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Gao S, Chen J, Liu J, Guan Y, Liu R, Yang J, Yang X. Decreased grey matter volume in dorsolateral prefrontal cortex and thalamus accompanied by compensatory increases in middle cingulate gyrus of premature ejaculation patients. Andrology 2024; 12:841-849. [PMID: 37902180 DOI: 10.1111/andr.13547] [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] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 09/18/2023] [Accepted: 10/10/2023] [Indexed: 10/31/2023]
Abstract
INTRODUCTION The prefrontal-cingulate-thalamic areas are associated with ejaculation control. Functional abnormalities of these areas and decreased grey matter volume (GMV) in the subcortical areas have been confirmed in premature ejaculation (PE) patients. However, no study has explored the corresponding GMV changes in the prefrontal-cingulate-thalamic areas, which are considered as the important basis for functional abnormalities. This study aimed to investigated whether PE patients exhibited impaired GMV in the brain, especially the prefrontal-cingulate-thalamic areas, and whether these structural deficits were associated with declined ejaculatory control. METHODS T1-weighted structural magnetic resonance imaging (MRI) data were acquired from 50 lifelong PE patients and 50 age-, and education-matched healthy controls (HCs). The PE diagnostic tool (PEDT) was applied to assess the subjective symptoms of PE. Based on the method of voxel-based morphometry (VBM), GMV were measured and compared between groups. In addition, the correlations between GMV of brain regions showed differences between groups and PEDT scores were evaluated in the patient group. RESULTS PE patients showed decreased GMV in the right dorsolateral superior frontal gyrus (clusters = 13, peak T-values = -4.30) and left thalamus (clusters = 47, T = -4.33), and increased GMV in the left middle cingulate gyrus (clusters = 12, T = 4.02) when compared with HCs. In the patient group, GMV of the left thalamus were negatively associated with PEDT scores (r = -0.35; P = 0.01). Receiver operating characteristic (ROC) analysis showed that GMV of the right dorsolateral superior frontal gyrus (AUC = 0.71, P < 0.01, sensitivity = 60%, specificity = 78%), left thalamus (AUC = 0.72, P < 0.01, sensitivity = 92%, specificity = 46%) and middle cingulate gyrus (AUC = 0.69, P < 0.01, sensitivity = 50%, specificity = 90%), and the combined model (AUC = 0.84, P < 0.01, sensitivity = 78%, specificity = 80%) all had the ability to distinguish PE patients from HCs. CONCLUSION Disturbances in GMV were revealed in the prefrontal-cingulate-thalamic areas of PE patients. The findings implied that decreased GMV in the dorsolateral prefrontal cortex and thalamus might be associated with the central pathological neural mechanism underlying the declined ejaculatory control while increased GMV in the middle cingulate gyrus might be the compensatory mechanism underlying PE.
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Affiliation(s)
- Songzhan Gao
- Department of Andrology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jianhuai Chen
- Department of Andrology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Jia Liu
- Department of clinical laboratory, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yichun Guan
- Department of Reproductive Medicine, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Rusheng Liu
- Department of Andrology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jie Yang
- Department of Urology, Jiangsu Provincial People's Hospital, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Department of Urology, People's Hospital of Xinjiang Kizilsu Kirgiz Autonomous Prefecture, Xinjiang Uygur Autonomous Region, China
| | - Xianfeng Yang
- Department of Andrology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Jeon YJ, Park SE, Baek HM. Predicting Brain Age and Gender from Brain Volume Data Using Variational Quantum Circuits. Brain Sci 2024; 14:401. [PMID: 38672050 DOI: 10.3390/brainsci14040401] [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: 03/26/2024] [Revised: 04/15/2024] [Accepted: 04/17/2024] [Indexed: 04/28/2024] Open
Abstract
The morphology of the brain undergoes changes throughout the aging process, and accurately predicting a person's brain age and gender using brain morphology features can aid in detecting atypical brain patterns. Neuroimaging-based estimation of brain age is commonly used to assess an individual's brain health relative to a typical aging trajectory, while accurately classifying gender from neuroimaging data offers valuable insights into the inherent neurological differences between males and females. In this study, we aimed to compare the efficacy of classical machine learning models with that of a quantum machine learning method called a variational quantum circuit in estimating brain age and predicting gender based on structural magnetic resonance imaging data. We evaluated six classical machine learning models alongside a quantum machine learning model using both combined and sub-datasets, which included data from both in-house collections and public sources. The total number of participants was 1157, ranging from ages 14 to 89, with a gender distribution of 607 males and 550 females. Performance evaluation was conducted within each dataset using training and testing sets. The variational quantum circuit model generally demonstrated superior performance in estimating brain age and gender classification compared to classical machine learning algorithms when using the combined dataset. Additionally, in benchmark sub-datasets, our approach exhibited better performance compared to previous studies that utilized the same dataset for brain age prediction. Thus, our results suggest that variational quantum algorithms demonstrate comparable effectiveness to classical machine learning algorithms for both brain age and gender prediction, potentially offering reduced error and improved accuracy.
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Affiliation(s)
- Yeong-Jae Jeon
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon 21999, Republic of Korea
- Department of BioMedical Science, Lee Gil Ya Cancer and Diabetes Institute, Gachon University, Incheon 21999, Republic of Korea
| | - Shin-Eui Park
- Department of BioMedical Science, Lee Gil Ya Cancer and Diabetes Institute, Gachon University, Incheon 21999, Republic of Korea
| | - Hyeon-Man Baek
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon 21999, Republic of Korea
- Department of Molecular Medicine, Lee Gil Ya Cancer and Diabetes Institute, Gachon University, Incheon 21999, Republic of Korea
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Li Z, Wu M, Yin C, Wang Z, Wang J, Chen L, Zhao W. Machine learning based on the EEG and structural MRI can predict different stages of vascular cognitive impairment. Front Aging Neurosci 2024; 16:1364808. [PMID: 38646447 PMCID: PMC11026635 DOI: 10.3389/fnagi.2024.1364808] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 03/22/2024] [Indexed: 04/23/2024] Open
Abstract
Background Vascular cognitive impairment (VCI) is a major cause of cognitive impairment in the elderly and a co-factor in the development and progression of most neurodegenerative diseases. With the continuing development of neuroimaging, multiple markers can be combined to provide richer biological information, but little is known about their diagnostic value in VCI. Methods A total of 83 subjects participated in our study, including 32 patients with vascular cognitive impairment with no dementia (VCIND), 21 patients with vascular dementia (VD), and 30 normal controls (NC). We utilized resting-state quantitative electroencephalography (qEEG) power spectra, structural magnetic resonance imaging (sMRI) for feature screening, and combined them with support vector machines to predict VCI patients at different disease stages. Results The classification performance of sMRI outperformed qEEG when distinguishing VD from NC (AUC of 0.90 vs. 0,82), and sMRI also outperformed qEEG when distinguishing VD from VCIND (AUC of 0.8 vs. 0,0.64), but both underperformed when distinguishing VCIND from NC (AUC of 0.58 vs. 0.56). In contrast, the joint model based on qEEG and sMRI features showed relatively good classification accuracy (AUC of 0.72) to discriminate VCIND from NC, higher than that of either qEEG or sMRI alone. Conclusion Patients at varying stages of VCI exhibit diverse levels of brain structure and neurophysiological abnormalities. EEG serves as an affordable and convenient diagnostic means to differentiate between different VCI stages. A machine learning model that utilizes EEG and sMRI as composite markers is highly valuable in distinguishing diverse VCI stages and in individually tailoring the diagnosis.
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Affiliation(s)
- Zihao Li
- Department of Neurology, Hongqi Hospital Affiliated to Mudanjiang Medical University, Mudanjiang, China
- Department of Neurology, Taizhou Second People’s Hospital, Taizhou, Zhejiang, China
| | - Meini Wu
- Department of Neurology, Hongqi Hospital Affiliated to Mudanjiang Medical University, Mudanjiang, China
- Department of Neurology, Taizhou Second People’s Hospital, Taizhou, Zhejiang, China
| | - Changhao Yin
- Department of Neurology, Hongqi Hospital Affiliated to Mudanjiang Medical University, Mudanjiang, China
| | - Zhenqi Wang
- Department of Neurology, Hongqi Hospital Affiliated to Mudanjiang Medical University, Mudanjiang, China
| | - Jianhang Wang
- Department of Neurology, Hongqi Hospital Affiliated to Mudanjiang Medical University, Mudanjiang, China
- Mudanjiang Medical College, Mudanjiang, China
| | - Lingyu Chen
- Department of Neurology, Hongqi Hospital Affiliated to Mudanjiang Medical University, Mudanjiang, China
- Mudanjiang Medical College, Mudanjiang, China
| | - Weina Zhao
- Department of Neurology, Hongqi Hospital Affiliated to Mudanjiang Medical University, Mudanjiang, China
- Center for Mudanjiang North Medicine Resource Development and Application Collaborative Innovation, Mudanjiang, China
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Peruzzo D, Ciceri T, Mascheretti S, Lampis V, Arrigoni F, Agarwal N, Giubergia A, Villa FM, Crippa A, Nobile M, Mani E, Russo A, D'Angelo MG. Brain Alteration Patterns in Children with Duchenne Muscular Dystrophy: A Machine Learning Approach to Magnetic Resonance Imaging. J Neuromuscul Dis 2024:JND230075. [PMID: 38578898 DOI: 10.3233/jnd-230075] [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] [Indexed: 04/07/2024]
Abstract
Background Duchenne Muscular Dystrophy (DMD) is a genetic disease in which lack of the dystrophin protein causes progressive muscular weakness, cardiomyopathy and respiratory insufficiency. DMD is often associated with other cognitive and behavioral impairments, however the correlation of abnormal dystrophin expression in the central nervous system with brain structure and functioning remains still unclear. Objective To investigate brain involvement in patients with DMD through a multimodal and multivariate approach accounting for potential comorbidities. Methods We acquired T1-weighted and Diffusion Tensor Imaging data from 18 patients with DMD and 18 age- and sex-matched controls with similar cognitive and behavioral profiles. Cortical thickness, structure volume, fractional anisotropy and mean diffusivity measures were used in a multivariate analysis performed using a Support Vector Machine classifier accounting for potential comorbidities in patients and controls. Results the classification experiment significantly discriminates between the two populations (97.2% accuracy) and the forward model weights showed that DMD mostly affects the microstructural integrity of long fiber bundles, in particular in the cerebellar peduncles (bilaterally), in the posterior thalamic radiation (bilaterally), in the fornix and in the medial lemniscus (bilaterally). We also reported a reduced cortical thickness, mainly in the motor cortex, cingulate cortex, hippocampal area and insula. Conclusions Our study identified a small pattern of alterations in the CNS likely associated with the DMD diagnosis.
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Affiliation(s)
- Denis Peruzzo
- Neuroimaging Unit,Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Tommaso Ciceri
- Neuroimaging Unit,Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Sara Mascheretti
- Child Psychopathology Unit,Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia (PV), Italy
| | - Valentina Lampis
- Child Psychopathology Unit,Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia (PV), Italy
| | - Filippo Arrigoni
- Neuroimaging Unit,Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
- Paediatric Radiology and Neuroradiology Department, V. Buzzi Children's Hospital, Milan, Italy
| | - Nivedita Agarwal
- Diagnostic Imaging and Neuroradiology Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Alice Giubergia
- Neuroimaging Unit,Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Filippo Maria Villa
- Child Psychopathology Unit,Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Alessandro Crippa
- Child Psychopathology Unit,Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Maria Nobile
- Child Psychopathology Unit,Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Elisa Mani
- Child Psychopathology Unit,Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Annamaria Russo
- Unit of Rehabilitation of Rare Diseases of the Central and Peripheral Nervous System, Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Maria Grazia D'Angelo
- Unit of Rehabilitation of Rare Diseases of the Central and Peripheral Nervous System, Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
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Mai Y, Cao Z, Zhao L, Yu Q, Xu J, Liu W, Liu B, Tang J, Luo Y, Liao W, Fang W, Ruan Y, Lei M, Mok VCT, Shi L, Liu J. The role of visual rating and automated brain volumetry in early detection and differential diagnosis of Alzheimer's disease. CNS Neurosci Ther 2024; 30:e14492. [PMID: 37864441 PMCID: PMC11017425 DOI: 10.1111/cns.14492] [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] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 09/07/2023] [Accepted: 09/26/2023] [Indexed: 10/22/2023] Open
Abstract
BACKGROUND Medial temporal lobe atrophy (MTA) is a diagnostic marker for mild cognitive impairment (MCI) and Alzheimer's disease (AD), but the accuracy of quantitative MTA (QMTA) in diagnosing early AD is unclear. This study aimed to investigate the accuracy of QMTA and its related components (inferior lateral ventricle [ILV] and hippocampus) with MTA in the early diagnosis of MCI and AD. METHODS This study included four groups: normal (NC), MCI stable (MCIs), MCI converted to AD (MCIs), and mild AD (M-AD) groups. Magnetic resonance image analysis software was used to quantify the hippocampus, ILV, and QMTA. MTA was rated by two experienced neurologists. Receiver operating characteristic area under the curve (AUC) analysis was performed to compare their capability in differentiating AD from NC and MCI, and optimal thresholds were determined using the Youden index. RESULTS QMTA distinguished M-AD from NC and MCI with higher diagnostic accuracy than MTA, hippocampus, and ILV (AUCNC = 0.976, AUCMCI = 0.836, AUCMCIs = 0.894, AUCMCIc = 0.730). The diagnostic accuracy of QMTA was superior to that of MTA, the hippocampus, and ILV in differentiating MCI from AD. The diagnostic accuracy of QMTA was found to remain the best across age, sex, and pathological subgroups analyzed. The sensitivity (92.45%) and specificity (90.64%) were higher in this study when a cutoff value of 0.635 was chosen for QMTA. CONCLUSIONS QMTA may be a better choice than the MTA scale or the associated quantitative components alone in identifying AD patients and MCI individuals with higher progression risk.
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Affiliation(s)
- Yingren Mai
- Department of NeurologyThe Second Affiliated Hospital of Guangzhou Medical UniversityGuangzhouChina
| | - Zhiyu Cao
- Department of Neurology, Sun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Lei Zhao
- BrainNow Research InstituteShenzhenChina
| | - Qun Yu
- Department of Neurology, Sun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Jiaxin Xu
- Department of Neurology, Sun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Wenyan Liu
- BrainNow Research InstituteShenzhenChina
| | - Bowen Liu
- Department of Statistics, College of Liberal Art and SciencesUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA
| | - Jingyi Tang
- Department of Neurology, Sun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Yishan Luo
- BrainNow Research InstituteShenzhenChina
| | - Wang Liao
- Department of NeurologyThe Second Affiliated Hospital of Guangzhou Medical UniversityGuangzhouChina
| | - Wenli Fang
- Department of Neurology, Sun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Yuting Ruan
- Department of RehabilitationThe Second Affiliated Hospital of Guangzhou Medical UniversityGuangzhouChina
| | - Ming Lei
- Department of Neurology, Sun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Vincent C. T. Mok
- BrainNow Research InstituteShenzhenChina
- Division of Neurology, Department of Medicine and Therapeutics, Gerald Choa Neuroscience Centre, Lui Che Woo Institute of Innovative MedicineThe Chinese University of Hong KongHong Kong, SARChina
| | - Lin Shi
- BrainNow Research InstituteShenzhenChina
- Department of Imaging and Interventional RadiologyThe Chinese University of Hong KongHong Kong, SARChina
| | - Jun Liu
- Department of NeurologyThe Second Affiliated Hospital of Guangzhou Medical UniversityGuangzhouChina
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Lyall AE, Breithaupt L, Ji C, Haidar A, Kotler E, Becker KR, Plessow F, Slattery M, Thomas JJ, Holsen LM, Misra M, Eddy KT, Lawson EA. Lower region-specific gray matter volume in females with atypical anorexia nervosa and anorexia nervosa. Int J Eat Disord 2024; 57:951-966. [PMID: 38366701 PMCID: PMC11018478 DOI: 10.1002/eat.24168] [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: 09/29/2023] [Revised: 01/29/2024] [Accepted: 01/30/2024] [Indexed: 02/18/2024]
Abstract
OBJECTIVE Few studies have focused on brain structure in atypical anorexia nervosa (atypical AN). This study investigates differences in gray matter volume (GMV) between females with anorexia nervosa (AN) and atypical AN, and healthy controls (HC). METHOD Structural magnetic resonance imaging data were acquired for 37 AN, 23 atypical AN, and 41 HC female participants. Freesurfer was used to extract GMV, cortical thickness, and surface area for six brain lobes and associated cortical regions of interest (ROI). Primary analyses employed linear mixed-effects models to compare group differences in lobar GMV, followed by secondary analyses on ROIs within significant lobes. We also explored relationships between cortical gray matter and both body mass index (BMI) and symptom severity. RESULTS Our primary analyses revealed significant lower GMV in frontal, temporal and parietal areas (FDR < .05) in AN and atypical AN when compared to HC. Lobar GMV comparisons were non-significant between atypical AN and AN. The parietal lobe exhibited the greatest proportion of affected cortical ROIs in both AN versus HC and atypical AN versus HC. BMI, but not symptom severity, was found to be associated with cortical GMV in the parietal, frontal, temporal, and cingulate lobes. No significant differences were observed in cortical thickness or surface area. DISCUSSION We observed lower GMV in frontal, temporal, and parietal areas, when compared to HC, but no differences between AN and atypical AN. This indicates potentially overlapping structural phenotypes between these disorders and evidence of brain changes among those who are not below the clinical underweight threshold. PUBLIC SIGNIFICANCE Despite individuals with atypical anorexia nervosa presenting above the clinical weight threshold, lower cortical gray matter volume was observed in partial, temporal, and frontal cortices, compared to healthy individuals. No significant differences were found in cortical gray matter volume between anorexia nervosa and atypical anorexia nervosa. This underscores the importance of continuing to assess and target weight gain in clinical care, even for those who are presenting above the low-weight clinical criteria.
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Affiliation(s)
- Amanda E. Lyall
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, MA
- Mass General Brigham Multidisciplinary Eating Disorders Research Collaborative, Mass General Brigham, Boston, MA
| | - Lauren Breithaupt
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, MA
- Eating Disorders Clinical and Research Program, Massachusetts General Hospital, MA
- Mass General Brigham Multidisciplinary Eating Disorders Research Collaborative, Mass General Brigham, Boston, MA
| | - Chunni Ji
- Eating Disorders Clinical and Research Program, Massachusetts General Hospital, MA
- Mass General Brigham Multidisciplinary Eating Disorders Research Collaborative, Mass General Brigham, Boston, MA
- Division of Women’s Health, Department of Medicine, and Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Anastasia Haidar
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, MA
| | - Elana Kotler
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, MA
| | - Kendra R Becker
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, MA
- Eating Disorders Clinical and Research Program, Massachusetts General Hospital, MA
- Mass General Brigham Multidisciplinary Eating Disorders Research Collaborative, Mass General Brigham, Boston, MA
| | - Franziska Plessow
- Neuroendocrine Unit, Massachusetts General Hospital, Harvard Medical School, MA
- Mass General Brigham Multidisciplinary Eating Disorders Research Collaborative, Mass General Brigham, Boston, MA
| | - Meghan Slattery
- Neuroendocrine Unit, Massachusetts General Hospital, Harvard Medical School, MA
| | - Jennifer J. Thomas
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, MA
- Eating Disorders Clinical and Research Program, Massachusetts General Hospital, MA
- Mass General Brigham Multidisciplinary Eating Disorders Research Collaborative, Mass General Brigham, Boston, MA
| | - Laura M. Holsen
- Mass General Brigham Multidisciplinary Eating Disorders Research Collaborative, Mass General Brigham, Boston, MA
- Division of Women’s Health, Department of Medicine, and Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Madhusmita Misra
- Division of Pediatric Endocrinology, Massachusetts General Hospital, Harvard Medical School, MA
- Neuroendocrine Unit, Massachusetts General Hospital, Harvard Medical School, MA
- Mass General Brigham Multidisciplinary Eating Disorders Research Collaborative, Mass General Brigham, Boston, MA
| | - Kamryn T. Eddy
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, MA
- Eating Disorders Clinical and Research Program, Massachusetts General Hospital, MA
- Mass General Brigham Multidisciplinary Eating Disorders Research Collaborative, Mass General Brigham, Boston, MA
| | - Elizabeth A. Lawson
- Neuroendocrine Unit, Massachusetts General Hospital, Harvard Medical School, MA
- Mass General Brigham Multidisciplinary Eating Disorders Research Collaborative, Mass General Brigham, Boston, MA
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Sun B, Zhang C, Huang K, Bhetuwal A, Yang X, Jing C, Li H, Lu H, Zhang Q, Yang H. The white matter characteristic of the genu of corpus callosum coupled with pain intensity and negative emotion scores in patients with trigeminal neuralgia: a multivariate analysis. Front Neurosci 2024; 18:1381085. [PMID: 38576866 PMCID: PMC10991788 DOI: 10.3389/fnins.2024.1381085] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 03/07/2024] [Indexed: 04/06/2024] Open
Abstract
Background Trigeminal neuralgia (TN) is a chronic neuropathic pain disorder that not only causes intense pain but also affects the psychological health of patients. Since TN pain intensity and negative emotion may be grounded in our own pain experiences, they exhibit huge inter-individual differences. This study investigates the effect of inter-individual differences in pain intensity and negative emotion on brain structure in patients with TN and the possible pathophysiology mechanism underlying this disease. Methods T1 weighted magnetic resonance imaging and diffusion tensor imaging scans were obtained in 46 patients with TN and 35 healthy controls. All patients with TN underwent pain-related and emotion-related questionnaires. Voxel-based morphometry and regional white matter diffusion property analysis were used to investigate whole brain grey and white matter quantitatively. Innovatively employing partial least squares correlation analysis to explore the relationship among pain intensity, negative emotion and brain microstructure in patients with TN. Results Significant difference in white matter integrity were identified in patients with TN compared to the healthy controls group; The most correlation brain region in the partial least squares correlation analysis was the genus of the corpus callosum, which was negatively associated with both pain intensity and negative emotion. Conclusion The genu of corpus callosum plays an important role in the cognition of pain perception, the generation and conduction of negative emotions in patients with TN. These findings may deepen our understanding of the pathophysiology of TN.
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Affiliation(s)
- Baijintao Sun
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Chuan Zhang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Kai Huang
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi, China
| | - Anup Bhetuwal
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xuezhao Yang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Chuan Jing
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Hongjian Li
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Hongyu Lu
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Qingwei Zhang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Hanfeng Yang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
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Triana AM, Saramäki J, Glerean E, Hayward NMEA. Neuroscience meets behavior: A systematic literature review on magnetic resonance imaging of the brain combined with real-world digital phenotyping. Hum Brain Mapp 2024; 45:e26620. [PMID: 38436603 PMCID: PMC10911114 DOI: 10.1002/hbm.26620] [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] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 01/19/2024] [Accepted: 01/24/2024] [Indexed: 03/05/2024] Open
Abstract
A primary goal of neuroscience is to understand the relationship between the brain and behavior. While magnetic resonance imaging (MRI) examines brain structure and function under controlled conditions, digital phenotyping via portable automatic devices (PAD) quantifies behavior in real-world settings. Combining these two technologies may bridge the gap between brain imaging, physiology, and real-time behavior, enhancing the generalizability of laboratory and clinical findings. However, the use of MRI and data from PADs outside the MRI scanner remains underexplored. Herein, we present a Preferred Reporting Items for Systematic Reviews and Meta-Analysis systematic literature review that identifies and analyzes the current state of research on the integration of brain MRI and PADs. PubMed and Scopus were automatically searched using keywords covering various MRI techniques and PADs. Abstracts were screened to only include articles that collected MRI brain data and PAD data outside the laboratory environment. Full-text screening was then conducted to ensure included articles combined quantitative data from MRI with data from PADs, yielding 94 selected papers for a total of N = 14,778 subjects. Results were reported as cross-frequency tables between brain imaging and behavior sampling methods and patterns were identified through network analysis. Furthermore, brain maps reported in the studies were synthesized according to the measurement modalities that were used. Results demonstrate the feasibility of integrating MRI and PADs across various study designs, patient and control populations, and age groups. The majority of published literature combines functional, T1-weighted, and diffusion weighted MRI with physical activity sensors, ecological momentary assessment via PADs, and sleep. The literature further highlights specific brain regions frequently correlated with distinct MRI-PAD combinations. These combinations enable in-depth studies on how physiology, brain function and behavior influence each other. Our review highlights the potential for constructing brain-behavior models that extend beyond the scanner and into real-world contexts.
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Affiliation(s)
- Ana María Triana
- Department of Computer Science, School of ScienceAalto UniversityEspooFinland
| | - Jari Saramäki
- Department of Computer Science, School of ScienceAalto UniversityEspooFinland
| | - Enrico Glerean
- Department of Neuroscience and Biomedical Engineering, School of ScienceAalto UniversityEspooFinland
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Wang J, Hill‐Jarrett T, Buto P, Pederson A, Sims KD, Zimmerman SC, DeVost MA, Ferguson E, Lacar B, Yang Y, Choi M, Caunca MR, La Joie R, Chen R, Glymour MM, Ackley SF. Comparison of approaches to control for intracranial volume in research on the association of brain volumes with cognitive outcomes. Hum Brain Mapp 2024; 45:e26633. [PMID: 38433682 PMCID: PMC10910271 DOI: 10.1002/hbm.26633] [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] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 01/30/2024] [Accepted: 02/07/2024] [Indexed: 03/05/2024] Open
Abstract
Most neuroimaging studies linking regional brain volumes with cognition correct for total intracranial volume (ICV), but methods used for this correction differ across studies. It is unknown whether different ICV correction methods yield consistent results. Using a brain-wide association approach in the MRI substudy of UK Biobank (N = 41,964; mean age = 64.5 years), we used regression models to estimate the associations of 58 regional brain volumetric measures with eight cognitive outcomes, comparing no correction and four ICV correction approaches. Approaches evaluated included: no correction; dividing regional volumes by ICV (proportional approach); including ICV as a covariate in the regression (adjustment approach); and regressing the regional volumes against ICV in different normative samples and using calculated residuals to determine associations (residual approach). We used Spearman-rank correlations and two consistency measures to quantify the extent to which associations were inconsistent across ICV correction approaches for each possible brain region and cognitive outcome pair across 2320 regression models. When the association between brain volume and cognitive performance was close to null, all approaches produced similar estimates close to the null. When associations between a regional volume and cognitive test were not null, the adjustment and residual approaches typically produced similar estimates, but these estimates were inconsistent with results from the crude and proportional approaches. For example, when using the crude approach, an increase of 0.114 (95% confidence interval [CI]: 0.103-0.125) in fluid intelligence was associated with each unit increase in hippocampal volume. However, when using the adjustment approach, the increase was 0.055 (95% CI: 0.043-0.068), while the proportional approach showed a decrease of -0.025 (95% CI: -0.035 to -0.014). Different commonly used methods to correct for ICV yielded inconsistent results. The proportional method diverges notably from other methods and results were sometimes biologically implausible. A simple regression adjustment for ICV produced biologically plausible associations.
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Affiliation(s)
- Jingxuan Wang
- Department of Epidemiology and BiostatisticsUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of EpidemiologyBoston UniversityBostonMassachusettsUSA
| | | | - Peter Buto
- Department of Epidemiology and BiostatisticsUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of EpidemiologyBoston UniversityBostonMassachusettsUSA
| | - Annie Pederson
- Department of Epidemiology and BiostatisticsUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of EpidemiologyBoston UniversityBostonMassachusettsUSA
| | - Kendra D. Sims
- Department of Epidemiology and BiostatisticsUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of EpidemiologyBoston UniversityBostonMassachusettsUSA
| | - Scott C. Zimmerman
- Department of Epidemiology and BiostatisticsUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Michelle A. DeVost
- Department of Epidemiology and BiostatisticsUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Erin Ferguson
- Department of Epidemiology and BiostatisticsUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Benjamin Lacar
- Bakar Computational Health Sciences InstituteUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Yulin Yang
- Department of Epidemiology and BiostatisticsUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Minhyuk Choi
- Department of Epidemiology and BiostatisticsUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Michelle R. Caunca
- Memory and Aging Center, Department of NeurologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Renaud La Joie
- Memory and Aging Center, Department of NeurologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Ruijia Chen
- Department of Epidemiology and BiostatisticsUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - M. Maria Glymour
- Department of EpidemiologyBoston UniversityBostonMassachusettsUSA
| | - Sarah F. Ackley
- Department of EpidemiologyBoston UniversityBostonMassachusettsUSA
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10
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Hoang KN, Huang Y, Fujiwara E, Malykhin N. Effects of healthy aging and mnemonic strategies on verbal memory performance across the adult lifespan: Mediating role of posterior hippocampus. Hippocampus 2024; 34:100-122. [PMID: 38145465 DOI: 10.1002/hipo.23592] [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] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 11/16/2023] [Accepted: 11/25/2023] [Indexed: 12/26/2023]
Abstract
In this study, we aimed to understand the contributions of hippocampal anteroposterior subregions (head, body, tail) and subfields (cornu ammonis 1-3 [CA1-3], dentate gyrus [DG], and subiculum [Sub]) and encoding strategies to the age-related verbal memory decline. Healthy participants were administered the California Verbal Learning Test-II to evaluate verbal memory performance and encoding strategies and underwent 4.7 T magnetic resonance imaging brain scan with subsequent hippocampal subregions and subfields manual segmentation. While total hippocampal volume was not associated with verbal memory performance, we found the volumes of the posterior hippocampus (body) and Sub showed significant effects on verbal memory performance. Additionally, the age-related volume decline in hippocampal body volume contributed to lower use of semantic clustering, resulting in lower verbal memory performance. The effect of Sub on verbal memory was statistically independent of encoding strategies. While total CA1-3 and DG volumes did not show direct or indirect effects on verbal memory, exploratory analyses with DG and CA1-3 volumes within the hippocampal body subregion suggested an indirect effect of age-related volumetric reduction on verbal memory performance through semantic clustering. As semantic clustering is sensitive to age-related hippocampal volumetric decline but not to the direct effect of age, further investigation of mechanisms supporting semantic clustering can have implications for early detection of cognitive impairments and decline.
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Affiliation(s)
- Kim Ngan Hoang
- Neuroscience and Mental Health Institute, Edmonton, Canada
| | - Yushan Huang
- Neuroscience and Mental Health Institute, Edmonton, Canada
| | - Esther Fujiwara
- Neuroscience and Mental Health Institute, Edmonton, Canada
- Department of Psychiatry, University of Alberta, Edmonton, Canada
| | - Nikolai Malykhin
- Neuroscience and Mental Health Institute, Edmonton, Canada
- Department of Psychiatry, University of Alberta, Edmonton, Canada
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11
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Yuan J, Liu Y, Liao H, Tan C, Cai S, Shen Q, Liu Q, Wang M, Tang Y, Li X, Liu J, Zi Y. Alterations in cortical volume and complexity in Parkinson's disease with depression. CNS Neurosci Ther 2024; 30:e14582. [PMID: 38421103 PMCID: PMC10851315 DOI: 10.1111/cns.14582] [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] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 11/09/2023] [Accepted: 12/17/2023] [Indexed: 03/02/2024] Open
Abstract
AIMS The aim of this study is to investigate differences in gray matter volume and cortical complexity between Parkinson's disease with depression (PDD) patients and Parkinson's disease without depression (PDND) patients. METHODS A total of 41 PDND patients, 36 PDD patients, and 38 healthy controls (HC) were recruited and analyzed by Voxel-based morphometry (VBM) and surface-based morphometry (SBM). Differences in gray matter volume and cortical complexity were compared using the one-way analysis of variance (ANOVA) and correlated with the Hamilton Depression Scale-17 (HAMD-17) scores. RESULTS PDD patients exhibited significant cortical atrophy in various regions, including bilateral medial parietal-occipital-temporal lobes, right dorsolateral temporal lobes, bilateral parahippocampal gyrus, and bilateral hippocampus, compared to HC and PDND groups. A negative correlation between the GMV of left precuneus and HAMD-17 scores in the PDD group tended to be significant (r = -0.318, p = 0.059). Decreased gyrification index was observed in the bilateral insular and dorsolateral temporal cortex. However, there were no significant differences found in fractal dimension and sulcal depth. CONCLUSION Our research shows extensive cortical structural changes in the insular cortex, parietal-occipital-temporal lobes, and hippocampal regions in PDD. This provides a morphological perspective for understanding the pathophysiological mechanism underlying depression in Parkinson's disease.
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Affiliation(s)
- Jiaying Yuan
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Yujing Liu
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Haiyan Liao
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
- Clinical Research Center For Medical Imaging in Hunan ProvinceChangshaChina
| | - Changlian Tan
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Sainan Cai
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Qin Shen
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Qinru Liu
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Min Wang
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Yuqing Tang
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Xu Li
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Jun Liu
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
- Clinical Research Center For Medical Imaging in Hunan ProvinceChangshaChina
| | - Yuheng Zi
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
- Department of Radiology, The First Affiliated Hospital, Hengyang Medical SchoolUniversity of South ChinaHengyangChina
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12
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Xu X, Chen P, Li W, Xiang Y, Xie Z, Yu Q, Tang Y, Wang P. Topological properties analysis and identification of mild cognitive impairment based on individual morphological brain network connectome. Cereb Cortex 2024; 34:bhad450. [PMID: 38012122 DOI: 10.1093/cercor/bhad450] [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] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 11/01/2023] [Accepted: 11/02/2023] [Indexed: 11/29/2023] Open
Abstract
Mild cognitive impairment is considered the prodromal stage of Alzheimer's disease. Accurate diagnosis and the exploration of the pathological mechanism of mild cognitive impairment are extremely valuable for targeted Alzheimer's disease prevention and early intervention. In all, 100 mild cognitive impairment patients and 86 normal controls were recruited in this study. We innovatively constructed the individual morphological brain networks and derived multiple brain connectome features based on 3D-T1 structural magnetic resonance imaging with the Jensen-Shannon divergence similarity estimation method. Our results showed that the most distinguishing morphological brain connectome features in mild cognitive impairment patients were consensus connections and nodal graph metrics, mainly located in the frontal, occipital, limbic lobes, and subcortical gray matter nuclei, corresponding to the default mode network. Topological properties analysis revealed that mild cognitive impairment patients exhibited compensatory changes in the frontal lobe, while abnormal cortical-subcortical circuits associated with cognition were present. Moreover, the combination of multidimensional brain connectome features using multiple kernel-support vector machine achieved the best classification performance in distinguishing mild cognitive impairment patients and normal controls, with an accuracy of 84.21%. Therefore, our findings are of significant importance for developing potential brain imaging biomarkers for early detection of Alzheimer's disease and understanding the neuroimaging mechanisms of the disease.
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Affiliation(s)
- Xiaowen Xu
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Peiying Chen
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Weikai Li
- School of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400064, China
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 276800, China
| | - Yongsheng Xiang
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Zhongfeng Xie
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Qiang Yu
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Ying Tang
- Department of Electrical and Computer Engineering, Rowan University, Glassboro, New Jersey 08028, USA
| | - Peijun Wang
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
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Cai M, Ma J, Wang Z, Zhao Y, Zhang Y, Wang H, Xue H, Chen Y, Zhang Y, Wang C, Zhao Q, Xue K, Liu F. Individual-level brain morphological similarity networks: Current methodologies and applications. CNS Neurosci Ther 2023; 29:3713-3724. [PMID: 37519018 PMCID: PMC10651978 DOI: 10.1111/cns.14384] [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] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/12/2023] [Accepted: 07/18/2023] [Indexed: 08/01/2023] Open
Abstract
AIMS The human brain is an extremely complex system in which neurons, clusters of neurons, or regions are connected to form a complex network. With the development of neuroimaging techniques, magnetic resonance imaging (MRI)-based brain networks play a key role in our understanding of the intricate architecture of human brain. Among them, the structural MRI-based brain morphological network approach has attracted increasing attention due to the advantages in data acquisition, image quality, and in revealing the structural organizing principles intrinsic to the brain. This review is to summarize the methodology and related applications of individual-level morphological networks. BACKGROUND There have been a growing number of studies related to brain morphological similarity networks. Conventional morphological networks are intersubject covariance networks constructed using a certain morphological indicator of a group of subjects; individual-level morphological networks, on the other hand, measure the morphological similarity between brain regions for individual brains and can reflect the morphological information of single subjects. In recent years, individual morphological networks have demonstrated significant worth in exploring the topological changes of the human brain under both normal and disease conditions. Such studies provided novel perspectives for understanding human brain development and exploring the pathological mechanisms of neuropsychiatric disorders. CONCLUSION This paper mainly focuses on the studies of brain morphological networks at the individual level, introduces several ways for network construction, reviews representative work in this field, and finally points out current problems and future directions.
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Affiliation(s)
- Mengjing Cai
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Juanwei Ma
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Zirui Wang
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Yao Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Yijing Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - He Wang
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Hui Xue
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Yayuan Chen
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Yujie Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Chunyang Wang
- Department of Scientific ResearchTianjin Medical University General HospitalTianjinChina
| | - Qiyu Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Kaizhong Xue
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
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14
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Stankeviciute L, Falcon C, Operto G, Garcia M, Shekari M, Iranzo Á, Niñerola-Baizán A, Perissinotti A, Minguillón C, Fauria K, Molinuevo JL, Zetterberg H, Blennow K, Suárez-Calvet M, Cacciaglia R, Gispert JD, Grau-Rivera O. Differential effects of sleep on brain structure and metabolism at the preclinical stages of AD. Alzheimers Dement 2023; 19:5371-5386. [PMID: 37194734 DOI: 10.1002/alz.13102] [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] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 05/18/2023]
Abstract
INTRODUCTION Poor sleep quality is associated with cognitive outcomes in Alzheimer's disease (AD). We analyzed the associations between self-reported sleep quality and brain structure and function in cognitively unimpaired (CU) individuals. METHODS CU adults (N = 339) underwent structural magnetic resonance imaging, lumbar puncture, and the Pittsburgh Sleep Quality Index (PSQI) questionnaire. A subset (N = 295) performed [18F] fluorodeoxyglucose positron emission tomography scans. Voxel-wise associations with gray matter volumes (GMv) and cerebral glucose metabolism (CMRGlu) were performed including interactions with cerebrospinal fluid (CSF) AD biomarkers status. RESULTS Poorer sleep quality was associated with lower GMv and CMRGlu in the orbitofrontal and cingulate cortices independently of AD pathology. Self-reported sleep quality interacted with altered core AD CSF biomarkers in brain areas known to be affected in preclinical AD stages. DISCUSSION Poor sleep quality may impact brain structure and function independently from AD pathology. Alternatively, AD-related neurodegeneration in areas involved in sleep-wake regulation may induce or worsen sleep disturbances. Highlights Poor sleep impacts brain structure and function independent of Alzheimer's disease (AD) pathology. Poor sleep exacerbates brain changes observed in preclinical AD. Sleep is an appealing therapeutic strategy for preventing AD.
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Affiliation(s)
- Laura Stankeviciute
- Universitat Pompeu Fabra, Barcelona, Spain
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
| | - Carles Falcon
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid, Spain
| | - Grégory Operto
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Marina Garcia
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Mahnaz Shekari
- Universitat Pompeu Fabra, Barcelona, Spain
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Álex Iranzo
- Neurology Service, Hospital Clínic de Barcelona and Institut D'Investigacions Biomèdiques, University of Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Aida Niñerola-Baizán
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid, Spain
- Nuclear Medicine Department, Hospital Clínic Barcelona, Barcelona, Spain
| | - Andrés Perissinotti
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid, Spain
- Nuclear Medicine Department, Hospital Clínic Barcelona, Barcelona, Spain
| | - Carolina Minguillón
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Karine Fauria
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Jose Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
| | - Henrik Zetterberg
- UK Dementia Research Institute at UCL, London, UK
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Kaj Blennow
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Marc Suárez-Calvet
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
- Servei de Neurologia, Hospital del Mar, Barcelona, Spain
| | - Raffaele Cacciaglia
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid, Spain
| | - Oriol Grau-Rivera
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
- Servei de Neurologia, Hospital del Mar, Barcelona, Spain
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Schmitz‐Koep B, Menegaux A, Zimmermann J, Thalhammer M, Neubauer A, Wendt J, Schinz D, Daamen M, Boecker H, Zimmer C, Priller J, Wolke D, Bartmann P, Sorg C, Hedderich DM. Altered gray-to-white matter tissue contrast in preterm-born adults. CNS Neurosci Ther 2023; 29:3199-3211. [PMID: 37365964 PMCID: PMC10580354 DOI: 10.1111/cns.14320] [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] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 06/01/2023] [Accepted: 06/10/2023] [Indexed: 06/28/2023] Open
Abstract
AIMS To investigate cortical organization in brain magnetic resonance imaging (MRI) of preterm-born adults using percent contrast of gray-to-white matter signal intensities (GWPC), which is an in vivo proxy measure for cortical microstructure. METHODS Using structural MRI, we analyzed GWPC at different percentile fractions across the cortex (0%, 10%, 20%, 30%, 40%, 50%, and 60%) in a large and prospectively collected cohort of 86 very preterm-born (<32 weeks of gestation and/or birth weight <1500 g, VP/VLBW) adults and 103 full-term controls at 26 years of age. Cognitive performance was assessed by full-scale intelligence quotient (IQ) using the Wechsler Adult Intelligence Scale. RESULTS GWPC was significantly decreased in VP/VLBW adults in frontal, parietal, and temporal associative cortices, predominantly in the right hemisphere. Differences were pronounced at 20%, 30%, and 40%, hence, in middle cortical layers. GWPC was significantly increased in right paracentral lobule in VP/VLBW adults. GWPC in frontal and temporal cortices was positively correlated with birth weight, and negatively with duration of ventilation (p < 0.05). Furthermore, GWPC in right paracentral lobule was negatively correlated with IQ (p < 0.05). CONCLUSIONS Widespread aberrant gray-to-white matter contrast suggests lastingly altered cortical microstructure after preterm birth, mainly in middle cortical layers, with differential effects on associative and primary cortices.
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Affiliation(s)
- Benita Schmitz‐Koep
- Department of Diagnostic and Interventional NeuroradiologyTechnical University of Munich; School of MedicineMunichGermany
- Technical University of Munich, School of Medicine, TUM‐NIC Neuroimaging CenterMunichGermany
| | - Aurore Menegaux
- Department of Diagnostic and Interventional NeuroradiologyTechnical University of Munich; School of MedicineMunichGermany
- Technical University of Munich, School of Medicine, TUM‐NIC Neuroimaging CenterMunichGermany
| | - Juliana Zimmermann
- Department of Diagnostic and Interventional NeuroradiologyTechnical University of Munich; School of MedicineMunichGermany
- Technical University of Munich, School of Medicine, TUM‐NIC Neuroimaging CenterMunichGermany
| | - Melissa Thalhammer
- Department of Diagnostic and Interventional NeuroradiologyTechnical University of Munich; School of MedicineMunichGermany
- Technical University of Munich, School of Medicine, TUM‐NIC Neuroimaging CenterMunichGermany
| | - Antonia Neubauer
- Department of Diagnostic and Interventional NeuroradiologyTechnical University of Munich; School of MedicineMunichGermany
- Technical University of Munich, School of Medicine, TUM‐NIC Neuroimaging CenterMunichGermany
| | - Jil Wendt
- Department of Diagnostic and Interventional NeuroradiologyTechnical University of Munich; School of MedicineMunichGermany
- Technical University of Munich, School of Medicine, TUM‐NIC Neuroimaging CenterMunichGermany
| | - David Schinz
- Department of Diagnostic and Interventional NeuroradiologyTechnical University of Munich; School of MedicineMunichGermany
- Technical University of Munich, School of Medicine, TUM‐NIC Neuroimaging CenterMunichGermany
| | - Marcel Daamen
- Department of Diagnostic and Interventional RadiologyUniversity Hospital Bonn, Clinical Functional Imaging GroupBonnGermany
- Department of Neonatology and Pediatric Intensive CareUniversity Hospital BonnBonnGermany
| | - Henning Boecker
- Department of Diagnostic and Interventional RadiologyUniversity Hospital Bonn, Clinical Functional Imaging GroupBonnGermany
| | - Claus Zimmer
- Department of Diagnostic and Interventional NeuroradiologyTechnical University of Munich; School of MedicineMunichGermany
- Technical University of Munich, School of Medicine, TUM‐NIC Neuroimaging CenterMunichGermany
| | - Josef Priller
- Department of PsychiatryTechnical University of Munich, School of MedicineMunichGermany
| | - Dieter Wolke
- Department of PsychologyUniversity of WarwickCoventryUK
- Warwick Medical SchoolUniversity of WarwickCoventryUK
| | - Peter Bartmann
- Department of Neonatology and Pediatric Intensive CareUniversity Hospital BonnBonnGermany
| | - Christian Sorg
- Department of Diagnostic and Interventional NeuroradiologyTechnical University of Munich; School of MedicineMunichGermany
- Technical University of Munich, School of Medicine, TUM‐NIC Neuroimaging CenterMunichGermany
- Department of PsychiatryTechnical University of Munich, School of MedicineMunichGermany
| | - Dennis M. Hedderich
- Department of Diagnostic and Interventional NeuroradiologyTechnical University of Munich; School of MedicineMunichGermany
- Technical University of Munich, School of Medicine, TUM‐NIC Neuroimaging CenterMunichGermany
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Zhou L, Liu X, Yan X, Liu Y, Xie Y, Sun C. Long-term effects of prenatal magnesium sulfate exposure on nervous system development in preterm-born children. Food Sci Nutr 2023; 11:7061-7069. [PMID: 37970388 PMCID: PMC10630835 DOI: 10.1002/fsn3.3630] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 08/01/2023] [Accepted: 08/04/2023] [Indexed: 11/17/2023] Open
Abstract
This study used structural magnetic resonance imaging to analyze changes in the gray matter volume (GMV) of preterm-born (PTB) and term-born (TB) children to help elucidate the influence of magnesium sulfate treatment on the nervous system development. A total of 51 subjects were recruited, including 28 PTB and 23 TB children. The intelligence scale and MRI scan were completed at the corrected age of 10 to 16 years. A whole-brain voxel-wise analysis tested the main effect of the status (PTB without magnesium, PTB with magnesium, and TB) using a factorial design in SPM8. The mean volumes of the regions that showed significant group effects on the GMV after the FDR correction were extracted in the common space for each subject. Verbal and full-scale intelligence quotient scores were significantly lower for PTB children without magnesium than for TB children; however, the scores of PTB children with magnesium and TB children were almost identical. Compared with TB children, PTB children had significantly reduced left straight gyrus and left inferior frontal gyrus GMVs; however, the volumes of PTB children with magnesium were closer to those of TB children. Changes in the GMV of the left inferior frontal gyrus were significantly correlated with full-scale and verbal intelligence quotient scores, whereas the lower gestational age at the time of mgsou4 treatment led to a larger GMV of the left inferior frontal gyrus. Brain structural abnormalities could exist in PTB children. The GMVs of the left straight gyrus and left inferior frontal gyrus were significantly reduced in these children. The influence of magnesium sulfate treatment was not significant, but the cognitive levels of these children were significantly increased and almost identical to those of TB children. Initiation of magnesium sulfate treatment during gestation is negatively correlated with the left inferior frontal gyrus GMV.
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Affiliation(s)
- Le Zhou
- Obstetrics and Gynecology Department, West China Second University HospitalSichuan UniversityChengduChina
| | - Xinghui Liu
- Obstetrics and Gynecology Department, West China Second University HospitalSichuan UniversityChengduChina
| | - Xiaoli Yan
- Obstetrics and Gynecology DepartmentThe Southwest Hospital of the Army Medical UniversityChongqingChina
| | - Yingwei Liu
- Obstetrics and Gynecology DepartmentThe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Yao Xie
- Obstetrics and Gynecology DepartmentSichuan Academy of Medical Sciences – Sichuan Provincial People's HospitalChengduChina
| | - Chuntang Sun
- Obstetrics and Gynecology Department, West China Second University HospitalSichuan UniversityChengduChina
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17
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Yang S, Wu Y, Sun L, Lu Y, Qian K, Kuang H, Meng J, Wu Y. Abnormal Topological Organization of Structural Covariance Networks in Patients with Temporal Lobe Epilepsy Comorbid Sleep Disorder. Brain Sci 2023; 13:1493. [PMID: 37891861 PMCID: PMC10605209 DOI: 10.3390/brainsci13101493] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/11/2023] [Accepted: 10/18/2023] [Indexed: 10/29/2023] Open
Abstract
OBJECTIVE The structural covariance network (SCN) alterations in patients with temporal lobe epilepsy and comorbid sleep disorder (PWSD) remain poorly understood. This study aimed to investigate changes in SCNs using structural magnetic resonance imaging. METHODS Thirty-four PWSD patients, thirty-three patients with temporal lobe epilepsy without sleep disorder (PWoSD), and seventeen healthy controls underwent high-resolution structural MRI imaging. Subsequently, SCNs were constructed based on gray matter volume and analyzed via graph-theoretical approaches. RESULTS PWSD exhibited significantly increased clustering coefficients, shortest path lengths, transitivity, and local efficiency. In addition, various distributions and numbers of SCN hubs were identified in PWSD. Furthermore, PWSD networks were less robust to random and target attacks than those of healthy controls and PWoSD patients. CONCLUSION This study identifies aberrant SCN changes in PWSD that may be related to the susceptibility of patients with epilepsy to sleep disorders.
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Affiliation(s)
| | | | | | | | | | | | | | - Yuan Wu
- Department of Neurology, The First Affiliated Hospital, Guangxi Medical University, Nanning 530021, China
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18
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Rashid-López R, Macías-García P, Sánchez-Fernández FL, Cano-Cano F, Sarrias-Arrabal E, Sanmartino F, Méndez-Bértolo C, Lozano-Soto E, Gutiérrez-Cortés R, González-Moraleda Á, Forero L, López-Sosa F, Zuazo A, Gómez-Molinero R, Gómez-Ramírez J, Paz-Expósito J, Rubio-Esteban G, Espinosa-Rosso R, Cruz-Gómez ÁJ, González-Rosa JJ. Neuroimaging and serum biomarkers of neurodegeneration and neuroplasticity in Parkinson's disease patients treated by intermittent theta-burst stimulation over the bilateral primary motor area: a randomized, double-blind, sham-controlled, crossover trial study. Front Aging Neurosci 2023; 15:1258315. [PMID: 37869372 PMCID: PMC10585115 DOI: 10.3389/fnagi.2023.1258315] [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: 07/13/2023] [Accepted: 09/12/2023] [Indexed: 10/24/2023] Open
Abstract
Background and objectives Intermittent theta-burst stimulation (iTBS) is a patterned form of excitatory transcranial magnetic stimulation that has yielded encouraging results as an adjunctive therapeutic option to alleviate the emergence of clinical deficits in Parkinson's disease (PD) patients. Although it has been demonstrated that iTBS influences dopamine-dependent corticostriatal plasticity, little research has examined the neurobiological mechanisms underlying iTBS-induced clinical enhancement. Here, our primary goal is to verify whether iTBS bilaterally delivered over the primary motor cortex (M1) is effective as an add-on treatment at reducing scores for both motor functional impairment and nonmotor symptoms in PD. We hypothesize that these clinical improvements following bilateral M1-iTBS could be driven by endogenous dopamine release, which may rebalance cortical excitability and restore compensatory striatal volume changes, resulting in increased striato-cortico-cerebellar functional connectivity and positively impacting neuroglia and neuroplasticity. Methods A total of 24 PD patients will be assessed in a randomized, double-blind, sham-controlled crossover study involving the application of iTBS over the bilateral M1 (M1 iTBS). Patients on medication will be randomly assigned to receive real iTBS or control (sham) stimulation and will undergo 5 consecutive sessions (5 days) of iTBS over the bilateral M1 separated by a 3-month washout period. Motor evaluation will be performed at different follow-up visits along with a comprehensive neurocognitive assessment; evaluation of M1 excitability; combined structural magnetic resonance imaging (MRI), resting-state electroencephalography and functional MRI; and serum biomarker quantification of neuroaxonal damage, astrocytic reactivity, and neural plasticity prior to and after iTBS. Discussion The findings of this study will help to clarify the efficiency of M1 iTBS for the treatment of PD and further provide specific neurobiological insights into improvements in motor and nonmotor symptoms in these patients. This novel project aims to yield more detailed structural and functional brain evaluations than previous studies while using a noninvasive approach, with the potential to identify prognostic neuroprotective biomarkers and elucidate the structural and functional mechanisms of M1 iTBS-induced plasticity in the cortico-basal ganglia circuitry. Our approach may significantly optimize neuromodulation paradigms to ensure state-of-the-art and scalable rehabilitative treatment to alleviate motor and nonmotor symptoms of PD.
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Affiliation(s)
- Raúl Rashid-López
- Psychophysiology and Neuroimaging Group, Institute of Biomedical Research Cadiz (INiBICA), Cadiz, Spain
- Department of Neurology, Puerta del Mar University Hospital, Cadiz, Spain
| | - Paloma Macías-García
- Psychophysiology and Neuroimaging Group, Institute of Biomedical Research Cadiz (INiBICA), Cadiz, Spain
- Department of Psychology, University of Cadiz, Cádiz, Spain
| | - F. Luis Sánchez-Fernández
- Psychophysiology and Neuroimaging Group, Institute of Biomedical Research Cadiz (INiBICA), Cadiz, Spain
- Department of Psychology, University of Cadiz, Cádiz, Spain
| | - Fátima Cano-Cano
- Psychophysiology and Neuroimaging Group, Institute of Biomedical Research Cadiz (INiBICA), Cadiz, Spain
| | - Esteban Sarrias-Arrabal
- Psychophysiology and Neuroimaging Group, Institute of Biomedical Research Cadiz (INiBICA), Cadiz, Spain
- Department of Psychology, University of Cadiz, Cádiz, Spain
| | - Florencia Sanmartino
- Psychophysiology and Neuroimaging Group, Institute of Biomedical Research Cadiz (INiBICA), Cadiz, Spain
- Department of Psychology, University of Cadiz, Cádiz, Spain
| | - Constantino Méndez-Bértolo
- Psychophysiology and Neuroimaging Group, Institute of Biomedical Research Cadiz (INiBICA), Cadiz, Spain
- Department of Psychology, University of Cadiz, Cádiz, Spain
| | - Elena Lozano-Soto
- Psychophysiology and Neuroimaging Group, Institute of Biomedical Research Cadiz (INiBICA), Cadiz, Spain
- Department of Psychology, University of Cadiz, Cádiz, Spain
| | - Remedios Gutiérrez-Cortés
- Psychophysiology and Neuroimaging Group, Institute of Biomedical Research Cadiz (INiBICA), Cadiz, Spain
| | - Álvaro González-Moraleda
- Psychophysiology and Neuroimaging Group, Institute of Biomedical Research Cadiz (INiBICA), Cadiz, Spain
- Department of Psychology, University of Cadiz, Cádiz, Spain
| | - Lucía Forero
- Psychophysiology and Neuroimaging Group, Institute of Biomedical Research Cadiz (INiBICA), Cadiz, Spain
- Department of Neurology, Puerta del Mar University Hospital, Cadiz, Spain
| | - Fernando López-Sosa
- Psychophysiology and Neuroimaging Group, Institute of Biomedical Research Cadiz (INiBICA), Cadiz, Spain
- Department of Psychology, University of Cadiz, Cádiz, Spain
| | - Amaya Zuazo
- Department of Radiodiagnostic and Medical Imaging, Puerta del Mar University Hospital, Cadiz, Spain
| | | | - Jaime Gómez-Ramírez
- Psychophysiology and Neuroimaging Group, Institute of Biomedical Research Cadiz (INiBICA), Cadiz, Spain
| | - José Paz-Expósito
- Department of Radiodiagnostic and Medical Imaging, Puerta del Mar University Hospital, Cadiz, Spain
| | | | - Raúl Espinosa-Rosso
- Psychophysiology and Neuroimaging Group, Institute of Biomedical Research Cadiz (INiBICA), Cadiz, Spain
- Department of Neurology, Jerez de la Frontera University Hospital, Jerez de la Frontera, Spain
| | - Álvaro J. Cruz-Gómez
- Psychophysiology and Neuroimaging Group, Institute of Biomedical Research Cadiz (INiBICA), Cadiz, Spain
- Department of Psychology, University of Cadiz, Cádiz, Spain
| | - Javier J. González-Rosa
- Psychophysiology and Neuroimaging Group, Institute of Biomedical Research Cadiz (INiBICA), Cadiz, Spain
- Department of Psychology, University of Cadiz, Cádiz, Spain
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Wei Z, Yue J, Li X, Zhao W, Cao D, Li A, Yang G, Zhang Q. A mini-review on functional magnetic resonance imaging on brain structure of vascular cognitive impairment. Front Neurol 2023; 14:1249147. [PMID: 37808504 PMCID: PMC10552639 DOI: 10.3389/fneur.2023.1249147] [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: 07/10/2023] [Accepted: 08/25/2023] [Indexed: 10/10/2023] Open
Abstract
Vascular cognitive impairment (VCI) is the initial stage of vascular dementia (VaD). Early diagnosis and treatment of VCI are crucial to prevent the progression of VaD. In order to gain a better understanding of VCI, this study aimed to investigate the use of advanced imaging techniques such as structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI). These techniques allow researchers to observe the structural and functional changes in the brain that are associated with VCI. Functional magnetic resonance imaging (fMRI) and sMRI techniques have been widely used in studies focusing on gray matter, brain networks, and functional abnormalities during rest. By searching and summarizing recent literature, this study has provided valuable evidence on the use of advanced imaging techniques in understanding and treating VCI. The findings from this study can aid in the development of early intervention strategies for patients with VCI, potentially slowing down or even halting the progression of VCI to full-blown VaD.
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Affiliation(s)
- Zeyi Wei
- Graduate School of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Jinhuan Yue
- Shenzhen Frontiers in Chinese Medicine Research Co., Ltd., Shenzhen, China
- Department of Acupuncture and Moxibustion, Vitality University, Hayward, CA, United States
| | - Xiaoling Li
- Division of CT and MRI, First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China
| | | | - Danna Cao
- Division of CT and MRI, First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Ang Li
- Servier (Beijing) Pharmaceutical Research & Development CO. Ltd., Beijing, China
| | - Guanhu Yang
- Department of Specialty Medicine, Ohio University, Athens, OH, United States
| | - Qinhong Zhang
- Shenzhen Frontiers in Chinese Medicine Research Co., Ltd., Shenzhen, China
- Department of Acupuncture and Moxibustion, Heilongjiang University of Chinese Medicine, Harbin, China
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20
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Chen HY, Parent JH, Ciampa CJ, Dahl MJ, Hämmerer D, Maass A, Winer JR, Yakupov R, Inglis B, Betts MJ, Berry AS. Interactive effects of locus coeruleus structure and catecholamine synthesis capacity on cognitive function. Front Aging Neurosci 2023; 15:1236335. [PMID: 37744395 PMCID: PMC10516288 DOI: 10.3389/fnagi.2023.1236335] [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: 06/07/2023] [Accepted: 08/24/2023] [Indexed: 09/26/2023] Open
Abstract
Background The locus coeruleus (LC) produces catecholamines (norepinephrine and dopamine) and is implicated in a broad range of cognitive functions including attention and executive function. Recent advancements in magnetic resonance imaging (MRI) approaches allow for the visualization and quantification of LC structure. Human research focused on the LC has since exploded given the LC's role in cognition and relevance to current models of psychopathology and neurodegenerative disease. However, it is unclear to what extent LC structure reflects underlying catecholamine function, and how LC structure and neurochemical function are collectively associated with cognitive performance. Methods A partial least squares correlation (PLSC) analysis was applied to 19 participants' LC structural MRI measures and catecholamine synthesis capacity measures assessed using [18F]Fluoro-m-tyrosine ([18F]FMT) positron emission tomography (PET). Results We found no direct association between LC-MRI and LC-[18F]FMT measures for rostral, middle, or caudal portions of the LC. We found significant associations between LC neuroimaging measures and neuropsychological performance that were driven by rostral and middle portions of the LC, which is in line with LC cortical projection patterns. Specifically, associations with executive function and processing speed arose from contributions of both LC structure and interactions between LC structure and catecholamine synthesis capacity. Conclusion These findings leave open the possibility that LC MRI and PET measures contribute unique information and suggest that their conjoint use may increase sensitivity to brain-behavior associations in small samples.
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Affiliation(s)
- Hsiang-Yu Chen
- Department of Psychology, Brandeis University, Waltham, MA, United States
| | - Jourdan H. Parent
- Department of Psychology, Brandeis University, Waltham, MA, United States
| | - Claire J. Ciampa
- Department of Psychology, Brandeis University, Waltham, MA, United States
| | - Martin J. Dahl
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
- USC Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States
| | - Dorothea Hämmerer
- Psychological Institute, University of Innsbruck, Innsbruck, Austria
| | - Anne Maass
- Deutsches Zentrum für Neurodegenerative Erkrankungen, Magdeburg, Germany
| | - Joseph R. Winer
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
| | - Renat Yakupov
- Deutsches Zentrum für Neurodegenerative Erkrankungen, Magdeburg, Germany
| | - Ben Inglis
- Henry H. Wheeler Jr. Brain Imaging Center, University of California, Berkeley, Berkeley, CA, United States
| | - Matthew J. Betts
- Deutsches Zentrum für Neurodegenerative Erkrankungen, Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Anne S. Berry
- Department of Psychology, Brandeis University, Waltham, MA, United States
- Lawrence Berkeley National Laboratory, Berkeley, CA, United States
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Förster K, Horstmann RH, Dannlowski U, Houenou J, Kanske P. Progressive grey matter alterations in bipolar disorder across the life span - A systematic review. Bipolar Disord 2023; 25:443-456. [PMID: 36872645 DOI: 10.1111/bdi.13318] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [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] [Indexed: 03/07/2023]
Abstract
OBJECTIVES To elucidate the relationship between the course of bipolar disorder (BD) and structural brain changes across the life span, we conducted a systematic review of longitudinal imaging studies in adolescent and adult BD patients. METHODS Eleven studies with 329 BD patients and 277 controls met our PICOS criteria (participants, intervention, comparison, outcome and study design): BD diagnosis based on DSM criteria, natural course of disease, comparison of grey matter changes in BD individuals over ≥1-year interval between scans. RESULTS The selected studies yielded heterogeneous findings, partly due to varying patient characteristics, data acquisition and statistical models. Mood episodes were associated with greater grey matter loss in frontal brain regions over time. Brain volume decreased or remained stable in adolescent patients, whereas it increased in healthy adolescents. Adult BD patients showed increased cortical thinning and brain structural decline. In particular, disease onset in adolescence was associated with amygdala volume reduction, which was not reported in adult BD. CONCLUSIONS The evidence collected suggests that the progression of BD impairs adolescent brain development and accelerates structural brain decline across the lifespan. Age-specific changes in amygdala volume in adolescent BD suggest that reduced amygdala volume is a correlate of early onset BD. Clarifying the role of BD in brain development across the lifespan promises a deeper understanding of the progression of BD patients through different developmental episodes.
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Affiliation(s)
- Katharina Förster
- Clinical Psychology and Behavioral Neuroscience, Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
| | - Rosa H Horstmann
- Clinical Psychology and Behavioral Neuroscience, Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Josselin Houenou
- Translational Neuropsychiatry, Fondation FondaMental, Université Paris Est Créteil, INSERM U955, IMRB, APHP, DMU IMPACT, Mondor University Hospitals, Créteil, France
- NeuroSpin, Psychiatry Team, UNIACT Lab, CEA, University Paris Saclay, Gif-sur-Yvette, France
| | - Philipp Kanske
- Clinical Psychology and Behavioral Neuroscience, Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
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22
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Chen MH, Lin HM, Sue YR, Yu YC, Yeh PY. Meta-analysis reveals a reduced surface area of the amygdala in individuals with attention deficit/hyperactivity disorder. Psychophysiology 2023; 60:e14308. [PMID: 37042481 DOI: 10.1111/psyp.14308] [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] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 02/15/2023] [Accepted: 03/03/2023] [Indexed: 04/13/2023]
Abstract
Despite the reported lack of structural alterations in the amygdala of individuals with attention deficit/hyperactivity disorder (ADHD) in previous meta-analyses, subsequent observational studies produced conflicting results. Through incorporating the updated data from observational studies on structural features of the amygdala in ADHD, the primary goal of this study was to examine the anatomical differences in amygdala between subjects with ADHD and their neurotypical controls. Using the appropriate keyword strings, we searched the PubMed, Embase, and Web of Science databases for English articles from inception to February 2022. Eligibility criteria included observational studies comparing the structure of the amygdala between ADHD subjects and their comparators using magnetic resonance imaging (MRI). Subgroup analyses were conducted focusing on the amygdala side, as well as the use of different scanners and approach to segmentation. The effects of other continuous variables, such as age, intelligence quotient, and male percentage, on amygdala size were also investigated. Of the 5703 participants in 16 eligible studies, 2928 were diagnosed with ADHD. Compared with neurotypical controls, subjects with ADHD had a smaller amygdala surface area (particularly in the left hemisphere) but without a significant difference in volume between the two groups. Subgroup analysis of MRI scanners and different approaches to segmentation showed no statistically significant difference. There was no significant correlation between continuous variables and amygdala size. Our results showed consistent surface morphological alterations of the amygdala, in particular on the left side, in subjects with ADHD. However, the preliminary findings based on the limited data available for analysis warrant future studies for verification.
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Affiliation(s)
- Meng-Hsiang Chen
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
- College of Medicine, Chang Gung University, Kaohsiung, Taiwan
| | - Hsiu-Man Lin
- Division of Child and Adolescent Psychiatry & Division of Developmental and Behavioral Pediatrics, China Medical University Children's Hospital, Taichung, Taiwan
| | - Yu-Ru Sue
- Department of Psychology, College of Medical and Health Science, Asia University, Taichung, Taiwan
| | - Yun-Chen Yu
- Department of Psychology, College of Medical and Health Science, Asia University, Taichung, Taiwan
| | - Pin-Yang Yeh
- Department of Psychology, College of Medical and Health Science, Asia University, Taichung, Taiwan
- Clinical Psychology Center, Asia University Hospital, Taichung, Taiwan
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Shi C, Deng H, Deng X, Rao D, Yue W. The Structural Changes of Frontal Subregions and Their Correlations with Cognitive Impairment in Patients with Alzheimer's Disease. J Integr Neurosci 2023; 22:99. [PMID: 37519164 DOI: 10.31083/j.jin2204099] [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: 11/16/2022] [Revised: 12/09/2022] [Accepted: 12/15/2022] [Indexed: 08/01/2023] Open
Abstract
BACKGROUND The frontal lobe is affected by Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, we still lack sufficient understanding of subregion atrophy in the frontal cortex, and the relationship between subregions volume and cognitive decline in AD or MCI remains unclear. METHODS This study enrolled 434 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI), including 150 cognitively normals (CN), 187 subjects with MCI, and 97 patients with AD. The gray matter of frontal regions and subregions was divided based on the BNA-246 atlas and its volume was measured by voxel-based morphometry (VBM). Analysis of covariance was performed to compare the differences in frontal regions and subregions volume. Then, receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to analyze the discriminative ability of subregion volume to distinguish the three groups. In addition, we investigated the association of subregion volume with Mini-Mental State Examination (MMSE) score and Alzheimer's Disease Assessment Scale-Cognitive Behavior section (ADAS-cog) scores with age, gender, education, and the estimated total intracranial volume (eTIV) as covariates. RESULTS In addition to the regions of frontal lobe atrophy found in previous studies, atrophy of the precentral gyrus (PrG) and some of its subregions were found in MCI. The volume of the right dorsal area 9/46 (MFG_7_1) was the best index to differentiate AD from CN, with an AUC value of 0.7. Moreover, we found that some subregions are associated with cognition in patients with MCI and AD. CONCLUSIONS Frontal lobe atrophy in MCI is more extensive than we assumed. In addition, the volume of right MFG_7_1 has the potential to distinguish AD from CN.
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Affiliation(s)
- Cailing Shi
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 637000 Nanchong, Sichuan, China
| | - Hao Deng
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 637000 Nanchong, Sichuan, China
| | - Xia Deng
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 637000 Nanchong, Sichuan, China
| | - Dingcai Rao
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 637000 Nanchong, Sichuan, China
| | - Wenjun Yue
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 637000 Nanchong, Sichuan, China
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AO YAWEN, LI YUSHUANG, ZHAO YILIN, ZHANG LIANG, YANG RENJIE, ZHA YUNFEI. Hippocampal Subfield Volumes in Amateur Marathon Runners. Med Sci Sports Exerc 2023; 55:1208-1217. [PMID: 36878015 PMCID: PMC10241426 DOI: 10.1249/mss.0000000000003144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
Abstract
PURPOSE Numerous studies have implicated the involvement of structure and function of the hippocampus in physical exercise, and the larger hippocampal volume is one of the relevant benefits reported in exercise. It remains to be determined how the different subfields of hippocampus respond to physical exercise. METHODS A 3D T1-weighted magnetic resonance imaging was acquired in 73 amateur marathon runners (AMR) and 52 healthy controls (HC) matched with age, sex, and education. The Montreal Cognitive Assessment, the Pittsburgh Sleep Quality Index (PSQI), and the Fatigue Severity Scale were assessed in all participants. We obtained hippocampal subfield volumes using FreeSurfer 6.0. We compared the volumes of the hippocampal subfield between the two groups and ascertained correlation between the significant subfield metrics and the significant behavioral measure in AMR group. RESULTS The AMR had significantly better sleep than HC, manifested as with lower score of PSQI. Sleep duration in AMR and HC was not significantly different from each other. In the AMR group, the left and right hippocampus, cornu ammonis 1 (CA1), CA4, granule cell and molecular layers of the dentate gyrus, molecular layer, left CA2-3, and left hippocampal-amygdaloid transition area volumes were significantly larger compared with those in the HC group. In AMR group, the correlations between the PSQI and the hippocampal subfield volumes were not significant. No correlations were found between hippocampal subfield volumes and sleep duration in AMR group. CONCLUSIONS We reported larger volumes of specific hippocampal subfields in AMR, which may provide a hippocampal volumetric reserve that protects against age-related hippocampal deterioration. These findings should be further investigated in longitudinal studies.
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Meng X, Deng K, Huang B, Lin X, Wu Y, Tao W, Lin C, Yang Y, Chen F. Classification of temporal lobe epilepsy based on neuropsychological tests and exploration of its underlying neurobiology. Front Hum Neurosci 2023; 17:1100683. [PMID: 37397855 PMCID: PMC10307531 DOI: 10.3389/fnhum.2023.1100683] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 05/09/2023] [Indexed: 07/04/2023] Open
Abstract
Objective To assist improving long-term postoperative seizure-free rate, we aimed to use machine learning algorithms based on neuropsychological data to differentiate temporal lobe epilepsy (TLE) from extratemporal lobe epilepsy (extraTLE), as well as explore the relationship between magnetic resonance imaging (MRI) and neuropsychological tests. Methods Twenty-three patients with TLE and 23 patients with extraTLE underwent neuropsychological tests and MRI scans before surgery. The least absolute shrinkage and selection operator were firstly employed for feature selection, and a machine learning approach with neuropsychological tests was employed to classify TLE using leave-one-out cross-validation. A generalized linear model was used to analyze the relationship between brain alterations and neuropsychological tests. Results We found that logistic regression with the selected neuropsychological tests generated classification accuracies of 87.0%, with an area under the receiver operating characteristic curve (AUC) of 0.89. Three neuropsychological tests were acquired as significant neuropsychological signatures for the diagnosis of TLE. We also found that the Right-Left Orientation Test difference was related to the superior temporal and the banks of the superior temporal sulcus (bankssts). The Conditional Association Learning Test (CALT) was associated with the cortical thickness difference in the lateral orbitofrontal area between the two groups, and the Component Verbal Fluency Test was associated with the cortical thickness difference in the lateral occipital cortex between the two groups. Conclusion These results showed that machine learning-based classification with the selected neuropsychological data can successfully classify TLE with high accuracy compared to previous studies, which could provide kind of warning sign for surgery candidate of TLE patients. In addition, understanding the mechanism of cognitive behavior by neuroimaging information could assist doctors in the presurgical evaluation of TLE.
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Affiliation(s)
- Xianghong Meng
- Department of Neurosurgery, Shenzhen University General Hospital, Shenzhen, China
| | - Kan Deng
- Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
- MSC Clinical and Technical Solutions, Philips Healthcare, Guangzhou, China
| | - Bingsheng Huang
- Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Xiaoyi Lin
- Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Yingtong Wu
- Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Wei Tao
- Department of Neurosurgery, Shenzhen University General Hospital, Shenzhen, China
| | - Chuxuan Lin
- Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Yang Yang
- Department of Radiology, Suining Central Hospital, Suining, China
| | - Fuyong Chen
- Department of Neurosurgery, Shenzhen University General Hospital, Shenzhen, China
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Shi Y, Cui D, Niu J, Zhang X, Sun F, Liu H, Dou R, Qiu J, Jiao Q, Cao W, Yu G. Sex differences in structural covariance network based on MRI cortical morphometry: effects on episodic memory. Cereb Cortex 2023:7152331. [PMID: 37143182 DOI: 10.1093/cercor/bhad147] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 04/10/2023] [Accepted: 04/12/2023] [Indexed: 05/06/2023] Open
Abstract
Sex differences in episodic memory (EM), remembering past events based on when and where they occurred, have been reported, but the neural mechanisms are unclear. T1-weighted images of 111 females and 61 males were acquired from the Dallas Lifespan Brain Study. Using surface-based morphometry and structural covariance (SC) analysis, we constructed structural covariance networks (SCN) based on cortical volume, and the global efficiency (Eglob) was computed to characterize network integration. The relationship between SCN and EM was examined by SC analysis among the top-n brain regions that were most relevant to EM performance. The number of SC connections (females: 3306; males: 437, P = 0.0212) and Eglob (females: 0.1845; males: 0.0417, P = 0.0408) of SCN in females were higher than those in males. The top-n brain regions with the strongest SC in females were located in auditory network, cingulo-opercular network (CON), and default mode network (DMN), and in males, they were located in frontoparietal network, CON, and DMN. These results confirmed that the Eglob of SCN in females was higher than males, sex differences in EM performance might be related to the differences in network-level integration. Our study highlights the importance of sex as a research variable in brain science.
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Affiliation(s)
- Yajun Shi
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Dong Cui
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Jinpeng Niu
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Xiaotong Zhang
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Fengzhu Sun
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Haiqin Liu
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Ruhai Dou
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Jianfeng Qiu
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Qing Jiao
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Weifang Cao
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Guanghui Yu
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an 271016, China
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27
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Wu H, Song Y, Yang X, Chen S, Ge H, Yan Z, Qi W, Yuan Q, Liang X, Lin X, Chen J. Functional and structural alterations of dorsal attention network in preclinical and early-stage Alzheimer's disease. CNS Neurosci Ther 2023; 29:1512-1524. [PMID: 36942514 PMCID: PMC10173716 DOI: 10.1111/cns.14092] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 12/31/2022] [Accepted: 01/02/2023] [Indexed: 03/23/2023] Open
Abstract
OBJECTIVES Subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) are known as the preclinical and early stage of Alzheimer's disease (AD). The dorsal attention network (DAN) is mainly responsible for the "top-down" attention process. However, previous studies mainly focused on single functional modality and limited structure. This study aimed to investigate the multimodal alterations of DAN in SCD and aMCI to assess their diagnostic value in preclinical and early-stage AD. METHODS Resting-state functional magnetic resonance imaging (MRI) was carried out to measure the fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and functional connectivity (FC). Structural MRI was used to calculate the gray matter volume (GMV) and cortical thickness. Moreover, receiver-operating characteristic (ROC) analysis was used to distinguish these alterations in SCD and aMCI. RESULTS The SCD and aMCI groups showed both decreased ReHo in the right middle temporal gyrus (MTG) and decreased GMV compared to healthy controls (HCs). Especially in the SCD group, there were increased fALFF and increased ReHo in the left inferior occipital gyrus (IOG), decreased fALFF and increased FC in the left inferior parietal lobule (IPL), and reduced cortical thickness in the right inferior temporal gyrus (ITG). Furthermore, functional and structural alterations in the SCD and aMCI groups were closely related to episodic memory (EM), executive function (EF), and information processing speed (IPS). The combination of multiple indicators of DAN had a high accuracy in differentiating clinical stages. CONCLUSIONS Our current study demonstrated functional and structural alterations of DAN in SCD and aMCI, especially in the MTG, IPL, and SPL. Furthermore, cognitive performance was closely related to these significant alterations. Our study further suggested that the combined multiple indicators of DAN could be acted as the latent neuroimaging markers of preclinical and early-stage AD for their high diagnostic value.
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Affiliation(s)
- Huimin Wu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yu Song
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xinyi Yang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shanshan Chen
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Honglin Ge
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Zheng Yan
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Wenzhang Qi
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Qianqian Yuan
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xuhong Liang
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xingjian Lin
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Department of Radiology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
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Sefik E, Boamah M, Addington J, Bearden CE, Cadenhead KS, Cornblatt BA, Keshavan MS, Mathalon DH, Perkins DO, Stone WS, Tsuang MT, Woods SW, Cannon TD, Walker EF. Sex- and Age-Specific Deviations in Cerebellar Structure and Their Link With Symptom Dimensions and Clinical Outcome in Individuals at Clinical High Risk for Psychosis. Schizophr Bull 2023; 49:350-363. [PMID: 36394426 PMCID: PMC10016422 DOI: 10.1093/schbul/sbac169] [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] [Indexed: 11/18/2022]
Abstract
BACKGROUND The clinical high-risk (CHR) period offers a temporal window into neurobiological deviations preceding psychosis onset, but little attention has been given to regions outside the cerebrum in large-scale studies of CHR. Recently, the North American Prodrome Longitudinal Study (NAPLS)-2 revealed altered functional connectivity of the cerebello-thalamo-cortical circuitry among individuals at CHR; however, cerebellar morphology remains underinvestigated in this at-risk population, despite growing evidence of its involvement in psychosis. STUDY DESIGN In this multisite study, we analyzed T1-weighted magnetic resonance imaging scans obtained from N = 469 CHR individuals (61% male, ages = 12-36 years) and N = 212 healthy controls (52% male, ages = 12-34 years) from NAPLS-2, with a focus on cerebellar cortex and white matter volumes separately. Symptoms were rated by the Structured Interview for Psychosis-Risk Syndromes (SIPS). The outcome by two-year follow-up was categorized as in-remission, symptomatic, prodromal-progression, or psychotic. General linear models were used for case-control comparisons and tests for volumetric associations with baseline SIPS ratings and clinical outcomes. STUDY RESULTS Cerebellar cortex and white matter volumes differed between the CHR and healthy control groups at baseline, with sex moderating the difference in cortical volumes, and both sex and age moderating the difference in white matter volumes. Baseline ratings for major psychosis-risk dimensions as well as a clinical outcome at follow-up had tissue-specific associations with cerebellar volumes. CONCLUSIONS These findings point to clinically relevant deviations in cerebellar cortex and white matter structures among CHR individuals and highlight the importance of considering the complex interplay between sex and age when studying the neuromaturational substrates of psychosis risk.
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Affiliation(s)
- Esra Sefik
- Department of Psychology, Emory University, Atlanta, GA, USA
- Department of Human Genetics, Emory University, Atlanta, GA, USA
| | - Michelle Boamah
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Jean Addington
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada
| | - Carrie E Bearden
- Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Kristin S Cadenhead
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | | | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Daniel H Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA
- Mental Health Service, San Francisco VA Medical Center, San Francisco, CA, USA
| | - Diana O Perkins
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - William S Stone
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Ming T Tsuang
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Scott W Woods
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Tyrone D Cannon
- Department of Psychiatry, Yale University, New Haven, CT, USA
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Elaine F Walker
- Department of Psychology, Emory University, Atlanta, GA, USA
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29
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Malykhin N, Pietrasik W, Aghamohammadi-Sereshki A, Ngan Hoang K, Fujiwara E, Olsen F. Emotional recognition across the adult lifespan: Effects of age, sex, cognitive empathy, alexithymia traits, and amygdala subnuclei volumes. J Neurosci Res 2023; 101:367-383. [PMID: 36478439 DOI: 10.1002/jnr.25152] [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: 04/22/2022] [Revised: 11/10/2022] [Accepted: 11/21/2022] [Indexed: 12/12/2022]
Abstract
The ability to recognize others' emotions is vital to everyday life. The goal of this study was to assess which emotions show age-related decline in recognition accuracy of facial emotional expressions across the entire adult lifespan and how this process is related to cognitive empathy (Theory of Mind [ToM]), alexithymia traits, and amygdala subnuclei volumes in a large cohort of healthy individuals. We recruited 140 healthy participants 18-85 years old. Facial affect processing was assessed with the Penn Emotion Recognition task (ER40) that contains images of the five basic emotions: Neutral, Happy, Sad, Angry, and Fearful. Structural magnetic resonance imaging (MRI) datasets were acquired on a 4.7T MRI system. Structural equation modeling was used to test the relationship between studied variables. We found that while both sexes demonstrated age-related reduction in recognition of happy emotions and preserved recognition of sadness, male participants showed age-related reduction in recognition of fear, while in female participants, age-related decline was linked to recognition of neutral and angry facial expressions. In both sexes, accurate recognition of sadness negatively correlated with alexithymia traits. On the other hand, better ToM capabilities in male participants were associated with improvement in recognition of positive and neutral emotions. Finally, none of the observed age-related reductions in emotional recognition were related to amygdala and its subnuclei volumes. In contrast, both global volume of amygdala and its cortical and centromedial subnuclei had significant direct effects on recognition of sad images.
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Affiliation(s)
- Nikolai Malykhin
- Department of Psychiatry, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, Canada.,Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Wojciech Pietrasik
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada.,Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | | | - Kim Ngan Hoang
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Esther Fujiwara
- Department of Psychiatry, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, Canada.,Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Fraser Olsen
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
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30
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Guo X, Zhang X, Chen H, Zhai G, Cao Y, Zhang T, Gao L. Exploring the heterogeneity of brain structure in autism spectrum disorder based on individual structural covariance network. Cereb Cortex 2023:7051065. [PMID: 36813465 DOI: 10.1093/cercor/bhad040] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [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: 12/06/2022] [Revised: 01/26/2023] [Accepted: 01/28/2023] [Indexed: 02/24/2023] Open
Abstract
Autism spectrum disorder (ASD) is characterized by highly structural heterogeneity. However, most previous studies analyzed between-group differences through a structural covariance network constructed based on the ASD group level, ignoring the effect of between-individual differences. We constructed the gray matter volume-based individual differential structural covariance network (IDSCN) using T1-weighted images of 207 children (ASD/healthy controls: 105/102). We analyzed structural heterogeneity of ASD and differences among ASD subtypes obtained by a K-means clustering analysis based on evidently different covariance edges relative to healthy controls. The relationship between the distortion coefficients (DCs) calculated at the whole-brain, intra- and interhemispheric levels and the clinical symptoms of ASD subtypes was then examined. Compared with the control group, ASD showed significantly altered structural covariance edges mainly involved in the frontal and subcortical regions. Given the IDSCN of ASD, we obtained 2 subtypes, and the positive DCs of the 2 ASD subtypes were significantly different. Intra- and interhemispheric positive and negative DCs can predict the severity of repetitive stereotyped behaviors in ASD subtypes 1 and 2, respectively. These findings highlight the crucial role of frontal and subcortical regions in the heterogeneity of ASD and the necessity of studying ASD from the perspective of individual differences.
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Affiliation(s)
- Xiaonan Guo
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China.,Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China
| | - Xia Zhang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China.,Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China
| | - Heng Chen
- School of Medicine, Guizhou University, Guiyang 550025, China
| | - Guangjin Zhai
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China.,Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China
| | - Yabo Cao
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China.,Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China
| | - Tao Zhang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China.,Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China
| | - Le Gao
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China.,Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China
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31
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Dai WZ, Liu L, Zhu MZ, Lu J, Ni JM, Li R, Ma T, Zhu XC. Morphological and Structural Network Analysis of Sporadic Alzheimer's Disease Brains Based on the APOE4 Gene. J Alzheimers Dis 2023; 91:1035-1048. [PMID: 36530087 DOI: 10.3233/jad-220877] [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] [Indexed: 12/23/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is an increasingly common type of dementia. Apolipoprotein E (APOE) gene is a strong risk factor for AD. OBJECTIVE Here, we explored alterations in grey matter structure (GMV) and networks in AD, as well as the effects of the APOEɛ4 allele on neuroimaging regions based on structural magnetic resonance imaging (sMRI). METHODS All subjects underwent an sMRI scan. GMV and cortical thickness were calculated using voxel-based morphological analysis, and structural networks were constructed based on graph theory analysis to compare differences between AD and normal controls. RESULTS The volumes of grey matter in the bilateral inferior temporal gyrus, right middle temporal gyrus, right inferior parietal lobule, right limbic lobe, right frontal lobe, left anterior cingulate gyrus, and bilateral olfactory cortex of patients with AD were significantly decreased. The cortical thickness in patients with AD was significantly reduced in the left lateral occipital lobe, inferior parietal lobe, orbitofrontal region, precuneus, superior parietal gyrus, right precentral gyrus, middle temporal gyrus, pars opercularis gyrus, insular gyrus, superior marginal gyrus, bilateral fusiform gyrus, and superior frontal gyrus. In terms of local properties, there were significant differences between the AD and control groups in these areas, including the right bank, right temporalis pole, bilateral middle temporal gyrus, right transverse temporal gyrus, left postcentral gyrus, and left parahippocampal gyrus. CONCLUSION There were significant differences in the morphological and structural covariate networks between AD patients and healthy controls under APOEɛ4 allele effects.
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Affiliation(s)
- Wen-Zhuo Dai
- Department of Neurology, Affiliated Wuxi Clinical College of Nantong University, Wuxi, Jiangsu Province, China.,Department of Neurology, The Affiliated Wuxi No. 2 People's Hospital of Nanjing Medical University, Wuxi, Jiangsu Province, China.,Department of Neurology, Affiliated Wuxi No. 2 Hospital of Jiangnan University, Wuxi, Jiangsu Province, China
| | - Lu Liu
- Department of Neurology, The Affiliated Wuxi No. 2 People's Hospital of Nanjing Medical University, Wuxi, Jiangsu Province, China
| | - Meng-Zhuo Zhu
- Department of Neurology, Affiliated Wuxi Clinical College of Nantong University, Wuxi, Jiangsu Province, China
| | - Jing Lu
- Department of Neurology, The Affiliated Wuxi No. 2 People's Hospital of Nanjing Medical University, Wuxi, Jiangsu Province, China
| | - Jian-Ming Ni
- Radiology Department, Nanjing Medical University, Wuxi, Jiangsu Province, China
| | - Rong Li
- Department of Pharmacy, The Affiliated Wuxi No. 2 People's Hospital of Nanjing Medical University, Wuxi, Jiangsu Province, China
| | - Tao Ma
- Department of Neurology, Affiliated Wuxi Clinical College of Nantong University, Wuxi, Jiangsu Province, China.,Department of Neurology, The Affiliated Wuxi No. 2 People's Hospital of Nanjing Medical University, Wuxi, Jiangsu Province, China.,Department of Neurology, Affiliated Wuxi No. 2 Hospital of Jiangnan University, Wuxi, Jiangsu Province, China
| | - Xi-Chen Zhu
- Department of Neurology, Affiliated Wuxi Clinical College of Nantong University, Wuxi, Jiangsu Province, China.,Department of Neurology, The Affiliated Wuxi No. 2 People's Hospital of Nanjing Medical University, Wuxi, Jiangsu Province, China.,Department of Neurology, Affiliated Wuxi No. 2 Hospital of Jiangnan University, Wuxi, Jiangsu Province, China
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Tan L, Xing J, Wang Z, Du X, Luo R, Wang J, Zhao J, Zhao W, Yin C. Study of gray matter atrophy pattern with subcortical ischemic vascular disease-vascular cognitive impairment no dementia based on structural magnetic resonance imaging. Front Aging Neurosci 2023; 15:1051177. [PMID: 36815175 PMCID: PMC9939744 DOI: 10.3389/fnagi.2023.1051177] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 01/16/2023] [Indexed: 02/08/2023] Open
Abstract
Objective This study explored the structural imaging changes in patients with subcortical ischemic vascular disease (SIVD)-vascular cognitive impairment no dementia (VCIND) and the correlation between the changes in gray matter volume and the field of cognitive impairment to provide new targets for early diagnosis and treatment. Methods Our study included 15 patients with SIVD-normal cognitive impairment (SIVD-NCI), 63 with SIVD-VCIND, 26 with SIVD-vascular dementia (SIVD-VD), and 14 normal controls (NC). T1-weighted images of all participants were collected, and DPABI and SPM12 software were used to process the gray matter of the four groups based on voxels. Fisher's exact test, one-way ANOVA and Kruskal-Wallis H test were used to evaluate all clinical and demographic data and compare the characteristics of diencephalic gray matter atrophy in each group. Finally, the region of interest (ROI) of the SIVD-VCIND was extracted, and Pearson correlation analysis was performed between the ROI and the results of the neuropsychological scale. Results Compared to the NC, changes in gray matter atrophy were observed in the bilateral orbitofrontal gyrus, right middle temporal gyrus, superior temporal gyrus, and precuneus in the SIVD-VCIND. Gray matter atrophy was observed in the left cerebellar region 6, cerebellar crural region 1, bilateral thalamus, right precuneus, and calcarine in the SIVD-VD. Compared with the SIVD-VCIND, gray matter atrophy changes were observed in the bilateral thalamus in the SIVD-VD (p < 0.05, family-wise error corrected). In the SIVD-VCIND, the total gray matter volume, bilateral medial orbital superior frontal gyrus, right superior temporal gyrus, middle temporal gyrus, and precuneus were positively correlated with Boston Naming Test score, whereas the total gray matter volume, right superior temporal gyrus, and middle temporal gyrus were positively correlated with overall cognition. Conclusion Structural magnetic resonance imaging can detect extensive and subtle structural changes in the gray matter of patients with SIVD-VCIND and SIVD-VD, providing valuable evidences to explain the pathogenesis of subcortical vascular cognitive impairment and contributing to the early diagnosis of SIVD-VCIND and early warning of SIVD-VD.
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Affiliation(s)
- Lin Tan
- Department of Neurology, Hongqi Hospital Affiliated to Mudanjiang Medical University, Mudanjiang, China,Department of Rehabilitation, The Sixth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Jian Xing
- Department of Imaging, Hongqi Hospital Affiliated to Mudanjiang Medical University, Mudanjiang, China
| | - Zhenqi Wang
- Department of Neurology, Hongqi Hospital Affiliated to Mudanjiang Medical University, Mudanjiang, China
| | - Xiao Du
- Department of Neurology, Hongqi Hospital Affiliated to Mudanjiang Medical University, Mudanjiang, China
| | - Ruidi Luo
- Department of Neurology, Hongqi Hospital Affiliated to Mudanjiang Medical University, Mudanjiang, China
| | - Jianhang Wang
- Department of Neurology, Hongqi Hospital Affiliated to Mudanjiang Medical University, Mudanjiang, China
| | - Jinyi Zhao
- Department of Imaging, Hongqi Hospital Affiliated to Mudanjiang Medical University, Mudanjiang, China
| | - Weina Zhao
- Department of Neurology, Hongqi Hospital Affiliated to Mudanjiang Medical University, Mudanjiang, China,Heilongjiang Key Laboratory of Ischemic Stroke Prevention and Treatment, Mudanjiang, China,*Correspondence: Weina Zhao, ; Changhao Yin,
| | - Changhao Yin
- Department of Neurology, Hongqi Hospital Affiliated to Mudanjiang Medical University, Mudanjiang, China,Heilongjiang Key Laboratory of Ischemic Stroke Prevention and Treatment, Mudanjiang, China,*Correspondence: Weina Zhao, ; Changhao Yin,
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Rivas-Fernández MÁ, Lindín M, Zurrón M, Díaz F, Lojo-Seoane C, Pereiro AX, Galdo-Álvarez S. Neuroanatomical and neurocognitive changes associated with subjective cognitive decline. Front Med (Lausanne) 2023; 10:1094799. [PMID: 36817776 PMCID: PMC9932036 DOI: 10.3389/fmed.2023.1094799] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 01/17/2023] [Indexed: 02/05/2023] Open
Abstract
Introduction Subjective Cognitive Decline (SCD) can progress to mild cognitive impairment (MCI) and Alzheimer's disease (AD) dementia and thus may represent a preclinical stage of the AD continuum. However, evidence about structural changes observed in the brain during SCD remains inconsistent. Materials and methods This cross-sectional study aimed to evaluate, in subjects recruited from the CompAS project, neurocognitive and neurostructural differences between a group of forty-nine control subjects and forty-nine individuals who met the diagnostic criteria for SCD and exhibited high levels of subjective cognitive complaints (SCCs). Structural magnetic resonance imaging was used to compare neuroanatomical differences in brain volume and cortical thickness between both groups. Results Relative to the control group, the SCD group displayed structural changes involving frontal, parietal, and medial temporal lobe regions of critical importance in AD etiology and functionally related to several cognitive domains, including executive control, attention, memory, and language. Conclusion Despite the absence of clinical deficits, SCD may constitute a preclinical entity with a similar (although subtle) pattern of neuroanatomical changes to that observed in individuals with amnestic MCI or AD dementia.
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Affiliation(s)
- Miguel Ángel Rivas-Fernández
- Department of Clinical Psychology and Psychobiology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain,Cognitive Neuroscience Research Group, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Mónica Lindín
- Department of Clinical Psychology and Psychobiology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain,Cognitive Neuroscience Research Group, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Montserrat Zurrón
- Department of Clinical Psychology and Psychobiology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain,Cognitive Neuroscience Research Group, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Fernando Díaz
- Department of Clinical Psychology and Psychobiology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain,Cognitive Neuroscience Research Group, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Cristina Lojo-Seoane
- Cognitive Neuroscience Research Group, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain,Department of Developmental and Educational Psychology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Arturo X. Pereiro
- Cognitive Neuroscience Research Group, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain,Department of Developmental and Educational Psychology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Santiago Galdo-Álvarez
- Department of Clinical Psychology and Psychobiology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain,Cognitive Neuroscience Research Group, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain,*Correspondence: Santiago Galdo-Álvarez,
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Pan D, Zeng A, Yang B, Lai G, Hu B, Song X, Jiang T. Deep Learning for Brain MRI Confirms Patterned Pathological Progression in Alzheimer's Disease. Adv Sci (Weinh) 2023; 10:e2204717. [PMID: 36575159 PMCID: PMC9951348 DOI: 10.1002/advs.202204717] [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] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 11/29/2022] [Indexed: 06/17/2023]
Abstract
Deep learning (DL) on brain magnetic resonance imaging (MRI) data has shown excellent performance in differentiating individuals with Alzheimer's disease (AD). However, the value of DL in detecting progressive structural MRI (sMRI) abnormalities linked to AD pathology has yet to be established. In this study, an interpretable DL algorithm named the Ensemble of 3-dimensional convolutional neural network (Ensemble 3DCNN) with enhanced parsing techniques is proposed to investigate the longitudinal trajectories of whole-brain sMRI changes denoting AD onset and progression. A set of 2369 T1-weighted images from the multi-centre Alzheimer's Disease Neuroimaging Initiative and Open Access Series of Imaging Studies cohorts are applied to model derivation, validation, testing, and pattern analysis. An Ensemble-3DCNN-based P-score is generated, based on which multiple brain regions, including amygdala, insular, parahippocampal, and temporal gyrus, exhibit early and connected progressive neurodegeneration. Complex individual variability in the sMRI is also observed. This study combining non-invasive sMRI and interpretable DL in detecting patterned sMRI changes confirmed AD pathological progression, shedding new light on predicting AD progression using whole-brain sMRI.
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Affiliation(s)
- Dan Pan
- School of Electronics and InformationGuangdong Polytechnic Normal UniversityGuangzhou510665China
| | - An Zeng
- Faculty of Computers, Guangdong University of TechnologyGuangzhou510006China
| | - Baoyao Yang
- Faculty of Computers, Guangdong University of TechnologyGuangzhou510006China
| | - Gangyong Lai
- Faculty of Computers, Guangdong University of TechnologyGuangzhou510006China
| | - Bing Hu
- Department of RadiologyThe Third Affiliated Hospital of SUN Yat‐sen UniversityGuangzhou510630China
| | - Xiaowei Song
- Clinical Research CentreSurrey Memorial HospitalFraser HealthSurreyBritish ColumbiaV3V 1Z2Canada
| | - Tianzi Jiang
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
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Zhou Z, Luo Y, Gao X, Zhu Y, Bai X, Yang H, Bi Q, Chen S, Duan L, Wang L, Gong F, Feng F, Gong G, Zhu H, Pan H. Alterations in brain structure and function associated with pediatric growth hormone deficiency: A multi-modal magnetic resonance imaging study. Front Neurosci 2023; 16:1043857. [PMID: 36685242 PMCID: PMC9853296 DOI: 10.3389/fnins.2022.1043857] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 12/02/2022] [Indexed: 01/07/2023] Open
Abstract
Introduction Pediatric growth hormone deficiency (GHD) is a disease resulting from impaired growth hormone/insulin-like growth factor-1 (IGF-1) axis but the effects of GHD on children's cognitive function, brain structure and brain function were not yet fully illustrated. Methods Full Wechsler Intelligence Scales for Children, structural imaging, diffusion tensor imaging, and resting-state functional magnetic resonance imaging were assessed in 11 children with GHD and 10 matched healthy controls. Results (1) The GHD group showed moderate cognitive impairment, and a positive correlation existed between IGF-1 levels and cognitive indices. (2) Mean diffusivity was significantly increased in both corticospinal tracts in GHD group. (3) There were significant positive correlations between IGF-1 levels and volume metrics of left thalamus, left pallidum and right putamen but a negative correlation between IGF-1 levels and cortical thickness of the occipital lobe. And IGF-1 levels negatively correlated with fractional anisotropy in the superior longitudinal fasciculus and right corticospinal tract. (4) Regional homogeneity (ReHo) in the left hippocampus/parahippocampal gyrus was negatively correlated with IGF-1 levels; the amplitude of low-frequency fluctuation (ALFF) and ReHo in the paracentral lobe, postcentral gyrus and precentral gyrus were also negatively correlated with IGF-1 levels, in which region ALFF fully mediates the effect of IGF-1 on working memory index. Conclusion Multiple subcortical, cortical structures, and regional neural activities might be influenced by serum IGF-1 levels. Thereinto, ALFF in the paracentral lobe, postcentral gyrus and precentral gyrus fully mediates the effect of IGF-1 on the working memory index.
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Affiliation(s)
- Zhibo Zhou
- Key Laboratory of Endocrinology of National Health Commission, State Key Laboratory of Complex Severe and Rare Diseases, Department of Endocrinology, Chinese Research Center for Behavior Medicine in Growth and Development, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yunyun Luo
- Key Laboratory of Endocrinology of National Health Commission, State Key Laboratory of Complex Severe and Rare Diseases, Department of Endocrinology, Chinese Research Center for Behavior Medicine in Growth and Development, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoxing Gao
- Department of Pulmonary and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yanlin Zhu
- Beijing Normal University, Beijing, China
| | - Xi Bai
- Key Laboratory of Endocrinology of National Health Commission, State Key Laboratory of Complex Severe and Rare Diseases, Department of Endocrinology, Chinese Research Center for Behavior Medicine in Growth and Development, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongbo Yang
- Key Laboratory of Endocrinology of National Health Commission, State Key Laboratory of Complex Severe and Rare Diseases, Department of Endocrinology, Chinese Research Center for Behavior Medicine in Growth and Development, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qiuhui Bi
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Shi Chen
- Key Laboratory of Endocrinology of National Health Commission, State Key Laboratory of Complex Severe and Rare Diseases, Department of Endocrinology, Chinese Research Center for Behavior Medicine in Growth and Development, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lian Duan
- Key Laboratory of Endocrinology of National Health Commission, State Key Laboratory of Complex Severe and Rare Diseases, Department of Endocrinology, Chinese Research Center for Behavior Medicine in Growth and Development, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Linjie Wang
- Key Laboratory of Endocrinology of National Health Commission, State Key Laboratory of Complex Severe and Rare Diseases, Department of Endocrinology, Chinese Research Center for Behavior Medicine in Growth and Development, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Fengying Gong
- Key Laboratory of Endocrinology of National Health Commission, State Key Laboratory of Complex Severe and Rare Diseases, Department of Endocrinology, Chinese Research Center for Behavior Medicine in Growth and Development, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Feng Feng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Huijuan Zhu
- Key Laboratory of Endocrinology of National Health Commission, State Key Laboratory of Complex Severe and Rare Diseases, Department of Endocrinology, Chinese Research Center for Behavior Medicine in Growth and Development, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China,Huijuan Zhu,
| | - Hui Pan
- Key Laboratory of Endocrinology of National Health Commission, State Key Laboratory of Complex Severe and Rare Diseases, Department of Endocrinology, Chinese Research Center for Behavior Medicine in Growth and Development, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China,*Correspondence: Hui Pan,
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Wang H, Xu J, Yu M, Zhou G, Ren J, Wang Y, Zheng H, Sun Y, Wu J, Liu W. Functional and structural alterations as diagnostic imaging markers for depression in de novo Parkinson's disease. Front Neurosci 2023; 17:1101623. [PMID: 36908791 PMCID: PMC9992430 DOI: 10.3389/fnins.2023.1101623] [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: 11/18/2022] [Accepted: 02/08/2023] [Indexed: 02/24/2023] Open
Abstract
Background Depression in Parkinson's disease (PD) is identified and diagnosed with behavioral observations and neuropsychological measurements. Due to the large overlaps of depression and PD symptoms in clinical manifestations, it is challenging for neurologists to distinguish and diagnose depression in PD (DPD) in the early clinical stage of PD. The advancement in magnetic resonance imaging (MRI) technology provides potential clinical utility in the diagnosis of DPD. This study aimed to explore the alterations of functional and structural MRI in DPD to produce neuroimaging markers in discriminating DPD from non-depressed PD (NDPD) and healthy controls (HC). Methods We recruited 20 DPD, 37 NDPD, and 41 HC matched in age, gender, and education years. The patients' diagnosis with PD was de novo. The differences in regional homogeneity (ReHo), voxel-wise degree centrality (DC), cortical thickness, cortical gray matter (GM) volumes, and subcortical GM volumes among these groups were detected, and the relationship between altered indicators and depression was analyzed. Moreover, the receiver operating characteristic (ROC) analysis was performed to assess the diagnostic efficacy of altered indicators for DPD. Results Compared to NDPD and HC, DPD showed significantly increased ReHo in left dorsolateral superior frontal gyrus (DSFG) and DC in left inferior temporal gyrus (ITG), and decreased GM volumes in left temporal lobe and right Amygdala. Among these altered indicators, ReHo value in left DSFG and DC values in left ITG and left DSFG were significantly correlated with the severity of depression in PD patients. Comparing DPD and NDPD, the ROC analysis revealed a better area under the curve value for the combination of ReHo value in left DSFG and DC value in left ITG, followed by each independent indicator. However, the difference is not statistically significant. Conclusion This study demonstrates that both functional and structural impairments are present in DPD. Among them, ReHo value of left DSFG and DC value of left ITG are equally well suited for the diagnosis and differential diagnosis of DPD, with a combination of them being slightly preferable. The multimodal MRI technique represents a promising approach for the classification of subjects with PD.
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Affiliation(s)
- Hui Wang
- Department of Neurology, Lianyungang Hospital of Traditional Chinese Medicine, Lianyungang Affiliated Hospital of Nanjing University of Chinese Medicine, Lianyungang, China
| | - Jianxia Xu
- Department of Neurology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, China
| | - Miao Yu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Gaiyan Zhou
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jingru Ren
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yajie Wang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Huifen Zheng
- Department of Neurology, Geriatric Hospital of Nanjing Medical University, Nanjing, China
| | - Yu Sun
- International Laboratory of Children Medical Imaging Research, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Jun Wu
- Department of Clinical Laboratory, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Weiguo Liu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
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Martelli C, Artiges E, Miranda R, Romeo B, Petillion A, Aubin HJ, Amirouche A, Chanraud S, Benyamina A, Martinot JL. Caudate gray matter volumes and risk of relapse in Type A alcohol-dependent patients: A 7-year MRI follow-up study. Front Psychiatry 2023; 14:1067326. [PMID: 36873223 PMCID: PMC9975333 DOI: 10.3389/fpsyt.2023.1067326] [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] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 01/20/2023] [Indexed: 02/17/2023] Open
Abstract
BACKGROUND Whether alteration in regional brain volumes can be detected in Type A alcoholics both at baseline and after a long follow-up remains to be confirmed. Therefore, we examined volume alterations at baseline, and longitudinal changes in a small follow-up subsample. METHODS In total of 26 patients and 24 healthy controls were assessed at baseline using magnetic resonance imaging and voxel-based morphometry, among which 17 patients and 6 controls were re-evaluated 7 years later. At baseline, regional cerebral volumes of patients were compared to controls. At follow-up, three groups were compared: abstainers (n = 11, more than 2 years of abstinence), relapsers (n = 6, <2 years of abstinence), and controls (n = 6). RESULTS The cross-sectional analyses detected, at both times, higher caudate nuclei volumes bilaterally in relapsers compared to abstainers. In abstainers, the longitudinal analysis indicated recovery of normal gray matter volumes in the middle and inferior frontal gyrus, and in the middle cingulate, while white matter volumes recovery was detected in the corpus callosum and in anterior and superior white matter specific regions. CONCLUSIONS Overall, the present investigation revealed larger caudate nuclei in the relapser AUD patient group both at baseline and at follow-up in the cross-sectional analyses. This finding suggest that a higher caudate volume could be a candidate risk factor of relapse. In patients with specific type A alcohol-dependence, we showed that long-term recovery in fronto-striato-limbic GM and WM volumes occurs during long-term abstinence. These results support the crucial role of frontal circuitry in AUD.
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Affiliation(s)
- Catherine Martelli
- Institut National de la Santé et de la Recherche Médicale (INSERM) Research Unit 1299 "Trajectoires développementales en psychiatrie", École Normale Supérieure Paris-Saclay, Université Paris-Saclay, Centre National de la Recherche Scientifique (CNRS) 9010, Centre Borelli, Gif-sur-Yvette, France.,Department of Psychiatry and Addictology, Assistance Publique - Hôpitaux de Paris, Paul-Brousse Hospital, Villejuif, France.,Psychiatry-Comorbidities-Addictions Research Unit (PSYCOMADD), Paris-Saclay University, Gif-sur-Yvette, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale (INSERM) Research Unit 1299 "Trajectoires développementales en psychiatrie", École Normale Supérieure Paris-Saclay, Université Paris-Saclay, Centre National de la Recherche Scientifique (CNRS) 9010, Centre Borelli, Gif-sur-Yvette, France.,Department of Psychiatry, Établissement Public de Santé (EPS) Barthélemy Durand, Etampes, France
| | - Rubén Miranda
- Institut National de la Santé et de la Recherche Médicale (INSERM) Research Unit 1299 "Trajectoires développementales en psychiatrie", École Normale Supérieure Paris-Saclay, Université Paris-Saclay, Centre National de la Recherche Scientifique (CNRS) 9010, Centre Borelli, Gif-sur-Yvette, France.,Department of Psychiatry and Addictology, Assistance Publique - Hôpitaux de Paris, Paul-Brousse Hospital, Villejuif, France.,Psychiatry-Comorbidities-Addictions Research Unit (PSYCOMADD), Paris-Saclay University, Gif-sur-Yvette, France
| | - Bruno Romeo
- Department of Psychiatry and Addictology, Assistance Publique - Hôpitaux de Paris, Paul-Brousse Hospital, Villejuif, France.,Psychiatry-Comorbidities-Addictions Research Unit (PSYCOMADD), Paris-Saclay University, Gif-sur-Yvette, France
| | - Amélie Petillion
- Department of Psychiatry and Addictology, Assistance Publique - Hôpitaux de Paris, Paul-Brousse Hospital, Villejuif, France.,Psychiatry-Comorbidities-Addictions Research Unit (PSYCOMADD), Paris-Saclay University, Gif-sur-Yvette, France
| | - Henri-Jean Aubin
- Department of Psychiatry and Addictology, Assistance Publique - Hôpitaux de Paris, Paul-Brousse Hospital, Villejuif, France.,Institut National de la Santé et de la Recherche Médicale Research Unit 1018, Centre de Recherche en Epidémiologie et Santé des Populations (CESP), Paris, France
| | - Ammar Amirouche
- Department of Psychiatry and Addictology, Assistance Publique - Hôpitaux de Paris, Paul-Brousse Hospital, Villejuif, France.,Psychiatry-Comorbidities-Addictions Research Unit (PSYCOMADD), Paris-Saclay University, Gif-sur-Yvette, France
| | - Sandra Chanraud
- Paris Sciences & Lettres (PSL) Research University-École Pratique des Hautes Études (EPHE), Paris, France.,Institut de Neurosciences Cognitives et Intégratives d'Aquitaine (INCIA), Centre National de la Recherche Scientifique (CNRS), Unité Mixte de Recherche (UMR) 5287, University of Bordeaux, Bordeaux, France
| | - Amine Benyamina
- Department of Psychiatry and Addictology, Assistance Publique - Hôpitaux de Paris, Paul-Brousse Hospital, Villejuif, France.,Psychiatry-Comorbidities-Addictions Research Unit (PSYCOMADD), Paris-Saclay University, Gif-sur-Yvette, France
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale (INSERM) Research Unit 1299 "Trajectoires développementales en psychiatrie", École Normale Supérieure Paris-Saclay, Université Paris-Saclay, Centre National de la Recherche Scientifique (CNRS) 9010, Centre Borelli, Gif-sur-Yvette, France
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Schmitz-Koep B, Menegaux A, Zimmermann J, Thalhammer M, Neubauer A, Wendt J, Schinz D, Wachinger C, Daamen M, Boecker H, Zimmer C, Priller J, Wolke D, Bartmann P, Sorg C, Hedderich DM. Aberrant allometric scaling of cortical folding in preterm-born adults. Brain Commun 2022; 5:fcac341. [PMID: 36632185 PMCID: PMC9830984 DOI: 10.1093/braincomms/fcac341] [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: 05/17/2022] [Revised: 10/24/2022] [Accepted: 12/22/2022] [Indexed: 12/27/2022] Open
Abstract
A universal allometric scaling law has been proposed to describe cortical folding of the mammalian brain as a function of the product of cortical surface area and the square root of cortical thickness across different mammalian species, including humans. Since these cortical properties are vulnerable to developmental disturbances caused by preterm birth in humans and since these alterations are related to cognitive impairments, we tested (i) whether cortical folding in preterm-born adults follows this cortical scaling law and (ii) the functional relevance of potential scaling aberrances. We analysed the cortical scaling relationship in a large and prospectively collected cohort of 91 very premature-born adults (<32 weeks of gestation and/or birthweight <1500 g, very preterm and/or very low birth weight) and 105 full-term controls at 26 years of age based on the total surface area, exposed surface area and average cortical thickness measured with structural magnetic resonance imaging and surface-based morphometry. We found that the slope of the log-transformed cortical scaling relationship was significantly altered in adults (very preterm and/or very low birth weight: 1.24, full-term: 1.14, P = 0.018). More specifically, the slope was significantly altered in male adults (very preterm and/or very low birth weight: 1.24, full-term: 1.00, P = 0.031), while there was no significant difference in the slope of female adults (very preterm and/or very low birth weight: 1.27, full-term: 1.12, P = 0.225). Furthermore, offset was significantly lower compared with full-term controls in both male (very preterm and/or very low birth weight: -0.546, full-term: -0.538, P = 0.001) and female adults (very preterm and/or very low birth weight: -0.545, full-term: -0.538, P = 0.023), indicating a systematic shift of the regression line after preterm birth. Gestational age had a significant effect on the slope in very preterm and/or very low birth weight adults and more specifically in male very preterm and/or very low birth weight adults, indicating that the difference in slope is specifically related to preterm birth. The shape or tension term of the scaling law had no significant effect on cognitive performance, while the size of the cortex did. Results demonstrate altered scaling of cortical surface and cortical thickness in very premature-born adults. Data suggest altered mechanical forces acting on the cortex after preterm birth.
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Affiliation(s)
- Benita Schmitz-Koep
- Correspondence to: Benita Schmitz-Koep, MD Department of Diagnostic and Interventional Neuroradiology Technical University of Munich, School of Medicine Klinikum rechts der Isar, Ismaninger Strasse 22 81675 Munich, Germany E-mail:
| | - Aurore Menegaux
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany
| | - Juliana Zimmermann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany
| | - Melissa Thalhammer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany
| | - Antonia Neubauer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany
| | - Jil Wendt
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany
| | - David Schinz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany
| | - Christian Wachinger
- Lab for Artificial Intelligence in Medical Imaging, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany
| | - Marcel Daamen
- Functional Neuroimaging Group, Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
- Department of Neonatology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Henning Boecker
- Functional Neuroimaging Group, Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany
| | - Josef Priller
- Department of Psychiatry, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany
| | - Dieter Wolke
- Department of Psychology, University of Warwick, University Road, Coventry CV4 7AL, UK
- Warwick Medical School, University of Warwick, University Road, Coventry CV4 7AL, UK
| | - Peter Bartmann
- Department of Neonatology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Christian Sorg
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany
- Department of Psychiatry, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany
| | - Dennis M Hedderich
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany
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Xie JY, Li RH, Yuan W, Du J, Zhou DS, Cheng YQ, Xu XM, Liu H, Yuan TF. Advances in neuroimaging studies of alcohol use disorder (AUD). Psychoradiology 2022; 2:146-155. [PMID: 38665276 PMCID: PMC11003430 DOI: 10.1093/psyrad/kkac018] [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] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/08/2022] [Accepted: 11/14/2022] [Indexed: 04/28/2024]
Abstract
Alcohol use disorder (AUD) is a worldwide problem and the most common substance use disorder. Chronic alcohol consumption may have negative effects on the body, the mind, the family, and even society. With the progress of current neuroimaging methods, an increasing number of imaging techniques are being used to objectively detect brain impairment induced by alcoholism and serve a vital role in the diagnosis, prognosis, and treatment assessment of AUD. This article organizes and analyzes the research on alcohol dependence concerning the main noninvasive neuroimaging methods, structural magnetic resonance imaging, functional magnetic resonance imaging, and electroencephalography, as well as the most common noninvasive brain stimulation - transcranial magnetic stimulation, and intersperses the article with joint intra- and intergroup studies, providing an outlook on future research directions.
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Affiliation(s)
- Ji-Yu Xie
- School of Mental Health, Wenzhou Medical University, Wenzho 325000, Zhejiangu, China
| | - Rui-Hua Li
- Shandong Mental Health Center, Shandong University, Jinan 250014, Shandong, China
| | - Wei Yuan
- Shandong Mental Health Center, Shandong University, Jinan 250014, Shandong, China
| | - Jiang Du
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai 200030, China
| | - Dong-Sheng Zhou
- Department of Psychiatry, Ningbo Kangning Hospital, Ningbo 315000, Zhejiang, China
| | - Yu-Qi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming 650000, Yunnan, China
| | - Xue-Ming Xu
- Department of Psychiatry, Taizhou Second People's Hospital, Taizhou 318000, Zhejiang, China
| | - Heng Liu
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi 563000, Guizhou, China
| | - Ti-Fei Yuan
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai 200030, China
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40
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Trevisan N, Jaillard A, Cattarinussi G, De Roni P, Sambataro F. Surface-Based Cortical Measures in Multimodal Association Brain Regions Predict Chess Expertise. Brain Sci 2022; 12. [PMID: 36421916 DOI: 10.3390/brainsci12111592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 11/10/2022] [Accepted: 11/17/2022] [Indexed: 11/22/2022] Open
Abstract
The complex structure of the brain supports high-order cognition, which is crucial for mastering chess. Surface-based measures, including the fractional dimension (FD) and gyrification index (GI), may be more sensitive in detecting cortical changes relative to volumetric indexes. For this reason, structural magnetic resonance imaging data from 29 chess experts and 29 novice participants were analyzed using the CAT12 toolbox. FD and GI for each brain region were compared between the groups. A multivariate model was used to identify surface-based brain measures that can predict chess expertise. In chess experts, FD is increased in the left frontal operculum (p < 0.01), and this change correlates with the starting age of chess practice (ρ = −0.54, p < 0.01). FD is decreased in the right superior parietal lobule (p < 0.01). Chess expertise is predicted by the FD in a network of fronto-parieto-temporal regions and is associated with GI changes in the middle cingulate gyrus (p < 0.01) and the superior temporal sulcus (p < 0.01). Our findings add to the evidence that chess expertise is based on the complex properties of the brain surface of a network of transmodal association areas important for flexible high-level cognitive functions. Interestingly, these changes are associated with long-lasting practice, suggesting that neuroplastic effects develop over time.
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Han J, Kim SY, Lee J, Lee WH. Brain Age Prediction: A Comparison between Machine Learning Models Using Brain Morphometric Data. Sensors (Basel) 2022; 22:8077. [PMID: 36298428 PMCID: PMC9608785 DOI: 10.3390/s22208077] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 10/13/2022] [Accepted: 10/17/2022] [Indexed: 06/16/2023]
Abstract
Brain structural morphology varies over the aging trajectory, and the prediction of a person's age using brain morphological features can help the detection of an abnormal aging process. Neuroimaging-based brain age is widely used to quantify an individual's brain health as deviation from a normative brain aging trajectory. Machine learning approaches are expanding the potential for accurate brain age prediction but are challenging due to the great variety of machine learning algorithms. Here, we aimed to compare the performance of the machine learning models used to estimate brain age using brain morphological measures derived from structural magnetic resonance imaging scans. We evaluated 27 machine learning models, applied to three independent datasets from the Human Connectome Project (HCP, n = 1113, age range 22-37), the Cambridge Centre for Ageing and Neuroscience (Cam-CAN, n = 601, age range 18-88), and the Information eXtraction from Images (IXI, n = 567, age range 19-86). Performance was assessed within each sample using cross-validation and an unseen test set. The models achieved mean absolute errors of 2.75-3.12, 7.08-10.50, and 8.04-9.86 years, as well as Pearson's correlation coefficients of 0.11-0.42, 0.64-0.85, and 0.63-0.79 between predicted brain age and chronological age for the HCP, Cam-CAN, and IXI samples, respectively. We found a substantial difference in performance between models trained on the same data type, indicating that the choice of model yields considerable variation in brain-predicted age. Furthermore, in three datasets, regularized linear regression algorithms achieved similar performance to nonlinear and ensemble algorithms. Our results suggest that regularized linear algorithms are as effective as nonlinear and ensemble algorithms for brain age prediction, while significantly reducing computational costs. Our findings can serve as a starting point and quantitative reference for future efforts at improving brain age prediction using machine learning models applied to brain morphometric data.
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Guo J, Jiang Z, Liu X, Li H, Biswal BB, Zhou B, Sheng W, Gao Q, Chen H, Fan Y, Zhu W, Wang J, Chen H, Liu C. Cerebello-cerebral resting-state functional connectivity in spinocerebellar ataxia type 3. Hum Brain Mapp 2022; 44:927-936. [PMID: 36250694 PMCID: PMC9875927 DOI: 10.1002/hbm.26113] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 08/24/2022] [Accepted: 09/26/2022] [Indexed: 01/28/2023] Open
Abstract
Spinocerebellar ataxia type 3 (SCA3) is a neurodegenerative disorder characterized by progressive motor and nonmotor deficits concomitant with degenerative pathophysiological changes within the cerebellum. The cerebellum is topographically organized into cerebello-cerebral circuits that create distinct functional networks regulating movement, cognition, and affect. SCA3-associated motor and nonmotor symptoms are possibly related not only to intracerebellar changes but also to disruption of the connectivity within these cerebello-cerebral circuits. However, to date, no comprehensive investigation of cerebello-cerebral connectivity in SCA3 has been conducted. The present study aimed to identify cerebello-cerebral functional connectivity alterations and associations with downstream clinical phenotypes and upstream topographic markers of cerebellar neurodegeneration in patients with SCA3. This study included 45 patients with SCA3 and 49 healthy controls. Voxel-based morphometry and resting-state functional magnetic resonance imaging (MRI) were performed to characterize the cerebellar atrophy and to examine the cerebello-cerebral functional connectivity patterns. Structural MRI confirmed widespread gray matter atrophy in the motor and cognitive cerebellum of patients with SCA3. We found reduced functional connectivity between the cerebellum and the cerebral cortical networks, including the somatomotor, frontoparietal, and default networks; however, increased connectivity was observed between the cerebellum and the dorsal attention network. These abnormal patterns correlated with the CAG repeat expansion and deficits in global cognition. Our results indicate the contribution of cerebello-cerebral networks to the motor and cognitive impairments in patients with SCA3 and reveal that such alterations occur in association with cerebellar atrophy. These findings add important insights into our understanding of the role of the cerebellum in SCA3.
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Affiliation(s)
- Jing Guo
- The Center of Psychosomatic MedicineSichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of ChinaChengduChina,The Clinical Hospital of Chengdu Brain Science InstituteSchool of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina,Department of RadiologySouthwest Hospital, Army Medical University (Third Military Medical University)ChongqingChina
| | - Zhouyu Jiang
- The Clinical Hospital of Chengdu Brain Science InstituteSchool of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina,MOE Key Lab for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Xinyuan Liu
- The Clinical Hospital of Chengdu Brain Science InstituteSchool of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina,MOE Key Lab for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Haoru Li
- The Clinical Hospital of Chengdu Brain Science InstituteSchool of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina,MOE Key Lab for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Bharat B. Biswal
- The Clinical Hospital of Chengdu Brain Science InstituteSchool of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina,Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNew JerseyUSA
| | - Bo Zhou
- The Center of Psychosomatic MedicineSichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of ChinaChengduChina
| | - Wei Sheng
- The Clinical Hospital of Chengdu Brain Science InstituteSchool of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina,MOE Key Lab for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Qing Gao
- The Clinical Hospital of Chengdu Brain Science InstituteSchool of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina,MOE Key Lab for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Hui Chen
- Department of RadiologySouthwest Hospital, Army Medical University (Third Military Medical University)ChongqingChina
| | - Yunshuang Fan
- The Clinical Hospital of Chengdu Brain Science InstituteSchool of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina,MOE Key Lab for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Wenyan Zhu
- Data Processing DepartmentYidu Cloud Technology, Inc.BeijingChina
| | - Jian Wang
- Department of RadiologySouthwest Hospital, Army Medical University (Third Military Medical University)ChongqingChina
| | - Huafu Chen
- The Center of Psychosomatic MedicineSichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of ChinaChengduChina,The Clinical Hospital of Chengdu Brain Science InstituteSchool of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina,Department of RadiologySouthwest Hospital, Army Medical University (Third Military Medical University)ChongqingChina
| | - Chen Liu
- Department of RadiologySouthwest Hospital, Army Medical University (Third Military Medical University)ChongqingChina
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Song YH, Yi JY, Noh Y, Jang H, Seo SW, Na DL, Seong JK. On the reliability of deep learning-based classification for Alzheimer's disease: Multi-cohorts, multi-vendors, multi-protocols, and head-to-head validation. Front Neurosci 2022; 16:851871. [PMID: 36161156 PMCID: PMC9490270 DOI: 10.3389/fnins.2022.851871] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 07/27/2022] [Indexed: 01/18/2023] Open
Abstract
Structural changes in the brain due to Alzheimer's disease dementia (ADD) can be observed through brain T1-weighted magnetic resonance imaging (MRI) images. Many ADD diagnostic studies using brain MRI images have been conducted with machine-learning and deep-learning models. Although reliability is a key in clinical application and applicability of low-resolution MRI (LRMRI) is a key to broad clinical application, both are not sufficiently studied in the deep-learning area. In this study, we developed a 2-dimensional convolutional neural network-based classification model by adopting several methods, such as using instance normalization layer, Mixup, and sharpness aware minimization. To train the model, MRI images from 2,765 cognitively normal individuals and 1,192 patients with ADD from the Samsung medical center cohort were exploited. To assess the reliability of our classification model, we designed external validation in multiple scenarios: (1) multi-cohort validation using four additional cohort datasets including more than 30 different centers in multiple countries, (2) multi-vendor validation using three different MRI vendor subgroups, (3) LRMRI image validation, and finally, (4) head-to-head validation using ten pairs of MRI images from ten individual subjects scanned in two different centers. For multi-cohort validation, we used the MRI images from 739 subjects from the Alzheimer's Disease Neuroimaging Initiative cohort, 125 subjects from the Dementia Platform of Korea cohort, 234 subjects from the Premier cohort, and 139 subjects from the Gachon University Gil Medical Center. We further assessed classification performance across different vendors and protocols for each dataset. We achieved a mean AUC and classification accuracy of 0.9868 and 0.9482 in 5-fold cross-validation. In external validation, we obtained a comparable AUC of 0.9396 and classification accuracy of 0.8757 to other cross-validation studies in the ADNI cohorts. Furthermore, we observed the possibility of broad clinical application through LRMRI image validation by achieving a mean AUC and classification accuracy of 0.9404 and 0.8765 at cross-validation and AUC and classification accuracy of 0.8749 and 0.8281 at the ADNI cohort external validation.
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Affiliation(s)
- Yeong-Hun Song
- Department of Artificial Intelligence, Korea University, Seoul, South Korea
| | - Jun-Young Yi
- Department of Artificial Intelligence, Korea University, Seoul, South Korea
| | - Young Noh
- Department of Neurology, Gil Medical Center, Gachon University College of Medicine, Incheon, South Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
| | - Duk L. Na
- Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
| | - Joon-Kyung Seong
- Department of Artificial Intelligence, Korea University, Seoul, South Korea
- School of Biomedical Engineering, Korea University, Seoul, South Korea
- Interdisciplinary Program in Precision Public Health, College of Health Science, Korea University, Seoul, South Korea
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44
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Du Y, He X, Kochunov P, Pearlson G, Hong LE, van Erp TGM, Belger A, Calhoun VD. A new multimodality fusion classification approach to explore the uniqueness of schizophrenia and autism spectrum disorder. Hum Brain Mapp 2022; 43:3887-3903. [PMID: 35484969 PMCID: PMC9294304 DOI: 10.1002/hbm.25890] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 03/24/2022] [Accepted: 04/08/2022] [Indexed: 11/13/2022] Open
Abstract
Schizophrenia (SZ) and autism spectrum disorder (ASD) sharing overlapping symptoms have a long history of diagnostic confusion. It is unclear what their differences at a brain level are. Here, we propose a multimodality fusion classification approach to investigate their divergence in brain function and structure. Using brain functional network connectivity (FNC) calculated from resting-state fMRI data and gray matter volume (GMV) estimated from sMRI data, we classify the two disorders using the main data (335 SZ and 380 ASD patients) via an unbiased 10-fold cross-validation pipeline, and also validate the classification generalization ability on an independent cohort (120 SZ and 349 ASD patients). The classification accuracy reached up to 83.08% for the testing data and 72.10% for the independent data, significantly better than the results from using the single-modality features. The discriminative FNCs that were automatically selected primarily involved the sub-cortical, default mode, and visual domains. Interestingly, all discriminative FNCs relating to the default mode network showed an intermediate strength in healthy controls (HCs) between SZ and ASD patients. Their GMV differences were mainly driven by the frontal gyrus, temporal gyrus, and insula. Regarding these regions, the mean GMV of HC fell intermediate between that of SZ and ASD, and ASD showed the highest GMV. The middle frontal gyrus was associated with both functional and structural differences. In summary, our work reveals the unique neuroimaging characteristics of SZ and ASD that can achieve high and generalizable classification accuracy, supporting their potential as disorder-specific neural substrates of the two entwined disorders.
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Affiliation(s)
- Yuhui Du
- School of Computer and Information TechnologyShanxi UniversityTaiyuanShanxiChina
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data ScienceGeorgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Xingyu He
- School of Computer and Information TechnologyShanxi UniversityTaiyuanShanxiChina
| | - Peter Kochunov
- Center for Brain Imaging ResearchUniversity of MarylandBaltimoreMarylandUSA
| | | | - L. Elliot Hong
- Center for Brain Imaging ResearchUniversity of MarylandBaltimoreMarylandUSA
| | - Theo G. M. van Erp
- Department of Psychiatry and Human BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
- Center for the Neurobiology of Learning and MemoryUniversity of CaliforniaIrvineCaliforniaUSA
| | - Aysenil Belger
- Department of PsychiatryUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - Vince D. Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data ScienceGeorgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
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Xie Y, Ding H, Du X, Chai C, Wei X, Sun J, Zhuo C, Wang L, Li J, Tian H, Liang M, Zhang S, Yu C, Qin W. Morphometric Integrated Classification Index: A Multisite Model-Based, Interpretable, Shareable and Evolvable Biomarker for Schizophrenia. Schizophr Bull 2022; 48:1217-1227. [PMID: 35925032 PMCID: PMC9673259 DOI: 10.1093/schbul/sbac096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND AND HYPOTHESIS Multisite massive schizophrenia neuroimaging data sharing is becoming critical in understanding the pathophysiological mechanism and making an objective diagnosis of schizophrenia; it remains challenging to obtain a generalizable and interpretable, shareable, and evolvable neuroimaging biomarker for schizophrenia diagnosis. STUDY DESIGN A Morphometric Integrated Classification Index (MICI) was proposed as a potential biomarker for schizophrenia diagnosis based on structural magnetic resonance imaging data of 1270 subjects from 10 sites (588 schizophrenia patients and 682 normal controls). An optimal XGBoost classifier plus sample-weighted SHapley Additive explanation algorithms were used to construct the MICI measure. STUDY RESULTS The MICI measure achieved comparable performance with the sample-weighted ensembling model and merged model based on raw data (Delong test, P > 0.82) while outperformed the single-site models (Delong test, P < 0.05) in either the independent-sample testing datasets from the 9 sites or the independent-site dataset (generalizable). Besides, when new sites were embedded in, the performance of this measure was gradually increasing (evolvable). Finally, MICI was strongly associated with the severity of schizophrenia brain structural abnormality, with the patients' positive and negative symptoms, and with the brain expression profiles of schizophrenia risk genes (interpretable). CONCLUSIONS In summary, the proposed MICI biomarker may provide a simple and explainable way to support clinicians for objectively diagnosing schizophrenia. Finally, we developed an online model share platform to promote biomarker generalization and provide free individual prediction services (http://micc.tmu.edu.cn/mici/index.html).
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Affiliation(s)
- Yingying Xie
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Hao Ding
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China,School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Xiaotong Du
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Chao Chai
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Xiaotong Wei
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Jie Sun
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Chuanjun Zhuo
- Department of Psychiatry Functional Neuroimaging Laboratory, Tianjin Mental Health Center, Tianjin Anding Hospital, Tianjin, China
| | - Lina Wang
- Department of Psychiatry Functional Neuroimaging Laboratory, Tianjin Mental Health Center, Tianjin Anding Hospital, Tianjin, China
| | - Jie Li
- Department of Psychiatry Functional Neuroimaging Laboratory, Tianjin Mental Health Center, Tianjin Anding Hospital, Tianjin, China
| | | | - Meng Liang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China,School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | | | | | - Wen Qin
- To whom correspondence should be addressed; Department of Radiology, and Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital. Anshan Road No 154, Heping District, Tianjin 300052, China.
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Hong J, Huang Y, Ye J, Wang J, Xu X, Wu Y, Li Y, Zhao J, Li R, Kang J, Lai X. 3D FRN-ResNet: An Automated Major Depressive Disorder Structural Magnetic Resonance Imaging Data Identification Framework. Front Aging Neurosci 2022; 14:912283. [PMID: 35645776 PMCID: PMC9136074 DOI: 10.3389/fnagi.2022.912283] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
Major Depressive Disorder (MDD) is the most prevalent psychiatric disorder, seriously affecting people's quality of life. Manually identifying MDD from structural magnetic resonance imaging (sMRI) images is laborious and time-consuming due to the lack of clear physiological indicators. With the development of deep learning, many automated identification methods have been developed, but most of them stay in 2D images, resulting in poor performance. In addition, the heterogeneity of MDD also results in slightly different changes reflected in patients' brain imaging, which constitutes a barrier to the study of MDD identification based on brain sMRI images. We propose an automated MDD identification framework in sMRI data (3D FRN-ResNet) to comprehensively address these challenges, which uses 3D-ResNet to extract features and reconstruct them based on feature maps. Notably, the 3D FRN-ResNet fully exploits the interlayer structure information in 3D sMRI data and preserves most of the spatial details as well as the location information when converting the extracted features into vectors. Furthermore, our model solves the feature map reconstruction problem in closed form to produce a straightforward and efficient classifier and dramatically improves model performance. We evaluate our framework on a private brain sMRI dataset of MDD patients. Experimental results show that the proposed model exhibits promising performance and outperforms the typical other methods, achieving the accuracy, recall, precision, and F1 values of 0.86776, 0.84237, 0.85333, and 0.84781, respectively.
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Affiliation(s)
- Jialin Hong
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yueqi Huang
- Department of Psychiatry, Hangzhou Seventh People’s Hospital, Hangzhou, China
| | - Jianming Ye
- First Affiliated Hospital, Gannan Medical University, Ganzhou, China
| | - Jianqing Wang
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Xiaomei Xu
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yan Wu
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yi Li
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Jialu Zhao
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Ruipeng Li
- Hangzhou Third People’s Hospital, Hangzhou, China
| | - Junlong Kang
- Zhongshan Hospital, Xiamen University, Xiamen, China
| | - Xiaobo Lai
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
- Department of Nephrology Surgery, Hangzhou Hospital of Traditional Chinese Medicine, Hangzhou, China
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47
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Ya Y, Ji L, Jia Y, Zou N, Jiang Z, Yin H, Mao C, Luo W, Wang E, Fan G. Machine Learning Models for Diagnosis of Parkinson's Disease Using Multiple Structural Magnetic Resonance Imaging Features. Front Aging Neurosci 2022; 14:808520. [PMID: 35493923 PMCID: PMC9043762 DOI: 10.3389/fnagi.2022.808520] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [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: 11/03/2021] [Accepted: 03/08/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose This study aimed to develop machine learning models for the diagnosis of Parkinson's disease (PD) using multiple structural magnetic resonance imaging (MRI) features and validate their performance. Methods Brain structural MRI scans of 60 patients with PD and 56 normal controls (NCs) were enrolled as development dataset and 69 patients with PD and 71 NCs from Parkinson's Progression Markers Initiative (PPMI) dataset as independent test dataset. First, multiple structural MRI features were extracted from cerebellar, subcortical, and cortical regions of the brain. Then, the Pearson's correlation test and least absolute shrinkage and selection operator (LASSO) regression were used to select the most discriminating features. Finally, using logistic regression (LR) classifier with the 5-fold cross-validation scheme in the development dataset, the cerebellar, subcortical, cortical, and a combined model based on all features were constructed separately. The diagnostic performance and clinical net benefit of each model were evaluated with the receiver operating characteristic (ROC) analysis and the decision curve analysis (DCA) in both datasets. Results After feature selection, 5 cerebellar (absolute value of left lobule crus II cortical thickness (CT) and right lobule IV volume, relative value of right lobule VIIIA CT and lobule VI/VIIIA gray matter volume), 3 subcortical (asymmetry index of caudate volume, relative value of left caudate volume, and absolute value of right lateral ventricle), and 4 cortical features (local gyrification index of right anterior circular insular sulcus and anterior agranular insula complex, local fractal dimension of right middle insular area, and CT of left supplementary and cingulate eye field) were selected as the most distinguishing features. The area under the curve (AUC) values of the cerebellar, subcortical, cortical, and combined models were 0.679, 0.555, 0.767, and 0.781, respectively, for the development dataset and 0.646, 0.632, 0.690, and 0.756, respectively, for the independent test dataset, respectively. The combined model showed higher performance than the other models (Delong's test, all p-values < 0.05). All models showed good calibration, and the DCA demonstrated that the combined model has a higher net benefit than other models. Conclusion The combined model showed favorable diagnostic performance and clinical net benefit and had the potential to be used as a non-invasive method for the diagnosis of PD.
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Affiliation(s)
- Yang Ya
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Lirong Ji
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yujing Jia
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Nan Zou
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Zhen Jiang
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Hongkun Yin
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Chengjie Mao
- Department of Neurology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Weifeng Luo
- Department of Neurology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Erlei Wang
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Guohua Fan
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
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Simonetti A, Saxena K, Koukopoulos AE, Janiri D, Lijffijt M, Swann AC, Kotzalidis GD, Sani G. Amygdala structure and function in paediatric bipolar disorder and high-risk youth: A systematic review of magnetic resonance imaging findings. World J Biol Psychiatry 2022; 23:103-126. [PMID: 34165050 DOI: 10.1080/15622975.2021.1935317] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
OBJECTIVE Converging evidence from structural and functional magnetic resonance imaging (MRI) studies points to amygdala alteration as crucial in the development of paediatric bipolar disorder (pBP). The high number of recent studies prompted us to comprehensively evaluate findings. We aimed to systematically review structural and functional MRI studies investigating the amygdala in patients with pBP and in youth at high-risk (HR) for developing pBP. METHODS We searched PubMed from any time to 25 September 2020 using: 'amygdala AND (MRI OR magnetic resonance imaging) AND bipolar AND (pediatr* OR child OR children OR childhood OR adolescent OR adolescents OR adolescence OR young OR familial OR at-risk OR sibling* OR offspring OR high risk)'. In this review, we adhered to the PRISMA statement. RESULTS Amygdala hyperactivity to emotional stimuli is the most commonly reported finding in youth with pBP and HR compared to healthy peers (HC), whereas findings from structural MRI studies are inconsistent. CONCLUSIONS Hyperactivation of the amygdala might be an endophenotype of pBP.
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Affiliation(s)
- Alessio Simonetti
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA.,Department of Psychiatry, Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy.,Centro Lucio Bini, Rome, Italy
| | - Kirti Saxena
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA.,Department of Psychiatry, Texas Children's Hospital, Houston, TX, USA
| | - Alexia E Koukopoulos
- Centro Lucio Bini, Rome, Italy.,Azienda Ospedaliera Universitaria Policlinico Umberto I, Sapienza School of Medicine and Dentistry, Sapienza University of Rome, Rome, Italy
| | - Delfina Janiri
- Centro Lucio Bini, Rome, Italy.,Department of Neurology and Psychiatry, Sapienza University of Rome, Rome, Italy
| | - Marijn Lijffijt
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA.,Michael E. DeBakey VA Medical Center, Houston, TX, USA
| | - Alan C Swann
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA.,Michael E. DeBakey VA Medical Center, Houston, TX, USA
| | - Georgios D Kotzalidis
- Centro Lucio Bini, Rome, Italy.,NESMOS Department, Faculty of Medicine and Psychology, Sant'Andrea University Hospital, Sapienza University of Rome, Rome, Italy
| | - Gabriele Sani
- Department of Psychiatry, Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy.,Institute of Psychiatry, Università Cattolica del Sacro Cuore, Roma, Italy
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49
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Zhao X, Zhu S, Cao Y, Cheng P, Lin Y, Sun Z, Jiang W, Du Y. Abnormalities of Gray Matter Volume and Its Correlation with Clinical Symptoms in Adolescents with High-Functioning Autism Spectrum Disorder. Neuropsychiatr Dis Treat 2022; 18:717-730. [PMID: 35401002 PMCID: PMC8983641 DOI: 10.2147/ndt.s349247] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 03/04/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Previous studies have indicated abnormal gray matter volume (GMV) in individuals with autism spectrum disorder (ASD); however, there is little consistency across the findings within these studies, partly due to small sample size and great heterogeneity among participants between studies. Additionally, few studies have explored the correlation between clinical symptoms and GMV abnormalities in individuals with ASD. Here, the current study examined GMV alterations in whole brain and their correlations with clinical symptoms in a relatively large and homogeneous sample of participants with ASD matched typically developing (TD) controls. METHODS Forty-eight adolescents with high-functioning ASD and 29 group-matched TD controls underwent structural magnetic resonance images. Voxel-based morphometry was applied to investigate regional GMV alterations. The participants with ASD were examined for the severity of clinical symptoms with Autism Behavior Checklist (ABC). The relationship between GMV abnormalities and clinical symptoms was explored in ASD group using voxel-wise correlation analysis within brain regions that showed significant GMV alterations in individuals with ASD compared with TD controls. RESULTS We found increased GMV in multiple brain regions, including the inferior frontal gyrus, medial frontal gyrus, superior frontal gyrus, superior temporal gyrus, occipital pole, anterior cingulate, cerebellum anterior lobe, cerebellum posterior lobe, and midbrain, as well as decreased GMV in cerebellum posterior lobe in individuals with ASD. The correlation analysis showed the GMV in the left fusiform was negatively associated with the scores of sensory factor, and the GMV in the right cerebellum anterior lobe was positively associated with the scores of social self-help factor. CONCLUSION Our results indicated that widespread GMV abnormalities of brain regions occurred in individuals with ASD, suggesting a potential neural basis for the pathogenesis and symptomatology of ASD.
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Affiliation(s)
- Xiaoxin Zhao
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Shuyi Zhu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Yang Cao
- Suzhou Guangji Hospital, Suzhou, People's Republic of China
| | - Peipei Cheng
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Yuxiong Lin
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Zhixin Sun
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Wenqing Jiang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Yasong Du
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
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50
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Tognin S, Richter A, Kempton MJ, Modinos G, Antoniades M, Azis M, Allen P, Bossong MG, Perez J, Pantelis C, Nelson B, Amminger P, Riecher-Rössler A, Barrantes-Vidal N, Krebs MO, Glenthøj B, Ruhrmann S, Sachs G, Rutten BPF, de Haan L, van der Gaag M, Valmaggia LR, McGuire P, Antoniades M, Pisani S, Modinos G, de Haan L, van der Gaag M, Velthorst E, Kraan TC, van Dam DS, Burger N, Nelson B, McGorry P, Amminger GP, Pantelis C, Politis A, Goodall J, Riecher-Rössler A, Borgwardt S, Studerus E, Bressan R, Gadelha A, Brietzke E, Asevedo G, Asevedo E, Zugman A, Barrantes-Vidal N, Domínguez-Martínez T, Racciopi A, Kwapil TR, Monsonet M, Hinojosa L, Kazes M, Daban C, Bourgin J, Gay O, Mam-Lam-Fook C, Krebs MO, Nordholm D, Randers L, Krakauer K, Glenthøj L, Glenthøj B, Nordentoft M, Ruhrmann S, Gebhard D, Arnhold J, Klosterkötter J, Sachs G, Lasser I, Winklbaur B, Aschauer H, Delespaul PA, Rutten BP, van Os J, Valmaggia LR, McGuire P. The Relationship Between Grey Matter Volume and Clinical and Functional Outcomes in People at Clinical High Risk for Psychosis. Schizophr Bull Open 2022; 3:sgac040. [PMID: 35903803 PMCID: PMC9309497 DOI: 10.1093/schizbullopen/sgac040] [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] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Objective To examine the association between baseline alterations in grey matter volume (GMV) and clinical and functional outcomes in people at clinical high risk (CHR) for psychosis. Methods 265 CHR individuals and 92 healthy controls were recruited as part of a prospective multi-center study. After a baseline assessment using magnetic resonance imaging (MRI), participants were followed for at least two years to determine clinical and functional outcomes, including transition to psychosis (according to the Comprehensive Assessment of an At Risk Mental State, CAARMS), level of functioning (according to the Global Assessment of Functioning), and symptomatic remission (according to the CAARMS). GMV was measured in selected cortical and subcortical regions of interest (ROI) based on previous studies (ie orbitofrontal gyrus, cingulate gyrus, gyrus rectus, inferior temporal gyrus, parahippocampal gyrus, striatum, and hippocampus). Using voxel-based morphometry, we analysed the relationship between GMV and clinical and functional outcomes. Results Within the CHR sample, a poor functional outcome (GAF < 65) was associated with relatively lower GMV in the right striatum at baseline (P < .047 after Family Wise Error correction). There were no significant associations between baseline GMV and either subsequent remission or transition to psychosis. Conclusions In CHR individuals, lower striatal GMV was associated with a poor level of overall functioning at follow-up. This finding was not related to effects of antipsychotic or antidepressant medication. The failure to replicate previous associations between GMV and later psychosis onset, despite studying a relatively large sample, is consistent with the findings of recent large-scale multi-center studies.
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Affiliation(s)
- Stefania Tognin
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Anja Richter
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Matthew J Kempton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Gemma Modinos
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Mathilde Antoniades
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Matilda Azis
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Paul Allen
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Matthijs G Bossong
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Jesus Perez
- CAMEO Early Intervention in Psychosis Services, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Christos Pantelis
- Department of Psychiatry, Melbourne Neuropsychiatry Centre, University of Melbourne & Melbourne Health, Carlton South, Victoria, Australia
| | | | | | | | - Neus Barrantes-Vidal
- Departament de Psicologia Clínica i de la Salut (Universitat Autònoma de Barcelona), Fundació Sanitària Sant Pere Claver (Spain), Center for Biomedical Research in Mental Health (CIBERSAM), Madrid, Spain
| | - Marie-Odile Krebs
- University of Paris, GHU-Paris, Sainte-Anne, C'JAAD, Hospitalo-Universitaire department SHU, Inserm U1266, Institut de Psychiatrie (CNRS 3557), Paris, France
| | - Birte Glenthøj
- Center for Neuropsychiatric Schizophrenia Research, CNSR, and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, Mental Health Services Capital Region of Denmark, Mental Health Center Glostrup, Glostrup, Denmark
| | - Stephan Ruhrmann
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital University of Cologne, Cologne, Germany
| | - Gabriele Sachs
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Bart P F Rutten
- Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, The Netherlands
| | - Lieuwe de Haan
- Early Psychosis Department, Amsterdam UMC, Amsterdam, The Netherlands
| | - Mark van der Gaag
- Department of Clinical Psychology and Amsterdam Public Mental Health Research Institute, Faculty of Behavioural and Movement Sciences, VU University, Amsterdam, The Netherlands
| | | | - Lucia R Valmaggia
- Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Lucia R Valmaggia
- Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience, King’s College London , UK
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London , UK
- National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) , UK
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