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Slováková A, Kúdelka J, Škoch A, Jakob L, Fialová M, Fürstová P, Bakštein E, Bankovská Motlová L, Knytl P, Španiel F. Time is the enemy: Negative symptoms are related to even slight differences in the duration of untreated psychosis. Compr Psychiatry 2024; 130:152450. [PMID: 38241816 DOI: 10.1016/j.comppsych.2024.152450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 12/26/2023] [Accepted: 01/12/2024] [Indexed: 01/21/2024] Open
Abstract
BACKGROUND Negative symptoms (NS) represent a detrimental symptomatic domain in schizophrenia affecting social and occupational outcomes. AIMS We aimed to identify factors from the baseline visit (V1) - with a mean illness duration of 0.47 years (SD = 0.45) - that predict the magnitude of NS at the follow-up visit (V3), occurring 4.4 years later (mean +/- 0.45). METHOD Using longitudinal data from 77 first-episode schizophrenia spectrum patients, we analysed eight predictors of NS severity at V3: (1) the age at disease onset, (2) age at V1, (3) sex, (4) diagnosis, (5) NS severity at V1, (6) the dose of antipsychotic medication at V3, (7) hospitalisation days before V1 and; (8) the duration of untreated psychosis /DUP/). Secondly, using a multiple linear regression model, we studied the longitudinal relationship between such identified predictors and NS severity at V3 using a multiple linear regression model. RESULTS DUP (Pearson's r = 0.37, p = 0.001) and NS severity at V1 (Pearson's r = 0.49, p < 0.001) survived correction for multiple comparisons. The logarithmic-like relationship between DUP and NS was responsible for the initial stunning incremental contribution of DUP to the severity of NS. For DUP < 6 months, with the sharpest DUP/NS correlation, prolonging DUP by five days resulted in a measurable one-point increase in the 6-item negative symptoms PANSS domain assessed 4.9 (+/- 0.6) years after the illness onset. Prolongation of DUP to 14.7 days doubled this NS gain, whereas 39 days longer DUP tripled NS increase. CONCLUSION The results suggest the petrification of NS during the early stages of the schizophrenia spectrum and a crucial dependence of this symptom domain on DUP. These findings are clinically significant and highlight the need for primary preventive actions.
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Affiliation(s)
- Andrea Slováková
- National Institute of Mental Health, Klecany, Czech Republic; 3rd Faculty of Medicine, Charles University, Prague, Czech Republic.
| | - Jan Kúdelka
- National Institute of Mental Health, Klecany, Czech Republic
| | - Antonín Škoch
- National Institute of Mental Health, Klecany, Czech Republic; Institute for Clinical and Experimental Medicine, Department of Diagnostic and Interventional Radiology, Prague, Czech Republic.
| | - Lea Jakob
- National Institute of Mental Health, Klecany, Czech Republic.
| | - Markéta Fialová
- National Institute of Mental Health, Klecany, Czech Republic; 3rd Faculty of Medicine, Charles University, Prague, Czech Republic.
| | - Petra Fürstová
- National Institute of Mental Health, Klecany, Czech Republic.
| | - Eduard Bakštein
- National Institute of Mental Health, Klecany, Czech Republic.
| | | | - Pavel Knytl
- National Institute of Mental Health, Klecany, Czech Republic.
| | - Filip Španiel
- National Institute of Mental Health, Klecany, Czech Republic; 3rd Faculty of Medicine, Charles University, Prague, Czech Republic.
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Svobodová V, Profant O, Škoch A, Tintěra J, Tóthová D, Chovanec M, Čapková D, Syka J. The effect of aging, hearing loss, and tinnitus on white matter in the human auditory system revealed with fixel-based analysis. Front Aging Neurosci 2024; 15:1283660. [PMID: 38264549 PMCID: PMC10803717 DOI: 10.3389/fnagi.2023.1283660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 12/12/2023] [Indexed: 01/25/2024] Open
Abstract
Introduction Aging negatively influences the structure of the human brain including the white matter. The objective of our study was to identify, using fixel-based morphometry, the age induced changes in the pathways connecting several regions of the central auditory system (inferior colliculus, Heschl's gyrus, planum temporale) and the pathways connecting these structures with parts of the limbic system (anterior insula, hippocampus and amygdala). In addition, we were interested in the extent to which the integrity of these pathways is influenced by hearing loss and tinnitus. Methods Tractographic data were acquired using a 3 T MRI in 79 volunteers. The participants were categorized into multiple groups in accordance with their age, auditory thresholds and tinnitus status. Fixel-based analysis was utilized to identify alterations in the subsequent three parameters: logarithm of fiber cross-section, fiber density, fiber density and cross-section. Two modes of analysis were used: whole brain analysis and targeted analysis using fixel mask, corresponding to the pathways connecting the aforementioned structures. Results A significantly negative effect of aging was present for all fixel-based metrics, namely the logarithm of the fiber cross-section, (7 % fixels in whole-brain, 14% fixels in fixel mask), fiber density (5 % fixels in whole-brain, 15% fixels in fixel mask), fiber density and cross section (7 % fixels in whole-brain, 19% fixels in fixel mask). Expressed age-related losses, exceeding 30% fixels, were particularly present in pathways connecting the auditory structures with limbic structures. The effect of hearing loss and/or tinnitus did not reach significance. Conclusions Our results show that although an age-related reduction of fibers is present in pathways connecting several auditory regions, the connections of these structures with limbic structures are even more reduced. To what extent this fact influences the symptoms of presbycusis, such as decreased speech comprehension, especially in noise conditions, remains to be elucidated.
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Affiliation(s)
- Veronika Svobodová
- Department of Auditory Neuroscience, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czechia
- Department of Otorhinolaryngology and Head and Neck Surgery, 1st Faculty of Medicine, Charles University in Prague, University Hospital Motol, Prague, Czechia
| | - Oliver Profant
- Department of Auditory Neuroscience, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czechia
- Department of Otorhinolaryngology, 3rd Faculty of Medicine, Charles University in Prague, University Hospital Královské Vinohrady, Prague, Czechia
| | - Antonín Škoch
- Department of Radiodiagnostic and Interventional Radiology, Institute of Clinical and Experimental Medicine, Prague, Czechia
| | - Jaroslav Tintěra
- Department of Radiodiagnostic and Interventional Radiology, Institute of Clinical and Experimental Medicine, Prague, Czechia
| | - Diana Tóthová
- Department of Auditory Neuroscience, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czechia
- Department of Otorhinolaryngology and Head and Neck Surgery, 1st Faculty of Medicine, Charles University in Prague, University Hospital Motol, Prague, Czechia
| | - Martin Chovanec
- Department of Otorhinolaryngology, 3rd Faculty of Medicine, Charles University in Prague, University Hospital Královské Vinohrady, Prague, Czechia
| | - Dora Čapková
- Department of Auditory Neuroscience, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czechia
- Department of Otorhinolaryngology, 3rd Faculty of Medicine, Charles University in Prague, University Hospital Královské Vinohrady, Prague, Czechia
| | - Josef Syka
- Department of Auditory Neuroscience, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czechia
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McWhinney SR, Brosch K, Calhoun VD, Crespo-Facorro B, Crossley NA, Dannlowski U, Dickie E, Dietze LMF, Donohoe G, Du Plessis S, Ehrlich S, Emsley R, Furstova P, Glahn DC, Gonzalez-Valderrama A, Grotegerd D, Holleran L, Kircher TTJ, Knytl P, Kolenic M, Lencer R, Nenadić I, Opel N, Pfarr JK, Rodrigue AL, Rootes-Murdy K, Ross AJ, Sim K, Škoch A, Spaniel F, Stein F, Švancer P, Tordesillas-Gutiérrez D, Undurraga J, Vázquez-Bourgon J, Voineskos A, Walton E, Weickert TW, Weickert CS, Thompson PM, van Erp TGM, Turner JA, Hajek T. Correction: Obesity and brain structure in schizophrenia - ENIGMA study in 3021 individuals. Mol Psychiatry 2024; 29:56. [PMID: 37015980 PMCID: PMC11078708 DOI: 10.1038/s41380-023-02055-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/06/2023]
Affiliation(s)
- Sean R McWhinney
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory University, Atlanta, GA, USA
| | - Benedicto Crespo-Facorro
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
- IBiS, University Hospital Virgen del Rocio, Sevilla, Spain
- Department of Psychiatry, School of Medicine, University of Sevilla, Sevilla, Spain
| | - Nicolas A Crossley
- Department of Psychiatry, School of Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Psychosis Studies, King's College London, London, UK
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Erin Dickie
- Centre for Addiction & Mental Health, Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | | | - Gary Donohoe
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Stefan Du Plessis
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- SAMRC Genomics of Brain Disorders Unit, Cape Town, South Africa
| | - Stefan Ehrlich
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Robin Emsley
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Petra Furstova
- National Institute of Mental Health, Klecany, Czech Republic
| | - David C Glahn
- Department of Psychiatry & Behavioral Sciences, Boston Children's Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
| | - Alfonso Gonzalez-Valderrama
- School of Medicine, Universidad Finis Terrae, Santiago, Chile
- Early Intervention in Psychosis Program, Instituto Psiquiátrico 'Dr. José Horwitz B.', Santiago, Chile
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Laurena Holleran
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Tilo T J Kircher
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Pavel Knytl
- National Institute of Mental Health, Klecany, Czech Republic
- Charles University, Third Faculty of Medicine, Prague, Czech Republic
| | - Marian Kolenic
- National Institute of Mental Health, Klecany, Czech Republic
- Charles University, Third Faculty of Medicine, Prague, Czech Republic
| | - Rebekka Lencer
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Department of Pscyhiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Department of Psychiatry, Jena University Hospital/Friedrich-Schiller-University Jena, Jena, Germany
| | - Julia-Katharina Pfarr
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Amanda L Rodrigue
- Department of Psychiatry & Behavioral Sciences, Boston Children's Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | | | - Alex J Ross
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Kang Sim
- West Region, Institute of Mental Health, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Antonín Škoch
- National Institute of Mental Health, Klecany, Czech Republic
- Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Filip Spaniel
- National Institute of Mental Health, Klecany, Czech Republic
- Charles University, Third Faculty of Medicine, Prague, Czech Republic
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Patrik Švancer
- National Institute of Mental Health, Klecany, Czech Republic
- Charles University, Third Faculty of Medicine, Prague, Czech Republic
| | - Diana Tordesillas-Gutiérrez
- Department of Radiology, Marqués de Valdecilla University Hospital, Valdecilla Biomedical Research Institute IDIVAL, Santander, Spain
- Computación Avanzada y Ciencia, Instituto de Física de Cantabria, CSIC, Santander, Spain
| | - Juan Undurraga
- Early Intervention in Psychosis Program, Instituto Psiquiátrico 'Dr. José Horwitz B.', Santiago, Chile
- Department of Neurology and Psychiatry. Faculty of Medicine, Clínica Alemana Universidad del Desarrollo, Santiago, Chile
| | - Javier Vázquez-Bourgon
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
- Department of Medicine and Psychiatry, School of Medicine, University of Cantabria, Santander, Spain
- Department of Psychiatry, Marqués de Valdecilla University Hospital, Valdecilla Biomedical Research Institute IDIVAL, Santander, Spain
| | - Aristotle Voineskos
- Centre for Addiction & Mental Health, Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Esther Walton
- Department of Psychology, University of Bath, Bath, UK
| | - Thomas W Weickert
- Department of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
- Neuroscience Research Australia, Randwick, NSW, Australia
| | - Cynthia Shannon Weickert
- Department of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
- Neuroscience Research Australia, Randwick, NSW, Australia
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Theo G M van Erp
- Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA, USA
| | - Jessica A Turner
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Tomas Hajek
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada.
- National Institute of Mental Health, Klecany, Czech Republic.
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Kosová E, Pajuelo D, Fajnerová I, Greguš D, Brunovský M, Stopková P, Škoch A, Fürstová P, Španiel F, Horáček J. Spectroscopic abnormalities in the pregenual anterior cingulate cortex in obsessive-compulsive disorder using proton magnetic resonance spectroscopy: a controlled study. BMC Psychiatry 2023; 23:734. [PMID: 37817131 PMCID: PMC10565966 DOI: 10.1186/s12888-023-05228-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 09/27/2023] [Indexed: 10/12/2023] Open
Abstract
BACKGROUND The main aim of the present study is to determine the role of metabolites observed using proton magnetic resonance spectroscopy (1H-MRS) in obsessive-compulsive disorder (OCD). As the literature describing biochemical changes in OCD yields conflicting results, we focused on accurate metabolite quantification of total N-acetyl aspartate (tNAA), total creatine (tCr), total choline-containing compounds (tCh), and myo-inositol (mI) in the anterior cingulate cortex (ACC) to capture the small metabolic changes between OCD patients and controls and between OCD patients with and without medication. METHODS In total 46 patients with OCD and 46 healthy controls (HC) matched for age and sex were included in the study. The severity of symptoms in the OCD was evaluated on the day of magnetic resonance imaging (MRI) using the Yale-Brown Obsessive-Compulsive Scale (YBOCS). Subjects underwent 1H-MRS from the pregenual ACC (pgACC) region to calculate concentrations of tNAA, tCr, tCho, and mI. Twenty-eight OCD and 28 HC subjects were included in the statistical analysis. We compared differences between groups for all selected metabolites and in OCD patients we analyzed the relationship between metabolite levels and symptom severity, medication status, age, and the duration of illness. RESULTS Significant decreases in tCr (U = 253.00, p = 0.022) and mI (U = 197.00, p = 0.001) in the pgACC were observed in the OCD group. No statistically significant differences were found in tNAA and tCho levels; however, tCho revealed a trend towards lower concentrations in OCD patients (U = 278.00, p = 0.062). Metabolic concentrations showed no significant correlations with the age and duration of illness. The correlation statistics found a significant negative correlation between tCr levels and YBOCS compulsions subscale (cor = -0.380, p = 0.046). tCho and YBOCS compulsions subscale showed a trend towards a negative correlation (cor = -0.351, p = 0.067). Analysis of subgroups with or without medication showed no differences. CONCLUSIONS Patients with OCD present metabolic disruption in the pgACC. The decrease in tCr shows an important relationship with OCD symptomatology. tCr as a marker of cerebral bioenergetics may also be considered as a biomarker of the severity of compulsions. The study failed to prove that metabolic changes correlate with the medication status or the duration of illness. It seems that a disruption in the balance between these metabolites and their transmission may play a role in the pathophysiology of OCD.
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Affiliation(s)
- Eliška Kosová
- National Institute of Mental Health, Klecany, Czech Republic
- Third Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Dita Pajuelo
- Third Faculty of Medicine, Charles University, Prague, Czech Republic.
- MR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic.
| | - Iveta Fajnerová
- National Institute of Mental Health, Klecany, Czech Republic
- Third Faculty of Medicine, Charles University, Prague, Czech Republic
| | - David Greguš
- National Institute of Mental Health, Klecany, Czech Republic
| | - Martin Brunovský
- National Institute of Mental Health, Klecany, Czech Republic
- Third Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Pavla Stopková
- National Institute of Mental Health, Klecany, Czech Republic
- Third Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Antonín Škoch
- National Institute of Mental Health, Klecany, Czech Republic
- MR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Petra Fürstová
- National Institute of Mental Health, Klecany, Czech Republic
| | - Filip Španiel
- National Institute of Mental Health, Klecany, Czech Republic
- Third Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Jiří Horáček
- National Institute of Mental Health, Klecany, Czech Republic
- Third Faculty of Medicine, Charles University, Prague, Czech Republic
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Rehák Bučková B, Mareš J, Škoch A, Kopal J, Tintěra J, Dineen R, Řasová K, Hlinka J. Multimodal-neuroimaging machine-learning analysis of motor disability in multiple sclerosis. Brain Imaging Behav 2023; 17:18-34. [PMID: 36396890 DOI: 10.1007/s11682-022-00737-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/07/2022] [Indexed: 11/19/2022]
Abstract
Motor disability is a dominant and restricting symptom in multiple sclerosis, yet its neuroimaging correlates are not fully understood. We apply statistical and machine learning techniques on multimodal neuroimaging data to discriminate between multiple sclerosis patients and healthy controls and to predict motor disability scores in the patients. We examine the data of sixty-four multiple sclerosis patients and sixty-five controls, who underwent the MRI examination and the evaluation of motor disability scales. The modalities used comprised regional fractional anisotropy, regional grey matter volumes, and functional connectivity. For analysis, we employ two approaches: high-dimensional support vector machines run on features selected by Fisher Score (aiming for maximal classification accuracy), and low-dimensional logistic regression on the principal components of data (aiming for increased interpretability). We apply analogous regression methods to predict symptom severity. While fractional anisotropy provides the classification accuracy of 96.1% and 89.9% with both approaches respectively, including other modalities did not bring further improvement. Concerning the prediction of motor impairment, the low-dimensional approach performed more reliably. The first grey matter volume component was significantly correlated (R = 0.28-0.46, p < 0.05) with most clinical scales. In summary, we identified the relationship between both white and grey matter changes and motor impairment in multiple sclerosis. Furthermore, we were able to achieve the highest classification accuracy based on quantitative MRI measures of tissue integrity between patients and controls yet reported, while also providing a low-dimensional classification approach with comparable results, paving the way to interpretable machine learning models of brain changes in multiple sclerosis.
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Affiliation(s)
- Barbora Rehák Bučková
- The Czech Technical University in Prague, Karlovo namesti 13, 121 35, Prague, Czech Republic.,Institute of Computer Science of the Czech Academy of Sciences, Pod Vodarenskou vezi 2/271, 182 00, Prague, Czech Republic.,National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic
| | - Jan Mareš
- National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic.,Institute for Clinical and Experimental Medicine, Videnska 1958, 140 21, Prague, Czech Republic
| | - Antonín Škoch
- National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic.,Institute for Clinical and Experimental Medicine, Videnska 1958, 140 21, Prague, Czech Republic
| | - Jakub Kopal
- Institute of Computer Science of the Czech Academy of Sciences, Pod Vodarenskou vezi 2/271, 182 00, Prague, Czech Republic
| | - Jaroslav Tintěra
- National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic.,Institute for Clinical and Experimental Medicine, Videnska 1958, 140 21, Prague, Czech Republic
| | - Robert Dineen
- University of Nottingham, Queen's Medical Centre, NG7 2UH, Nottingham, UK.,National Institute for Health Research, Nottingham Biomedical Research Centre, NG1 5DU, Nottingham, UK
| | - Kamila Řasová
- Charles University, Ruska 87, 100 00, Prague, Czech Republic
| | - Jaroslav Hlinka
- Institute of Computer Science of the Czech Academy of Sciences, Pod Vodarenskou vezi 2/271, 182 00, Prague, Czech Republic. .,National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic.
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Bučková BR, Kala D, Kořenek J, Matušková V, Kumpošt V, Svobodová L, Otáhal J, Škoch A, Šulc V, Olšerová A, Vyhnálek M, Janský P, Tomek A, Marusič P, Jiruška P, Hlinka J. Structural connectivity-based predictors of cognitive impairment in stroke patients attributable to aging. PLoS One 2023; 18:e0280892. [PMID: 37058495 PMCID: PMC10104329 DOI: 10.1371/journal.pone.0280892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 01/09/2023] [Indexed: 04/15/2023] Open
Abstract
Despite the rising global burden of stroke and its socio-economic implications, the neuroimaging predictors of subsequent cognitive impairment are still poorly understood. We address this issue by studying the relationship of white matter integrity assessed within ten days after stroke and patients' cognitive status one year after the attack. Using diffusion-weighted imaging, we apply the Tract-Based Spatial Statistics analysis and construct individual structural connectivity matrices by employing deterministic tractography. We further quantify the graph-theoretical properties of individual networks. The Tract-Based Spatial Statistic did identify lower fractional anisotropy as a predictor of cognitive status, although this effect was mostly attributable to the age-related white matter integrity decline. We further observed the effect of age propagating into other levels of analysis. Specifically, in the structural connectivity approach we identified pairs of regions significantly correlated with clinical scales, namely memory, attention, and visuospatial functions. However, none of them persisted after the age correction. Finally, the graph-theoretical measures appeared to be more robust towards the effect of age, but still were not sensitive enough to capture a relationship with clinical scales. In conclusion, the effect of age is a dominant confounder especially in older cohorts, and unless appropriately addressed, may falsely drive the results of the predictive modelling.
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Affiliation(s)
- Barbora Rehák Bučková
- Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic
- Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
- National Institute of Mental Health, Klecany, Czech Republic
| | - David Kala
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
- Department of Developmental Epileptology, Institute of Physiology, Academy of Sciences of Czech Republic, Prague, Czech Republic
| | - Jakub Kořenek
- Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic
- Department of Mathematics, Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Veronika Matušková
- Department of Neurology, Second Faculty of Medicine, Charles University, Motol University Hospital, Prague, Czech Republic
| | - Vojtěch Kumpošt
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
- Department of Developmental Epileptology, Institute of Physiology, Academy of Sciences of Czech Republic, Prague, Czech Republic
| | - Lenka Svobodová
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Jakub Otáhal
- Department of Developmental Epileptology, Institute of Physiology, Academy of Sciences of Czech Republic, Prague, Czech Republic
| | - Antonín Škoch
- National Institute of Mental Health, Klecany, Czech Republic
- MR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Vlastimil Šulc
- Department of Neurology, Second Faculty of Medicine, Charles University, Motol University Hospital, Prague, Czech Republic
| | - Anna Olšerová
- Department of Neurology, Second Faculty of Medicine, Charles University, Motol University Hospital, Prague, Czech Republic
| | - Martin Vyhnálek
- Department of Neurology, Second Faculty of Medicine, Charles University, Motol University Hospital, Prague, Czech Republic
| | - Petr Janský
- Department of Neurology, Second Faculty of Medicine, Charles University, Motol University Hospital, Prague, Czech Republic
| | - Aleš Tomek
- Department of Neurology, Second Faculty of Medicine, Charles University, Motol University Hospital, Prague, Czech Republic
| | - Petr Marusič
- Department of Neurology, Second Faculty of Medicine, Charles University, Motol University Hospital, Prague, Czech Republic
| | - Přemysl Jiruška
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Jaroslav Hlinka
- Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic
- National Institute of Mental Health, Klecany, Czech Republic
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Ibrahim I, Škoch A, Herynek V, Humhej I, Beran J, Flusserová V, Rolencová E, Juhaňáková M, Brzák M, Nagy M, Tintěra J. Magnetic resonance tractography of the brachial plexus: step-by-step. Quant Imaging Med Surg 2022; 12:4488-4501. [PMID: 36060587 PMCID: PMC9403600 DOI: 10.21037/qims-22-30] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 06/06/2022] [Indexed: 11/17/2022]
Abstract
Background Magnetic resonance (MR) tractography of the brachial plexus (BP) is challenging due to different factors such as motion artifacts, pulsation artifacts, signal-to-noise ratio, spatial resolution; eddy currents induced geometric distortions, sequence parameters and choice of used coils. Notably challenging is the separation of the peripheral nerve bundles and skeletal muscles as both structures have similar fractional anisotropy values. We proposed an algorithm for robust visualization and assessment of BP root bundles using the segmentation of the spinal cord (SSC, C4-T1) as seed points for the initial starting area for the fibre tracking algorithm. Methods Twenty-seven healthy volunteers and four patients with root avulsions underwent magnetic resonance imaging (MRI) examinations on a 3T MR scanner with optimized measurement protocols for diffusion-weighted images and coronal T2 weighted 3D short-term inversion recovery sampling perfection with application optimized contrast using varying flip angle evaluation sequences used for BP fibre reconstruction and MR neurography (MRN). The fibre bundles reconstruction was optimized in terms of eliminating the skeletal muscle fibres contamination using the SSC and the tracking threshold of the normalized quantitative anisotropy (NQA) on reconstruction of the BP. In our study, the NQA parameter has been used for fiber tracking instead of fractional anisotropy (FA). The diffusion data were processed in individual C4-T1 root bundles using the generalized q-sampling imaging (GQI) algorithm. Calculated diffusion parameters were statistically analysed using the two-sample t-test. The MRN was performed in MedINRIA and post-processed using the maximum intensity projection (MIP) method to demonstrate BP root bundles in multiple planes. Results In control subjects, no significant effect of laterality in diffusion parameters was found (P>0.05) in the BP. In the central part of the BP, a significant difference between control subjects and patients at P=0.02 was found in the NQA values. Other diffusion parameters were not significantly different. Conclusions Using NQA instead of FA in the proposed algorithm allowed for a better separation of muscle and root nerve bundles. The presented algorithm yields a high quality reconstruction of the BP bundles that may be helpful both in research and clinical practice.
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Affiliation(s)
- Ibrahim Ibrahim
- Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Antonín Škoch
- Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Vít Herynek
- Center for Advanced Preclinical Imaging, First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Ivan Humhej
- Department of Neurosurgery, J. E. Purkyně University, Masaryk Hospital, Ústí nad Labem, Czech Republic
| | - Jan Beran
- Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Vlasta Flusserová
- Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Eva Rolencová
- Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Martina Juhaňáková
- Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Michal Brzák
- Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Markéta Nagy
- Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Jaroslav Tintěra
- Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
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8
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McWhinney SR, Brosch K, Calhoun VD, Crespo-Facorro B, Crossley NA, Dannlowski U, Dickie E, Dietze LMF, Donohoe G, Du Plessis S, Ehrlich S, Emsley R, Furstova P, Glahn DC, Gonzalez-Valderrama A, Grotegerd D, Holleran L, Kircher TTJ, Knytl P, Kolenic M, Lencer R, Nenadić I, Opel N, Pfarr JK, Rodrigue AL, Rootes-Murdy K, Ross AJ, Sim K, Škoch A, Spaniel F, Stein F, Švancer P, Tordesillas-Gutiérrez D, Undurraga J, Vázquez-Bourgon J, Voineskos A, Walton E, Weickert TW, Weickert CS, Thompson PM, van Erp TGM, Turner JA, Hajek T. Obesity and brain structure in schizophrenia - ENIGMA study in 3021 individuals. Mol Psychiatry 2022; 27:3731-3737. [PMID: 35739320 PMCID: PMC9902274 DOI: 10.1038/s41380-022-01616-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 04/27/2022] [Accepted: 05/06/2022] [Indexed: 02/08/2023]
Abstract
Schizophrenia is frequently associated with obesity, which is linked with neurostructural alterations. Yet, we do not understand how the brain correlates of obesity map onto the brain changes in schizophrenia. We obtained MRI-derived brain cortical and subcortical measures and body mass index (BMI) from 1260 individuals with schizophrenia and 1761 controls from 12 independent research sites within the ENIGMA-Schizophrenia Working Group. We jointly modeled the statistical effects of schizophrenia and BMI using mixed effects. BMI was additively associated with structure of many of the same brain regions as schizophrenia, but the cortical and subcortical alterations in schizophrenia were more widespread and pronounced. Both BMI and schizophrenia were primarily associated with changes in cortical thickness, with fewer correlates in surface area. While, BMI was negatively associated with cortical thickness, the significant associations between BMI and surface area or subcortical volumes were positive. Lastly, the brain correlates of obesity were replicated among large studies and closely resembled neurostructural changes in major depressive disorders. We confirmed widespread associations between BMI and brain structure in individuals with schizophrenia. People with both obesity and schizophrenia showed more pronounced brain alterations than people with only one of these conditions. Obesity appears to be a relevant factor which could account for heterogeneity of brain imaging findings and for differences in brain imaging outcomes among people with schizophrenia.
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Affiliation(s)
- Sean R McWhinney
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory University, Atlanta, GA, USA
| | - Benedicto Crespo-Facorro
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
- IBiS, University Hospital Virgen del Rocio, Sevilla, Spain
- Department of Psychiatry, School of Medicine, University of Sevilla, Sevilla, Spain
| | - Nicolas A Crossley
- Department of Psychiatry, School of Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Psychosis Studies, King's College London, London, UK
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Erin Dickie
- Centre for Addiction & Mental Health, Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | | | - Gary Donohoe
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Stefan Du Plessis
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- SAMRC Genomics of Brain Disorders Unit, Cape Town, South Africa
| | - Stefan Ehrlich
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Robin Emsley
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Petra Furstova
- National Institute of Mental Health, Klecany, Czech Republic
| | - David C Glahn
- Department of Psychiatry & Behavioral Sciences, Boston Children's Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
| | - Alfonso Gonzalez-Valderrama
- School of Medicine, Universidad Finis Terrae, Santiago, Chile
- Early Intervention in Psychosis Program, Instituto Psiquiátrico 'Dr. José Horwitz B.', Santiago, Chile
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Laurena Holleran
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Tilo T J Kircher
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Pavel Knytl
- National Institute of Mental Health, Klecany, Czech Republic
- Charles University, Third Faculty of Medicine, Prague, Czech Republic
| | - Marian Kolenic
- National Institute of Mental Health, Klecany, Czech Republic
- Charles University, Third Faculty of Medicine, Prague, Czech Republic
| | - Rebekka Lencer
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Department of Pscyhiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Department of Psychiatry, Jena University Hospital/Friedrich-Schiller-University Jena, Jena, Germany
| | - Julia-Katharina Pfarr
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Amanda L Rodrigue
- Department of Psychiatry & Behavioral Sciences, Boston Children's Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | | | - Alex J Ross
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Kang Sim
- West Region, Institute of Mental Health, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Antonín Škoch
- National Institute of Mental Health, Klecany, Czech Republic
- Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Filip Spaniel
- National Institute of Mental Health, Klecany, Czech Republic
- Charles University, Third Faculty of Medicine, Prague, Czech Republic
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Patrik Švancer
- National Institute of Mental Health, Klecany, Czech Republic
- Charles University, Third Faculty of Medicine, Prague, Czech Republic
| | - Diana Tordesillas-Gutiérrez
- Department of Radiology, Marqués de Valdecilla University Hospital, Valdecilla Biomedical Research Institute IDIVAL, Santander, Spain
- Computación Avanzada y Ciencia, Instituto de Física de Cantabria, CSIC, Santander, Spain
| | - Juan Undurraga
- Early Intervention in Psychosis Program, Instituto Psiquiátrico 'Dr. José Horwitz B.', Santiago, Chile
- Department of Neurology and Psychiatry. Faculty of Medicine, Clínica Alemana Universidad del Desarrollo, Santiago, Chile
| | - Javier Vázquez-Bourgon
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
- Department of Medicine and Psychiatry, School of Medicine, University of Cantabria, Santander, Spain
- Department of Psychiatry, Marqués de Valdecilla University Hospital, Valdecilla Biomedical Research Institute IDIVAL, Santander, Spain
| | - Aristotle Voineskos
- Centre for Addiction & Mental Health, Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Esther Walton
- Department of Psychology, University of Bath, Bath, UK
| | - Thomas W Weickert
- Department of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
- Neuroscience Research Australia, Randwick, NSW, Australia
| | - Cynthia Shannon Weickert
- Department of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
- Neuroscience Research Australia, Randwick, NSW, Australia
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Theo G M van Erp
- Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA, USA
| | - Jessica A Turner
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Tomas Hajek
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada.
- National Institute of Mental Health, Klecany, Czech Republic.
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9
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Gerster M, Taher H, Škoch A, Hlinka J, Guye M, Bartolomei F, Jirsa V, Zakharova A, Olmi S. Patient-Specific Network Connectivity Combined With a Next Generation Neural Mass Model to Test Clinical Hypothesis of Seizure Propagation. Front Syst Neurosci 2021; 15:675272. [PMID: 34539355 PMCID: PMC8440880 DOI: 10.3389/fnsys.2021.675272] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 07/07/2021] [Indexed: 11/13/2022] Open
Abstract
Dynamics underlying epileptic seizures span multiple scales in space and time, therefore, understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. In this view, mathematical models have been developed, ranging from single neuron to neural population. In this study, we consider a neural mass model able to exactly reproduce the dynamics of heterogeneous spiking neural networks. We combine mathematical modeling with structural information from non invasive brain imaging, thus building large-scale brain network models to explore emergent dynamics and test the clinical hypothesis. We provide a comprehensive study on the effect of external drives on neuronal networks exhibiting multistability, in order to investigate the role played by the neuroanatomical connectivity matrices in shaping the emergent dynamics. In particular, we systematically investigate the conditions under which the network displays a transition from a low activity regime to a high activity state, which we identify with a seizure-like event. This approach allows us to study the biophysical parameters and variables leading to multiple recruitment events at the network level. We further exploit topological network measures in order to explain the differences and the analogies among the subjects and their brain regions, in showing recruitment events at different parameter values. We demonstrate, along with the example of diffusion-weighted magnetic resonance imaging (dMRI) connectomes of 20 healthy subjects and 15 epileptic patients, that individual variations in structural connectivity, when linked with mathematical dynamic models, have the capacity to explain changes in spatiotemporal organization of brain dynamics, as observed in network-based brain disorders. In particular, for epileptic patients, by means of the integration of the clinical hypotheses on the epileptogenic zone (EZ), i.e., the local network where highly synchronous seizures originate, we have identified the sequence of recruitment events and discussed their links with the topological properties of the specific connectomes. The predictions made on the basis of the implemented set of exact mean-field equations turn out to be in line with the clinical pre-surgical evaluation on recruited secondary networks.
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Affiliation(s)
- Moritz Gerster
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
| | - Halgurd Taher
- Inria Sophia Antipolis Méditerranée Research Centre, MathNeuro Team, Valbonne, France
| | - Antonín Škoch
- National Institute of Mental Health, Klecany, Czechia
- MR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czechia
| | - Jaroslav Hlinka
- National Institute of Mental Health, Klecany, Czechia
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czechia
| | - Maxime Guye
- Faculté de Médecine de la Timone, Centre de Résonance Magnétique et Biologique et Médicale (CRMBM, UMR CNRS-AMU 7339), Medical School of Marseille, Aix-Marseille Université, Marseille, France
- Assistance Publique -Hôpitaux de Marseille, Hôpital de la Timone, Pôle d'Imagerie, Marseille, France
| | - Fabrice Bartolomei
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, Service de Neurophysiologie Clinique, Marseille, France
| | - Viktor Jirsa
- Aix Marseille Université, Inserm, Institut de Neurosciences des Systèmes, UMRS 1106, Marseille, France
| | - Anna Zakharova
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
| | - Simona Olmi
- Inria Sophia Antipolis Méditerranée Research Centre, MathNeuro Team, Valbonne, France
- Consiglio Nazionale delle Ricerche, Istituto dei Sistemi Complessi, Sesto Fiorentino, Italy
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10
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Ibrahim I, Škoch A, Herynek V, Jírů F, Tintěra J. Magnetic resonance tractography of the lumbosacral plexus: Step-by-step. Medicine (Baltimore) 2021; 100:e24646. [PMID: 33578590 PMCID: PMC10545402 DOI: 10.1097/md.0000000000024646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 12/22/2020] [Accepted: 01/13/2021] [Indexed: 11/25/2022] Open
Abstract
ABSTRACT MR tractography of the lumbosacral plexus (LSP) is challenging due to the difficulty of acquiring high quality data and accurately estimating the neuronal tracts. We proposed an algorithm for an accurate visualization and assessment of the major LSP bundles using the segmentation of the cauda equina as seed points for the initial starting area for the fiber tracking algorithm.Twenty-six healthy volunteers underwent MRI examinations on a 3T MR scanner using the phased array coils with optimized measurement protocols for diffusion-weighted images and coronal T2 weighted 3D short-term inversion recovery sampling perfection with application optimized contrast using varying flip angle evaluation sequences used for LSP fiber reconstruction and MR neurography (MRN).The fiber bundles reconstruction was optimized in terms of eliminating the muscle fibers contamination using the segmentation of cauda equina, the effects of the normalized quantitative anisotropy (NQA) and angular threshold on reconstruction of the LSP. In this study, the NQA parameter has been used for fiber tracking instead of fractional anisotropy (FA) and the regions of interest positioning was precisely adjusted bilaterally and symmetrically in each individual subject.The diffusion data were processed in individual L3-S2 nerve fibers using the generalized Q-sampling imaging algorithm. Data (mean FA, mean diffusivity, axial diffusivity and radial diffusivity, and normalized quantitative anisotropy) were statistically analyzed using the linear mixed-effects model. The MR neurography was performed in MedINRIA and post-processed using the maximum intensity projection method to demonstrate LSP tracts in multiple planes.FA values significantly decreased towards the sacral region (P < .001); by contrast, mean diffusivity, axial diffusivity, radial diffusivity and NQA values significantly increased towards the sacral region (P < .001).Fiber tractography of the LSP was feasible in all examined subjects and closely corresponded with the nerves visible in the maximum intensity projection images of MR neurography. Usage of NQA instead of FA in the proposed algorithm enabled better separation of muscle and nerve fibers.The presented algorithm yields a high quality reconstruction of the LSP bundles that may be helpful both in research and clinical practice.
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Affiliation(s)
- Ibrahim Ibrahim
- Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, MR Unit
| | - Antonín Škoch
- Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, MR Unit
| | - Vít Herynek
- Center for Advanced Preclinical Imaging, First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Filip Jírů
- Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, MR Unit
| | - Jaroslav Tintěra
- Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, MR Unit
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11
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Profant O, Škoch A, Tintěra J, Svobodová V, Kuchárová D, Svobodová Burianová J, Syka J. The Influence of Aging, Hearing, and Tinnitus on the Morphology of Cortical Gray Matter, Amygdala, and Hippocampus. Front Aging Neurosci 2020; 12:553461. [PMID: 33343328 PMCID: PMC7746808 DOI: 10.3389/fnagi.2020.553461] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Accepted: 11/12/2020] [Indexed: 12/11/2022] Open
Abstract
Age related hearing loss (presbycusis) is a natural process represented by elevated auditory thresholds and decreased speech intelligibility, especially in noisy conditions. Tinnitus is a phantom sound that also potentially leads to cortical changes, with its highest occurrence coinciding with the clinical onset of presbycusis. The aim of our project was to identify age, hearing loss and tinnitus related structural changes, within the auditory system and associated structures. Groups of subjects with presbycusis and tinnitus (22 subjects), with only presbycusis (24 subjects), young tinnitus patients with normal hearing (10 subjects) and young controls (17 subjects), underwent an audiological examination to characterize hearing loss and tinnitus. In addition, MRI (3T MR system, analysis in Freesurfer software) scans were used to identify changes in the cortical and subcortical structures. The following areas of the brain were analyzed: Heschl gyrus (HG), planum temporale (PT), primary visual cortex (V1), gyrus parahippocampus (PH), anterior insula (Ins), amygdala (Amg), and hippocampus (HP). A statistical analysis was performed in R framework using linear mixed-effects models with explanatory variables: age, tinnitus, laterality and hearing. In all of the cortical structures, the gray matter thickness decreased significantly with aging without having an effect on laterality (differences between the left and right hemispheres). The decrease in the gray matter thickness was faster in the HG, PT and Ins in comparison with the PH and V1. Aging did not influence the surface of the cortical areas, however there were differences between the surface size of the reported regions in the left and right hemispheres. Hearing loss caused only a borderline decrease of the cortical surface in the HG. Tinnitus was accompanied by a borderline decrease of the Ins surface and led to an increase in the volume of Amy and HP. In summary, aging is accompanied by a decrease in the cortical gray matter thickness; hearing loss only has a limited effect on the structure of the investigated cortical areas and tinnitus causes structural changes which are predominantly within the limbic system and insula, with the structure of the auditory system only being minimally affected.
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Affiliation(s)
- Oliver Profant
- Department of Auditory Neuroscience, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czechia.,Department of Otorhinolaryngology, 3rd Faculty of Medicine, Faculty Hospital Kralovske Vinohrady, Charles University, Prague, Czechia
| | - Antonín Škoch
- MR Unit, Institute of Clinical and Experimental Medicine, Prague, Czechia
| | - Jaroslav Tintěra
- MR Unit, Institute of Clinical and Experimental Medicine, Prague, Czechia
| | - Veronika Svobodová
- Department of Auditory Neuroscience, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czechia.,Department of Otorhinolaryngology and Head and Neck Surgery, 1st Faculty of Medicine, University Hospital Motol, Charles University, Prague, Czechia
| | - Diana Kuchárová
- Department of Auditory Neuroscience, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czechia.,Department of Otorhinolaryngology and Head and Neck Surgery, 1st Faculty of Medicine, University Hospital Motol, Charles University, Prague, Czechia
| | - Jana Svobodová Burianová
- Department of Auditory Neuroscience, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czechia
| | - Josef Syka
- Department of Auditory Neuroscience, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czechia
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12
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Gerster M, Berner R, Sawicki J, Zakharova A, Škoch A, Hlinka J, Lehnertz K, Schöll E. FitzHugh-Nagumo oscillators on complex networks mimic epileptic-seizure-related synchronization phenomena. Chaos 2020; 30:123130. [PMID: 33380049 DOI: 10.1063/5.0021420] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 11/23/2020] [Indexed: 06/12/2023]
Abstract
We study patterns of partial synchronization in a network of FitzHugh-Nagumo oscillators with empirical structural connectivity measured in human subjects. We report the spontaneous occurrence of synchronization phenomena that closely resemble the ones seen during epileptic seizures in humans. In order to obtain deeper insights into the interplay between dynamics and network topology, we perform long-term simulations of oscillatory dynamics on different paradigmatic network structures: random networks, regular nonlocally coupled ring networks, ring networks with fractal connectivities, and small-world networks with various rewiring probability. Among these networks, a small-world network with intermediate rewiring probability best mimics the findings achieved with the simulations using the empirical structural connectivity. For the other network topologies, either no spontaneously occurring epileptic-seizure-related synchronization phenomena can be observed in the simulated dynamics, or the overall degree of synchronization remains high throughout the simulation. This indicates that a topology with some balance between regularity and randomness favors the self-initiation and self-termination of episodes of seizure-like strong synchronization.
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Affiliation(s)
- Moritz Gerster
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstr. 36, 10623 Berlin, Germany
| | - Rico Berner
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstr. 36, 10623 Berlin, Germany
| | - Jakub Sawicki
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstr. 36, 10623 Berlin, Germany
| | - Anna Zakharova
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstr. 36, 10623 Berlin, Germany
| | - Antonín Škoch
- National Institute of Mental Health, Topolová 748, 250 67 Klecany, Czech Republic
| | - Jaroslav Hlinka
- National Institute of Mental Health, Topolová 748, 250 67 Klecany, Czech Republic
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127 Bonn, Germany
| | - Eckehard Schöll
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstr. 36, 10623 Berlin, Germany
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13
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Gajdošík M, Hingerl L, Škoch A, Freudenthaler A, Krumpolec P, Ukropec J, Ukropcová B, Šedivý P, Hájek M, Itariu BK, Maier B, Baumgartner‐Parzer S, Krebs M, Trattnig S, Krššák M. Ultralong TE In Vivo 1 H MR Spectroscopy of Omega-3 Fatty Acids in Subcutaneous Adipose Tissue at 7 T. J Magn Reson Imaging 2019; 50:71-82. [PMID: 30578581 PMCID: PMC6618283 DOI: 10.1002/jmri.26605] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Revised: 11/27/2018] [Accepted: 11/28/2018] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Omega-3 (n-3) fatty acids (FA) play and important role in neural development and other metabolic diseases such as obesity and diabetes. The knowledge about the in vivo content and distribution of n-3 FA in human body tissues is not well established and the standard quantification of FA is invasive and costly. PURPOSE To detect omega-3 (n-3 CH3 ) and non-omega-3 (CH3 ) methyl group resonance lines with echo times up to 1200 msec, in oils, for the assessment of n-3 FA content, and the n-3 FA fraction in adipose tissue in vivo. STUDY TYPE Prospective technical development. POPULATION Three oils with different n-3 FA content and 24 healthy subjects. FIELD STRENGTH/SEQUENCE Single-voxel MR spectroscopy (SVS) with a point-resolved spectroscopy (PRESS) sequence with an echo time (TE) of 1000 msec at 7 T. ASSESSMENT Knowledge about the J-coupling evolution of both CH3 resonances was used for the optimal detection of the n-3 CH3 resonance line at a TE of 1000 msec. The accuracy of the method in oils and in vivo was validated from a biopsy sample with gas chromatography analysis. STATISTICAL TESTS SVS data were compared to gas chromatography with the Pearson correlation coefficient. RESULTS T2 relaxation times in oils were assessed as follows: CH2 , 65 ± 22 msec; CH3 , 325 ± 7 msec; and n-3 CH3 , 628 ± 34 msec. The n-3 FA fractions from oil phantom experiments (n = 3) were in agreement with chromatography analysis and the comparison of in vivo obtained data with the results of chromatography analysis (n = 5) yielded a significant correlation (P = 0.029). DATA CONCLUSION PRESS with ultralong-TE can detect and quantify the n-3 CH3 signal in vivo at 7 T. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:71-82.
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Affiliation(s)
- Martin Gajdošík
- High‐field MR Centre, Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
- Division of Endocrinology and Metabolism, Department of Internal Medicine IIIMedical University of ViennaViennaAustria
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of RadiologyNew York University School of MedicineNew YorkNew York
| | - Lukas Hingerl
- High‐field MR Centre, Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
| | - Antonín Škoch
- National Institute of Mental HealthKlecanyCzech Republic
- MR Unit, Department of Diagnostic and Interventional RadiologyInstitute for Clinical and Experimental MedicinePragueCzech Republic
| | - Angelika Freudenthaler
- Division of Endocrinology and Metabolism, Department of Internal Medicine IIIMedical University of ViennaViennaAustria
| | - Patrik Krumpolec
- High‐field MR Centre, Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
- Institute of Experimental EndocrinologyBiomedical Research Center, Slovak Academy of SciencesBratislavaSlovakia
| | - Jozef Ukropec
- Institute of Experimental EndocrinologyBiomedical Research Center, Slovak Academy of SciencesBratislavaSlovakia
| | - Barbara Ukropcová
- Institute of Experimental EndocrinologyBiomedical Research Center, Slovak Academy of SciencesBratislavaSlovakia
| | - Petr Šedivý
- MR Unit, Department of Diagnostic and Interventional RadiologyInstitute for Clinical and Experimental MedicinePragueCzech Republic
| | - Milan Hájek
- MR Unit, Department of Diagnostic and Interventional RadiologyInstitute for Clinical and Experimental MedicinePragueCzech Republic
| | - Bianca K. Itariu
- Division of Endocrinology and Metabolism, Department of Internal Medicine IIIMedical University of ViennaViennaAustria
| | - Bernhard Maier
- University Clinic for Trauma Surgery, Medical University of ViennaViennaAustria
| | - Sabina Baumgartner‐Parzer
- Division of Endocrinology and Metabolism, Department of Internal Medicine IIIMedical University of ViennaViennaAustria
| | - Michael Krebs
- Division of Endocrinology and Metabolism, Department of Internal Medicine IIIMedical University of ViennaViennaAustria
| | - Siegfried Trattnig
- High‐field MR Centre, Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
- Christian Doppler Laboratory for Clinical Molecular MR ImagingViennaAustria
| | - Martin Krššák
- High‐field MR Centre, Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
- Division of Endocrinology and Metabolism, Department of Internal Medicine IIIMedical University of ViennaViennaAustria
- Christian Doppler Laboratory for Clinical Molecular MR ImagingViennaAustria
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Kratochvílová S, Škoch A, Wohl P, Švehlíková E, Dezortová M, Hill M, Hájek M, Pelikánová T. Intramyocellular lipid content in subjects with impaired fasting glucose after telmisartan treatment, a randomised cross-over trial. Magn Reson Imaging 2015; 34:353-8. [PMID: 26523653 DOI: 10.1016/j.mri.2015.10.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2015] [Accepted: 10/21/2015] [Indexed: 10/22/2022]
Abstract
Ectopic lipid accumulation in skeletal muscle is associated with insulin resistance. Telmisartan improves metabolic parameters in type 2 diabetic patients. The aim of our study was to evaluate the in vivo effect of telmisartan on intramyocellular lipid content (IMCL) in subjects with impaired fasting glucose (IFG) by magnetic resonance spectroscopy (MRS). We enrolled 10 subjects with IFG in a cross-over, placebo-controlled, randomized, double-blind trial, treated with 3 weeks of telmisartan (160 mg daily) or placebo. After completing each treatment, a hyperinsulinaemic euglycaemic clamp (1 mU/kg per min; 5 mmol/l; 120 min) to assess insulin action (metabolic clearance rate of glucose, MCR) and (1)H MRS of the m. tibialis anterior using a MR Scanner Siemens Vision operating at 1.5 T to evaluate IMCL content, were performed. Plasma adipokine levels were determined simultaneously. Telmisartan treatment resulted in a lower fasting plasma glucose (FPG) (p < 0.05), but insulin action was comparable to after placebo. Telmisartan did not affect IMCL content. After placebo, IMCL correlated negatively with total cholesterol (p < 0.001), MCR (p < 0.05) and adiponectin (p < 0.05) and positively with FPG (p < 0.05). After telmisartan treatment there was only a positive correlation between IMCL and TNFα (p < 0.05). IMCL content is related to parameters of glucose metabolism and insulin action in sedentary IFG subjects. A short telmisartan treatment did not affect the IMCL content despite its positive effect on FPG. The improvement in FPG was probably mediated through interference with other metabolic pathways.
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Affiliation(s)
- Simona Kratochvílová
- Diabetes Center, Institute for Clinical and Experimental Medicine, Vídeňská 1958/4, Prague 140 21, Czech Republic
| | - Antonín Škoch
- MR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Vídeňská 1958/4, Prague 140 21, Czech Republic
| | - Petr Wohl
- Diabetes Center, Institute for Clinical and Experimental Medicine, Vídeňská 1958/4, Prague 140 21, Czech Republic
| | - Eva Švehlíková
- Diabetes Center, Institute for Clinical and Experimental Medicine, Vídeňská 1958/4, Prague 140 21, Czech Republic
| | - Monika Dezortová
- MR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Vídeňská 1958/4, Prague 140 21, Czech Republic
| | - Martin Hill
- Institute of Endocrinology, Národní 8, Prague 116 94, Czech Republic
| | - Milan Hájek
- MR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Vídeňská 1958/4, Prague 140 21, Czech Republic
| | - Terezie Pelikánová
- Diabetes Center, Institute for Clinical and Experimental Medicine, Vídeňská 1958/4, Prague 140 21, Czech Republic.
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Wagnerová D, Herynek V, Dezortová M, Marusič P, Kršek P, Zámečník J, Jírů F, Škoch A, Hájek M. The Relationships Between Quantitative MR Parameters in Hippocampus in Healthy Subjects and Patients With Temporal Lobe Epilepsy. Physiol Res 2015; 64:407-17. [PMID: 25536324 DOI: 10.33549/physiolres.932846] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
We introduce a new magnetic resonance (MR) method based on a pixel-by-pixel image processing to examine relationships between metabolic and structural processes in the pathologic hippocampus. The method was tested for lateralization of the epileptogenic zone in patients with temporal lobe epilepsy (TLE). Twenty patients with drug-resistant TLE and fifteen healthy controls were examined at 3T. The measurement protocol contained T2-weighted MR images, spectroscopic imaging, diffusion tensor imaging and T2 relaxometry. Correlations between quantitative MR parameters were calculated on a pixel-by-pixel basis using the CORIMA program which enables automated pixel identification in the normal tissue according to control data. All MR parameters changed in the anteroposterior direction in the hippocampus and correlation patterns and their slopes differed between patients and controls. Combinations of T2 relaxation times with metabolite values represent the best biomarkers of the epileptogenic zone. Correlations with mean diffusivity did not provide sufficiently accurate results due to diffusion image distortions. Quantitative MR analysis non-invasively provides a detailed description of hippocampal pathology and may represent complementary tool to the standard clinical protocol. However, the automated processing should be carefully monitored in order to avoid possible errors caused by MR artifacts.
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Affiliation(s)
- D Wagnerová
- Institute for Clinical and Experimental Medicine, Prague, Czech Republic.
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Profant O, Škoch A, Balogová Z, Tintěra J, Hlinka J, Syka J. Diffusion tensor imaging and MR morphometry of the central auditory pathway and auditory cortex in aging. Neuroscience 2014; 260:87-97. [PMID: 24333969 DOI: 10.1016/j.neuroscience.2013.12.010] [Citation(s) in RCA: 76] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2013] [Revised: 11/13/2013] [Accepted: 12/05/2013] [Indexed: 01/12/2023]
Affiliation(s)
- O Profant
- Department of Auditory Neuroscience, Institute of Experimental Medicine, Academy of Sciences of the Czech Republic, Prague, Czech Republic; Department of Otorhinolaryngology and Head and Neck Surgery, 1st Medical Faculty of Charles University, University Hospital Motol, Prague, Czech Republic.
| | - A Škoch
- MR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Z Balogová
- Department of Auditory Neuroscience, Institute of Experimental Medicine, Academy of Sciences of the Czech Republic, Prague, Czech Republic; Department of Otorhinolaryngology and Head and Neck Surgery, 1st Medical Faculty of Charles University, University Hospital Motol, Prague, Czech Republic
| | - J Tintěra
- MR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - J Hlinka
- Department of Nonlinear Dynamics and Complex Systems, Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic
| | - J Syka
- Department of Auditory Neuroscience, Institute of Experimental Medicine, Academy of Sciences of the Czech Republic, Prague, Czech Republic
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Mikoláš P, Vyhnánek J, Škoch A, Horáček J. Analysis of fMRI time-series by entropy measures. Neuro Endocrinol Lett 2012; 33:471-476. [PMID: 23090262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Received: 06/15/2012] [Accepted: 09/10/2012] [Indexed: 06/01/2023]
Abstract
Entropy is a measure of information content or complexity. Information-theoretic modeling has been successfully used in various biological data analyses including functional magnetic resonance (fMRI). Several studies have tested and evaluated entropy measures on simulated datasets and real fMRI data. The efficiency of entropy algorithms has been compared to classical methods based on the linear model. Here we explain and summarize entropy algorithms that have been used in fMRI analysis, their advantages over classical methods and their potential use in event-related and block design fMRI.
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Affiliation(s)
- Pavol Mikoláš
- Prague Psychiatric Centre, 3rd Faculty of Medicine, Charles University, Prague, Czech Republic.
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