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Liloia D, Zamfira DA, Tanaka M, Manuello J, Crocetta A, Keller R, Cozzolino M, Duca S, Cauda F, Costa T. Disentangling the role of gray matter volume and concentration in autism spectrum disorder: A meta-analytic investigation of 25 years of voxel-based morphometry research. Neurosci Biobehav Rev 2024; 164:105791. [PMID: 38960075 DOI: 10.1016/j.neubiorev.2024.105791] [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: 10/26/2023] [Revised: 05/22/2024] [Accepted: 06/27/2024] [Indexed: 07/05/2024]
Abstract
Despite over two decades of neuroimaging research, a unanimous definition of the pattern of structural variation associated with autism spectrum disorder (ASD) has yet to be found. One potential impeding issue could be the sometimes ambiguous use of measurements of variations in gray matter volume (GMV) or gray matter concentration (GMC). In fact, while both can be calculated using voxel-based morphometry analysis, these may reflect different underlying pathological mechanisms. We conducted a coordinate-based meta-analysis, keeping apart GMV and GMC studies of subjects with ASD. Results showed distinct and non-overlapping patterns for the two measures. GMV decreases were evident in the cerebellum, while GMC decreases were mainly found in the temporal and frontal regions. GMV increases were found in the parietal, temporal, and frontal brain regions, while GMC increases were observed in the anterior cingulate cortex and middle frontal gyrus. Age-stratified analyses suggested that such variations are dynamic across the ASD lifespan. The present findings emphasize the importance of considering GMV and GMC as distinct yet synergistic indices in autism research.
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Affiliation(s)
- Donato Liloia
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Denisa Adina Zamfira
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy; Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Masaru Tanaka
- HUN-REN-SZTE Neuroscience Research Group, Hungarian Research Network, University of Szeged (HUN-REN-SZTE), Danube Neuroscience Research Laboratory, Szeged, Hungary
| | - Jordi Manuello
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Annachiara Crocetta
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Roberto Keller
- Adult Autism Center, DSM Local Health Unit, ASL TO, Turin, Italy
| | - Mauro Cozzolino
- Department of Humanities, Philosophical and Educational Sciences, University of Salerno, Fisciano, Italy
| | - Sergio Duca
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Franco Cauda
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy
| | - Tommaso Costa
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy
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Grecucci A, Monachesi B, Messina I. Reduced GM-WM concentration inside the Default Mode Network in individuals with high emotional intelligence and low anxiety: a data fusion mCCA+jICA approach. Soc Cogn Affect Neurosci 2024; 19:nsae018. [PMID: 38451879 PMCID: PMC10919484 DOI: 10.1093/scan/nsae018] [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: 10/08/2023] [Revised: 01/05/2024] [Accepted: 02/16/2024] [Indexed: 03/09/2024] Open
Abstract
The concept of emotional intelligence (EI) refers to the ability to recognize and regulate emotions to appropriately guide cognition and behaviour. Unfortunately, studies on the neural bases of EI are scant, and no study so far has exhaustively investigated grey matter (GM) and white matter (WM) contributions to it. To fill this gap, we analysed trait measure of EI and structural MRI data from 128 healthy participants to shed new light on where and how EI is encoded in the brain. In addition, we explored the relationship between the neural substrates of trait EI and trait anxiety. A data fusion unsupervised machine learning approach (mCCA + jICA) was used to decompose the brain into covarying GM-WM networks and to assess their association with trait-EI. Results showed that high levels trait-EI are associated with decrease in GM-WM concentration in a network spanning from frontal to parietal and temporal regions, among which insula, cingulate, parahippocampal gyrus, cuneus and precuneus. Interestingly, we also found that the higher the GM-WM concentration in the same network, the higher the trait anxiety. These findings encouragingly highlight the neural substrates of trait EI and their relationship with anxiety. The network is discussed considering its overlaps with the Default Mode Network.
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Affiliation(s)
- Alessandro Grecucci
- Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Rovereto (TN), Italy 38068, Italy
- Centre for Medical Sciences, CISMed, University of Trento, Trento, Italy 38122, Italy
| | - Bianca Monachesi
- Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Rovereto (TN), Italy 38068, Italy
| | - Irene Messina
- Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Rovereto (TN), Italy 38068, Italy
- Faculty of Social and Communication Sciences, Universitas Mercatorum, Rome, Italy
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Richter N, Brand S, Nellessen N, Dronse J, Gramespacher H, Schmieschek MHT, Fink GR, Kukolja J, Onur OA. Fine-grained age-matching improves atrophy-based detection of mild cognitive impairment more than amyloid-negative reference subjects. Neuroimage Clin 2023; 40:103508. [PMID: 37717383 PMCID: PMC10514218 DOI: 10.1016/j.nicl.2023.103508] [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: 04/24/2023] [Revised: 09/07/2023] [Accepted: 09/08/2023] [Indexed: 09/19/2023]
Abstract
INTRODUCTION In clinical practice, differentiating between age-related gray matter (GM) atrophy and neurodegeneration-related atrophy at early disease stages, such as mild cognitive impairment (MCI), remains challenging. We hypothesized that fined-grained adjustment for age effects and using amyloid-negative reference subjects could increase classification accuracy. METHODS T1-weighted magnetic resonance imaging (MRI) data of 131 cognitively normal (CN) individuals and 91 patients with MCI from the Alzheimer's disease neuroimaging initiative (ADNI) characterized concerning amyloid status, as well as 19 CN individuals and 19 MCI patients from an independent validation sample were segmented, spatially normalized and analyzed in the framework of voxel-based morphometry (VBM). For each participant, statistical maps of GM atrophy were computed as the deviation from the GM of CN reference groups at the voxel level. CN reference groups composed with different degrees of age-matching, and mixed and strictly amyloid-negative CN reference groups were examined regarding their effect on the accuracy in distinguishing between CN and MCI. Furthermore, the effects of spatial smoothing and atrophy threshold were assessed. RESULTS Approaches with a specific reference group for each age significantly outperformed all other age-adjustment strategies with a maximum area under the curve of 1.0 in the ADNI sample and 0.985 in the validation sample. Accounting for age in a regression-based approach improved classification accuracy over that of a single CN reference group in the age range of the patient sample. Using strictly amyloid-negative reference groups improved classification accuracy only when age was not considered. CONCLUSION Our results demonstrate that VBM can differentiate between age-related and MCI-associated atrophy with high accuracy. Crucially, age-specific reference groups significantly increased accuracy, more so than regression-based approaches and using amyloid-negative reference groups.
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Affiliation(s)
- Nils Richter
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, 52425 Jülich, Germany; Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany.
| | - Stefanie Brand
- Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany
| | - Nils Nellessen
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, 52425 Jülich, Germany; Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany; Department of Neurology and Clinical Neurophysiology, Helios University Hospital Wuppertal, 42283 Wuppertal, Germany; Faculty of Health, Witten/Herdecke University, 58448 Witten, Germany
| | - Julian Dronse
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, 52425 Jülich, Germany; Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany
| | - Hannes Gramespacher
- Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany
| | - Maximilian H T Schmieschek
- Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany
| | - Gereon R Fink
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, 52425 Jülich, Germany; Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany
| | - Juraj Kukolja
- Department of Neurology and Clinical Neurophysiology, Helios University Hospital Wuppertal, 42283 Wuppertal, Germany; Faculty of Health, Witten/Herdecke University, 58448 Witten, Germany
| | - Oezguer A Onur
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, 52425 Jülich, Germany; Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany
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Nemoto K, Sakaguchi H, Kasai W, Hotta M, Kamei R, Noguchi T, Minamimoto R, Arai T, Asada T. Differentiating Dementia with Lewy Bodies and Alzheimer's Disease by Deep Learning to Structural MRI. J Neuroimaging 2021; 31:579-587. [PMID: 33476487 DOI: 10.1111/jon.12835] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 12/14/2020] [Accepted: 01/03/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND AND PURPOSE Dementia with Lewy bodies (DLB) is the second most prevalent cause of degenerative dementia next to Alzheimer's disease (AD). Though current DLB diagnostic criteria employ several indicative biomarkers, relative preservation of the medial temporal lobe as revealed by structural MRI suffers from low sensitivity and specificity, making them unreliable as sole supporting biomarkers. In this study, we investigated how a deep learning approach would be able to differentiate DLB from AD with structural MRI data. METHODS Two-hundred and eight patients (101 DLB, 69 AD, and 38 controls) participated in this retrospective study. Gray matter images were extracted using voxel-based morphometry (VBM). In order to compare the conventional statistical analysis with deep-learning feature extraction, we built a classification model for DLB and AD with a residual neural network (ResNet) type of convolutional neural network architecture, which is one of the deep learning models. The anatomically standardized gray matter images extracted in the same way as for the VBM process were used as inputs, and the classification performance achieved by our model was evaluated. RESULTS Conventional statistical analysis detected no significant atrophy other than fine differences on the middle temporal pole and hippocampal regions. The feature extracted by the deep learning method differentiated DLB from AD with 79.15% accuracy compared to the 68.41% of the conventional method. CONCLUSIONS Our results confirmed that the deep learning method with gray matter images can detect fine differences between DLB and AD that may be underestimated by the conventional method.
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Affiliation(s)
- Kiyotaka Nemoto
- Department of Psychiatry, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | | | | | - Masatoshi Hotta
- Center Hospital of the National Center for Global Health and Medicine, Tokyo, Japan
| | - Ryotaro Kamei
- Center Hospital of the National Center for Global Health and Medicine, Tokyo, Japan
| | - Tomoyuki Noguchi
- National Hospital Organization, Kyushu Medical Center, Fukuoka, Japan
| | - Ryogo Minamimoto
- Center Hospital of the National Center for Global Health and Medicine, Tokyo, Japan
| | - Tetsuaki Arai
- Department of Psychiatry, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Takashi Asada
- Center for Brain Integration Research, Tokyo Medical and Dental University, Tokyo, Japan.,Memory Clinic Ochanomizu, Tokyo, Japan
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Noguchi-Shinohara M, Domoto C, Yoshida T, Niwa K, Yuki-Nozaki S, Samuraki-Yokohama M, Sakai K, Hamaguchi T, Ono K, Iwasa K, Matsunari I, Komai K, Nakamura H, Yamada M. A new computerized assessment battery for cognition (C-ABC) to detect mild cognitive impairment and dementia around 5 min. PLoS One 2020; 15:e0243469. [PMID: 33306697 PMCID: PMC7732101 DOI: 10.1371/journal.pone.0243469] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 11/21/2020] [Indexed: 11/24/2022] Open
Abstract
This study aimed to develop a new computerized assessment battery for cognition (C-ABC) to detect mild cognitive impairment (MCI) and dementia. We performed C-ABC in subjects with dementia (n = 422), MCI (n = 145), and normal cognition (NC; n = 574), and analyzed by age stratum (50s, 60s, and 70–85 years). To distinguish MCI from NC, the C-ABC total combined score, which were calculated by dividing the C-ABC total score by the C-ABC required time, revealed the best area under the curves (AUC) at 0.838 and 0.735 in the 50s and 60s age groups, respectively; notably, this entire procedure took approximately 5 min. To distinguish dementia from NC and MCI, the partial items of C-ABC (items 3 + 6 combined score) revealed the best AUCs at 0.910, 0.874, and 0.882 in the 50s, 60s, and 70–85 age groups, respectively. Furthermore, the items 3 + 6 combined score established the best AUC at 0.794 in the 70–85 age group to distinguish MCI from NC; this entire procedure took around 2 min. Hence, this study suggests that C-ABC could be a useful tool for detecting dementia or MCI in a short time.
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Affiliation(s)
- Moeko Noguchi-Shinohara
- Department of Neurology and Neurobiology of Aging, Kanazawa University Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan.,Department of Preemptive Medicine for Dementia, Kanazawa University Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan
| | - Chiaki Domoto
- Department of Neurology and Neurobiology of Aging, Kanazawa University Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan
| | - Taketoshi Yoshida
- Department of Knowledge Science of Japan Advanced Institute of Science and Technology (JAIST), Nomi, Ishikawa, Japan
| | - Kozue Niwa
- Department of Neurology and Neurobiology of Aging, Kanazawa University Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan
| | - Sohshi Yuki-Nozaki
- Department of Neurology and Neurobiology of Aging, Kanazawa University Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan
| | - Miharu Samuraki-Yokohama
- Department of Neurology and Neurobiology of Aging, Kanazawa University Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan
| | - Kenji Sakai
- Department of Neurology and Neurobiology of Aging, Kanazawa University Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan
| | - Tsuyoshi Hamaguchi
- Department of Neurology and Neurobiology of Aging, Kanazawa University Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan
| | - Kenjiro Ono
- Department of Neurology and Neurobiology of Aging, Kanazawa University Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan.,Division of Neurology, Department of Internal Medicine, Showa University School of Medicine, Tokyo, Japan
| | - Kazuo Iwasa
- Department of Neurology and Neurobiology of Aging, Kanazawa University Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan.,Department of Health and Medical Sciences, Ishikawa Prefectural Nursing University, Kahoku, Ishikawa, Japan
| | - Ichiro Matsunari
- Division of Nuclear Medicine, Department of Radiology, Saitama Medical University Hospital, Saitama, Japan
| | - Kiyonobu Komai
- Department of Neurology, Hokuriku Brain and Neuromuscular Disease Center, National Hospital Organization Iou National Hospital, Kanazawa, Japan
| | - Hiroyuki Nakamura
- Department of Public Health, Kanazawa University Graduate School of Advanced Preventive Medical Sciences, Kanazawa, Japan.,Kanazawa University Advanced Preventive Medical Sciences Research Center, Kanazawa, Japan.,Department of Environmental and Preventive Medicine, Kanazawa University Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan
| | - Masahito Yamada
- Department of Neurology and Neurobiology of Aging, Kanazawa University Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan
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Comparing VBM and ROI analyses for detection of gray matter abnormalities in patients with bipolar disorder using MRI. MIDDLE EAST CURRENT PSYCHIATRY 2020. [DOI: 10.1186/s43045-020-00076-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Abstract
Background
With the increasing efforts to a better understanding of psychiatric diseases, detection of brain morphological alterations is necessary. This study compared two methods—voxel-based morphometry (VBM) and region of interest (ROI) analyses—to identify significant gray matter changes of patients with bipolar disorder type I (BP I).
Results
The VBM findings suggested gray matter reductions in the left precentral gyrus and right precuneus of the patients compared to healthy subjects (α = 0.0005, uncorrected). However, no regions reached the level of significance in ROI analysis using the three atlases, i.e., hammers, lpba40, and neuromorphometrics atlases (α = 0.0005).
Conclusion
It can be concluded that VBM analysis seems to be more sensitive to partial changes in this study. If ROI analysis is employed in studies to detect structural brain alterations between groups, it is highly recommended to use VBM analysis besides.
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