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For: Mazeaud C, Salazar BH, Braun M, Hossu G, Khavari R. Functional MRI in neuro-urology: A narrative review. Prog Urol 2023:S1166-7087(23)00082-9. [PMID: 37062631 DOI: 10.1016/j.purol.2023.03.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 03/26/2023] [Indexed: 04/18/2023]

For:	Mazeaud C, Salazar BH, Braun M, Hossu G, Khavari R. Functional MRI in neuro-urology: A narrative review. Prog Urol 2023:S1166-7087(23)00082-9. [PMID: 37062631 DOI: 10.1016/j.purol.2023.03.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 03/26/2023] [Indexed: 04/18/2023]

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Cited by Other Article(s)

Yang X, Liechti MD, Kanber B, Sudre CH, Castellazzi G, Zhang J, Yiannakas MC, Gonzales G, Prados F, Toosy AT, Gandini Wheeler-Kingshott CAM, Panicker JN. White Matter Magnetic Resonance Diffusion Measures in Multiple Sclerosis with Overactive Bladder. Brain Sci 2024;14:975. [PMID: 39451989 PMCID: PMC11506346 DOI: 10.3390/brainsci14100975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2024] [Revised: 09/22/2024] [Accepted: 09/25/2024] [Indexed: 10/26/2024] Open

Abstract

BACKGROUND

Lower urinary tract (LUT) symptoms are reported in more than 80% of patients with multiple sclerosis (MS), most commonly an overactive bladder (OAB). The relationship between brain white matter (WM) changes in MS and OAB symptoms is poorly understood.

OBJECTIVES

We aim to evaluate (i) microstructural WM differences across MS patients (pwMS) with OAB symptoms, patients without LUT symptoms, and healthy subjects using diffusion tensor imaging (DTI), and (ii) associations between clinical OAB symptom scores and DTI indices.

METHODS

Twenty-nine female pwMS [mean age (SD) 43.3 years (9.4)], including seventeen with OAB [mean age (SD) 46.1 years (8.6)] and nine without LUT symptoms [mean age (SD) 37.5 years (8.9)], and fourteen healthy controls (HCs) [mean age (SD) 48.5 years (20)] were scanned in a 3T MRI with a DTI protocol. Additionally, clinical scans were performed for WM lesion segmentation. Group differences in fractional anisotropy (FA) were evaluated using tract-based spatial statistics. The Urinary Symptom Profile questionnaire assessed OAB severity.

RESULTS

A statistically significant reduction in FA (p = 0.004) was identified in microstructural WM in pwMS, compared with HCs. An inverse correlation was found between FA in frontal and parietal WM lobes and OAB scores (p = 0.021) in pwMS. Areas of lower FA, although this did not reach statistical significance, were found in both frontal lobes and the rest of the non-dominant hemisphere in pwMS with OAB compared with pwMS without LUT symptoms (p = 0.072).

CONCLUSIONS

This study identified that lesions affecting different WM tracts in MS can result in OAB symptoms and demonstrated the role of the WM in the neural control of LUT functions. By using DTI, the association between OAB symptom severity and WM changes were identified, adding knowledge to the current LUT working model. As MS is predominantly a WM disease, these findings suggest that regional WM involvement, including of the anterior corona radiata, anterior thalamic radiation, superior longitudinal fasciculus, and superior frontal-occipital fasciculus and a non-dominant prevalence in WM, can result in OAB symptoms. OAB symptoms in MS correlate with anisotropy changes in different white matter tracts as demonstrated by DTI. Structural impairment in WM tracts plays an important role in LUT symptoms in MS.

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Affiliation(s)

Xixi Yang Department of Neurology, Xuan Wu Hospital of Capital Medical University, Beijing 100053, China Department of Brain Repair and Rehabilitation, Faculty of Brain Sciences, Queen Square Institute of Neurology, University College London, London WC1E 6BT, UK; (M.D.L.); (J.N.P.) Department of Uro-Neurology, The National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London WC1E 6BT, UK; (B.K.); (G.C.); (M.C.Y.); (F.P.); (A.T.T.); (C.A.M.G.W.-K.)
Martina D. Liechti Department of Brain Repair and Rehabilitation, Faculty of Brain Sciences, Queen Square Institute of Neurology, University College London, London WC1E 6BT, UK; (M.D.L.); (J.N.P.) Department of Uro-Neurology, The National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London WC1E 6BT, UK; (B.K.); (G.C.); (M.C.Y.); (F.P.); (A.T.T.); (C.A.M.G.W.-K.) Department of Neuro-Urology, Balgrist University Hospital, University of Zürich, 8006 Zürich, Switzerland
Baris Kanber NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London WC1E 6BT, UK; (B.K.); (G.C.); (M.C.Y.); (F.P.); (A.T.T.); (C.A.M.G.W.-K.) Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK;
Carole H. Sudre Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK Dementia Research Centre, Institute of Neurology, University College London, London WC1E 6BT, UK
Gloria Castellazzi NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London WC1E 6BT, UK; (B.K.); (G.C.); (M.C.Y.); (F.P.); (A.T.T.); (C.A.M.G.W.-K.) Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy
Jiaying Zhang School of Artificial Intelligence, Beijing University of Post and Communications, Beijing 100876, China; Department of Computer Science and Centre for Medical Image Computing, University College London, London WC1E 6BT, UK
Marios C. Yiannakas NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London WC1E 6BT, UK; (B.K.); (G.C.); (M.C.Y.); (F.P.); (A.T.T.); (C.A.M.G.W.-K.)
Gwen Gonzales Department of Uro-Neurology, The National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK;
Ferran Prados NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London WC1E 6BT, UK; (B.K.); (G.C.); (M.C.Y.); (F.P.); (A.T.T.); (C.A.M.G.W.-K.) Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; e-Health Centre, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
Ahmed T. Toosy NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London WC1E 6BT, UK; (B.K.); (G.C.); (M.C.Y.); (F.P.); (A.T.T.); (C.A.M.G.W.-K.)
Claudia A. M. Gandini Wheeler-Kingshott NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London WC1E 6BT, UK; (B.K.); (G.C.); (M.C.Y.); (F.P.); (A.T.T.); (C.A.M.G.W.-K.) Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy Digital Neuroscience Centre, IRCCS Mondino Foundation, 27100 Pavia, Italy
Jalesh N. Panicker Department of Brain Repair and Rehabilitation, Faculty of Brain Sciences, Queen Square Institute of Neurology, University College London, London WC1E 6BT, UK; (M.D.L.); (J.N.P.) Department of Uro-Neurology, The National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK;

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de Rijk MM, Peter S, Wolf-Johnston A, Heesakkers J, van Koeveringe GA, Birder LA. Quantification of Aging-Related Decreases in Sensory Innervation of the Bladder Trigone in Rats. Int Neurourol J 2024;28:40-45. [PMID: 38461855 DOI: 10.5213/inj.2346220.110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 11/13/2023] [Indexed: 03/12/2024] Open

Botter SM, Kessler TM. Neuro-Urology and Biobanking: An Integrated Approach for Advancing Research and Improving Patient Care. Int J Mol Sci 2023;24:14281. [PMID: 37762582 PMCID: PMC10531693 DOI: 10.3390/ijms241814281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/12/2023] [Accepted: 09/14/2023] [Indexed: 09/29/2023] Open

Abstract

Understanding the molecular mechanisms underlying neuro-urological disorders is crucial for the development of targeted therapeutic interventions. Through the establishment of comprehensive biobanks, researchers can collect and store various biological specimens, including urine, blood, tissue, and DNA samples, to study these mechanisms. In the context of neuro-urology, biobanking facilitates the identification of genetic variations, epigenetic modifications, and gene expression patterns associated with neurogenic lower urinary tract dysfunction. These conditions often present as symptoms of neurological diseases such as Alzheimer's disease, multiple sclerosis, Parkinson's disease, spinal cord injury, and many others. Biobanking of tissue specimens from such patients is essential to understand why these diseases cause the respective symptoms and what can be done to alleviate them. The utilization of high-throughput technologies, such as next-generation sequencing and gene expression profiling, enables researchers to explore the molecular landscape of these conditions in an unprecedented manner. The development of specific and reliable biomarkers resulting from these efforts may help in early detection, accurate diagnosis, and effective monitoring of neuro-urological conditions, leading to improved patient care and management. Furthermore, these biomarkers could potentially facilitate the monitoring of novel therapies currently under investigation in neuro-urological clinical trials. This comprehensive review explores the synergistic integration of neuro-urology and biobanking, with particular emphasis on the translation of biobanking approaches in molecular research in neuro-urology. We discuss the advantages of biobanking in neuro-urological studies, the types of specimens collected and their applications in translational research. Furthermore, we highlight the importance of standardization and quality assurance when collecting samples and discuss challenges that may compromise sample quality and impose limitations on their subsequent utilization. Finally, we give recommendations for sampling in multicenter studies, examine sustainability issues associated with biobanking, and provide future directions for this dynamic field.

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