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Liu F, Yang J, Feng M, Cui Z, He X, Zhou L, Feng J, Shen D. Does perfect filtering really guarantee perfect phase correction for diffusion MRI data? Comput Med Imaging Graph 2023; 103:102160. [PMID: 36528017 DOI: 10.1016/j.compmedimag.2022.102160] [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: 08/25/2022] [Revised: 12/05/2022] [Accepted: 12/05/2022] [Indexed: 12/14/2022]
Abstract
Owing to its merit of avoiding noise-floor, phase correction is recently used to reconstruct real-valued diffusion MRI data by employing an image filter to estimate the noise-free background phase. However, several studies report an unexpected signal-loss issue for their reconstruction results, with its causing reason still remaining unclear. Although phase correction has achieved promising results in mitigating the signal-loss issue via improving the employed image filter, we have observed counterintuitive results that an advanced filter generates severe artifacts in our previous work. Considering the potential issues with phase correction procedures, in this paper, we argue that even a perfect image filter is insufficient to produce perfect phase correction. To point out the reason why phase correction introduces signal-loss and address this issue, we first propose a complex polar coordinate system (CPCS) to analyze its procedures in detail; second, based on CPCS, we find that phase correction has not sufficiently utilized the background phase, and thus propose a quantitative criterion to fully exploit the background phase; eventually, we propose a phase calibration procedure to remedy current phase correction. Extensive experimental results, including those on synthetic and real diffusion MRI data, demonstrate that our proposed method significantly reduces signal-loss and also eliminates artifacts in FA maps, particularly with improved accuracy on FA.
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Affiliation(s)
- Feihong Liu
- School of Information Science and Technology, Northwest University, Xi'an, China; School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Junwei Yang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Mingyue Feng
- Department of Informatics, Technische Universität München, Garching, Germany
| | - Zhiming Cui
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Xiaowei He
- School of Information Science and Technology, Northwest University, Xi'an, China; State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, School of Information Science and Technology, Northwest University, Xi'an, China
| | - Luping Zhou
- School of Electrical and Information Engineering, University of Sydney, Sydney, Australia.
| | - Jun Feng
- School of Information Science and Technology, Northwest University, Xi'an, China; State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, School of Information Science and Technology, Northwest University, Xi'an, China.
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; Shanghai Clinical Research and Trial Center, Shanghai, China.
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2
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Abdollah Zadegan S, Coco HM, Reddy KS, Anderson KM, Teixeira AL, Stimming EF. Frequency and Pathophysiology of Apathy in Huntington Disease: A Systematic Review and Meta-Analysis. J Neuropsychiatry Clin Neurosci 2022; 35:121-132. [PMID: 36353818 DOI: 10.1176/appi.neuropsych.20220033] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Apathy is a common behavioral symptom of Huntington disease (HD). This systematic review describes current evidence on the pathophysiology, assessment, and frequency of apathy in HD. METHODS This systematic review was conducted in accordance with PRISMA guidelines. Using a comprehensive search strategy, the investigators searched the MEDLINE, Embase, and PsycINFO databases. All studies that evaluated apathy in HD patients with a valid scale and reported apathy frequency or scores were included. Apathy scores were analyzed by mean or standardized mean differences in accordance with Cochrane guidelines. RESULTS A total of 1,085 records were screened and 80 studies were ultimately included. The Problem Behaviors Assessment-Short was the most frequently used apathy assessment tool. Apathy frequency generally ranged from 10%-33% in premanifest HD to 24%-76% in manifest HD. A meta-analysis of 5,311 records of patients with premanifest HD showed significantly higher apathy scores, with a standardized mean difference of 0.41 (CI=0.29-0.52; p<0.001). A comparison of 1,247 patients showed significantly higher apathy scores in manifest than premanifest HD, with a mean difference of 1.87 (CI=1.48-2.26; p<0.001). There was evidence of involvement of various cortical and subcortical brain regions in HD patients with apathy. CONCLUSIONS Apathy was more frequent among individuals with premanifest HD compared with those in a control group and among individuals with manifest HD compared with those with premanifest HD. Considering the complexity and unique pattern of development in neurodegenerative disease, further studies are required to explore the pathophysiology of apathy in HD.
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Affiliation(s)
- Shayan Abdollah Zadegan
- Department of Neurology (Zadegan, Furr Stimming), Huntington's Disease Society of America Center of Excellence (Zadegan, Anderson, Teixeira, Furr Stimming), McGovern Medical School (Coco, Reddy), Department of Psychiatry and Behavioral Sciences (Anderson, Teixeira), all at the University of Texas Health Science Center at Houston
| | - Hannah M Coco
- Department of Neurology (Zadegan, Furr Stimming), Huntington's Disease Society of America Center of Excellence (Zadegan, Anderson, Teixeira, Furr Stimming), McGovern Medical School (Coco, Reddy), Department of Psychiatry and Behavioral Sciences (Anderson, Teixeira), all at the University of Texas Health Science Center at Houston
| | - Kirthan S Reddy
- Department of Neurology (Zadegan, Furr Stimming), Huntington's Disease Society of America Center of Excellence (Zadegan, Anderson, Teixeira, Furr Stimming), McGovern Medical School (Coco, Reddy), Department of Psychiatry and Behavioral Sciences (Anderson, Teixeira), all at the University of Texas Health Science Center at Houston
| | - Kendra M Anderson
- Department of Neurology (Zadegan, Furr Stimming), Huntington's Disease Society of America Center of Excellence (Zadegan, Anderson, Teixeira, Furr Stimming), McGovern Medical School (Coco, Reddy), Department of Psychiatry and Behavioral Sciences (Anderson, Teixeira), all at the University of Texas Health Science Center at Houston
| | - Antonio L Teixeira
- Department of Neurology (Zadegan, Furr Stimming), Huntington's Disease Society of America Center of Excellence (Zadegan, Anderson, Teixeira, Furr Stimming), McGovern Medical School (Coco, Reddy), Department of Psychiatry and Behavioral Sciences (Anderson, Teixeira), all at the University of Texas Health Science Center at Houston
| | - Erin Furr Stimming
- Department of Neurology (Zadegan, Furr Stimming), Huntington's Disease Society of America Center of Excellence (Zadegan, Anderson, Teixeira, Furr Stimming), McGovern Medical School (Coco, Reddy), Department of Psychiatry and Behavioral Sciences (Anderson, Teixeira), all at the University of Texas Health Science Center at Houston
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3
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Xie S, McDonnell E, Wang Y. Conditional Gaussian graphical model for estimating personalized disease symptom networks. Stat Med 2022; 41:543-553. [PMID: 34866214 PMCID: PMC8792223 DOI: 10.1002/sim.9274] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 10/13/2021] [Accepted: 11/15/2021] [Indexed: 11/10/2022]
Abstract
The co-occurrence of symptoms may result from the direct interactions between these symptoms and the symptoms can be treated as a system. In addition, subject-specific risk factors (eg, genetic variants, age) can also exert external influence on the system. In this work, we develop a covariate-dependent conditional Gaussian graphical model to obtain personalized symptom networks. The strengths of network connections are modeled as a function of covariates to capture the heterogeneity among individuals and subgroups of individuals. We assess the performance of our proposed method by simulation studies and an application to a large natural history study of Huntington's disease to investigate the networks of symptoms in multiple clinical domains (motor, cognitive, psychiatric) and identify important brain imaging biomarkers that are associated with the connections. We show that the symptoms in the same clinical domain interact more often with each other than cross domains and the psychiatric subnetwork is the densest network. We validate the findings using the subjects' symptom measurements at follow-up visits.
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Affiliation(s)
- Shanghong Xie
- School of Statistics and Center of Statistical Research, Southwestern University of Finance and Economics, Chengdu, China
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, U.S.A
| | - Erin McDonnell
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, U.S.A
| | - Yuanjia Wang
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, U.S.A
- Department of Psychiatry, Columbia University Medical Center, New York, NY, U.S.A
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4
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Aracil-Bolaños I, Martínez-Horta S, González-de-Echávarri JM, Sampedro F, Pérez-Pérez J, Horta A, Campolongo A, Izquierdo C, Gómez-Ansón B, Pagonabarraga J, Kulisevsky J. Structure and Dynamics of Large-Scale Cognitive Networks in Huntington's Disease. Mov Disord 2021; 37:343-353. [PMID: 34752656 DOI: 10.1002/mds.28839] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/10/2021] [Accepted: 10/04/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Huntington's disease is a neurodegenerative disorder characterized by clinical alterations in the motor, behavioral, and cognitive domains. However, the structure and disruptions to large-scale brain cognitive networks have not yet been established. OBJECTIVE We aimed to profile changes in large-scale cognitive networks in premanifest and symptomatic patients with Huntington's disease. METHODS We prospectively recruited premanifest and symptomatic Huntington's disease mutation carriers as well as healthy controls. Clinical and sociodemographic data were obtained from all participants, and resting-state functional connectivity data, using both time-averaged and dynamic functional connectivity, was acquired from whole-brain and cognitively oriented brain parcellations. RESULTS A total of 64 gene mutation carriers and 23 healthy controls were included; 21 patients with Huntington's disease were classified as premanifest and 43 as symptomatic Huntington's disease. Compared with healthy controls, patients with Huntington's disease showed decreased network connectivity within the posterior hubs of the default-mode network and the medial prefrontal cortex, changes that correlated with cognitive (t = 2.25, P = 0.01) and disease burden scores (t = -2.42, P = 0.009). The salience network showed decreased functional connectivity between insular and supramarginal cortices and also correlated with cognitive (t = 2.11, P = 0.02) and disease burden scores (t = -2.35, P = 0.01). Dynamic analyses showed that network variability was decreased for default-central executive networks, a feature already present in premanifest mutation carriers (dynamic factor 8, P = 0.02). CONCLUSIONS Huntington's disease shows an early and widespread disruption of large-scale cognitive networks. Importantly, these changes are related to cognitive and disease burden scores, and novel dynamic functional analyses uncovered subtler network changes even in the premanifest stages.
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Affiliation(s)
- Ignacio Aracil-Bolaños
- Movement Disorders Unit, Neurology Department, Sant Pau Hospital, Barcelona, Spain.,Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain.,Institut d'Investigacions Biomèdiques-Sant Pau, Barcelona, Spain.,Centro de Investigación en Red-Enfermedades Neurodegenerativas, Madrid, Spain
| | - Saül Martínez-Horta
- Movement Disorders Unit, Neurology Department, Sant Pau Hospital, Barcelona, Spain.,Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain.,Institut d'Investigacions Biomèdiques-Sant Pau, Barcelona, Spain.,Centro de Investigación en Red-Enfermedades Neurodegenerativas, Madrid, Spain
| | - Jose M González-de-Echávarri
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation and Hospital del Mar Medical Research Institute, Barcelona, Spain
| | - Frederic Sampedro
- Movement Disorders Unit, Neurology Department, Sant Pau Hospital, Barcelona, Spain.,Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain.,Institut d'Investigacions Biomèdiques-Sant Pau, Barcelona, Spain.,Centro de Investigación en Red-Enfermedades Neurodegenerativas, Madrid, Spain
| | - Jesús Pérez-Pérez
- Movement Disorders Unit, Neurology Department, Sant Pau Hospital, Barcelona, Spain.,Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain.,Institut d'Investigacions Biomèdiques-Sant Pau, Barcelona, Spain.,Centro de Investigación en Red-Enfermedades Neurodegenerativas, Madrid, Spain
| | - Andrea Horta
- Movement Disorders Unit, Neurology Department, Sant Pau Hospital, Barcelona, Spain.,Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain.,Institut d'Investigacions Biomèdiques-Sant Pau, Barcelona, Spain.,Centro de Investigación en Red-Enfermedades Neurodegenerativas, Madrid, Spain
| | - Antonia Campolongo
- Movement Disorders Unit, Neurology Department, Sant Pau Hospital, Barcelona, Spain.,Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain.,Institut d'Investigacions Biomèdiques-Sant Pau, Barcelona, Spain.,Centro de Investigación en Red-Enfermedades Neurodegenerativas, Madrid, Spain
| | - Cristina Izquierdo
- Movement Disorders Unit, Neurology Department, Sant Pau Hospital, Barcelona, Spain.,Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain.,Institut d'Investigacions Biomèdiques-Sant Pau, Barcelona, Spain.,Centro de Investigación en Red-Enfermedades Neurodegenerativas, Madrid, Spain
| | - Beatriz Gómez-Ansón
- Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain.,Institut d'Investigacions Biomèdiques-Sant Pau, Barcelona, Spain.,Centro de Investigación en Red-Enfermedades Neurodegenerativas, Madrid, Spain.,Neuroradiology Unit, Sant Pau Hospital, Barcelona, Spain
| | - Javier Pagonabarraga
- Movement Disorders Unit, Neurology Department, Sant Pau Hospital, Barcelona, Spain.,Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain.,Institut d'Investigacions Biomèdiques-Sant Pau, Barcelona, Spain.,Centro de Investigación en Red-Enfermedades Neurodegenerativas, Madrid, Spain
| | - Jaime Kulisevsky
- Movement Disorders Unit, Neurology Department, Sant Pau Hospital, Barcelona, Spain.,Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain.,Institut d'Investigacions Biomèdiques-Sant Pau, Barcelona, Spain.,Centro de Investigación en Red-Enfermedades Neurodegenerativas, Madrid, Spain
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5
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Nair A, Razi A, Gregory S, Rutledge RR, Rees G, Tabrizi SJ. Imbalanced basal ganglia connectivity is associated with motor deficits and apathy in Huntington's disease. Brain 2021; 145:991-1000. [PMID: 34633421 PMCID: PMC9050569 DOI: 10.1093/brain/awab367] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 08/16/2021] [Accepted: 08/24/2021] [Indexed: 11/12/2022] Open
Abstract
The gating of movement depends on activity within the cortico-striato-thalamic loops. Within these loops, emerging from the cells of the striatum, run two opponent pathways—the direct and indirect basal ganglia pathways. Both are complex and polysynaptic, but the overall effect of activity within these pathways is thought to encourage and inhibit movement, respectively. In Huntington’s disease, the preferential early loss of striatal neurons forming the indirect pathway is thought to lead to disinhibition, giving rise to the characteristic motor features of the condition. But early Huntington’s disease is also associated with apathy, a loss of motivation and failure to engage in goal-directed movement. We hypothesized that in Huntington’s disease, motor signs and apathy may be selectively correlated with indirect and direct pathway dysfunction, respectively. We used spectral dynamic casual modelling of resting-state functional MRI data to model effective connectivity in a model of these cortico-striatal pathways. We tested both of these hypotheses in vivo for the first time in a large cohort of patients with prodromal Huntington’s disease. Using an advanced approach at the group level we combined parametric empirical Bayes and Bayesian model reduction procedures to generate a large number of competing models and compare them using Bayesian model comparison. With this automated Bayesian approach, associations between clinical measures and connectivity parameters emerge de novo from the data. We found very strong evidence (posterior probability > 0.99) to support both of our hypotheses. First, more severe motor signs in Huntington’s disease were associated with altered connectivity in the indirect pathway components of our model and, by comparison, loss of goal-direct behaviour or apathy, was associated with changes in the direct pathway component. The empirical evidence we provide here demonstrates that imbalanced basal ganglia connectivity may play an important role in the pathogenesis of some of commonest and disabling features of Huntington’s disease and may have important implications for therapeutics.
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Affiliation(s)
- Akshay Nair
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, Russell Square House, London, WC1B 5EH, UK.,UCL Institute of Cognitive Neuroscience, University College London, Alexandra House, 17-19 Queen Square, Bloomsbury, London, WC1N 3AZ, UK
| | - Adeel Razi
- Turner Institute for Brain and Mental Health, Monash Biomedical Imaging, Monash University, 770 Blackburn Road, Clayton 3800, Australia.,Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, 12 Queen Square, London, WC1N 3AR, UK
| | - Sarah Gregory
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, Russell Square House, London, WC1B 5EH, UK
| | - Robb R Rutledge
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, 12 Queen Square, London, WC1N 3AR, UK.,Max Planck UCL Centre for Computational Psychiatry and Ageing Research, UCL Queen Square Institute of Neurology, University College London, Russell Square House, London, WC1B 5EH, UK.,Department of Psychology, Yale University, New Haven, CT 06511, USA
| | - Geraint Rees
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, Russell Square House, London, WC1B 5EH, UK.,UCL Institute of Cognitive Neuroscience, University College London, Alexandra House, 17-19 Queen Square, Bloomsbury, London, WC1N 3AZ, UK.,Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, 12 Queen Square, London, WC1N 3AR, UK
| | - Sarah J Tabrizi
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, Russell Square House, London, WC1B 5EH, UK.,Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, 12 Queen Square, London, WC1N 3AR, UK
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6
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De Paepe AE, Ara A, Garcia-Gorro C, Martinez-Horta S, Perez-Perez J, Kulisevsky J, Rodriguez-Dechicha N, Vaquer I, Subira S, Calopa M, Muñoz E, Santacruz P, Ruiz-Idiago J, Mareca C, de Diego-Balaguer R, Camara E. Gray Matter Vulnerabilities Predict Longitudinal Development of Apathy in Huntington's Disease. Mov Disord 2021; 36:2162-2172. [PMID: 33998063 DOI: 10.1002/mds.28638] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 04/15/2021] [Accepted: 04/16/2021] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Apathy, a common neuropsychiatric disturbance in Huntington's disease (HD), is subserved by a complex neurobiological network. However, no study has yet employed a whole-brain approach to examine underlying regional vulnerabilities that may precipitate apathy changes over time. OBJECTIVES To identify whole-brain gray matter volume (GMV) vulnerabilities that may predict longitudinal apathy development in HD. METHODS Forty-five HD individuals (31 female) were scanned and evaluated for apathy and other neuropsychiatric features using the short-Problem Behavior Assessment for a maximum total of six longitudinal visits (including baseline). In order to identify regions where changes in GMV may describe changes in apathy, we performed longitudinal voxel-based morphometry (VBM) on those 33 participants with a magnetic resonance imaging (MRI) scan on their second visit at 18 ± 6 months follow-up (78 MRI datasets). We next employed a generalized linear mixed-effects model (N = 45) to elucidate whether initial and specific GMV may predict apathy development over time. RESULTS Utilizing longitudinal VBM, we revealed a relationship between increases in apathy and specific GMV atrophy in the right middle cingulate cortex (MCC). Furthermore, vulnerability in the right MCC volume at baseline successfully predicted the severity and progression of apathy over time. CONCLUSIONS This study highlights that individual differences in apathy in HD may be explained by variability in atrophy and initial vulnerabilities in the right MCC, a region implicated in action-initiation. These findings thus serve to facilitate the prediction of an apathetic profile, permitting targeted, time-sensitive interventions in neurodegenerative disease with potential implications in otherwise healthy populations. © 2021 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Audrey E De Paepe
- Cognition and Brain Plasticity Unit, Bellvitge Biomedical Research Institute - IDIBELL, Barcelona, Spain.,Department of Cognition, Development and Educational Psychology, Universitat de Barcelona, Barcelona, Spain
| | - Alberto Ara
- Cognition and Brain Plasticity Unit, Bellvitge Biomedical Research Institute - IDIBELL, Barcelona, Spain.,Department of Cognition, Development and Educational Psychology, Universitat de Barcelona, Barcelona, Spain.,Institute of Neurosciences, Universitat de Barcelona, Barcelona, Spain
| | - Clara Garcia-Gorro
- Cognition and Brain Plasticity Unit, Bellvitge Biomedical Research Institute - IDIBELL, Barcelona, Spain.,Department of Cognition, Development and Educational Psychology, Universitat de Barcelona, Barcelona, Spain
| | - Saül Martinez-Horta
- European Huntington's Disease Network, Ulm, Germany.,Movement Disorders Unit, Department of Neurology, Biomedical Research Institute Sant Pau (IIB-Sant Pau), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain.,CIBERNED (Center for Networked Biomedical Research on Neurodegenerative Diseases), Carlos III Institute, Madrid, Spain
| | - Jesus Perez-Perez
- European Huntington's Disease Network, Ulm, Germany.,Movement Disorders Unit, Department of Neurology, Biomedical Research Institute Sant Pau (IIB-Sant Pau), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain.,CIBERNED (Center for Networked Biomedical Research on Neurodegenerative Diseases), Carlos III Institute, Madrid, Spain
| | - Jaime Kulisevsky
- European Huntington's Disease Network, Ulm, Germany.,Movement Disorders Unit, Department of Neurology, Biomedical Research Institute Sant Pau (IIB-Sant Pau), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain.,CIBERNED (Center for Networked Biomedical Research on Neurodegenerative Diseases), Carlos III Institute, Madrid, Spain
| | | | - Irene Vaquer
- Hestia Duran i Reynals, Hospital Duran i Reynals, Hospitalet de Llobregat, Barcelona, Spain
| | - Susana Subira
- Hestia Duran i Reynals, Hospital Duran i Reynals, Hospitalet de Llobregat, Barcelona, Spain.,Departament de Psicologia Clínica i de la Salut, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Matilde Calopa
- Movement Disorders Unit, Neurology Service, Hospital Universitari de Bellvitge, Barcelona, Spain
| | - Esteban Muñoz
- European Huntington's Disease Network, Ulm, Germany.,Movement Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain.,IDIBAPS (Institut d'Investigacions Biomèdiques August Pi i Sunyer), Barcelona, Spain.,Facultat de Medicina, University of Barcelona, Barcelona, Spain
| | - Pilar Santacruz
- Movement Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain
| | - Jesus Ruiz-Idiago
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain.,Hospital Mare de Deu de la Mercè, Barcelona, Spain
| | - Celia Mareca
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Ruth de Diego-Balaguer
- Cognition and Brain Plasticity Unit, Bellvitge Biomedical Research Institute - IDIBELL, Barcelona, Spain.,Department of Cognition, Development and Educational Psychology, Universitat de Barcelona, Barcelona, Spain.,Institute of Neurosciences, Universitat de Barcelona, Barcelona, Spain.,European Huntington's Disease Network, Ulm, Germany.,ICREA (Catalan Institute for Research and Advanced Studies), Barcelona, Spain
| | - Estela Camara
- Cognition and Brain Plasticity Unit, Bellvitge Biomedical Research Institute - IDIBELL, Barcelona, Spain.,Department of Cognition, Development and Educational Psychology, Universitat de Barcelona, Barcelona, Spain.,European Huntington's Disease Network, Ulm, Germany
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7
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Insights into the Pathophysiology of Psychiatric Symptoms in Central Nervous System Disorders: Implications for Early and Differential Diagnosis. Int J Mol Sci 2021; 22:ijms22094440. [PMID: 33922780 PMCID: PMC8123079 DOI: 10.3390/ijms22094440] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 04/16/2021] [Accepted: 04/21/2021] [Indexed: 12/12/2022] Open
Abstract
Different psychopathological manifestations, such as affective, psychotic, obsessive-compulsive symptoms, and impulse control disturbances, may occur in most central nervous system (CNS) disorders including neurodegenerative and neuroinflammatory diseases. Psychiatric symptoms often represent the clinical onset of such disorders, thus potentially leading to misdiagnosis, delay in treatment, and a worse outcome. In this review, psychiatric symptoms observed along the course of several neurological diseases, namely Alzheimer’s disease, fronto-temporal dementia, Parkinson’s disease, Huntington’s disease, and multiple sclerosis, are discussed, as well as the involved brain circuits and molecular/synaptic alterations. Special attention has been paid to the emerging role of fluid biomarkers in early detection of these neurodegenerative diseases. The frequent occurrence of psychiatric symptoms in neurological diseases, even as the first clinical manifestations, should prompt neurologists and psychiatrists to share a common clinico-biological background and a coordinated diagnostic approach.
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8
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Has Silemek AC, Ranjeva J, Audoin B, Heesen C, Gold SM, Kühn S, Weygandt M, Stellmann J. Delayed access to conscious processing in multiple sclerosis: Reduced cortical activation and impaired structural connectivity. Hum Brain Mapp 2021; 42:3379-3395. [PMID: 33826184 PMCID: PMC8249884 DOI: 10.1002/hbm.25440] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/26/2021] [Accepted: 03/28/2021] [Indexed: 01/24/2023] Open
Abstract
Although multiple sclerosis (MS) is frequently accompanied by visuo‐cognitive impairment, especially functional brain mechanisms underlying this impairment are still not well understood. Consequently, we used a functional MRI (fMRI) backward masking task to study visual information processing stratifying unconscious and conscious in MS. Specifically, 30 persons with MS (pwMS) and 34 healthy controls (HC) were shown target stimuli followed by a mask presented 8–150 ms later and had to compare the target to a reference stimulus. Retinal integrity (via optical coherence tomography), optic tract integrity (visual evoked potential; VEP) and whole brain structural connectivity (probabilistic tractography) were assessed as complementary structural brain integrity markers. On a psychophysical level, pwMS reached conscious access later than HC (50 vs. 16 ms, p < .001). The delay increased with disease duration (p < .001, β = .37) and disability (p < .001, β = .24), but did not correlate with conscious information processing speed (Symbol digit modality test, β = .07, p = .817). No association was found for VEP and retinal integrity markers. Moreover, pwMS were characterized by decreased brain activation during unconscious processing compared with HC. No group differences were found during conscious processing. Finally, a complementary structural brain integrity analysis showed that a reduced fractional anisotropy in corpus callosum and an impaired connection between right insula and primary visual areas was related to delayed conscious access in pwMS. Our study revealed slowed conscious access to visual stimulus material in MS and a complex pattern of functional and structural alterations coupled to unconscious processing of/delayed conscious access to visual stimulus material in MS.
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Affiliation(s)
- Arzu C. Has Silemek
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS)Universitätsklinikum Hamburg‐Eppendorf (UKE)HamburgGermany
| | - Jean‐Philippe Ranjeva
- Aix‐Marseille UniversityCNRS, CRMBMMarseille CedexFrance
- APHMHopital de la Timone, CEMEREMMarseilleFrance
| | - Bertrand Audoin
- Aix‐Marseille UniversityCNRS, CRMBMMarseille CedexFrance
- APHMHopital de la Timone, CEMEREMMarseilleFrance
| | - Christoph Heesen
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS)Universitätsklinikum Hamburg‐Eppendorf (UKE)HamburgGermany
- Klinik und Poliklinik für NeurologieUniversitätsklinikum Hamburg‐EppendorfHamburgGermany
| | - Stefan M. Gold
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS)Universitätsklinikum Hamburg‐Eppendorf (UKE)HamburgGermany
- Charité ‐ Universitätsmedizin Berlin, Freie Universität BerlinHumboldt Universität zu Berlin, and Berlin Institute of Health (BIH), Klinik für Psychiatrie & Psychotherapie und Medizinische Klinik m.S. Psychosomatik, Campus Benjamin Franklin (CBF)BerlinGermany
| | - Simone Kühn
- Clinic for Psychiatry and PsychotherapyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
- Lise Meitner Group for Environmental NeuroscienceMax Planck Institute for Human DevelopmentBerlinGermany
| | - Martin Weygandt
- Max Delbrück Center for Molecular Medicine and Charité – Universitätsmedizin Berlin, corporate member of Freie Universität BerlinHumboldt‐Universität zu Berlin, and Berlin Institute of Health, Experimental and Clinical Research CenterBerlinGermany
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität BerlinHumboldt‐Universität zu Berlin, and Berlin Institute of Health, NeuroCure Clinical Research CenterBerlinGermany
| | - Jan‐Patrick Stellmann
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS)Universitätsklinikum Hamburg‐Eppendorf (UKE)HamburgGermany
- Aix‐Marseille UniversityCNRS, CRMBMMarseille CedexFrance
- APHMHopital de la Timone, CEMEREMMarseilleFrance
- Klinik und Poliklinik für NeurologieUniversitätsklinikum Hamburg‐EppendorfHamburgGermany
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9
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Abstract
The biomarker networks measured by different modalities of data (e.g., structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI)) may share the same true underlying biological model. In this work, we propose a node-wise biomarker graphical model to leverage the shared mechanism between multi-modality data to provide a more reliable estimation of the target modality network and account for the heterogeneity in networks due to differences between subjects and networks of external modality. Latent variables are introduced to represent the shared unobserved biological network and the information from the external modality is incorporated to model the distribution of the underlying biological network. We propose an efficient approximation to the posterior expectation of the latent variables that reduces computational cost by at least 50%. The performance of the proposed method is demonstrated by extensive simulation studies and an application to construct gray matter brain atrophy network of Huntington's disease by using sMRI data and DTI data. The identified network connections are more consistent with clinical literature and better improve prediction in follow-up clinical outcomes and separate subjects into clinically meaningful subgroups with different prognosis than alternative methods.
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Affiliation(s)
- Shanghong Xie
- Department of Biostatistics, Mailman School of Public Health, Columbia University
| | - Donglin Zeng
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill
| | - Yuanjia Wang
- Department of Biostatistics, Mailman School of Public Health, Columbia University
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10
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Estevez-Fraga C, Scahill R, Rees G, Tabrizi SJ, Gregory S. Diffusion imaging in Huntington's disease: comprehensive review. J Neurol Neurosurg Psychiatry 2020; 92:jnnp-2020-324377. [PMID: 33033167 PMCID: PMC7803908 DOI: 10.1136/jnnp-2020-324377] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 09/07/2020] [Accepted: 09/07/2020] [Indexed: 12/31/2022]
Abstract
Huntington's disease (HD) is a monogenic disorder with 100% penetrance. With the advent of genetic testing in adults, disease-related, structural brain changes can be investigated from the earliest, premorbid stages of HD. While examining macrostructural change characterises global neuronal damage, investigating microstructural alterations provides information regarding brain organisation and its underlying biological properties. Diffusion MRI can be used to track the progression of microstructural anomalies in HD decades prior to clinical disease onset, providing a greater understanding of neurodegeneration. Multiple approaches, including voxelwise, region of interest and tractography, have been used in HD cohorts, showing a centrifugal pattern of white matter (WM) degeneration starting from deep brain areas, which is consistent with neuropathological studies. The corpus callosum, longer WM tracts and areas that are more densely connected, in particular the sensorimotor network, also tend to be affected early during premanifest stages. Recent evidence supports the routine inclusion of diffusion analyses within clinical trials principally as an additional measure to improve understanding of treatment effects, while the advent of novel techniques such as multitissue compartment models and connectomics can help characterise the underpinnings of progressive functional decline in HD.
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Affiliation(s)
- Carlos Estevez-Fraga
- Huntington's Disease Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Rachael Scahill
- Huntington's Disease Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Geraint Rees
- Wellcome Centre for Neuroimaging, University College London, London, UK
- Institute of Cognitive Neuroscience, University College London, London, UK
| | - Sarah J Tabrizi
- Huntington's Disease Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah Gregory
- Huntington's Disease Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
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11
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McColgan P, Joubert J, Tabrizi SJ, Rees G. The human motor cortex microcircuit: insights for neurodegenerative disease. Nat Rev Neurosci 2020; 21:401-415. [PMID: 32555340 DOI: 10.1038/s41583-020-0315-1] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/04/2020] [Indexed: 12/22/2022]
Abstract
The human motor cortex comprises a microcircuit of five interconnected layers with different cell types. In this Review, we use a layer-specific and cell-specific approach to integrate physiological accounts of this motor cortex microcircuit with the pathophysiology of neurodegenerative diseases affecting motor functions. In doing so we can begin to link motor microcircuit pathology to specific disease stages and clinical phenotypes. Based on microcircuit physiology, we can make future predictions of axonal loss and microcircuit dysfunction. With recent advances in high-resolution neuroimaging we can then test these predictions in humans in vivo, providing mechanistic insights into neurodegenerative disease.
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Affiliation(s)
- Peter McColgan
- Huntington's Disease Research Centre, UCL Institute of Neurology, University College London, London, UK.
| | - Julie Joubert
- Huntington's Disease Research Centre, UCL Institute of Neurology, University College London, London, UK
| | - Sarah J Tabrizi
- Huntington's Disease Research Centre, UCL Institute of Neurology, University College London, London, UK.,Dementia Research Institute at UCL, London, UK
| | - Geraint Rees
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, UK.,UCL Institute of Cognitive Neuroscience, University College London, London, UK
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12
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Gubert C, Renoir T, Hannan AJ. Why Woody got the blues: The neurobiology of depression in Huntington's disease. Neurobiol Dis 2020; 142:104958. [PMID: 32526274 DOI: 10.1016/j.nbd.2020.104958] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 05/02/2020] [Accepted: 06/03/2020] [Indexed: 02/03/2023] Open
Abstract
Huntington's disease (HD) is an extraordinary disorder that usually strikes when individuals are in the prime of their lives, as was the case for the influential 20th century musician Woody Guthrie. HD demonstrates the exceptionally fine line between life and death in such 'genetic diseases', as the only difference between those who suffer horribly and die slowly of this disease is often just a handful of extra tandem repeats (beyond the normal polymorphic range) in a genome that constitutes over 3 billion paired nucleotides of DNA. Furthermore, HD presents as a complex and heterogenous combination of psychiatric, cognitive and motor symptoms, so can appear as an unholy trinity of 'three disorders in one'. The autosomal dominant nature of the disorder is also extremely challenging for affected families, as a 'flip of a coin' dictates which children inherit the mutation from their affected parent, and the gene-negative family members bear the burden of caring for the other half of the family that is affected. In this review, we will focus on one of the earliest, and most devastating, symptoms associated with HD, depression, which has been reported to affect approximately half of gene-positive HD family members. We will discuss the pathogenesis of HD, and depressive symptoms in particular, including molecular and cellular mechanisms, and potential genetic and environmental modifiers. This expanding understanding of HD pathogenesis may not only lead to novel therapeutic options for HD families, but may also provide insights into depression in the wider population, which has the greatest burden of disease of any disorder and an enormous unmet need for new therapies.
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Affiliation(s)
- Carolina Gubert
- Florey Institute of Neuroscience and Mental Health, Melbourne Brain Centre, University of Melbourne, Parkville, Victoria, Australia
| | - Thibault Renoir
- Florey Institute of Neuroscience and Mental Health, Melbourne Brain Centre, University of Melbourne, Parkville, Victoria, Australia
| | - Anthony J Hannan
- Florey Institute of Neuroscience and Mental Health, Melbourne Brain Centre, University of Melbourne, Parkville, Victoria, Australia; Department of Anatomy and Neuroscience, University of Melbourne, Parkville, Victoria, Australia.
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13
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Seghier ML, Fahim MA, Habak C. Educational fMRI: From the Lab to the Classroom. Front Psychol 2019; 10:2769. [PMID: 31866920 PMCID: PMC6909003 DOI: 10.3389/fpsyg.2019.02769] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 11/25/2019] [Indexed: 12/23/2022] Open
Abstract
Functional MRI (fMRI) findings hold many potential applications for education, and yet, the translation of fMRI findings to education has not flowed. Here, we address the types of fMRI that could better support applications of neuroscience to the classroom. This 'educational fMRI' comprises eight main challenges: (1) collecting artifact-free fMRI data in school-aged participants and in vulnerable young populations, (2) investigating heterogenous cohorts with wide variability in learning abilities and disabilities, (3) studying the brain under natural and ecological conditions, given that many practical topics of interest for education can be addressed only in ecological contexts, (4) depicting complex age-dependent associations of brain and behaviour with multi-modal imaging, (5) assessing changes in brain function related to developmental trajectories and instructional intervention with longitudinal designs, (6) providing system-level mechanistic explanations of brain function, so that useful individualized predictions about learning can be generated, (7) reporting negative findings, so that resources are not wasted on developing ineffective interventions, and (8) sharing data and creating large-scale longitudinal data repositories to ensure transparency and reproducibility of fMRI findings for education. These issues are of paramount importance to the development of optimal fMRI practices for educational applications.
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Affiliation(s)
- Mohamed L Seghier
- Cognitive Neuroimaging Unit, Emirates College for Advanced Education (ECAE), Abu Dhabi, United Arab Emirates
| | - Mohamed A Fahim
- Cognitive Neuroimaging Unit, Emirates College for Advanced Education (ECAE), Abu Dhabi, United Arab Emirates
| | - Claudine Habak
- Cognitive Neuroimaging Unit, Emirates College for Advanced Education (ECAE), Abu Dhabi, United Arab Emirates
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14
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Pini L, Jacquemot C, Cagnin A, Meneghello F, Semenza C, Mantini D, Vallesi A. Aberrant brain network connectivity in presymptomatic and manifest Huntington's disease: A systematic review. Hum Brain Mapp 2019; 41:256-269. [PMID: 31532053 PMCID: PMC7268025 DOI: 10.1002/hbm.24790] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 07/29/2019] [Accepted: 08/26/2019] [Indexed: 12/12/2022] Open
Abstract
Resting‐state functional magnetic resonance imaging (rs‐fMRI) has the potential to shed light on the pathophysiological mechanisms of Huntington's disease (HD), paving the way to new therapeutic interventions. A systematic literature review was conducted in three online databases according to PRISMA guidelines, using keywords for HD, functional connectivity, and rs‐fMRI. We included studies investigating connectivity in presymptomatic (pre‐HD) and manifest HD gene carriers compared to healthy controls, implementing seed‐based connectivity, independent component analysis, regional property, and graph analysis approaches. Visual network showed reduced connectivity in manifest HD, while network/areas underpinning motor functions were consistently altered in both manifest HD and pre‐HD, showing disease stage‐dependent changes. Cognitive networks underlying executive and attentional functions showed divergent anterior–posterior alterations, possibly reflecting compensatory mechanisms. The involvement of these networks in pre‐HD is still unclear. In conclusion, aberrant connectivity of the sensory‐motor network is observed in the early stage of HD while, as pathology spreads, other networks might be affected, such as the visual and executive/attentional networks. Moreover, sensory‐motor and executive networks exhibit hyper‐ and hypo‐connectivity patterns following different spatiotemporal trajectories. These findings could potentially help to implement future huntingtin‐lowering interventions.
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Affiliation(s)
- Lorenzo Pini
- Department of Neuroscience & Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Charlotte Jacquemot
- Département d'Etudes Cognitives, Ecole Normale Supérieure-PSL University, Paris, France.,Laboratoire de NeuroPsychologie Interventionnelle, Institut Mondor de Recherche Biomédicale, Institut National de la Santé et Recherche Médical (INSERM) U955, Equipe 01, Créteil, France.,Faculté de Médecine, Université Paris Est Créteil, Créteil, France
| | - Annachiara Cagnin
- Department of Neuroscience & Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Francesca Meneghello
- Cognitive Neuroscience Research Group, IRCCS San Camillo Hospital, Venice, Italy
| | - Carlo Semenza
- Department of Neuroscience & Padova Neuroscience Center, University of Padova, Padova, Italy.,Cognitive Neuroscience Research Group, IRCCS San Camillo Hospital, Venice, Italy
| | - Dante Mantini
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium.,Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
| | - Antonino Vallesi
- Department of Neuroscience & Padova Neuroscience Center, University of Padova, Padova, Italy.,Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
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15
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De Paepe AE, Sierpowska J, Garcia-Gorro C, Martinez-Horta S, Perez-Perez J, Kulisevsky J, Rodriguez-Dechicha N, Vaquer I, Subira S, Calopa M, Muñoz E, Santacruz P, Ruiz-Idiago J, Mareca C, de Diego-Balaguer R, Camara E. White matter cortico-striatal tracts predict apathy subtypes in Huntington's disease. Neuroimage Clin 2019; 24:101965. [PMID: 31401404 PMCID: PMC6700450 DOI: 10.1016/j.nicl.2019.101965] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 07/23/2019] [Accepted: 07/28/2019] [Indexed: 01/24/2023]
Abstract
BACKGROUND Apathy is the neuropsychiatric syndrome that correlates most highly with Huntington's disease progression, and, like early patterns of neurodegeneration, is associated with lesions to cortico-striatal connections. However, due to its multidimensional nature and elusive etiology, treatment options are limited. OBJECTIVES To disentangle underlying white matter microstructural correlates across the apathy spectrum in Huntington's disease. METHODS Forty-six Huntington's disease individuals (premanifest (N = 22) and manifest (N = 24)) and 35 healthy controls were scanned at 3-tesla and underwent apathy evaluation using the short-Problem Behavior Assessment and short-Lille Apathy Rating Scale, with the latter being characterized into three apathy domains, namely emotional, cognitive, and auto-activation deficit. Diffusion tensor imaging was used to study whether individual differences in specific cortico-striatal tracts predicted global apathy and its subdomains. RESULTS We elucidate that apathy profiles may develop along differential timelines, with the auto-activation deficit domain manifesting prior to motor onset. Furthermore, diffusion tensor imaging revealed that inter-individual variability in the disruption of discrete cortico-striatal tracts might explain the heterogeneous severity of apathy profiles. Specifically, higher levels of auto-activation deficit symptoms significantly correlated with increased mean diffusivity in the right uncinate fasciculus. Conversely, those with severe cognitive apathy demonstrated increased mean diffusivity in the right frontostriatal tract and left dorsolateral prefrontal cortex to caudate nucleus tract. CONCLUSIONS The current study provides evidence that white matter correlates associated with emotional, cognitive, and auto-activation subtypes may elucidate the heterogeneous nature of apathy in Huntington's disease, as such opening a door for individualized pharmacological management of apathy as a multidimensional syndrome in other neurodegenerative disorders.
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Affiliation(s)
- Audrey E De Paepe
- Department of Neuroscience, Pomona College, Claremont, CA, United States; Cognition and Brain Plasticity Unit, Bellvitge Biomedical Research Institute - IDIBELL, 08097 L'Hospitalet de Llobregat, Barcelona, Spain
| | - Joanna Sierpowska
- Cognition and Brain Plasticity Unit, Bellvitge Biomedical Research Institute - IDIBELL, 08097 L'Hospitalet de Llobregat, Barcelona, Spain; Department of Cognition, Development and Education Psychology, Universitat de Barcelona, Barcelona, Spain; Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands; Radboud University Medical Center, Donders Institute for Brain Cognition and Behaviour, Department of Medical Psychology, Nijmegen, The Netherlands
| | - Clara Garcia-Gorro
- Cognition and Brain Plasticity Unit, Bellvitge Biomedical Research Institute - IDIBELL, 08097 L'Hospitalet de Llobregat, Barcelona, Spain; Department of Cognition, Development and Education Psychology, Universitat de Barcelona, Barcelona, Spain
| | - Saül Martinez-Horta
- European Huntington's Disease Network, Germany; Movement Disorders Unit, Department of Neurology, Biomedical Research Institute Sant Pau (IIB-Sant Pau), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Jesus Perez-Perez
- European Huntington's Disease Network, Germany; Movement Disorders Unit, Department of Neurology, Biomedical Research Institute Sant Pau (IIB-Sant Pau), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Jaime Kulisevsky
- European Huntington's Disease Network, Germany; Movement Disorders Unit, Department of Neurology, Biomedical Research Institute Sant Pau (IIB-Sant Pau), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain; CIBERNED (Center for Networked Biomedical Research on Neurodegenerative Diseases), Carlos III Institute, Madrid, Spain
| | | | - Irene Vaquer
- Hestia Duran i Reynals. Hospital Duran i Reynals, Hospitalet de Llobregat, Barcelona, Spain
| | - Susana Subira
- Hestia Duran i Reynals. Hospital Duran i Reynals, Hospitalet de Llobregat, Barcelona, Spain; Department of Clinical and Health Psychology, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Matilde Calopa
- Movement Disorders Unit, Neurology Service, Hospital Universitari de Bellvitge, Barcelona, Spain
| | - Esteban Muñoz
- Movement Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain; IDIBAPS (Institut d'Investigacions Biomèdiques August Pi i Sunyer), Barcelona, Spain; Facultat de Medicina, University of Barcelona, Barcelona, Spain
| | - Pilar Santacruz
- Movement Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain
| | - Jesus Ruiz-Idiago
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Spain; Hospital Mare de Deu de la Mercè, Barcelona, Spain
| | - Celia Mareca
- Hospital Mare de Deu de la Mercè, Barcelona, Spain
| | - Ruth de Diego-Balaguer
- Cognition and Brain Plasticity Unit, Bellvitge Biomedical Research Institute - IDIBELL, 08097 L'Hospitalet de Llobregat, Barcelona, Spain; Department of Cognition, Development and Education Psychology, Universitat de Barcelona, Barcelona, Spain; Institute of Neurosciences, Universitat de Barcelona, Barcelona, Spain; ICREA (Catalan Institute for Research and Advanced Studies), Barcelona, Spain
| | - Estela Camara
- Cognition and Brain Plasticity Unit, Bellvitge Biomedical Research Institute - IDIBELL, 08097 L'Hospitalet de Llobregat, Barcelona, Spain.
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16
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Neuroimaging depression and anxiety in essential tremor: A diffusion tensor imaging study. Clin Imaging 2019; 58:96-104. [PMID: 31284179 DOI: 10.1016/j.clinimag.2019.06.016] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 05/26/2019] [Accepted: 06/26/2019] [Indexed: 01/25/2023]
Abstract
OBJECTIVE Patients with essential tremor (ET) may exhibit non-motor features, including those that are neuropsychiatric. Depression and anxiety are the most common among these. This study aims to investigate the possible relationship between microstructural brain changes and symptoms of depression and anxiety in ET. METHODS We assessed 62 ET patients (40 women and 22 men, mean age 46.0 ± 20.4) for symptoms of depression and anxiety using the Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI). Thirty-two patients had severe or moderate symptoms of anxiety, and 15 patients had severe or moderate depressive symptoms. Microstructural brain changes were evaluated using diffusion tensor imaging (DTI), which was reported using fractional anisotropy (FA), mean diffusivity (MD), apparent diffusion coefficient (ADC), radial diffusivity (RD), and axial diffusivity (AD) values calculated for 17 regions of interest including the prefrontal cortex, paralimbic and limbic structures and cerebellar peduncles. We evaluated the relationship between observed changes in brain regions and symptoms of depression and anxiety. RESULTS Decreased left amygdala FA (p = 0.003) and increased left amygdala RD (p = 0.04) were detected in depressed vs. non-depressed ET patients. Left ventrolateral prefrontal cortex (VLPFC) FA (p = 0.02) and left precuneus FA (p = 0.02) values differed between anxious patients vs. non-anxious ET patients. BDI scores were correlated with left amygdala FA and left RD, while BAI scores were correlated with left VLPFC FA and left precuneus FA. DISCUSSION Our results provide evidence that symptoms of depression and anxiety could be based in structural brain changes observed in patients with ET.
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17
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Johnson EB, Gregory S. Huntington's disease: Brain imaging in Huntington's disease. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2019; 165:321-369. [PMID: 31481169 DOI: 10.1016/bs.pmbts.2019.04.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Huntington's disease (HD) gene-carriers show prominent neuronal loss by end-stage disease, and the use of magnetic resonance imaging (MRI) has been increasingly used to quantify brain changes during earlier stages of the disease. MRI offers an in vivo method of measuring structural and functional brain change. The images collected via MRI are processed to measure different anatomical features, such as brain volume, macro- and microstructural changes within white matter and functional brain activity. Structural imaging has demonstrated significant volume loss across multiple white and gray matter regions in HD, particularly within subcortical structures. There also appears to be increasing disorganization of white matter tracts and between-region connectivity with increasing disease progression. Finally, functional changes are thought to represent changes in brain activity underlying compensatory mechanisms in HD. This chapter will provide an overview of the principles of MRI and practicalities associated with using MRI in HD studies, and summarize findings from MRI studies investigating brain structure and function in HD.
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Affiliation(s)
- Eileanoir B Johnson
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sarah Gregory
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.
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18
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Functional Magnetic Resonance Imaging in Huntington's Disease. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2018; 142:381-408. [PMID: 30409260 DOI: 10.1016/bs.irn.2018.09.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Huntington's disease is an inherited neurodegenerative condition characterized by motor dysfunction, cognitive impairment and neuropsychiatric disturbance. The effects of the underlying pathology on brain morphology are relatively well understood. Numerous structural Magnetic Resonance Imaging (MRI) studies have demonstrated macrostructural change with widespread striatal and cortical atrophy and microstructural white matter loss in premanifest and manifest HD gene carriers. However, disease effects on brain function are less well characterized. Functional MRI provides an opportunity to examine differences in brain activity either in response to a particular task or in the brain at rest. There is increasing evidence that HD gene carriers exhibit altered activation patterns and functional connectivity between brain regions in response to the neurodegenerative process. Here we review the growing literature in this area and critically evaluate the utility of this imaging modality.
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19
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Baggio HC, Abos A, Segura B, Campabadal A, Garcia-Diaz A, Uribe C, Compta Y, Marti MJ, Valldeoriola F, Junque C. Statistical inference in brain graphs using threshold-free network-based statistics. Hum Brain Mapp 2018; 39:2289-2302. [PMID: 29450940 PMCID: PMC6619254 DOI: 10.1002/hbm.24007] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 01/30/2018] [Accepted: 02/06/2018] [Indexed: 01/06/2023] Open
Abstract
The description of brain networks as graphs where nodes represent different brain regions and edges represent a measure of connectivity between a pair of nodes is an increasingly used approach in neuroimaging research. The development of powerful methods for edge‐wise group‐level statistical inference in brain graphs while controlling for multiple‐testing associated false‐positive rates, however, remains a difficult task. In this study, we use simulated data to assess the properties of threshold‐free network‐based statistics (TFNBS). The TFNBS combines threshold‐free cluster enhancement, a method commonly used in voxel‐wise statistical inference, and network‐based statistic (NBS), which is frequently used for statistical analysis of brain graphs. Unlike the NBS, TFNBS generates edge‐wise significance values and does not require the a priori definition of a hard cluster‐defining threshold. Other test parameters, nonetheless, need to be set. We show that it is possible to find parameters that make TFNBS sensitive to strong and topologically clustered effects, while appropriately controlling false‐positive rates. Our results show that the TFNBS is an adequate technique for the statistical assessment of brain graphs.
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Affiliation(s)
- Hugo C Baggio
- Medical Psychology Unit, Department of Medicine, Institute of Neuroscience, University of Barcelona, Barcelona, Catalonia, Spain
| | - Alexandra Abos
- Medical Psychology Unit, Department of Medicine, Institute of Neuroscience, University of Barcelona, Barcelona, Catalonia, Spain
| | - Barbara Segura
- Medical Psychology Unit, Department of Medicine, Institute of Neuroscience, University of Barcelona, Barcelona, Catalonia, Spain
| | - Anna Campabadal
- Medical Psychology Unit, Department of Medicine, Institute of Neuroscience, University of Barcelona, Barcelona, Catalonia, Spain
| | - Anna Garcia-Diaz
- Medical Psychology Unit, Department of Medicine, Institute of Neuroscience, University of Barcelona, Barcelona, Catalonia, Spain
| | - Carme Uribe
- Medical Psychology Unit, Department of Medicine, Institute of Neuroscience, University of Barcelona, Barcelona, Catalonia, Spain
| | - Yaroslau Compta
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic de Barcelona, Barcelona, Catalonia, Spain.,Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona. Institute of Neuroscience, University of Barcelona, Barcelona, Catalonia, Spain
| | - Maria Jose Marti
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic de Barcelona, Barcelona, Catalonia, Spain.,Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona. Institute of Neuroscience, University of Barcelona, Barcelona, Catalonia, Spain
| | - Francesc Valldeoriola
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic de Barcelona, Barcelona, Catalonia, Spain.,Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona. Institute of Neuroscience, University of Barcelona, Barcelona, Catalonia, Spain
| | - Carme Junque
- Medical Psychology Unit, Department of Medicine, Institute of Neuroscience, University of Barcelona, Barcelona, Catalonia, Spain.,Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic de Barcelona, Barcelona, Catalonia, Spain.,Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain
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