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Yin Y, Qiu X, Nie L, Wang F, Luo X, Zhao C, Yu H, Luo D, Wang J, Liu H. Individual-based morphological brain network changes in children with Rolandic epilepsy. Clin Neurophysiol 2024; 165:90-96. [PMID: 38991378 DOI: 10.1016/j.clinph.2024.06.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 04/09/2024] [Accepted: 06/15/2024] [Indexed: 07/13/2024]
Abstract
OBJECTIVE To investigate the local cortical morphology and individual-based morphological brain networks (MBNs) changes in children with Rolandic epilepsy (RE). METHODS Based on the structural MRI data of 56 children with RE and 56 healthy controls (HC), we constructed four types of individual-based MBNs using morphological indices (cortical thickness [CT], fractal dimension [FD], gyrification index [GI], and sulcal depth [SD]). The global and nodal properties of the brain networks were analyzed using graph theory. The between-group difference in local morphology and network topology was estimated, and partial correlation analysis was further analyzed. RESULTS Compared with the HC, children with RE showed regional GI increases in the right posterior cingulate gyrus and SD increases in the right anterior cingulate gyrus and medial prefrontal cortex. Regarding the network level, RE exhibited increased characteristic path length in CT-based and FD-based networks, while decreased FD-based network node efficiency in the right inferior frontal gyrus. No significant correlation between altered morphological features and clinical variables was found in RE. CONCLUSIONS These findings indicated that children with RE have disrupted morphological brain network organization beyond local morphology changes. SIGNIFICANCE The present study could provide more theoretical basis for exploring the neuropathological mechanisms in RE.
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Affiliation(s)
- Yu Yin
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Engineering Research Center of Intelligent Medical Imaging in Guizhou Higher Education Institutions, Zunyi 563003, China; Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Xiaofan Qiu
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Lisha Nie
- GE Healthcare, MR Research China, Beijing, China
| | - Fuqin Wang
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Engineering Research Center of Intelligent Medical Imaging in Guizhou Higher Education Institutions, Zunyi 563003, China
| | - Xinyu Luo
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Engineering Research Center of Intelligent Medical Imaging in Guizhou Higher Education Institutions, Zunyi 563003, China
| | - Chunfeng Zhao
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Engineering Research Center of Intelligent Medical Imaging in Guizhou Higher Education Institutions, Zunyi 563003, China
| | - Haoyue Yu
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Engineering Research Center of Intelligent Medical Imaging in Guizhou Higher Education Institutions, Zunyi 563003, China
| | - Dan Luo
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Engineering Research Center of Intelligent Medical Imaging in Guizhou Higher Education Institutions, Zunyi 563003, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Heng Liu
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Engineering Research Center of Intelligent Medical Imaging in Guizhou Higher Education Institutions, Zunyi 563003, China.
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Zhang P, Wan X, Jiang J, Liu Y, Wang D, Ai K, Liu G, Zhang X, Zhang J. A causal effect study of cortical morphology and related covariate networks in classical trigeminal neuralgia patients. Cereb Cortex 2024; 34:bhae337. [PMID: 39123310 DOI: 10.1093/cercor/bhae337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 07/17/2024] [Accepted: 07/27/2024] [Indexed: 08/12/2024] Open
Abstract
Structural covariance networks and causal effects within can provide critical information on gray matter reorganization and disease-related hierarchical changes. Based on the T1WI data of 43 classical trigeminal neuralgia patients and 45 controls, we constructed morphological similarity networks of cortical thickness, sulcal depth, fractal dimension, and gyrification index. Moreover, causal structural covariance network analyses were conducted in regions with morphological abnormalities or altered nodal properties, respectively. We found that patients showed reduced sulcal depth, gyrification index, and fractal dimension, especially in the salience network and the default mode network. Additionally, the integration of the fractal dimension and sulcal depth networks was significantly reduced, accompanied by decreased nodal efficiency of the bilateral temporal poles, and right pericalcarine cortex within the sulcal depth network. Negative causal effects existed from the left insula to the right caudal anterior cingulate cortex in the gyrification index map, also from bilateral temporal poles to right pericalcarine cortex within the sulcal depth network. Collectively, patients exhibited impaired integrity of the covariance networks in addition to the abnormal gray matter morphology in the salience network and default mode network. Furthermore, the patients may experience progressive impairment in the salience network and from the limbic system to the sensory system in network topology, respectively.
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Affiliation(s)
- Pengfei Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, Sichuan 610041, China
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Medical MRI Equipment Application Industry Technology Center, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Xinyue Wan
- Department of Radiology, Huashan Hospital, Fudan University, No. 12, Urumqi Middle Road, Jingan District, Shanghai 200040, China
| | - Jingqi Jiang
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Medical MRI Equipment Application Industry Technology Center, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Yang Liu
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Medical MRI Equipment Application Industry Technology Center, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Danyang Wang
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Medical MRI Equipment Application Industry Technology Center, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Kai Ai
- Department of Clinical and Technical Supports, Philips Healthcare, No. 64 West Section, South 2nd Ring Road, Yanta District, Xi'an 710000, China
| | - Guangyao Liu
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Medical MRI Equipment Application Industry Technology Center, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Xinding Zhang
- Department of Neurosurgery and Laboratory of Neurosurgery, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
| | - Jing Zhang
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Medical MRI Equipment Application Industry Technology Center, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
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Lin Q, Jin S, Yin G, Li J, Asgher U, Qiu S, Wang J. Cortical Morphological Networks Differ Between Gyri and Sulci. Neurosci Bull 2024:10.1007/s12264-024-01262-7. [PMID: 39044060 DOI: 10.1007/s12264-024-01262-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 03/28/2024] [Indexed: 07/25/2024] Open
Abstract
This study explored how the human cortical folding pattern composed of convex gyri and concave sulci affected single-subject morphological brain networks, which are becoming an important method for studying the human brain connectome. We found that gyri-gyri networks exhibited higher morphological similarity, lower small-world parameters, and lower long-term test-retest reliability than sulci-sulci networks for cortical thickness- and gyrification index-based networks, while opposite patterns were observed for fractal dimension-based networks. Further behavioral association analysis revealed that gyri-gyri networks and connections between gyral and sulcal regions significantly explained inter-individual variance in Cognition and Motor domains for fractal dimension- and sulcal depth-based networks. Finally, the clinical application showed that only sulci-sulci networks exhibited morphological similarity reductions in major depressive disorder for cortical thickness-, fractal dimension-, and gyrification index-based networks. Taken together, these findings provide novel insights into the constraint of the cortical folding pattern to the network organization of the human brain.
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Affiliation(s)
- Qingchun Lin
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Suhui Jin
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Guole Yin
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Junle Li
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Umer Asgher
- Department of Air Transport, Faculty of Transportation Sciences, Czech Technical University in Prague (CTU), Prague, 128 00, Czech Republic
- School of Interdisciplinary Engineering and Sciences (SINES), National University of Science and Technology (NUST), Islamabad, 44000, Pakistan
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510405, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China.
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Guangzhou, 510631, China.
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China.
- Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China.
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4
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Porcaro C, Diciotti S, Madan CR, Marzi C. Editorial: Methods and application in fractal analysis of neuroimaging data. Front Hum Neurosci 2024; 18:1453284. [PMID: 39050380 PMCID: PMC11266171 DOI: 10.3389/fnhum.2024.1453284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2024] [Accepted: 06/26/2024] [Indexed: 07/27/2024] Open
Affiliation(s)
- Camillo Porcaro
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua, Italy
- Institute of Cognitive Sciences and Technologies—National Research Council, Rome, Italy
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi” – DEI, University of Bologna, Cesena, Italy
- Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, Bologna, Italy
| | | | - Chiara Marzi
- Department of Statistics, Computer Science and Applications “Giuseppe Parenti,” University of Florence, Florence, Italy
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Marzi C, Giannelli M, Barucci A, Tessa C, Mascalchi M, Diciotti S. Efficacy of MRI data harmonization in the age of machine learning: a multicenter study across 36 datasets. Sci Data 2024; 11:115. [PMID: 38263181 PMCID: PMC10805868 DOI: 10.1038/s41597-023-02421-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 07/27/2023] [Indexed: 01/25/2024] Open
Abstract
Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups of subjects, increase statistical power, and promote data reuse with machine learning techniques. The harmonization of multicenter data is necessary to reduce the confounding effect associated with non-biological sources of variability in the data. However, when applied to the entire dataset before machine learning, the harmonization leads to data leakage, because information outside the training set may affect model building, and potentially falsely overestimate performance. We propose a 1) measurement of the efficacy of data harmonization; 2) harmonizer transformer, i.e., an implementation of the ComBat harmonization allowing its encapsulation among the preprocessing steps of a machine learning pipeline, avoiding data leakage by design. We tested these tools using brain T1-weighted MRI data from 1740 healthy subjects acquired at 36 sites. After harmonization, the site effect was removed or reduced, and we showed the data leakage effect in predicting individual age from MRI data, highlighting that introducing the harmonizer transformer into a machine learning pipeline allows for avoiding data leakage by design.
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Affiliation(s)
- Chiara Marzi
- Department of Statistics, Computer Science and Applications "Giuseppe Parenti", University of Florence, 50134, Florence, Italy
- "Nello Carrara" Institute of Applied Physics (IFAC), National Research Council (CNR), 50019, Sesto Fiorentino, Florence, Italy
| | - Marco Giannelli
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", 56126, Pisa, Italy
| | - Andrea Barucci
- "Nello Carrara" Institute of Applied Physics (IFAC), National Research Council (CNR), 50019, Sesto Fiorentino, Florence, Italy
| | - Carlo Tessa
- Radiology Unit Apuane e Lunigiana, Azienda USL Toscana Nord Ovest, 54100, Massa, Italy
| | - Mario Mascalchi
- Department of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, 50139, Florence, Italy
- Division of Epidemiology and Clinical Governance, Institute for Study, Prevention and netwoRk in Oncology (ISPRO), 50139, Florence, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" - DEI, University of Bologna, 47522, Cesena, Italy.
- Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, 40121, Bologna, Italy.
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Karperien AL, Jelinek HF. Morphology and Fractal-Based Classifications of Neurons and Microglia in Two and Three Dimensions. ADVANCES IN NEUROBIOLOGY 2024; 36:149-172. [PMID: 38468031 DOI: 10.1007/978-3-031-47606-8_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Microglia and neurons live physically intertwined, intimately related structurally and functionally in a dynamic relationship in which microglia change continuously over a much shorter timescale than do neurons. Although microglia may unwind and depart from the neurons they attend under certain circumstances, in general, together both contribute to the fractal topology of the brain that defines its computational capabilities. Both neuronal and microglial morphologies are well-described using fractal analysis complementary to more traditional measures. For neurons, the fractal dimension has proved valuable for classifying dendritic branching and other neuronal features relevant to pathology and development. For microglia, fractal geometry has substantially contributed to classifying functional categories, where, in general, the more pathological the biological status, the lower the fractal dimension for individual cells, with some exceptions, including hyper-ramification. This chapter provides a review of the intimate relationships between neurons and microglia, by introducing 2D and 3D fractal analysis methodology and its applications in neuron-microglia function in health and disease.
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Affiliation(s)
- Audrey L Karperien
- School of Community Health, Charles Sturt University, Albury, NSW, Australia
| | - Herbert F Jelinek
- Department of Medical Sciences and Biotechnology Center, Khalifa University, Abu Dhabi, UAE
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Díaz Beltrán L, Madan CR, Finke C, Krohn S, Di Ieva A, Esteban FJ. Fractal Dimension Analysis in Neurological Disorders: An Overview. ADVANCES IN NEUROBIOLOGY 2024; 36:313-328. [PMID: 38468040 DOI: 10.1007/978-3-031-47606-8_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Fractal analysis has emerged as a powerful tool for characterizing irregular and complex patterns found in the nervous system. This characterization is typically applied by estimating the fractal dimension (FD), a scalar index that describes the topological complexity of the irregular components of the nervous system, both at the macroscopic and microscopic levels, that may be viewed as geometric fractals. Moreover, temporal properties of neurophysiological signals can also be interpreted as dynamic fractals. Given its sensitivity for detecting changes in brain morphology, FD has been explored as a clinically relevant marker of brain damage in several neuropsychiatric conditions as well as in normal and pathological cerebral aging. In this sense, evidence is accumulating for decreases in FD in Alzheimer's disease, frontotemporal dementia, Parkinson's disease, multiple sclerosis, and many other neurological disorders. In addition, it is becoming increasingly clear that fractal analysis in the field of clinical neurology opens the possibility of detecting structural alterations in the early stages of the disease, which highlights FD as a potential diagnostic and prognostic tool in clinical practice.
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Affiliation(s)
- Leticia Díaz Beltrán
- Department of Medical Oncology, Clinical Research Unit, University Hospital of Jaén, Jaén, Spain
| | | | - Carsten Finke
- Department of Neurology, Charité-Universitätsmedizin, Berlin, Germany
| | - Stephan Krohn
- Department of Neurology, Charité-Universitätsmedizin, Berlin, Germany
| | - Antonio Di Ieva
- Computational NeuroSurgery (CNS) Lab, Faculty of Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia
| | - Francisco J Esteban
- Systems Biology Unit, Department of Experimental Biology, University of Jaén, Jaén, Spain.
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Honea RA, Hunt S, Lepping RJ, Vidoni ED, Morris JK, Watts A, Michaelis E, Burns JM, Swerdlow RH. Alzheimer's disease cortical morphological phenotypes are associated with TOMM40'523-APOE haplotypes. Neurobiol Aging 2023; 132:131-144. [PMID: 37804609 PMCID: PMC10763175 DOI: 10.1016/j.neurobiolaging.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 09/05/2023] [Accepted: 09/07/2023] [Indexed: 10/09/2023]
Abstract
Both the APOE ε4 and TOMM40 rs10524523 ("523") genes have been associated with risk for Alzheimer's disease (AD) and neuroimaging biomarkers of AD. No studies have investigated the relationship of TOMM40'523-APOE ε4 on the structural complexity of the brain in AD individuals. We quantified brain morphology and multiple cortical attributes in individuals with mild cognitive impairment (MCI) and AD, then tested whether APOE ε4 or TOMM40 poly-T genotypes were related to AD morphological biomarkers in cognitively unimpaired (CU) and MCI/AD individuals. We identified several AD-specific phenotypes in brain morphology and found that TOMM40 poly-T short alleles are associated with early, AD-specific brain morphological differences in healthy aging. We observed decreased cortical thickness, sulcal depth, and fractal dimension in CU individuals with the poly-T short alleles. Moreover, in MCI/AD participants, the APOE ε4 (TOMM40 L) individuals had a higher rate of gene-related morphological markers indicative of AD. Our data suggest that TOMM40'523 is associated with early brain structure variations in the precuneus, temporal, and limbic cortices.
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Affiliation(s)
- Robyn A Honea
- University of Kansas Alzheimer's Disease Center, University of Kansas School of Medicine, Kansas City, KS, USA; Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA.
| | - Suzanne Hunt
- University of Kansas Alzheimer's Disease Center, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Rebecca J Lepping
- University of Kansas Alzheimer's Disease Center, University of Kansas School of Medicine, Kansas City, KS, USA; Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, USA; Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Eric D Vidoni
- University of Kansas Alzheimer's Disease Center, University of Kansas School of Medicine, Kansas City, KS, USA; Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Jill K Morris
- University of Kansas Alzheimer's Disease Center, University of Kansas School of Medicine, Kansas City, KS, USA; Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Amber Watts
- University of Kansas Alzheimer's Disease Center, University of Kansas School of Medicine, Kansas City, KS, USA; Department of Psychology, University of Kansas, Lawrence, KS, USA
| | - Elias Michaelis
- University of Kansas Alzheimer's Disease Center, University of Kansas School of Medicine, Kansas City, KS, USA; Department of Pharmacology and Toxicology, University of Kansas, Lawrence, KS, USA
| | - Jeffrey M Burns
- University of Kansas Alzheimer's Disease Center, University of Kansas School of Medicine, Kansas City, KS, USA; Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Russell H Swerdlow
- University of Kansas Alzheimer's Disease Center, University of Kansas School of Medicine, Kansas City, KS, USA; Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA
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Marzi C, Scheda R, Salvadori E, Giorgio A, De Stefano N, Poggesi A, Inzitari D, Pantoni L, Mascalchi M, Diciotti S. Fractal dimension of the cortical gray matter outweighs other brain MRI features as a predictor of transition to dementia in patients with mild cognitive impairment and leukoaraiosis. Front Hum Neurosci 2023; 17:1231513. [PMID: 37822707 PMCID: PMC10562576 DOI: 10.3389/fnhum.2023.1231513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 08/31/2023] [Indexed: 10/13/2023] Open
Abstract
Background The relative contribution of changes in the cerebral white matter (WM) and cortical gray matter (GM) to the transition to dementia in patients with mild cognitive impairment (MCI) is not yet established. In this longitudinal study, we aimed to analyze MRI features that may predict the transition to dementia in patients with MCI and T2 hyperintensities in the cerebral WM, also known as leukoaraiosis. Methods Sixty-four participants with MCI and moderate to severe leukoaraiosis underwent baseline MRI examinations and annual neuropsychological testing over a 2 year period. The diagnosis of dementia was based on established criteria. We evaluated demographic, neuropsychological, and several MRI features at baseline as predictors of the clinical transition. The MRI features included visually assessed MRI features, such as the number of lacunes, microbleeds, and dilated perivascular spaces, and quantitative MRI features, such as volumes of the cortical GM, hippocampus, T2 hyperintensities, and diffusion indices of the cerebral WM. Additionally, we examined advanced quantitative features such as the fractal dimension (FD) of cortical GM and WM, which represents an index of tissue structural complexity derived from 3D-T1 weighted images. To assess the prediction of transition to dementia, we employed an XGBoost-based machine learning system using SHapley Additive exPlanations (SHAP) values to provide explainability to the machine learning model. Results After 2 years, 18 (28.1%) participants had transitioned from MCI to dementia. The area under the receiving operator characteristic curve was 0.69 (0.53, 0.85) [mean (90% confidence interval)]. The cortical GM-FD emerged as the top-ranking predictive feature of transition. Furthermore, aggregated quantitative neuroimaging features outperformed visually assessed MRI features in predicting conversion to dementia. Discussion Our findings confirm the complementary roles of cortical GM and WM changes as underlying factors in the development of dementia in subjects with MCI and leukoaraiosis. FD appears to be a biomarker potentially more sensitive than other brain features.
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Affiliation(s)
- Chiara Marzi
- Department of Statistics, Computer Science, Applications "Giuseppe Parenti, " University of Florence, Florence, Italy
| | - Riccardo Scheda
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi, " University of Bologna, Cesena, Italy
| | - Emilia Salvadori
- NEUROFARBA Department, Neuroscience Section, University of Florence, Florence, Italy
| | - Antonio Giorgio
- Department of Medicine, Surgery, and Neuroscience, University of Siena, Siena, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery, and Neuroscience, University of Siena, Siena, Italy
| | - Anna Poggesi
- NEUROFARBA Department, Neuroscience Section, University of Florence, Florence, Italy
| | - Domenico Inzitari
- NEUROFARBA Department, Neuroscience Section, University of Florence, Florence, Italy
| | - Leonardo Pantoni
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Mario Mascalchi
- Department of Experimental and Clinical Biomedical Sciences "Mario Serio, " University of Florence, Florence, Italy
- Division of Epidemiology and Clinical Governance, Institute for Study, Prevention and Network in Oncology (ISPRO), Florence, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi, " University of Bologna, Cesena, Italy
- Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, Bologna, Italy
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Yin G, Li T, Jin S, Wang N, Li J, Wu C, He H, Wang J. A comprehensive evaluation of multicentric reliability of single-subject cortical morphological networks on traveling subjects. Cereb Cortex 2023:7169131. [PMID: 37197789 DOI: 10.1093/cercor/bhad178] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 04/29/2023] [Accepted: 04/30/2023] [Indexed: 05/19/2023] Open
Abstract
Despite the prevalence of research on single-subject cerebral morphological networks in recent years, whether they can offer a reliable way for multicentric studies remains largely unknown. Using two multicentric datasets of traveling subjects, this work systematically examined the inter-site test-retest (TRT) reliabilities of single-subject cerebral morphological networks, and further evaluated the effects of several key factors. We found that most graph-based network measures exhibited fair to excellent reliabilities regardless of different analytical pipelines. Nevertheless, the reliabilities were affected by choices of morphological index (fractal dimension > sulcal depth > gyrification index > cortical thickness), brain parcellation (high-resolution > low-resolution), thresholding method (proportional > absolute), and network type (binarized > weighted). For the factor of similarity measure, its effects depended on the thresholding method used (absolute: Kullback-Leibler divergence > Jensen-Shannon divergence; proportional: Jensen-Shannon divergence > Kullback-Leibler divergence). Furthermore, longer data acquisition intervals and different scanner software versions significantly reduced the reliabilities. Finally, we showed that inter-site reliabilities were significantly lower than intra-site reliabilities for single-subject cerebral morphological networks. Altogether, our findings propose single-subject cerebral morphological networks as a promising approach for multicentric human connectome studies, and offer recommendations on how to determine analytical pipelines and scanning protocols for obtaining reliable results.
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Affiliation(s)
- Guole Yin
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Ting Li
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610066, China
| | - Suhui Jin
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Ningkai Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Junle Li
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Changwen Wu
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou 310058, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
- Key Laboratory of Cognition and Education Sciences, Ministry of Education, Beijing 100816, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou 510000, China
- Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510000, China
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11
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Armonaite K, Nobili L, Paulon L, Balsi M, Conti L, Tecchio F. Local neurodynamics as a signature of cortical areas: new insights from sleep. Cereb Cortex 2023; 33:3284-3292. [PMID: 35858209 DOI: 10.1093/cercor/bhac274] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 06/02/2022] [Accepted: 06/04/2022] [Indexed: 11/13/2022] Open
Abstract
Sleep crucial for the animal survival is accompanied by huge changes in neuronal electrical activity over time, the neurodynamics. Here, drawing on intracranial stereo-electroencephalographic (sEEG) recordings from the Montreal Neurological Institute (MNI), we analyzed local neurodynamics in the waking state at rest and during the N2, N3, and rapid eye movement (REM) sleep phases. Higuchi fractal dimension (HFD)-a measure of signal complexity-was studied as a feature of the local neurodynamics of the primary motor (M1), somatosensory (S1), and auditory (A1) cortices. The key working hypothesis, that the relationships between local neurodynamics preserve in all sleep phases despite the neurodynamics complexity reduces in sleep compared with wakefulness, was supported by the results. In fact, while HFD awake > REM > N2 > N3 (P < 0.001 consistently), HFD in M1 > S1 > A1 in awake and all sleep stages (P < 0.05 consistently). Also power spectral density was studied for consistency with previous investigations. Meaningfully, we found a local specificity of neurodynamics, well quantified by the fractal dimension, expressed in wakefulness and during sleep. We reinforce the idea that neurodynamic may become a new criterion for cortical parcellation, prospectively improving the understanding and ability of compensatory interventions for behavioral disorders.
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Affiliation(s)
- Karolina Armonaite
- Faculty of Psychology, Uninettuno University, Corso V. Emanuele II, n. 39, 00186, Rome, Italy
- Laboratory of Electrophysiology for Translational NeuroScience (LET'S), Institute of Cognitive Sciences and Technologies - Consiglio Nazionale delle Ricerche, Via Palestro, n. 32, 00185, Rome, Italy
| | - Lino Nobili
- Child Neurology and Psychiatry, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini, n. 5, 16147, Genoa, Italy
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health (DINOGMI), University of Genoa, Largo Paolo Daneo, n. 3, 16132, Genoa, Italy
| | - Luca Paulon
- Laboratory of Electrophysiology for Translational NeuroScience (LET'S), Institute of Cognitive Sciences and Technologies - Consiglio Nazionale delle Ricerche, Via Palestro, n. 32, 00185, Rome, Italy
| | - Marco Balsi
- Department of Information Engineering, Electronics and Telecommunications, Sapienza University, Via Eudossiana, n. 18, 00184, Rome
| | - Livio Conti
- Faculty of Engineering, Uninettuno University, Corso V. Emanuele II, n. 39, 00186, Rome, Italy
- INFN - Istituto Nazionale di Fisica Nucleare, Sezione Roma Tor Vergata, Via della Ricerca Scientifica, n.1, 00133, Rome, Italy
| | - Franca Tecchio
- Laboratory of Electrophysiology for Translational NeuroScience (LET'S), Institute of Cognitive Sciences and Technologies - Consiglio Nazionale delle Ricerche, Via Palestro, n. 32, 00185, Rome, Italy
- Faculty of Psychology, Uninettuno University, Corso V. Emanuele II, n. 39, 00186, Rome, Italy
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12
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Nazlee N, Waiter GD, Sandu A. Age-associated sex and asymmetry differentiation in hemispheric and lobar cortical ribbon complexity across adulthood: A UK Biobank imaging study. Hum Brain Mapp 2023; 44:49-65. [PMID: 36574599 PMCID: PMC9783444 DOI: 10.1002/hbm.26076] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 07/28/2022] [Accepted: 08/21/2022] [Indexed: 02/01/2023] Open
Abstract
Cortical morphology changes with ageing and age-related neurodegenerative diseases. Previous studies suggest that the age effect is more pronounced in the frontal lobe. However, our knowledge of structural complexity changes in male and female brains is still limited. We measured cortical ribbon complexity through fractal dimension (FD) analysis at the hemisphere and lobe level in 7010 individuals from the UK Biobank imaging cohort to study age-related sex differences (3332 males, age ranged 45-79 years). FD decreases significantly with age and sexual dimorphism exists. With correction for brain size, females showed higher complexity in the left hemisphere and left and right parietal lobes whereas males showed higher complexity in the right temporal and left and right occipital lobes. A nonlinear age effect was observed in the left and right frontal, and right temporal lobes. Differential patterns of age effects were observed in both sexes with relatively more age-affected regions in males. Significantly higher rightward asymmetries at hemisphere, frontal, parietal, and occipital lobe level and higher leftward asymmetry in temporal lobe were observed. There was no age-by-sex-by asymmetry interaction in any region. When controlling for brain size, the leftward hemispheric, and temporal lobe asymmetry decreased with age. Males had significantly lower asymmetry between hemispheres and higher asymmetry in the parietal and occipital lobes than females. This work provides distinct patterns of age-related sex and asymmetry differences that can aid in the future development of sex-specific models of the normal brain to ascribe cognitive functional significance of these patterns in ageing.
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Affiliation(s)
- Nafeesa Nazlee
- Aberdeen Biomedical Imaging CentreUniversity of AberdeenAberdeenScotland
| | - Gordon D. Waiter
- Aberdeen Biomedical Imaging CentreUniversity of AberdeenAberdeenScotland
| | - Anca‐Larisa Sandu
- Aberdeen Biomedical Imaging CentreUniversity of AberdeenAberdeenScotland
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13
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Chen Y, Zuo Y, Kang S, Pan L, Jiang S, Yan A, Li L. Using fractal dimension analysis to assess the effects of normal aging and sex on subregional cortex alterations across the lifespan from a Chinese dataset. Cereb Cortex 2022; 33:5289-5296. [PMID: 36300622 DOI: 10.1093/cercor/bhac417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 09/23/2022] [Accepted: 09/25/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
Fractal dimension (FD) is used to quantify brain structural complexity and is more sensitive to morphological variability than other cortical measures. However, the effects of normal aging and sex on FD are not fully understood. In this study, age- and sex-related differences in FD were investigated in a sample of 448 adults age of 19–80 years from a Chinese dataset. The FD was estimated with the surface-based morphometry (SBM) approach, sex differences were analyzed on a vertex level, and correlations between FD and age were examined. Generalized additive models (GAMs) were used to characterize the trajectories of age-related changes in 68 regions based on the Desikan–Killiany atlas. The SBM results showed sex differences in the entire sample and 3 subgroups defined by age. GAM results demonstrated that the FD values of 51 regions were significantly correlated with age. The trajectories of changes can be classified into 4 main patterns. Our results indicate that sex differences in FD are evident across developmental stages. Age-related trajectories in FD are not homogeneous across the cerebral cortex. Our results extend previous findings and provide a foundation for future investigation of the underlying mechanism.
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Affiliation(s)
- Yiyong Chen
- Ningbo University School of Medicine, , Ningbo, 315211, Zhejiang, PR China
| | - Yizhi Zuo
- Nanjing Medical University Human Anatomy Department, , Nanjing, 211166, Jiangsu, PR China
| | - Shaofang Kang
- Ningbo University College of Teacher Education, , Ningbo, 315211, Zhejiang, PR China
| | - Liliang Pan
- Ningbo University School of Medicine, , Ningbo, 315211, Zhejiang, PR China
| | - Siyu Jiang
- Ningbo University School of Medicine, , Ningbo, 315211, Zhejiang, PR China
| | - Aohui Yan
- Ningbo University School of Medicine, , Ningbo, 315211, Zhejiang, PR China
| | - Lin Li
- Nanjing Medical University Human Anatomy Department, , Nanjing, 211166, Jiangsu, PR China
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14
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Tu Y, Wang J, Xiong F, Gao F. Cortical abnormalities in patients with fibromyalgia: a pilot study of surface-based morphometry analysis. PAIN MEDICINE 2022; 23:1939-1946. [PMID: 35881694 DOI: 10.1093/pm/pnac101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 06/05/2022] [Accepted: 06/16/2022] [Indexed: 11/13/2022]
Abstract
BACKGROUND Although neuroanatomical studies correlated to fibromyalgia (FM) are gaining increasing interest, the cortical morphology of patients are largely unknown, and data on cortical gyrification are scarce. The objective of the present study is to assess the cortical morphology in female patients with FM compared with healthy controls (HC) using surface-based morphometry (SBM) analysis of magnetic resonance imaging (MRI). METHODS T1-MRIs and clinical data of 20 FM patients and 20 HC subjects were obtained from a public databset via OpenNeuro. For each subject, surface parameters including cortical thickness, local gyrification index (LGI), sulcal depth, and fractal dimensionality were estimated using SBM analysis. These data were compared between two groups controlled by age. The correlations between regional SBM parameters showing group differences and clinical profiles were analyzed. RESULTS Compared with HC subjects, FM patients showed reduced cortical thickness in right primary motor cortex, lower LGI in right rostral anterior cingulate and higher sulcal depth in right precuneus (p < 0.05 cluster level family- wise error corrected). In FM patients, correlation analysis showed that the cortical thickness in right primary motor cortex were inversely correlated with scores of pain catastrophizing scale (r = -0.498, p = 0.030) and pain self-perception scale (r = -0.527, p = 0.020), and disease duration (r = -0.488, p = 0.034), respectively. CONCLUSIONS Our findings provide evidence of neuroanatomical aberrations in FM patients, which may provide insight into the neuropathology of FM.
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Affiliation(s)
- Ye Tu
- Department of Anesthesiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jihong Wang
- Department of Anesthesiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fei Xiong
- Department of Radiology, PLA Central Theater General Hospital, Wuhan, China
| | - Feng Gao
- Department of Anesthesiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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15
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Explanations of Machine Learning Models in Repeated Nested Cross-Validation: An Application in Age Prediction Using Brain Complexity Features. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12136681] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
SHAP (Shapley additive explanations) is a framework for explainable AI that makes explanations locally and globally. In this work, we propose a general method to obtain representative SHAP values within a repeated nested cross-validation procedure and separately for the training and test sets of the different cross-validation rounds to assess the real generalization abilities of the explanations. We applied this method to predict individual age using brain complexity features extracted from MRI scans of 159 healthy subjects. In particular, we used four implementations of the fractal dimension (FD) of the cerebral cortex—a measurement of brain complexity. Representative SHAP values highlighted that the most recent implementation of the FD had the highest impact over the others and was among the top-ranking features for predicting age. SHAP rankings were not the same in the training and test sets, but the top-ranking features were consistent. In conclusion, we propose a method—and share all the source code—that allows a rigorous assessment of the SHAP explanations of a trained model in a repeated nested cross-validation setting.
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16
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Sandu AL, Waiter GD, Staff RT, Nazlee N, Habota T, McNeil CJ, Chapko D, Williams JH, Fall CHD, Chandak GR, Pene S, Krishna M, McIntosh AM, Whalley HC, Kumaran K, Krishnaveni GV, Murray AD. Sexual dimorphism in the relationship between brain complexity, volume and general intelligence (g): a cross-cohort study. Sci Rep 2022; 12:11025. [PMID: 35773463 PMCID: PMC9247090 DOI: 10.1038/s41598-022-15208-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 06/20/2022] [Indexed: 01/20/2023] Open
Abstract
Changes in brain morphology have been reported during development, ageing and in relation to different pathologies. Brain morphology described by the shape complexity of gyri and sulci can be captured and quantified using fractal dimension (FD). This measure of brain structural complexity, as well as brain volume, are associated with intelligence, but less is known about the sexual dimorphism of these relationships. In this paper, sex differences in the relationship between brain structural complexity and general intelligence (g) in two diverse geographic and cultural populations (UK and Indian) are investigated. 3D T1-weighted magnetic resonance imaging (MRI) data and a battery of cognitive tests were acquired from participants belonging to three different cohorts: Mysore Parthenon Cohort (MPC); Aberdeen Children of the 1950s (ACONF) and UK Biobank. We computed MRI derived structural brain complexity and g estimated from a battery of cognitive tests for each group. Brain complexity and volume were both positively corelated with intelligence, with the correlations being significant in women but not always in men. This relationship is seen across populations of differing ages and geographical locations and improves understanding of neurobiological sex-differences.
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Affiliation(s)
- Anca-Larisa Sandu
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Lilian Sutton Building, Foresterhill, Aberdeen, AB25 2ZD, UK.
| | - Gordon D Waiter
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Lilian Sutton Building, Foresterhill, Aberdeen, AB25 2ZD, UK
| | - Roger T Staff
- Aberdeen Royal Infirmary, NHS Grampian, Aberdeen, UK
| | - Nafeesa Nazlee
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Lilian Sutton Building, Foresterhill, Aberdeen, AB25 2ZD, UK
| | - Tina Habota
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Lilian Sutton Building, Foresterhill, Aberdeen, AB25 2ZD, UK
| | - Chris J McNeil
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Lilian Sutton Building, Foresterhill, Aberdeen, AB25 2ZD, UK
| | - Dorota Chapko
- School of Public Health, Imperial College London, London, UK
| | | | - Caroline H D Fall
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - Giriraj R Chandak
- Genomic Research on Complex Diseases, CSIR - Centre for Cellular and Molecular Biology, Hyderabad, India
| | - Shailesh Pene
- Department of Imaging and Interventional Radiology, Narayana Multispecialty Hospital, Mysore, India
| | - Murali Krishna
- Foundation for Research and Advocacy in Mental Health, Mysore, India
| | - Andrew M McIntosh
- Division of Psychiatry, Centre for Clinical Brain Sciences, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Heather C Whalley
- Division of Psychiatry, Centre for Clinical Brain Sciences, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Kalyanaraman Kumaran
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
- Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India
| | | | - Alison D Murray
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Lilian Sutton Building, Foresterhill, Aberdeen, AB25 2ZD, UK
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17
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Fractal dimension of the brain in neurodegenerative disease and dementia: A systematic review. Ageing Res Rev 2022; 79:101651. [PMID: 35643264 DOI: 10.1016/j.arr.2022.101651] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 04/26/2022] [Accepted: 05/23/2022] [Indexed: 12/25/2022]
Abstract
Sensitive and specific antemortem biomarkers of neurodegenerative disease and dementia are crucial to the pursuit of effective treatments, required both to reliably identify disease and to track its progression. Atrophy is the structural magnetic resonance imaging (MRI) hallmark of neurodegeneration. However in most cases it likely indicates a relatively advanced stage of disease less susceptible to treatment as some disease processes begin decades prior to clinical onset. Among emerging metrics that characterise brain shape rather than volume, fractal dimension (FD) quantifies shape complexity. FD has been applied in diverse fields of science to measure subtle changes in elaborate structures. We review its application thus far to structural MRI of the brain in neurodegenerative disease and dementia. We identified studies involving subjects who met criteria for mild cognitive impairment, Alzheimer's Disease, Vascular Dementia, Lewy Body Dementia, Frontotemporal Dementia, Amyotrophic Lateral Sclerosis, Parkinson's Disease, Huntington's Disease, Multiple Systems Atrophy, Spinocerebellar Ataxia and Multiple Sclerosis. The early literature suggests that neurodegenerative disease processes are usually associated with a decline in FD of the brain. The literature includes examples of disease-related change in FD occurring independently of atrophy, which if substantiated would represent a valuable advantage over other structural imaging metrics. However, it is likely to be non-specific and to exhibit complex spatial and temporal patterns. A more harmonious methodological approach across a larger number of studies as well as careful attention to technical factors associated with image processing and FD measurement will help to better elucidate the metric's utility.
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18
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Longitudinal study of the effect of a 5-year exercise intervention on structural brain complexity in older adults. A Generation 100 substudy. Neuroimage 2022; 256:119226. [PMID: 35447353 DOI: 10.1016/j.neuroimage.2022.119226] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 03/15/2022] [Accepted: 04/16/2022] [Indexed: 12/17/2022] Open
Abstract
Physical inactivity has been identified as an important risk factor for dementia. High levels of cardiorespiratory fitness (CRF) have been shown to reduce the risk of dementia. However, the mechanism by which exercise affects brain health is still debated. Fractal dimension (FD) is an index that quantifies the structural complexity of the brain. The purpose of this study was to investigate the effects of a 5-year exercise intervention on the structural complexity of the brain, measured through the FD, in a subset of 105 healthy older adults participating in the randomized controlled trial Generation 100 Study. The subjects were randomized into control, moderate intensity continuous training, and high intensity interval training groups. Both brain MRI and CRF were acquired at baseline and at 1-, 3- and 5-years follow-ups. Cortical thickness and volume data were extracted with FreeSurfer, and FD of the cortical lobes, cerebral and cerebellar gray and white matter were computed. CRF was measured as peak oxygen uptake (VO2peak) using ergospirometry during graded maximal exercise testing. Linear mixed models were used to investigate exercise group differences and possible CRF effects on the brain's structural complexity. Associations between change over time in CRF and FD were performed if there was a significant association between CRF and FD. There were no effects of group membership on the structural complexity. However, we found a positive association between CRF and the cerebral gray matter FD (p < 0.001) and the temporal lobe gray matter FD (p < 0.001). This effect was not present for cortical thickness, suggesting that FD is a more sensitive index of structural changes. The change over time in CRF was associated with the change in temporal lobe gray matter FD from baseline to 5-year follow-up (p < 0.05). No association of the change was found between CRF and cerebral gray matter FD. These results demonstrated that entering old age with high and preserved CRF levels protected against loss of structural complexity in areas sensitive to aging and age-related pathology.
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19
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Meregalli V, Alberti F, Madan CR, Meneguzzo P, Miola A, Trevisan N, Sambataro F, Favaro A, Collantoni E. Cortical Complexity Estimation Using Fractal Dimension: A Systematic Review of the Literature on Clinical and Nonclinical Samples. Eur J Neurosci 2022; 55:1547-1583. [PMID: 35229388 PMCID: PMC9313853 DOI: 10.1111/ejn.15631] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/27/2022] [Accepted: 02/20/2022] [Indexed: 12/04/2022]
Abstract
Fractal geometry has recently been proposed as a useful tool for characterizing the complexity of the brain cortex, which is likely to derive from the recurrence of sulci–gyri convolution patterns. The index used to describe the cortical complexity is called fractal dimensional (FD) and was employed by different research exploring the neurobiological correlates of distinct pathological and nonpathological conditions. This review aims to describe the literature on the application of this index, summarize the heterogeneities between studies and inform future research on this topic. Sixty‐two studies were included in the systematic review. The main research lines concern neurodevelopment, aging and the neurobiology of specific psychiatric and neurological disorders. Overall, the included papers indicate that cortical complexity is likely to reduce during aging and in various pathological processes affecting the brain. Nevertheless, the high heterogeneity between studies strongly prevents the possibility of drawing conclusions. Further research considering this index besides other morphological values is needed to better clarify the role of FD in characterizing the cortical structure. Fractal dimension (FD) is a useful tool for the assessment of cortical complexity. In healthy controls, FD is associated with development, aging and cognition. Alterations in FD have been observed in different neurological and psychiatric disorders.
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Affiliation(s)
- Valentina Meregalli
- Department of Neurosciences, University of Padua, Padova, Italy.,Padua Neuroscience Center, University of Padua, Padova, Italy
| | | | | | - Paolo Meneguzzo
- Department of Neurosciences, University of Padua, Padova, Italy
| | - Alessandro Miola
- Department of Neurosciences, University of Padua, Padova, Italy.,Padua Neuroscience Center, University of Padua, Padova, Italy
| | - Nicolò Trevisan
- Department of Neurosciences, University of Padua, Padova, Italy.,Padua Neuroscience Center, University of Padua, Padova, Italy
| | - Fabio Sambataro
- Department of Neurosciences, University of Padua, Padova, Italy.,Padua Neuroscience Center, University of Padua, Padova, Italy
| | - Angela Favaro
- Department of Neurosciences, University of Padua, Padova, Italy.,Padua Neuroscience Center, University of Padua, Padova, Italy
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20
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Kritikos M, Clouston SAP, Huang C, Pellecchia AC, Mejia-Santiago S, Carr MA, Kotov R, Lucchini RG, Gandy SE, Bromet EJ, Luft BJ. Cortical complexity in world trade center responders with chronic posttraumatic stress disorder. Transl Psychiatry 2021; 11:597. [PMID: 34815383 PMCID: PMC8611009 DOI: 10.1038/s41398-021-01719-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 10/18/2021] [Accepted: 10/29/2021] [Indexed: 12/19/2022] Open
Abstract
Approximately 23% of World Trade Center (WTC) responders are experiencing chronic posttraumatic stress disorder (PTSD) associated with their exposures at the WTC following the terrorist attacks of 9/11/2001, which has been demonstrated to be a risk factor for cognitive impairment raising concerns regarding their brain health. Cortical complexity, as measured by analyzing Fractal Dimension (FD) from T1 MRI brain images, has been reported to be reduced in a variety of psychiatric and neurological conditions. In this report, we hypothesized that FD would be also reduced in a case-control sample of 99 WTC responders as a result of WTC-related PTSD. The results of our surface-based morphometry cluster analysis found alterations in vertex clusters of complexity in WTC responders with PTSD, with marked reductions in regions within the frontal, parietal, and temporal cortices, in addition to whole-brain absolute bilateral and unilateral complexity. Furthermore, region of interest analysis identified that the magnitude of changes in regional FD severity was associated with increased PTSD symptoms (reexperiencing, avoidance, hyperarousal, negative affect) severity. This study confirms prior findings on FD and psychiatric disorders and extends our understanding of FD associations with posttraumatic symptom severity. The complex and traumatic experiences that led to WTC-related PTSD were associated with reductions in cortical complexity. Future work is needed to determine whether reduced cortical complexity arose prior to, or concurrently with, onset of PTSD.
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Affiliation(s)
- Minos Kritikos
- Program in Public Health and Department of Family, Population, and Preventive Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Sean A P Clouston
- Program in Public Health and Department of Family, Population, and Preventive Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.
| | - Chuan Huang
- Department of Radiology, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, USA
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Alison C Pellecchia
- World Trade Center Health and Wellness Program, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Stephanie Mejia-Santiago
- World Trade Center Health and Wellness Program, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Melissa A Carr
- World Trade Center Health and Wellness Program, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Roman Kotov
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Roberto G Lucchini
- Department of Environmental Health Sciences, Robert Stempel School of Public Health, Florida International University, Miami, FL, USA
| | - Samuel E Gandy
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry and Mount Sinai Alzheimer's Disease Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Evelyn J Bromet
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Benjamin J Luft
- World Trade Center Health and Wellness Program, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
- Department of Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
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21
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Armonaite K, Bertoli M, Paulon L, Gianni E, Balsi M, Conti L, Tecchio F. Neuronal Electrical Ongoing Activity as Cortical Areas Signature: An Insight from MNI Intracerebral Recording Atlas. Cereb Cortex 2021; 32:2895-2906. [PMID: 34727186 DOI: 10.1093/cercor/bhab389] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 09/28/2021] [Accepted: 09/29/2021] [Indexed: 11/14/2022] Open
Abstract
The time course of the neuronal activity in the brain network, the neurodynamics, reflects the structure and functionality of the generating neuronal pools. Here, using the intracranial stereo-electroencephalographic (sEEG) recordings of the public Montreal Neurological Institute (MNI) atlas, we investigated the neurodynamics of primary motor (M1), somatosensory (S1) and auditory (A1) cortices measuring power spectral densities (PSD) and Higuchi fractal dimension (HFD) in the same subject (M1 vs. S1 in 16 subjects, M1 vs. A1 in 9, S1 vs. A1 in 6). We observed specific spectral features in M1, which prevailed above beta band, S1 in the alpha band, and A1 in the delta band. M1 HFD was higher than S1, both higher than A1. A clear distinction of neurodynamics properties of specific primary cortices supports the efforts in cortical parceling based on this expression of the local cytoarchitecture and connectivity. In this perspective, we selected within the MNI intracortical database a first set of primary motor, somatosensory and auditory cortices' representatives to query in recognizing ongoing patterns of neuronal communication. Potential clinical impact stands primarily in exploiting such exchange patterns to enhance the efficacy of neuromodulation intervention to cure symptoms secondary to neuronal activity unbalances.
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Affiliation(s)
| | - Massimo Bertoli
- Laboratory of Electrophysiology for Translational NeuroScience (LET'S), Institute of Cognitive Sciences and Technologies - Consiglio Nazionale delle Ricerche, Rome 00185, Italy.,Department of Neuroscience, Imaging and Clinical Sciences, University 'Gabriele D'Annunzio' of Chieti-Pescara, Chieti 66100, Italy
| | - Luca Paulon
- Laboratory of Electrophysiology for Translational NeuroScience (LET'S), Institute of Cognitive Sciences and Technologies - Consiglio Nazionale delle Ricerche, Rome 00185, Italy
| | - Eugenia Gianni
- Laboratory of Electrophysiology for Translational NeuroScience (LET'S), Institute of Cognitive Sciences and Technologies - Consiglio Nazionale delle Ricerche, Rome 00185, Italy.,Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico di Roma, Rome 00128, Italy
| | - Marco Balsi
- Department of Information Engineering, Electronics and Telecommunications, Sapienza University, Rome 00185, Italy
| | - Livio Conti
- Faculty of Engineering, Uninettuno University, Rome 00186, Italy.,INFN - Istituto Nazionale di Fisica Nucleare, Sezione Roma Tor Vergata, Rome 00133, Italy
| | - Franca Tecchio
- Laboratory of Electrophysiology for Translational NeuroScience (LET'S), Institute of Cognitive Sciences and Technologies - Consiglio Nazionale delle Ricerche, Rome 00185, Italy
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22
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Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy. Phys Med 2021; 83:221-241. [DOI: 10.1016/j.ejmp.2021.04.010] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 03/31/2021] [Accepted: 04/03/2021] [Indexed: 02/06/2023] Open
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23
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Using Fractal Dimension Analysis with the Desikan-Killiany Atlas to Assess the Effects of Normal Aging on Subregional Cortex Alterations in Adulthood. Brain Sci 2021; 11:brainsci11010107. [PMID: 33466961 PMCID: PMC7829920 DOI: 10.3390/brainsci11010107] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 12/23/2020] [Accepted: 12/31/2020] [Indexed: 12/11/2022] Open
Abstract
Normal aging is associated with functional and structural alterations in the human brain. The effects of normal aging and gender on morphological changes in specific regions of the brain are unknown. The fractal dimension (FD) can be a quantitative measure of cerebral folding. In this study, we used 3D-FD analysis with the Desikan–Killiany (DK) atlas to assess subregional morphological changes in adulthood. A total of 258 participants (112 women and 146 men) aged 30–85 years participated in this study. Participants in the middle-age group exhibited a decreased FD in the lateral frontal lobes, which then spread to the temporal and parietal lobes. Men exhibited an earlier and more significant decrease in FD values, mainly in the right frontal and left parietal lobes. Men exhibited more of a decrease in FD values in the subregions on the left than those in the right, whereas women exhibited more of a decrease in the lateral subregions. Older men were at a higher risk of developing mild cognitive impairment (MCI) and exhibited age-related memory decline earlier than women. Our FD analysis using the DK atlas-based prediagnosis may provide a suitable tool for assessing normal aging and neurodegeneration between groups or in individual patients.
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