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Takahashi T, Sasabayashi D, Yücel M, Whittle S, Lorenzetti V, Walterfang M, Suzuki M, Pantelis C, Malhi GS, Allen NB. Different Frequency of Heschl’s Gyrus Duplication Patterns in Neuropsychiatric Disorders: An MRI Study in Bipolar and Major Depressive Disorders. Front Hum Neurosci 2022; 16:917270. [PMID: 35769254 PMCID: PMC9234751 DOI: 10.3389/fnhum.2022.917270] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 05/26/2022] [Indexed: 01/13/2023] Open
Abstract
An increased prevalence of duplicated Heschl’s gyrus (HG) has been repeatedly demonstrated in various stages of schizophrenia as a potential neurodevelopmental marker, but it remains unknown whether other neuropsychiatric disorders also exhibit this macroscopic brain feature. The present magnetic resonance imaging study aimed to examine the disease specificity of the established finding of altered HG patterns in schizophrenia by examining independent cohorts of bipolar disorder (BD) and major depressive disorder (MDD). Twenty-six BD patients had a significantly higher prevalence of HG duplication bilaterally compared to 24 age- and sex-matched controls, while their clinical characteristics (e.g., onset age, number of episodes, and medication) did not relate to HG patterns. No significant difference was found for the HG patterns between 56 MDD patients and 33 age- and sex-matched controls, but the patients with a single HG were characterized by more severe depressive/anxiety symptoms compared to those with a duplicated HG. Thus, in keeping with previous findings, the present study suggests that neurodevelopmental pathology associated with gyral formation of the HG during the late gestation period partly overlaps between schizophrenia and BD, but that HG patterns may make a somewhat distinct contribution to the phenomenology of MDD.
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Affiliation(s)
- Tsutomu Takahashi
- Department of Neuropsychiatry, School of Medicine, University of Toyama, Toyama, Japan
- Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
- *Correspondence: Tsutomu Takahashi,
| | - Daiki Sasabayashi
- Department of Neuropsychiatry, School of Medicine, University of Toyama, Toyama, Japan
- Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Murat Yücel
- Brain Park, Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, VIC, Australia
| | - Sarah Whittle
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia
| | - Valentina Lorenzetti
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Psychology, Faculty of Health Sciences, Australian Catholic University, Melbourne, VIC, Australia
| | - Mark Walterfang
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia
- Department of Neuropsychiatry, Royal Melbourne Hospital, Melbourne, VIC, Australia
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC, Australia
| | - Michio Suzuki
- Department of Neuropsychiatry, School of Medicine, University of Toyama, Toyama, Japan
- Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC, Australia
- North Western Mental Health, Western Hospital Sunshine, St Albans, VIC, Australia
| | - Gin S. Malhi
- Academic Department of Psychiatry, Kolling Institute, Northern Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
- CADE Clinic, Royal North Shore Hospital, Northern Sydney Local Health District, St Leonards, NSW, Australia
| | - Nicholas B. Allen
- Department of Psychology, University of Oregon, Eugene, OR, United States
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Sushentsev N, Rundo L, Blyuss O, Gnanapragasam VJ, Sala E, Barrett T. MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance. Sci Rep 2021; 11:12917. [PMID: 34155265 PMCID: PMC8217549 DOI: 10.1038/s41598-021-92341-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 06/03/2021] [Indexed: 02/05/2023] Open
Abstract
Nearly half of patients with prostate cancer (PCa) harbour low- or intermediate-risk disease considered suitable for active surveillance (AS). However, up to 44% of patients discontinue AS within the first five years, highlighting the unmet clinical need for robust baseline risk-stratification tools that enable timely and accurate prediction of tumour progression. In this proof-of-concept study, we sought to investigate the added value of MRI-derived radiomic features to standard-of-care clinical parameters for improving baseline prediction of PCa progression in AS patients. Tumour T2-weighted imaging (T2WI) and apparent diffusion coefficient radiomic features were extracted, with rigorous calibration and pre-processing methods applied to select the most robust features for predictive modelling. Following leave-one-out cross-validation, the addition of T2WI-derived radiomic features to clinical variables alone improved the area under the ROC curve for predicting progression from 0.61 (95% confidence interval [CI] 0.481-0.743) to 0.75 (95% CI 0.64-0.86). These exploratory findings demonstrate the potential benefit of MRI-derived radiomics to add incremental benefit to clinical data only models in the baseline prediction of PCa progression on AS, paving the way for future multicentre studies validating the proposed model and evaluating its impact on clinical outcomes.
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Affiliation(s)
- Nikita Sushentsev
- Department of Radiology, Addenbrooke's Hospital, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK.
| | - Leonardo Rundo
- Department of Radiology, Addenbrooke's Hospital, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | - Oleg Blyuss
- School of Physics, Engineering & Computer Science, University of Hertfordshire, Hatfield, UK
- Department of Paediatrics and Paediatric Infectious Diseases, Sechenov First Moscow State Medical University, Moscow, Russia
- Department of Applied Mathematics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Vincent J Gnanapragasam
- Division of Urology, Department of Surgery, University of Cambridge, Cambridge, UK
- Cambridge Urology Translational Research and Clinical Trials Office, University of Cambridge, Cambridge, UK
| | - Evis Sala
- Department of Radiology, Addenbrooke's Hospital, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | - Tristan Barrett
- Department of Radiology, Addenbrooke's Hospital, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
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First-Step PPG Signal Analysis for Evaluation of Stress Induced during Scanning in the Open-Air MRI Device. SENSORS 2020; 20:s20123532. [PMID: 32580364 PMCID: PMC7349840 DOI: 10.3390/s20123532] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 06/10/2020] [Accepted: 06/16/2020] [Indexed: 12/22/2022]
Abstract
The paper describes first-step experiments with parallel measurement of cardiovascular parameters using a photoplethysmographic optical sensor and standard portable blood pressure monitors in different situations of body relaxation and stimulation. Changes in the human cardiovascular system are mainly manifested by differences in the Oliva–Roztocil index, the instantaneous heart rate, and variations in blood pressure. In the auxiliary experiments, different physiological and psychological stimuli were applied to test whether relaxation and activation phases produce different measured parameters suitable for further statistical analysis and processing. The principal investigation is aimed at analysis of vibration and acoustic noise impact on a physiological and psychological state of a person lying inside the low-field open-air magnetic resonance imager (MRI). The obtained results will be used to analyze, quantify, and suppress a possible stress factor that has an impact on the speech signal recorded during scanning in the MRI device in the research aimed at 3D modeling of the human vocal tract.
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Flor D, Pena D, Pena L, A. de Sousa V, Martins A. Characterization of Noise Level Inside a Vehicle under Different Conditions. SENSORS 2020; 20:s20092471. [PMID: 32349298 PMCID: PMC7249658 DOI: 10.3390/s20092471] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 01/29/2020] [Accepted: 02/20/2020] [Indexed: 11/20/2022]
Abstract
Vehicular acoustic noise evaluations are a concern of researchers due to health and comfort effects on humans and are fundamental for anyone interested in mitigating audio noise. This paper focuses on the evaluation of the noise level inside a vehicle by using statistical tools. First, an experimental setup was developed with microphones and a microcomputer located strategically on the car’s panel, and measurements were carried out with different conditions such as car window position, rain, traffic, and car speed. Regression analysis was performed to evaluate the similarity of the noise level from those conditions. Thus, we were able to discuss the relevance of the variables that contribute to the noise level inside a car. Finally, our results revealed that the car speed is strongly correlated to interior noise levels, suggesting the most relevant noise sources are in the vehicle itself.
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Affiliation(s)
- Daniel Flor
- Department of Communications Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil
- Correspondence: ; Tel.: +55-84-99132-5962
| | - Danilo Pena
- Department of Electrical Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil
| | - Luan Pena
- Department of Electrical Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil
| | - Vicente A. de Sousa
- Department of Communications Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil
| | - Allan Martins
- Department of Electrical Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil
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