis is a method for quantifying the volume or surface morphology of structures using magnetic resonance imaging. In this review, we discuss the recent advances in morphological analysis techniques for studying the subcortical structures in PD in vivo. This approach includes available pipelines for volume and shape analysis, focusing on the morphological features of volume and surface area.
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Affiliation(s)
- Jin-Huan Deng
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, Shenzhen 518035, Guangdong Province, China
| | - Han-Wen Zhang
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, Shenzhen 518035, Guangdong Province, China
| | - Xiao-Lei Liu
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, Shenzhen 518035, Guangdong Province, China
| | - Hua-Zhen Deng
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, Shenzhen 518035, Guangdong Province, China
| | - Fan Lin
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, Shenzhen 518035, Guangdong Province, China
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14
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Optic radiation atrophy in Lewy body disease with visual hallucination on phase difference enhanced magnetic resonance images. Sci Rep 2022; 12:18556. [PMID: 36329069 PMCID: PMC9633778 DOI: 10.1038/s41598-022-21847-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 10/04/2022] [Indexed: 11/06/2022] Open
Abstract
Visual hallucinations (VH) occur commonly in Lewy body disease (LBD), including Parkinson's disease (PD), PD with dementia, and dementia with Lewy bodies. We aimed to use phase difference enhanced imaging (PADRE) to assess structural abnormalities of optic radiation (OR) in patients with Lewy body disease (LBD) concomitant with VH. Firstly, two radiologists reviewed the OR appearances in healthy subjects (HS) on PADRE. Next, based on the OR abnormalities, two reviewers assessed the PADRE images from 18 HS and 38 and 110 patients with LBD, with and without VH, respectively, in a blinded manner. Finally, all patients with LBD without VH were eventually followed up for at least 5 years after magnetic resonance imaging to determine the appearance of VH. The radiologists identified three layers, namely external sagittal stratum, internal sagittal stratum, and tapetum, in OR on the PADRE in HS. Moreover, they were able to consensually define the OR as abnormal when the layers were obscured and the disappearance of the cranial side. The sensitivity/specificity of abnormal OR for each case was 68%/81% (LBD with VH vs. LBD without VH). Furthermore, VH appeared in 12 of the 21 (57%) patients with LBD and abnormal OR during the follow-up period. However, no patients without abnormal OR reported VH. Patients with LBD and VH demonstrated the abnormal OR. This, in turn, might be a useful marker to distinguish the patients with VH from those without VH and HS. Moreover, abnormal OR on PADRE may precede the appearance of VH in LBD.
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15
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Weintraub D, Aarsland D, Biundo R, Dobkin R, Goldman J, Lewis S. Management of psychiatric and cognitive complications in Parkinson's disease. BMJ 2022; 379:e068718. [PMID: 36280256 DOI: 10.1136/bmj-2021-068718] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Neuropsychiatric symptoms (NPSs) such as affective disorders, psychosis, behavioral changes, and cognitive impairment are common in Parkinson's disease (PD). However, NPSs remain under-recognized and under-treated, often leading to adverse outcomes. Their epidemiology, presentation, risk factors, neural substrate, and management strategies are incompletely understood. While psychological and psychosocial factors may contribute, hallmark PD neuropathophysiological changes, plus the associations between exposure to dopaminergic medications and occurrence of some symptoms, suggest a neurobiological basis for many NPSs. A range of psychotropic medications, psychotherapeutic techniques, stimulation therapies, and other non-pharmacological treatments have been studied, are used clinically, and are beneficial for managing NPSs in PD. Appropriate management of NPSs is critical for comprehensive PD care, from recognizing their presentations and timing throughout the disease course, to the incorporation of different therapeutic strategies (ie, pharmacological and non-pharmacological) that utilize a multidisciplinary approach.
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Affiliation(s)
- Daniel Weintraub
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
- Parkinson's Disease Research, Education and Clinical Center (PADRECC), Philadelphia Veterans Affairs Medical Center, Philadelphia, PA
| | - Dag Aarsland
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, England
- Centre for Age-Related Diseases, Stavanger University Hospital, Stavanger, Norway
| | - Roberta Biundo
- Department of General Psychology, University of Padua, Padua, Italy
- Study Center for Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
| | - Roseanne Dobkin
- Department of Psychiatry, Rutgers-The State University of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ
| | - Jennifer Goldman
- Shirley Ryan AbilityLab, Parkinson's Disease and Movement Disorders, Chicago, IL
- Departments of Physical Medicine and Rehabilitation and Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Simon Lewis
- ForeFront Parkinson's Disease Research Clinic, Brain and Mind Centre, School of Medical Sciences, University of Sydney, Sydney, New South Wales, Australia
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16
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Imaging the Limbic System in Parkinson's Disease-A Review of Limbic Pathology and Clinical Symptoms. Brain Sci 2022; 12:brainsci12091248. [PMID: 36138984 PMCID: PMC9496800 DOI: 10.3390/brainsci12091248] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/05/2022] [Accepted: 09/13/2022] [Indexed: 01/09/2023] Open
Abstract
The limbic system describes a complex of brain structures central for memory, learning, as well as goal directed and emotional behavior. In addition to pathological studies, recent findings using in vivo structural and functional imaging of the brain pinpoint the vulnerability of limbic structures to neurodegeneration in Parkinson's disease (PD) throughout the disease course. Accordingly, dysfunction of the limbic system is critically related to the symptom complex which characterizes PD, including neuropsychiatric, vegetative, and motor symptoms, and their heterogeneity in patients with PD. The aim of this systematic review was to put the spotlight on neuroimaging of the limbic system in PD and to give an overview of the most important structures affected by the disease, their function, disease related alterations, and corresponding clinical manifestations. PubMed was searched in order to identify the most recent studies that investigate the limbic system in PD with the help of neuroimaging methods. First, PD related neuropathological changes and corresponding clinical symptoms of each limbic system region are reviewed, and, finally, a network integration of the limbic system within the complex of PD pathology is discussed.
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17
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The structural changes of gray matter in Parkinson disease patients with mild cognitive impairments. PLoS One 2022; 17:e0269787. [PMID: 35857782 PMCID: PMC9299333 DOI: 10.1371/journal.pone.0269787] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 05/30/2022] [Indexed: 11/19/2022] Open
Abstract
Objectives
Parkinson disease (PD) is associated with cognitive impairments. However, the underlying neural mechanism of cognitive impairments in PD is still not clear. This study aimed to investigate the anatomic alternations of gray matter in PD patients with mild cognitive impairment (MCI) and their associations with neurocognitive measurements.
Methods
T1-weighted magnetic resonance imaging (MRI) data were acquired from 23 PD patients with MCI, 23 PD patients without MCI, and 23 matched healthy controls. The MRI data were analyzed using voxel-based morphometry (VBM) and surfaced-based morphometry (SBM) methods to assess the structural changes in gray matter volume and cortical thickness respectively. Receiver operating characteristic (ROC) analysis was used to examine the diagnostic accuracies of the indexes of interest. The correlations between the structural metrics and neurocognitive assessments (e.g., Montreal cognitive assessment, MOCA; Mini-mental state examination, MMSE) were further examined.
Results
PD patients with MCI showed reduced gray matter volume (GMV) in the frontal cortex (e.g., right inferior frontal gyrus and middle frontal gyrus) and extended to insula as well as cerebellum compared with the healthy controls and PD patients without MIC. Thinner of cortical thickens in the temporal lobe (e.g., left middle temporal gyrus and right superior temporal gyrus) extending to parietal cortex (e.g., precuneus) were found in the PD patients with MCI relative to the healthy controls and PD patients without MCI.ROC analysis indicated that the area under the ROC curve (AUC) values in the frontal, temporal, and subcortical structures (e.g., insula and cerebellum) could differentiate the PD patients with MCI and without MCI and healthy controls. Furthermore, GMV of the right middle frontal gyrus and cortical thickness of the right superior temporal gyrus were correlated with neurocognitive dysfunctions (e.g., MOCA and MMSE) in PD patients with MCI.
Conclusion
This study provided further evidence that PD with MCI was associated with structural alternations of brain. Morphometric analysis focusing on the cortical and subcortical regions could be biomarkers of cognitive impairments in PD patients.
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Lawn T, Dipasquale O, Vamvakas A, Tsougos I, Mehta MA, Howard MA. Differential contributions of serotonergic and dopaminergic functional connectivity to the phenomenology of LSD. Psychopharmacology (Berl) 2022; 239:1797-1808. [PMID: 35322297 PMCID: PMC9166846 DOI: 10.1007/s00213-022-06117-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 03/11/2022] [Indexed: 02/25/2023]
Abstract
RATIONALE LSD is the prototypical psychedelic. Despite a clear central role of the 5HT2a receptor in its mechanism of action, the contributions of additional receptors for which it shows affinity and agonist activity remain unclear. OBJECTIVES We employed receptor-enriched analysis of functional connectivity by targets (REACT) to explore differences in functional connectivity (FC) associated with the distributions of the primary targets of LSD-the 5HT1a, 5HT1b, 5HT2a, D1 and D2 receptors. METHODS We performed secondary analyses of an openly available dataset (N = 15) to estimate the LSD-induced alterations in receptor-enriched FC maps associated with these systems. Principal component analysis (PCA) was employed as a dimension reduction strategy for subjective experiences associated with LSD captured by the Altered States of Consciousness (ASC) questionnaire. Correlations between these principal components as well as VAS ratings of subjective effects with receptor-enriched FC were explored. RESULTS Compared to placebo, LSD produced differences in FC when the analysis was enriched with each of the primary serotonergic and dopaminergic receptors. Altered receptor-enriched FC showed relationships with the subjective effects of LSD on conscious experience, with serotonergic and dopaminergic systems being predominantly associated with perceptual effects and perceived selfhood as well as cognition respectively. These relationships were dissociable, with different receptors showing the same relationships within, but not between, the serotonergic and dopaminergic systems. CONCLUSIONS These exploratory findings provide new insights into the pharmacology of LSD and highlight the need for additional investigation of non-5HT2a-mediated mechanisms.
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Affiliation(s)
- Timothy Lawn
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Ottavia Dipasquale
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Alexandros Vamvakas
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Medical Physics Department, School of Medicine, University of Thessaly, Larissa, Greece
| | - Ioannis Tsougos
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Medical Physics Department, School of Medicine, University of Thessaly, Larissa, Greece
| | - Mitul A. Mehta
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Matthew A. Howard
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
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