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Chattopadhyay T, Ozarkar SS, Buwa K, Joshy NA, Komandur D, Naik J, Thomopoulos SI, Ver Steeg G, Ambite JL, Thompson PM. Comparison of deep learning architectures for predicting amyloid positivity in Alzheimer's disease, mild cognitive impairment, and healthy aging, from T1-weighted brain structural MRI. Front Neurosci 2024; 18:1387196. [PMID: 39015378 PMCID: PMC11250587 DOI: 10.3389/fnins.2024.1387196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 06/14/2024] [Indexed: 07/18/2024] Open
Abstract
Abnormal β-amyloid (Aβ) accumulation in the brain is an early indicator of Alzheimer's disease (AD) and is typically assessed through invasive procedures such as PET (positron emission tomography) or CSF (cerebrospinal fluid) assays. As new anti-Alzheimer's treatments can now successfully target amyloid pathology, there is a growing interest in predicting Aβ positivity (Aβ+) from less invasive, more widely available types of brain scans, such as T1-weighted (T1w) MRI. Here we compare multiple approaches to infer Aβ + from standard anatomical MRI: (1) classical machine learning algorithms, including logistic regression, XGBoost, and shallow artificial neural networks, (2) deep learning models based on 2D and 3D convolutional neural networks (CNNs), (3) a hybrid ANN-CNN, combining the strengths of shallow and deep neural networks, (4) transfer learning models based on CNNs, and (5) 3D Vision Transformers. All models were trained on paired MRI/PET data from 1,847 elderly participants (mean age: 75.1 yrs. ± 7.6SD; 863 females/984 males; 661 healthy controls, 889 with mild cognitive impairment (MCI), and 297 with Dementia), scanned as part of the Alzheimer's Disease Neuroimaging Initiative. We evaluated each model's balanced accuracy and F1 scores. While further tests on more diverse data are warranted, deep learning models trained on standard MRI showed promise for estimating Aβ + status, at least in people with MCI. This may offer a potential screening option before resorting to more invasive procedures.
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Affiliation(s)
- Tamoghna Chattopadhyay
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Saket S. Ozarkar
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Ketaki Buwa
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Neha Ann Joshy
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Dheeraj Komandur
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Jayati Naik
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Sophia I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | | | - Jose Luis Ambite
- Information Sciences Institute, University of Southern California, Marina del Rey, CA, United States
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
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Cao C, Li Y, Hu F, Gao X. Modeling refined differences of cortical folding patterns via spatial, morphological, and temporal fusion representations. Cereb Cortex 2024; 34:bhae146. [PMID: 38602743 DOI: 10.1093/cercor/bhae146] [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: 12/19/2023] [Revised: 03/18/2024] [Accepted: 03/20/2024] [Indexed: 04/12/2024] Open
Abstract
The gyrus, a pivotal cortical folding pattern, is essential for integrating brain structure-function. This study focuses on 2-Hinge and 3-Hinge folds, characterized by the gyral convergence from various directions. Existing voxel-level studies may not adequately capture the precise spatial relationships within cortical folding patterns, especially when relying solely on local cortical characteristics due to their variable shapes and homogeneous frequency-specific features. To overcome these challenges, we introduced a novel model that combines spatial distribution, morphological structure, and functional magnetic resonance imaging data. We utilized spatio-morphological residual representations to enhance and extract subtle variations in cortical spatial distribution and morphological structure during blood oxygenation, integrating these with functional magnetic resonance imaging embeddings using self-attention for spatio-morphological-temporal representations. Testing these representations for identifying cortical folding patterns, including sulci, gyri, 2-Hinge, and 2-Hinge folds, and evaluating the impact of phenotypic data (e.g. stimulus) on recognition, our experimental results demonstrate the model's superior performance, revealing significant differences in cortical folding patterns under various stimulus. These differences are also evident in the characteristics of sulci and gyri folds between genders, with 3-Hinge showing more variations. Our findings indicate that our representations of cortical folding patterns could serve as biomarkers for understanding brain structure-function correlations.
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Affiliation(s)
- Chunhong Cao
- The MOE Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, 411005 Xiangtan, China
| | - Yongquan Li
- The MOE Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, 411005 Xiangtan, China
| | - Fang Hu
- The Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University, 423043 Chenzhou, China
| | - Xieping Gao
- The Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, 410081 Changsha, China
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Ben-Zion Z, Korem N, Fine NB, Katz S, Siddhanta M, Funaro MC, Duek O, Spiller TR, Danböck SK, Levy I, Harpaz-Rotem I. Structural Neuroimaging of Hippocampus and Amygdala Subregions in Posttraumatic Stress Disorder: A Scoping Review. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:120-134. [PMID: 38298789 PMCID: PMC10829655 DOI: 10.1016/j.bpsgos.2023.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 06/28/2023] [Accepted: 07/02/2023] [Indexed: 02/02/2024] Open
Abstract
Numerous studies have explored the relationship between posttraumatic stress disorder (PTSD) and the hippocampus and the amygdala because both regions are implicated in the disorder's pathogenesis and pathophysiology. Nevertheless, those key limbic regions consist of functionally and cytoarchitecturally distinct substructures that may play different roles in the etiology of PTSD. Spurred by the availability of automatic segmentation software, structural neuroimaging studies of human hippocampal and amygdala subregions have proliferated in recent years. Here, we present a preregistered scoping review of the existing structural neuroimaging studies of the hippocampus and amygdala subregions in adults diagnosed with PTSD. A total of 3513 studies assessing subregion volumes were identified, 1689 of which were screened, and 21 studies were eligible for this review (total N = 2876 individuals). Most studies examined hippocampal subregions and reported decreased CA1, CA3, dentate gyrus, and subiculum volumes in PTSD. Fewer studies investigated amygdala subregions and reported altered lateral, basal, and central nuclei volumes in PTSD. This review further highlights the conceptual and methodological limitations of the current literature and identifies future directions to increase understanding of the distinct roles of hippocampal and amygdalar subregions in posttraumatic psychopathology.
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Affiliation(s)
- Ziv Ben-Zion
- Yale School of Medicine, Yale University, New Haven, Connecticut
- US Department of Veterans Affairs National Center for PTSD, Clinical Neuroscience Division, VA Connecticut Healthcare System, West Haven, Connecticut
- Wu Tsai Institute, Yale University, New Haven, Connecticut
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Nachshon Korem
- Yale School of Medicine, Yale University, New Haven, Connecticut
- US Department of Veterans Affairs National Center for PTSD, Clinical Neuroscience Division, VA Connecticut Healthcare System, West Haven, Connecticut
| | - Naomi B Fine
- Sagol Brain Institute Tel-Aviv, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Faculty of Social Sciences, School of Psychological Science, Tel Aviv University, Tel Aviv, Israel
| | - Sophia Katz
- Yale School of Medicine, Yale University, New Haven, Connecticut
| | - Megha Siddhanta
- Yale School of Medicine, Yale University, New Haven, Connecticut
| | - Melissa C Funaro
- Harvey Cushing/John Hay Whitney Medical Library, Yale University, New Haven, Connecticut
| | - Or Duek
- Yale School of Medicine, Yale University, New Haven, Connecticut
- US Department of Veterans Affairs National Center for PTSD, Clinical Neuroscience Division, VA Connecticut Healthcare System, West Haven, Connecticut
- Department of Epidemiology, Biostatistics and Community Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Tobias R Spiller
- Yale School of Medicine, Yale University, New Haven, Connecticut
- US Department of Veterans Affairs National Center for PTSD, Clinical Neuroscience Division, VA Connecticut Healthcare System, West Haven, Connecticut
- Department of Consultation-Liaison Psychiatry and Psychosomatic Medicine, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Sarah K Danböck
- Yale School of Medicine, Yale University, New Haven, Connecticut
- Division of Clinical Psychology and Psychopathology, Department of Psychology, Paris London University of Salzburg, Salzburg, Austria
| | - Ifat Levy
- Yale School of Medicine, Yale University, New Haven, Connecticut
- Wu Tsai Institute, Yale University, New Haven, Connecticut
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Ilan Harpaz-Rotem
- Yale School of Medicine, Yale University, New Haven, Connecticut
- US Department of Veterans Affairs National Center for PTSD, Clinical Neuroscience Division, VA Connecticut Healthcare System, West Haven, Connecticut
- Wu Tsai Institute, Yale University, New Haven, Connecticut
- Department of Psychology, Yale University, New Haven, Connecticut
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Siadat MR, Elisevich K, Soltanian-Zadeh H, Eetemadi A, Smith B. Curvature analysis of perisylvian epilepsy. Acta Neurol Belg 2023; 123:2303-2313. [PMID: 37368146 DOI: 10.1007/s13760-023-02238-6] [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: 10/05/2022] [Accepted: 03/10/2023] [Indexed: 06/28/2023]
Abstract
PURPOSE We assess whether alterations in the convolutional anatomy of the deep perisylvian area (DPSA) might indicate focal epileptogenicity. MATERIALS AND METHODS The DPSA of each hemisphere was segmented on MRI and a 3D gray-white matter interface (GWMI) geometrical model was constructed. Comparative visual and quantitative assessment of the convolutional anatomy of both the left and right DPSA models was performed. Both the density of thorn-like contours (peak percentage) and coarse interface curvatures was computed using Gaussian curvature and shape index, respectively. The proposed method was applied to a total of 14 subjects; 7 patients with an epileptogenic DPSA and 7 non-epileptic subjects. RESULTS A high peak percentage correlated well with the epileptogenic DPSA. It distinguished between patients and non-epileptic subjects (P = 0.029) and identified laterality of the epileptic focus in all but one case. A diminished regional curvature also identified epileptogenicity (P = 0.016) and, moreover, its laterality (P = 0.001). CONCLUSION An increased peak percentage from a global view of the GWMI of the DPSA provides some indication of a propensity toward a focal or regional DPSA epileptogenicity. A diminished convolutional anatomy (i.e., smoothing effect) appears also to coincide with the epileptogenic site in the DPSA and to distinguish laterality.
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Affiliation(s)
- Mohammad-Reza Siadat
- Department of Computer Science and Engineering, Oakland University, 115 Library Dr., #540, Rochester, MI, 48309, USA.
| | - Kost Elisevich
- Department of Surgery, Michigan State University, East Lansing, MI, 48824, USA
| | - Hamid Soltanian-Zadeh
- Department of Diagnostic Radiology, Henry Ford Health System, Detroit, MI, 48202, USA
| | - Ameen Eetemadi
- Department of Computer Science, University of California, Davis, CA, 95616, USA
| | - Brien Smith
- Department of Neurosurgery, Ohio Health, Columbus, OH, 43228, USA
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Zhang J, Shi Y. Personalized Patch-based Normality Assessment of Brain Atrophy in Alzheimer's Disease. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2023; 14224:55-62. [PMID: 38501074 PMCID: PMC10948101 DOI: 10.1007/978-3-031-43904-9_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
Cortical thickness is an important biomarker associated with gray matter atrophy in neurodegenerative diseases. In order to conduct meaningful comparisons of cortical thickness between different subjects, it is imperative to establish correspondence among surface meshes. Conventional methods achieve this by projecting surface onto canonical domains such as the unit sphere or averaging feature values in anatomical regions of interest (ROIs). However, due to the natural variability in cortical topography, perfect anatomically meaningful one-to-one mapping can be hardly achieved and the practice of averaging leads to the loss of detailed information. For example, two subjects may have different number of gyral structures in the same region, and thus mapping can result in gyral/sulcal mismatch which introduces noise and averaging in detailed local information loss. Therefore, it is necessary to develop new method that can overcome these intrinsic problems to construct more meaningful comparison for atrophy detection. To address these limitations, we propose a novel personalized patch-based method to improve cortical thickness comparison across subjects. Our model segments the brain surface into patches based on gyral and sulcal structures to reduce mismatches in mapping method while still preserving detailed topological information which is potentially discarded in averaging. Moreover,the personalized templates for each patch account for the variability of folding patterns, as not all subjects are comparable. Finally, through normality assessment experiments, we demonstrate that our model performs better than standard spherical registration in detecting atrophy in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD).
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Affiliation(s)
- Jianwei Zhang
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA 90033, USA
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California (USC), Los Angeles, CA 90089, USA
| | - Yonggang Shi
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA 90033, USA
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California (USC), Los Angeles, CA 90089, USA
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Guo Y, Chen Q, Choi GPT, Lui LM. Automatic landmark detection and registration of brain cortical surfaces via quasi-conformal geometry and convolutional neural networks. Comput Biol Med 2023; 163:107185. [PMID: 37418897 DOI: 10.1016/j.compbiomed.2023.107185] [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: 12/05/2022] [Revised: 05/24/2023] [Accepted: 06/13/2023] [Indexed: 07/09/2023]
Abstract
In medical imaging, surface registration is extensively used for performing systematic comparisons between anatomical structures, with a prime example being the highly convoluted brain cortical surfaces. To obtain a meaningful registration, a common approach is to identify prominent features on the surfaces and establish a low-distortion mapping between them with the feature correspondence encoded as landmark constraints. Prior registration works have primarily focused on using manually labeled landmarks and solving highly nonlinear optimization problems, which are time-consuming and hence hinder practical applications. In this work, we propose a novel framework for the automatic landmark detection and registration of brain cortical surfaces using quasi-conformal geometry and convolutional neural networks. We first develop a landmark detection network (LD-Net) that allows for the automatic extraction of landmark curves given two prescribed starting and ending points based on the surface geometry. We then utilize the detected landmarks and quasi-conformal theory for achieving the surface registration. Specifically, we develop a coefficient prediction network (CP-Net) for predicting the Beltrami coefficients associated with the desired landmark-based registration and a mapping network called the disk Beltrami solver network (DBS-Net) for generating quasi-conformal mappings from the predicted Beltrami coefficients, with the bijectivity guaranteed by quasi-conformal theory. Experimental results are presented to demonstrate the effectiveness of our proposed framework. Altogether, our work paves a new way for surface-based morphometry and medical shape analysis.
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Affiliation(s)
- Yuchen Guo
- Department of Mathematics, The Chinese University of Hong Kong, Hong Kong
| | - Qiguang Chen
- Department of Mathematics, The Chinese University of Hong Kong, Hong Kong
| | - Gary P T Choi
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lok Ming Lui
- Department of Mathematics, The Chinese University of Hong Kong, Hong Kong.
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7
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Kochunov P, Ma Y, Hatch KS, Gao S, Acheson A, Jahanshad N, Thompson PM, Adhikari BM, Bruce H, Van der Vaart A, Chiappelli J, Du X, Sotiras A, Kvarta MD, Ma T, Chen S, Hong LE. Ancestral, Pregnancy, and Negative Early-Life Risks Shape Children's Brain (Dis)similarity to Schizophrenia. Biol Psychiatry 2023; 94:332-340. [PMID: 36948435 PMCID: PMC10511664 DOI: 10.1016/j.biopsych.2023.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 03/07/2023] [Accepted: 03/07/2023] [Indexed: 03/24/2023]
Abstract
BACKGROUND Familial, obstetric, and early-life environmental risks for schizophrenia spectrum disorder (SSD) alter normal cerebral development, leading to the formation of characteristic brain deficit patterns prior to onset of symptoms. We hypothesized that the insidious effects of these risks may increase brain similarity to adult SSD deficit patterns in prepubescent children. METHODS We used data collected by the Adolescent Brain Cognitive Development (ABCD) Study (N = 8940, age = 9.9 ± 0.1 years, 4307/4633 female/male), including 727 (age = 9.9 ± 0.1 years, 351/376 female/male) children with family history of SSD, to evaluate unfavorable cerebral effects of ancestral SSD history, pre/perinatal environment, and negative early-life environment. We used a regional vulnerability index to measure the alignment of a child's cerebral patterns with the adult SSD pattern derived from a large meta-analysis of case-control differences. RESULTS In children with a family history of SSD, the regional vulnerability index captured significantly more variance in ancestral history than traditional whole-brain and regional brain measurements. In children with and without family history of SSD, the regional vulnerability index also captured more variance associated with negative pre/perinatal environment and early-life experiences than traditional brain measurements. CONCLUSIONS In summary, in a cohort in which most children will not develop SSD, familial, pre/perinatal, and early developmental risks can alter brain patterns in the direction observed in adult patients with SSD. Individual similarity to adult SSD patterns may provide an early biomarker of the effects of genetic and developmental risks on the brain prior to psychotic or prodromal symptom onset.
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Affiliation(s)
- Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland.
| | - Yizhou Ma
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Kathryn S Hatch
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Si Gao
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Ashley Acheson
- Department of Psychiatry, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of University of the Sunshine Coast, Marina del Rey, California
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of University of the Sunshine Coast, Marina del Rey, California
| | - Bhim M Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Heather Bruce
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Andrew Van der Vaart
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Joshua Chiappelli
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Xiaoming Du
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Aris Sotiras
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - Mark D Kvarta
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, University of Maryland, College Park, Maryland
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
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Cao C, Li Y, Zhang L, Hu F, Gao X. Identification for the cortical 3-Hinges folding pattern based on cortical morphological and structural features. Front Neurosci 2023; 17:1125666. [PMID: 36968484 PMCID: PMC10034048 DOI: 10.3389/fnins.2023.1125666] [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: 12/16/2022] [Accepted: 02/22/2023] [Indexed: 03/11/2023] Open
Abstract
The Cortical 3-Hinges Folding Pattern (i.e., 3-Hinges) is one of the brain's hallmarks, and it is of great reference for predicting human intelligence, diagnosing eurological diseases and understanding the brain functional structure differences among gender. Given the significant morphological variability among individuals, it is challenging to identify 3-Hinges, but current 3-Hinges researches are mainly based on the computationally expensive Gyral-net method. To address this challenge, this paper aims to develop a deep network model to realize the fast identification of 3-Hinges based on cortical morphological and structural features. The main work includes: (1) The morphological and structural features of the cerebral cortex are extracted to relieve the imbalance between the number of 3-Hinges and each brain image's voxels; (2) The feature vector is constructed with the K nearest neighbor algorithm from the extracted scattered features of the morphological and structural features to alleviate over-fitting in training; (3) The squeeze excitation module combined with the deep U-shaped network structure is used to learn the correlation of the channels among the feature vectors; (4) The functional structure roles that 3-Hinges plays between adolescent males and females are discussed in this work. The experimental results on both adolescent and adult MRI datasets show that the proposed model achieves better performance in terms of time consumption. Moreover, this paper reveals that cortical sulcus information plays a critical role in the procedure of identification, and the cortical thickness, cortical surface area, and volume characteristics can supplement valuable information for 3-Hinges identification to some extent. Furthermore, there are significant structural differences on 3-Hinges among adolescent gender.
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Affiliation(s)
- Chunhong Cao
- The MOE Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, Xiangtan, China
| | - Yongquan Li
- The MOE Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, Xiangtan, China
| | - Lele Zhang
- The MOE Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, Xiangtan, China
| | - Fang Hu
- Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University, Chenzhou, China
| | - Xieping Gao
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, China
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Zhang L, Zhao L, Liu D, Wu Z, Wang X, Liu T, Zhu D. Cortex2vector: anatomical embedding of cortical folding patterns. Cereb Cortex 2022; 33:5851-5862. [PMID: 36487182 PMCID: PMC10183757 DOI: 10.1093/cercor/bhac465] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 11/01/2022] [Accepted: 11/02/2022] [Indexed: 12/13/2022] Open
Abstract
Abstract
Current brain mapping methods highly depend on the regularity, or commonality, of anatomical structure, by forcing the same atlas to be matched to different brains. As a result, individualized structural information can be overlooked. Recently, we conceptualized a new type of cortical folding pattern called the 3-hinge gyrus (3HG), which is defined as the conjunction of gyri coming from three directions. Many studies have confirmed that 3HGs are not only widely existing on different brains, but also possess both common and individual patterns. In this work, we put further effort, based on the identified 3HGs, to establish the correspondences of individual 3HGs. We developed a learning-based embedding framework to encode individual cortical folding patterns into a group of anatomically meaningful embedding vectors (cortex2vector). Each 3HG can be represented as a combination of these embedding vectors via a set of individual specific combining coefficients. In this way, the regularity of folding pattern is encoded into the embedding vectors, while the individual variations are preserved by the multi-hop combination coefficients. Results show that the learned embeddings can simultaneously encode the commonality and individuality of cortical folding patterns, as well as robustly infer the complicated many-to-many anatomical correspondences among different brains.
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Affiliation(s)
- Lu Zhang
- Department of Computer Science and Engineering, The University of Texas at Arlington , Arlington, 76010, USA
| | - Lin Zhao
- Department of Computer Science, The University of Georgia , Athens, 30602, USA
| | | | - Zihao Wu
- Department of Computer Science, The University of Georgia , Athens, 30602, USA
| | - Xianqiao Wang
- College of Engineering, The University of Georgia , Athens, 30602, USA
| | - Tianming Liu
- Department of Computer Science, The University of Georgia , Athens, 30602, USA
| | - Dajiang Zhu
- Department of Computer Science and Engineering, The University of Texas at Arlington , Arlington, 76010, USA
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Wang C, Wei Y, Li J, Li X, Liu Y, Hu Q, Wang Y. Asymmetry-enhanced attention network for Alzheimer's diagnosis with structural Magnetic Resonance Imaging. Comput Biol Med 2022; 151:106282. [PMID: 36413817 DOI: 10.1016/j.compbiomed.2022.106282] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 10/25/2022] [Accepted: 10/30/2022] [Indexed: 11/11/2022]
Abstract
BACKGROUND AND OBJECTIVE With the aging of the global population becoming severe, Alzheimer's disease (AD) has become one of the world's most common senile diseases. Many studies have suggested that the brain's left-right asymmetry is one of the possible diagnostic landmarks for AD. However, most published approaches to classification problems may not adequately explore the asymmetry between the left and right hemispheres. At the same time, the relationship between asymmetry traits and other classifier features remains understudied. METHODS In this paper, we proposed an asymmetry enhanced attention network (ASEAN) for AD diagnosis that effectively combines the anatomical asymmetry characteristics of the brain to enhance the accuracy and stability of classification tasks. First, we proposed a multi-scale asymmetry feature extraction module (MSAF) that can extract the asymmetry features of the brain from various scales. Second, we proposed an asymmetry refinement module (ARM) that considers the dependency between feature maps to suppress the irrelevant regions of the asymmetric feature maps. In addition, a parameter-free attention module was introduced to infer 4D attention weights and improve the network's representation capability. RESULTS The proposed method achieved performance improvements on two databases: Alzheimer's Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarkers and Lifestyle (AIBL). For the classification tasks on ADNI, the proposed method achieves 92.1% accuracy, 96.2% sensitivity, and 91.3% specificity on the AD vs. CN (Cognitively Normal) task. Compared with state-of-the-art methods, the proposed method could achieve comparable results. CONCLUSION The proposed model can extract long-range left-right brain similarity as complementary information and improve the model's diagnostic performance. A large number of experiments also support the model's validity. At the same time, this work provides a valuable reference for other neurological diseases, particularly those that exhibit left-right brain asymmetry during development.
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Affiliation(s)
- Chuyuan Wang
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Ying Wei
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Information Technology R&D Innovation Center of Peking University, Shaoxing, China; Changsha Hisense Intelligent System Research Institute Co., Ltd., China.
| | - Jiaguang Li
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Xiang Li
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Yue Liu
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Qian Hu
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Yuefeng Wang
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
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11
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Zhou H, Wang D, Cao B, Zhang X. Association of reduced cortical thickness and psychopathological symptoms in patients with first-episode drug-naïve schizophrenia. Int J Psychiatry Clin Pract 2022; 27:42-50. [PMID: 36193901 DOI: 10.1080/13651501.2022.2129067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/10/2022]
Abstract
OBJECTIVE There is growing evidence that reduced cortical thickness has been considered to be a central abnormality in schizophrenia. Brain imaging studies have demonstrated that the cerebral cortex becomes thinner in patients with first-episode schizophrenia. This study aimed to examine whether cortical thickness is altered in drug-naïve schizophrenia in a Chinese Han population and the relationship between cortical thickness and clinical symptoms. METHODS We compared cortical thickness in 41 schizophrenia patients and 30 healthy controls. Psychopathology of patients with schizophrenia was assessed using the Positive and Negative Syndrome Scale (PANSS). RESULTS The cortical thickness of left banks of superior temporal sulcus, left lateral occipital gyrus, left rostral middle frontal gyrus, right inferior parietal lobule and right lateral occipital gyrus in schizophrenia patients was generally thinner compared with healthy controls. Correlation analysis revealed a negative correlation between cortical thickness of the left banks of superior temporal sulcus and general psychopathology of PANSS. CONCLUSIONS Our results suggest that cortical thickness abnormalities are already present early in the onset of schizophrenia and are associated with psychopathological symptoms, suggesting that it plays an important role in the pathogenesis and symptomatology of schizophrenia.Key points(1) The first-episode drug-naïve schizophrenia had reduced cortical thickness than the controls.(2) Cortical thickness was associated with psychopathological symptoms in patients with schizophrenia.
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Affiliation(s)
- Huixia Zhou
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, PR China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Dongmei Wang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, PR China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Bo Cao
- Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada.,Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xiangyang Zhang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, PR China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
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12
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NAS-optimized topology-preserving transfer learning for differentiating cortical folding patterns. Med Image Anal 2021; 77:102316. [PMID: 34979433 DOI: 10.1016/j.media.2021.102316] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 11/21/2021] [Accepted: 11/23/2021] [Indexed: 11/21/2022]
Abstract
Increasing evidence suggests that cortical folding patterns of human cerebral cortex manifest overt structural and functional differences. However, for interpretability, few studies leverage advanced techniques (e.g., deep learning) to investigate the difference among cortical folds, resulting in more differences yet to be extensively explored. To this end, we proposed an effective topology-preserving transfer learning framework to differentiate cortical fMRI time series extracted from cortical folds. Our framework consists of three main parts: (1) Neural architecture search (NAS), which is used to devise a well-performing network structure based on an initialized hand-designed super-graph in an image dataset; (2) Topology-preserving transfer, which takes the model searched by NAS as the source network, keeping the topological connectivity in the network unchanged, while transforming all 2D operations including convolution and pooling into 1D, therefore resulting in a topology-preserving network, named TPNAS-Net; (3) Classification and correlation analysis, which involves using the TPNAS-Net to classify 1D cortical fMRI time series for each individual brain, and performing a group difference analysis between autism spectrum disorder (ASD) and healthy control (HC) and correlation analysis with clinical information (i.e., age). Extensive experiments on two ASD datasets obtain consistent results, demonstrating that the TPNAS-Net not only discriminates cortical folding patterns at high classification accuracy, but also captures subtle differences between ASD and HC (p-value = 0.042). In addition, we discover that there is a positive correlation between the classification accuracy and age in ASD (r = 0.39, p-value = 0.04). These findings together suggest that structural and functional differences in cortical folding patterns between ASD and HC may provide a potentially useful biomarker for the diagnosis of ASD.
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13
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Zhang J, Dong Q, Shi J, Li Q, Stonnington CM, Gutman BA, Chen K, Reiman EM, Caselli RJ, Thompson PM, Ye J, Wang Y. Predicting future cognitive decline with hyperbolic stochastic coding. Med Image Anal 2021; 70:102009. [PMID: 33711742 PMCID: PMC8049149 DOI: 10.1016/j.media.2021.102009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2019] [Revised: 08/10/2020] [Accepted: 02/16/2021] [Indexed: 01/18/2023]
Abstract
Hyperbolic geometry has been successfully applied in modeling brain cortical and subcortical surfaces with general topological structures. However, such approaches, similar to other surface-based brain morphology analysis methods, usually generate high dimensional features. It limits their statistical power in cognitive decline prediction research, especially in datasets with limited subject numbers. To address the above limitation, we propose a novel framework termed as hyperbolic stochastic coding (HSC). We first compute diffeomorphic maps between general topological surfaces by mapping them to a canonical hyperbolic parameter space with consistent boundary conditions and extracts critical shape features. Secondly, in the hyperbolic parameter space, we introduce a farthest point sampling with breadth-first search method to obtain ring-shaped patches. Thirdly, stochastic coordinate coding and max-pooling algorithms are adopted for feature dimension reduction. We further validate the proposed system by comparing its classification accuracy with some other methods on two brain imaging datasets for Alzheimer's disease (AD) progression studies. Our preliminary experimental results show that our algorithm achieves superior results on various classification tasks. Our work may enrich surface-based brain imaging research tools and potentially result in a diagnostic and prognostic indicator to be useful in individualized treatment strategies.
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Affiliation(s)
- Jie Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287 USA
| | - Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287 USA
| | - Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287 USA
| | - Qingyang Li
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287 USA
| | | | - Boris A Gutman
- Armour College of Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | | | | | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Jieping Ye
- Department of Computational Medicine and Bioinformatics & Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287 USA.
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14
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Mori S, Osawa A, Maeshima S, Sakurai T, Ozaki K, Kondo I, Saitoh E. Possibility of Using Quantitative Assessment with the Cube Copying Test for Evaluation of Visuo-spatial Function in Patients with Alzheimer's Disease. Prog Rehabil Med 2021; 6:20210021. [PMID: 33937549 PMCID: PMC8080165 DOI: 10.2490/prm.20210021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Accepted: 04/15/2021] [Indexed: 11/10/2022] Open
Abstract
Objectives: The aim of this study was to investigate the clinical usefulness of the Cube Copying
Test (CCT) for quantitative assessment of visuo-spatial function in patients with
Alzheimer’s disease (AD). Methods: The CCT, Raven’s Colored Progressive Matrices (RCPM), and other neuropsychological
tests were administered to 152 AD outpatients. For the quantitative assessment of CCT,
we scored the points of connection (POC) and the number of plane-drawing errors (PDE)
and categorized the pattern classification (PAC). We also measured Functional Assessment
Staging (FAST) to assess the severity of AD. The relationships among CCT, RCPM, and FAST
were then analyzed. Results: The mean POC and PDE scores were 2.7 and 3.6, respectively, and the median PAC score
was 6.0. PDE and PAC showed a linear relationship, but POC and PDE, and POC and PAC did
not. Each component of CCT showed a significant correlation with RCPM scores. PDE and
PAC had closer correlations with RCPM scores than POC did. The PDE and PAC results were
significantly different among most of the FAST stages. Conclusions: Quantitative assessment using CCT may be effective for the quick determination of the
visuo-spatial function in AD patients.
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Affiliation(s)
- Shino Mori
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan
| | - Aiko Osawa
- Department of Rehabilitation Medicine, National Center for Geriatrics and Gerontology, Obu, Japan.,Center for Comprehensive Care and Research on Memory Disorders, National Center for Geriatrics and Gerontology, Obu, Japan
| | | | - Takashi Sakurai
- Center for Comprehensive Care and Research on Memory Disorders, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Kenichi Ozaki
- Department of Rehabilitation Medicine, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Izumi Kondo
- Department of Rehabilitation Medicine, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Eiichi Saitoh
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan
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15
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Wu Z, Peng Y, Hong M, Zhang Y. Gray Matter Deterioration Pattern During Alzheimer's Disease Progression: A Regions-of-Interest Based Surface Morphometry Study. Front Aging Neurosci 2021; 13:593898. [PMID: 33613265 PMCID: PMC7886803 DOI: 10.3389/fnagi.2021.593898] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 01/13/2021] [Indexed: 11/17/2022] Open
Abstract
Accurate detection of the regions of Alzheimer's disease (AD) lesions is critical for early intervention to effectively slow down the progression of the disease. Although gray matter volumetric abnormalities are commonly detected in patients with mild cognition impairment (MCI) and patients with AD, the gray matter surface-based deterioration pattern associated with the progression of the disease from MCI to AD stages is largely unknown. To identify group differences in gray matter surface morphometry, including cortical thickness, the gyrification index (GI), and the sulcus depth, 80 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were split into healthy controls (HCs; N = 20), early MCIs (EMCI; N = 20), late MCIs (LMCI; N = 20), and ADs (N = 20). Regions-of-interest (ROI)-based surface morphometry was subsequently studied and compared across the four stage groups to characterize the gray matter deterioration during AD progression. Co-alteration patterns (Spearman's correlation coefficient) across the whole brain were also examined. Results showed that patients with MCI and AD exhibited a significant reduction in cortical thickness (p < 0.001) mainly in the cingulate region (four subregions) and in the temporal (thirteen subregions), parietal (five subregions), and frontal (six subregions) lobes compared to HCs. The sulcus depth of the eight temporal, four frontal, four occipital, and eight parietal subregions were also significantly affected (p < 0.001) by the progression of AD. The GI was shown to be insensitive to AD progression (only three subregions were detected with a significant difference, p < 0.001). Moreover, Spearman's correlation analysis confirmed that the co-alteration pattern of the cortical thickness and sulcus depth indices is predominant during AD progression. The findings highlight the relevance between gray matter surface morphometry and the stages of AD, laying the foundation for in vivo tracking of AD progression. The co-alteration pattern of surface-based morphometry would improve the researchers' knowledge of the underlying pathologic mechanisms in AD.
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Affiliation(s)
- Zhanxiong Wu
- School of Electronic Information, Hangzhou Dianzi University, Hangzhou, China.,Department of Biomedical Engineering, University of Houston, Houston, TX, United States
| | - Yun Peng
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
| | - Ming Hong
- School of Electronic Information, Hangzhou Dianzi University, Hangzhou, China
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
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16
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Profant O, Škoch A, Tintěra J, Svobodová V, Kuchárová D, Svobodová Burianová J, Syka J. The Influence of Aging, Hearing, and Tinnitus on the Morphology of Cortical Gray Matter, Amygdala, and Hippocampus. Front Aging Neurosci 2020; 12:553461. [PMID: 33343328 PMCID: PMC7746808 DOI: 10.3389/fnagi.2020.553461] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Accepted: 11/12/2020] [Indexed: 12/11/2022] Open
Abstract
Age related hearing loss (presbycusis) is a natural process represented by elevated auditory thresholds and decreased speech intelligibility, especially in noisy conditions. Tinnitus is a phantom sound that also potentially leads to cortical changes, with its highest occurrence coinciding with the clinical onset of presbycusis. The aim of our project was to identify age, hearing loss and tinnitus related structural changes, within the auditory system and associated structures. Groups of subjects with presbycusis and tinnitus (22 subjects), with only presbycusis (24 subjects), young tinnitus patients with normal hearing (10 subjects) and young controls (17 subjects), underwent an audiological examination to characterize hearing loss and tinnitus. In addition, MRI (3T MR system, analysis in Freesurfer software) scans were used to identify changes in the cortical and subcortical structures. The following areas of the brain were analyzed: Heschl gyrus (HG), planum temporale (PT), primary visual cortex (V1), gyrus parahippocampus (PH), anterior insula (Ins), amygdala (Amg), and hippocampus (HP). A statistical analysis was performed in R framework using linear mixed-effects models with explanatory variables: age, tinnitus, laterality and hearing. In all of the cortical structures, the gray matter thickness decreased significantly with aging without having an effect on laterality (differences between the left and right hemispheres). The decrease in the gray matter thickness was faster in the HG, PT and Ins in comparison with the PH and V1. Aging did not influence the surface of the cortical areas, however there were differences between the surface size of the reported regions in the left and right hemispheres. Hearing loss caused only a borderline decrease of the cortical surface in the HG. Tinnitus was accompanied by a borderline decrease of the Ins surface and led to an increase in the volume of Amy and HP. In summary, aging is accompanied by a decrease in the cortical gray matter thickness; hearing loss only has a limited effect on the structure of the investigated cortical areas and tinnitus causes structural changes which are predominantly within the limbic system and insula, with the structure of the auditory system only being minimally affected.
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Affiliation(s)
- Oliver Profant
- Department of Auditory Neuroscience, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czechia.,Department of Otorhinolaryngology, 3rd Faculty of Medicine, Faculty Hospital Kralovske Vinohrady, Charles University, Prague, Czechia
| | - Antonín Škoch
- MR Unit, Institute of Clinical and Experimental Medicine, Prague, Czechia
| | - Jaroslav Tintěra
- MR Unit, Institute of Clinical and Experimental Medicine, Prague, Czechia
| | - Veronika Svobodová
- Department of Auditory Neuroscience, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czechia.,Department of Otorhinolaryngology and Head and Neck Surgery, 1st Faculty of Medicine, University Hospital Motol, Charles University, Prague, Czechia
| | - Diana Kuchárová
- Department of Auditory Neuroscience, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czechia.,Department of Otorhinolaryngology and Head and Neck Surgery, 1st Faculty of Medicine, University Hospital Motol, Charles University, Prague, Czechia
| | - Jana Svobodová Burianová
- Department of Auditory Neuroscience, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czechia
| | - Josef Syka
- Department of Auditory Neuroscience, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czechia
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17
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Zhang T, Li X, Jiang X, Ge F, Zhang S, Zhao L, Liu H, Huang Y, Wang X, Yang J, Guo L, Hu X, Liu T. Cortical 3-hinges could serve as hubs in cortico-cortical connective network. Brain Imaging Behav 2020; 14:2512-2529. [PMID: 31950404 PMCID: PMC7647986 DOI: 10.1007/s11682-019-00204-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Mapping the relation between cortical convolution and structural/functional brain architectures could provide deep insights into the mechanisms of brain development, evolution and diseases. In our previous studies, we found a unique gyral folding pattern, termed a 3-hinge, which was defined as the conjunction of three gyral crests. The uniqueness of the 3-hinge was evidenced by its thicker cortex and stronger fiber connections than other gyral regions. However, the role that 3-hinges play in cortico-cortical connective architecture remains unclear. To this end, we conducted MRI studies by constructing structural cortico-cortical connective networks based on a fine-granular cortical parcellation, the parcels of which were automatically labeled as 3-hinge, 2-hinge (ordinary gyrus) or sulcus. On human brains, 3-hinges possess significantly higher degrees, strengths and betweennesses than 2-hinges, suggesting that 3-hinges could serve more like hubs in the cortico-cortical connective network. This hypothesis gains supports from human functional network analyses, in which 3-hinges are involved in more global functional networks than ordinary gyri. In addition, 3-hinges could serve as 'connector' hubs rather than 'provincial' hubs and they account for a dominant proportion of nodes in the high-level 'backbone' of the network. These structural results are reproduced on chimpanzee and macaque brains, while the roles of 3-hinges as hubs become more pronounced in higher order primates. Our new findings could provide a new window to the relation between cortical convolution, anatomical connection and brain function.
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Affiliation(s)
- Tuo Zhang
- School of Automation, Northwestern Polytechnical University, #127, West Youyi Road, Xi'an, 710072, Shaanxi, China.
| | - Xiao Li
- School of Automation, Northwestern Polytechnical University, #127, West Youyi Road, Xi'an, 710072, Shaanxi, China
| | - Xi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Fangfei Ge
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
| | - Shu Zhang
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
| | - Lin Zhao
- School of Automation, Northwestern Polytechnical University, #127, West Youyi Road, Xi'an, 710072, Shaanxi, China
| | - Huan Liu
- School of Automation, Northwestern Polytechnical University, #127, West Youyi Road, Xi'an, 710072, Shaanxi, China
| | - Ying Huang
- School of Automation, Northwestern Polytechnical University, #127, West Youyi Road, Xi'an, 710072, Shaanxi, China
| | - Xianqiao Wang
- College of Engineering, The University of Georgia, Athens, GA, USA
| | - Jian Yang
- Radiology Department of the First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, #127, West Youyi Road, Xi'an, 710072, Shaanxi, China
| | - Xiaoping Hu
- Department of Bioengineering, University of California Riverside, Riverside, CA, USA
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
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18
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Popuri K, Ma D, Wang L, Beg MF. Using machine learning to quantify structural MRI neurodegeneration patterns of Alzheimer's disease into dementia score: Independent validation on 8,834 images from ADNI, AIBL, OASIS, and MIRIAD databases. Hum Brain Mapp 2020; 41:4127-4147. [PMID: 32614505 PMCID: PMC7469784 DOI: 10.1002/hbm.25115] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Revised: 04/15/2020] [Accepted: 06/08/2020] [Indexed: 12/29/2022] Open
Abstract
Biomarkers for dementia of Alzheimer's type (DAT) are sought to facilitate accurate prediction of the disease onset, ideally predating the onset of cognitive deterioration. T1-weighted magnetic resonance imaging (MRI) is a commonly used neuroimaging modality for measuring brain structure in vivo, potentially providing information enabling the design of biomarkers for DAT. We propose a novel biomarker using structural MRI volume-based features to compute a similarity score for the individual's structural patterns relative to those observed in the DAT group. We employed ensemble-learning framework that combines structural features in most discriminative ROIs to create an aggregate measure of neurodegeneration in the brain. This classifier is trained on 423 stable normal control (NC) and 330 DAT subjects, where clinical diagnosis is likely to have the highest certainty. Independent validation on 8,834 unseen images from ADNI, AIBL, OASIS, and MIRIAD Alzheimer's disease (AD) databases showed promising potential to predict the development of DAT depending on the time-to-conversion (TTC). Classification performance on stable versus progressive mild cognitive impairment (MCI) groups achieved an AUC of 0.81 for TTC of 6 months and 0.73 for TTC of up to 7 years, achieving state-of-the-art results. The output score, indicating similarity to patterns seen in DAT, provides an intuitive measure of how closely the individual's brain features resemble the DAT group. This score can be used for assessing the presence of AD structural atrophy patterns in normal aging and MCI stages, as well as monitoring the progression of the individual's brain along with the disease course.
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Affiliation(s)
- Karteek Popuri
- School of Engineering ScienceSimon Fraser UniversityBarnabyBritish ColumbiaCanada
| | - Da Ma
- School of Engineering ScienceSimon Fraser UniversityBarnabyBritish ColumbiaCanada
| | - Lei Wang
- Feinberg School of MedicineNorthwestern UniversityEvanstonIllinoisUSA
| | - Mirza Faisal Beg
- School of Engineering ScienceSimon Fraser UniversityBarnabyBritish ColumbiaCanada
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19
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Dong Q, Zhang W, Stonnington CM, Wu J, Gutman BA, Chen K, Su Y, Baxter LC, Thompson PM, Reiman EM, Caselli RJ, Wang Y. Applying surface-based morphometry to study ventricular abnormalities of cognitively unimpaired subjects prior to clinically significant memory decline. NEUROIMAGE-CLINICAL 2020; 27:102338. [PMID: 32683323 PMCID: PMC7371915 DOI: 10.1016/j.nicl.2020.102338] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 06/15/2020] [Accepted: 07/02/2020] [Indexed: 12/31/2022]
Abstract
A completely automated surface-based ventricular morphometry system. Generate a whole connected 3D ventricular shape model. Test-retest the system in two independent CU subject cohorts. Subregional ventricular abnormalities prior to clinically memory decline.
Ventricular volume (VV) is a widely used structural magnetic resonance imaging (MRI) biomarker in Alzheimer’s disease (AD) research. Abnormal enlargements of VV can be detected before clinically significant memory decline. However, VV does not pinpoint the details of subregional ventricular expansions. Here we introduce a ventricular morphometry analysis system (VMAS) that generates a whole connected 3D ventricular shape model and encodes a great deal of ventricular surface deformation information that is inaccessible by VV. VMAS contains an automated segmentation approach and surface-based multivariate morphometry statistics. We applied VMAS to two independent datasets of cognitively unimpaired (CU) groups. To our knowledge, it is the first work to detect ventricular abnormalities that distinguish normal aging subjects from those who imminently progress to clinically significant memory decline. Significant bilateral ventricular morphometric differences were first shown in 38 members of the Arizona APOE cohort, which included 18 CU participants subsequently progressing to the clinically significant memory decline within 2 years after baseline visits (progressors), and 20 matched CU participants with at least 4 years of post-baseline cognitive stability (non-progressors). VMAS also detected significant differences in bilateral ventricular morphometry in 44 Alzheimer’s Disease Neuroimaging Initiative (ADNI) subjects (18 CU progressors vs. 26 CU non-progressors) with the same inclusion criterion. Experimental results demonstrated that the ventricular anterior horn regions were affected bilaterally in CU progressors, and more so on the left. VMAS may track disease progression at subregional levels and measure the effects of pharmacological intervention at a preclinical stage.
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Affiliation(s)
- Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Wen Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | | | - Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Boris A Gutman
- Armour College of Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | - Yi Su
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | - Leslie C Baxter
- Human Brain Imaging Laboratory, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | | | | | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
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20
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Wang X, Li W, Marcus J, Pearson M, Song L, Smith K, Terracina G, Lee J, Hong KLK, Lu SX, Hyde L, Chen SC, Kinsley D, Melchor JP, Rubins DJ, Meng X, Hostetler E, Sur C, Zhang L, Schachter JB, Hess JF, Selnick HG, Vocadlo DJ, McEachern EJ, Uslaner JM, Duffy JL, Smith SM. MK-8719, a Novel and Selective O-GlcNAcase Inhibitor That Reduces the Formation of Pathological Tau and Ameliorates Neurodegeneration in a Mouse Model of Tauopathy. J Pharmacol Exp Ther 2020; 374:252-263. [PMID: 32493725 DOI: 10.1124/jpet.120.266122] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 06/01/2020] [Indexed: 01/01/2023] Open
Abstract
Deposition of hyperphosphorylated and aggregated tau protein in the central nervous system is characteristic of Alzheimer disease and other tauopathies. Tau is subject to O-linked N-acetylglucosamine (O-GlcNAc) modification, and O-GlcNAcylation of tau has been shown to influence tau phosphorylation and aggregation. Inhibition of O-GlcNAcase (OGA), the enzyme that removes O-GlcNAc moieties, is a novel strategy to attenuate the formation of pathologic tau. Here we described the in vitro and in vivo pharmacological properties of a novel and selective OGA inhibitor, MK-8719. In vitro, this compound is a potent inhibitor of the human OGA enzyme with comparable activity against the corresponding enzymes from mouse, rat, and dog. In vivo, oral administration of MK-8719 elevates brain and peripheral blood mononuclear cell O-GlcNAc levels in a dose-dependent manner. In addition, positron emission tomography imaging studies demonstrate robust target engagement of MK-8719 in the brains of rats and rTg4510 mice. In the rTg4510 mouse model of human tauopathy, MK-8719 significantly increases brain O-GlcNAc levels and reduces pathologic tau. The reduction in tau pathology in rTg4510 mice is accompanied by attenuation of brain atrophy, including reduction of forebrain volume loss as revealed by volumetric magnetic resonance imaging analysis. These findings suggest that OGA inhibition may reduce tau pathology in tauopathies. However, since hundreds of O-GlcNAcylated proteins may be influenced by OGA inhibition, it will be critical to understand the physiologic and toxicological consequences of chronic O-GlcNAc elevation in vivo. SIGNIFICANCE STATEMENT: MK-8719 is a novel, selective, and potent O-linked N-acetylglucosamine (O-GlcNAc)-ase (OGA) inhibitor that inhibits OGA enzyme activity across multiple species with comparable in vitro potency. In vivo, MK-8719 elevates brain O-GlcNAc levels, reduces pathological tau, and ameliorates brain atrophy in the rTg4510 mouse model of tauopathy. These findings indicate that OGA inhibition may be a promising therapeutic strategy for the treatment of Alzheimer disease and other tauopathies.
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Affiliation(s)
- Xiaohai Wang
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Wenping Li
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Jacob Marcus
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Michelle Pearson
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Lixin Song
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Karen Smith
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Giuseppe Terracina
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Julie Lee
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Kwok-Lam Karen Hong
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Sherry X Lu
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Lynn Hyde
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Shu-Cheng Chen
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - David Kinsley
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Jerry P Melchor
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Daniel J Rubins
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Xiangjun Meng
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Eric Hostetler
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Cyrille Sur
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Lili Zhang
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Joel B Schachter
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - J Fred Hess
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Harold G Selnick
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - David J Vocadlo
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Ernest J McEachern
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Jason M Uslaner
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Joseph L Duffy
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Sean M Smith
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
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Heidekum AE, Vogel SE, Grabner RH. Associations Between Individual Differences in Mathematical Competencies and Surface Anatomy of the Adult Brain. Front Hum Neurosci 2020; 14:116. [PMID: 32292335 PMCID: PMC7118203 DOI: 10.3389/fnhum.2020.00116] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 03/13/2020] [Indexed: 01/18/2023] Open
Abstract
Previously conducted structural magnetic resonance imaging (MRI) studies on the neuroanatomical correlates of mathematical abilities and competencies have several methodological limitations. Besides small sample sizes, the majority of these studies have employed voxel-based morphometry (VBM)-a method that, although it is easy to implement, has some major drawbacks. Taking this into account, the current study is the first to investigate in a large sample of typically developed adults the associations between mathematical abilities and variations in brain surface structure by using surface-based morphometry (SBM). SBM is a method that also allows the investigation of brain morphometry by avoiding the pitfalls of VBM. Eighty-nine young adults were tested with a large battery of psychometric tests to measure mathematical competencies in four different areas: (1) simple arithmetic; (2) complex arithmetic; (3) higher-order mathematics; and (4) numerical intelligence. Also, we asked participants for their mathematics grades for their final school exams. Inside the MRI scanner, we collected high-resolution T1-weighted anatomical images from each subject. SBM analyses were performed with the computational anatomy toolbox (CAT12) and indices for cortical thickness, for cortical surface complexity, for gyrification, and sulcal depth were calculated. Further analyses revealed associations between: (1) the cortical surface complexity of the right superior temporal gyrus and numerical intelligence; (2) the depth of the right central sulcus and adults' ability to solve complex arithmetic problems; and (3) the depth of the left parieto-occipital sulcus and adults' higher-order mathematics competence. Interestingly, no relationships with previously reported brain regions were observed, thus, suggesting the importance of similar research to confirm the role of the brain regions found in this study.
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Affiliation(s)
- Alexander E. Heidekum
- Educational Neuroscience, Institute of Psychology, University of Graz, Graz, Austria
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22
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Reduced gray matter volume and cortical thickness associated with traffic-related air pollution in a longitudinally studied pediatric cohort. PLoS One 2020; 15:e0228092. [PMID: 31978108 PMCID: PMC6980590 DOI: 10.1371/journal.pone.0228092] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Accepted: 01/07/2020] [Indexed: 12/20/2022] Open
Abstract
Early life exposure to air pollution poses a significant risk to brain development from direct exposure to toxicants or via indirect mechanisms involving the circulatory, pulmonary or gastrointestinal systems. In children, exposure to traffic related air pollution has been associated with adverse effects on cognitive, behavioral and psychomotor development. We aimed to determine whether childhood exposure to traffic related air pollution is associated with regional differences in brain volume and cortical thickness among children enrolled in a longitudinal cohort study of traffic related air pollution and child health. We used magnetic resonance imaging to obtain anatomical brain images from a nested subset of 12 year old participants characterized with either high or low levels of traffic related air pollution exposure during their first year of life. We employed voxel-based morphometry to examine group differences in regional brain volume, and with separate analyses, changes in cortical thickness. Smaller regional gray matter volumes were determined in the left pre- and post-central gyri, the cerebellum, and inferior parietal lobe of participants in the high traffic related air pollution exposure group relative to participants with low exposure. Reduced cortical thickness was observed in participants with high exposure relative to those with low exposure, primarily in sensorimotor regions of the brain including the pre- and post-central gyri and the paracentral lobule, but also within the frontal and limbic regions. These results suggest that significant childhood exposure to traffic related air pollution is associated with structural alterations in brain.
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23
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Dong Q, Zhang J, Li Q, Wang J, Leporé N, Thompson PM, Caselli RJ, Ye J, Wang Y. Integrating Convolutional Neural Networks and Multi-Task Dictionary Learning for Cognitive Decline Prediction with Longitudinal Images. J Alzheimers Dis 2020; 75:971-992. [PMID: 32390615 PMCID: PMC7427104 DOI: 10.3233/jad-190973] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Disease progression prediction based on neuroimaging biomarkers is vital in Alzheimer's disease (AD) research. Convolutional neural networks (CNN) have been proved to be powerful for various computer vision research by refining reliable and high-level feature maps from image patches. OBJECTIVE A key challenge in applying CNN to neuroimaging research is the limited labeled samples with high dimensional features. Another challenge is how to improve the prediction accuracy by joint analysis of multiple data sources (i.e., multiple time points or multiple biomarkers). To address these two challenges, we propose a novel multi-task learning framework based on CNN. METHODS First, we pre-trained CNN on the ImageNet dataset and transferred the knowledge from the pre-trained model to neuroimaging representation. We used this deep model as feature extractor to generate high-level feature maps of different tasks. Then a novel unsupervised learning method, termed Multi-task Stochastic Coordinate Coding (MSCC), was proposed for learning sparse features of multi-task feature maps by using shared and individual dictionaries. Finally, Lasso regression was performed on these multi-task sparse features to predict AD progression measured by the Mini-Mental State Examination (MMSE) and the Alzheimer's Disease Assessment Scale cognitive subscale (ADAS-Cog). RESULTS We applied this novel CNN-MSCC system on the Alzheimer's Disease Neuroimaging Initiative dataset to predict future MMSE/ADAS-Cog scales. We found our method achieved superior performances compared with seven other methods. CONCLUSION Our work may add new insights into data augmentation and multi-task deep model research and facilitate the adoption of deep models in neuroimaging research.
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Affiliation(s)
- Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Jie Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Qingyang Li
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Junwen Wang
- Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ, 85259, USA
| | - Natasha Leporé
- Department of Radiology, Children’s Hospital Los Angeles, Los Angeles, CA, USA
| | - Paul M. Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | | | - Jieping Ye
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
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24
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Boutzoukas EM, Crutcher J, Somoza E, Sepeta LN, You X, Gaillard WD, Wallace GL, Berl MM. Cortical thickness in childhood left focal epilepsy: Thinning beyond the seizure focus. Epilepsy Behav 2020; 102:106825. [PMID: 31816479 PMCID: PMC6962541 DOI: 10.1016/j.yebeh.2019.106825] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 11/20/2019] [Accepted: 11/24/2019] [Indexed: 11/18/2022]
Abstract
OBJECTIVE Structural brain differences are found in adults and children with epilepsy, yet pediatric samples have been heterogeneous regarding seizure type, magnetic resonance imaging (MRI) findings, and hemisphere of seizure focus. This study examines whether cortical thickness and surface area differ between children with left-hemisphere focal epilepsy (LHE) and age-matched typically developing (TD) peers. We examined whether age differentially moderated cortical thickness between groups and if cortical thickness was associated with duration of epilepsy, seizure frequency, or neuropsychological functioning. METHODS Thirty-five children with LHE and 35 TD children completed neuropsychological testing and 3T MR imaging. Neuropsychological measures included general intelligence and executive functioning. All MRIs were normal. Surface-based morphometric processing and analyses were conducted using FreeSurfer 6.0. Regression analyses compared age by cortical thickness differences between groups. Correlational analyses examined associations between cortical thickness in these areas with neuropsychological functioning or epilepsy characteristics. RESULTS Left-hemisphere focal epilepsy displayed decreased cortical thickness bilaterally compared to TD controls across 6 brain regions but no differences in surface area. Moderation analyses revealed quadratic relationships between age and cortical thickness for left frontoparietal-cingulate and right superior frontal regions. Higher performance intelligence quotient (IQ) (PIQ) and verbal IQ (VIQ) and fewer parent reported executive function problems were associated with greater cortical thickness in TD children. SIGNIFICANCE Children with LHE displayed thinner cortex extending beyond the hemisphere of seizure focus. The nonlinear pattern of cortical thickness across age occurring in TD children is not evident in the same manner in children with LHE. These differences in cortical thickness patterns were greatest in children 8-12 years old. Greater cortical thickness was associated with higher IQ and fewer executive control problems in daily activities in TD children. Thus, differences in cortical thickness in the absence of differences in surface area, suggest cortical thickness may be a sensitive proxy of subtle neuroanatomical changes that are related to neuropsychological functioning.
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Affiliation(s)
- Emanuel M Boutzoukas
- Comprehensive Pediatric Epilepsy Program, Children's National Medical Center, Washington, DC, USA
| | - Jason Crutcher
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
| | - Eduardo Somoza
- Comprehensive Pediatric Epilepsy Program, Children's National Medical Center, Washington, DC, USA
| | - Leigh N Sepeta
- Comprehensive Pediatric Epilepsy Program, Children's National Medical Center, Washington, DC, USA; Department of Psychiatry and Behavioral Sciences, The George Washington University, Washington, DC, USA
| | - Xiaozhen You
- Comprehensive Pediatric Epilepsy Program, Children's National Medical Center, Washington, DC, USA; Department of Pediatrics and Neurology, The George Washington University, Washington, DC, USA
| | - William D Gaillard
- Comprehensive Pediatric Epilepsy Program, Children's National Medical Center, Washington, DC, USA; Department of Pediatrics and Neurology, The George Washington University, Washington, DC, USA
| | - Gregory L Wallace
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA; Department of Speech, Language, and Hearing Sciences, The George Washington University, Washington, DC, USA
| | - Madison M Berl
- Comprehensive Pediatric Epilepsy Program, Children's National Medical Center, Washington, DC, USA; Department of Psychiatry and Behavioral Sciences, The George Washington University, Washington, DC, USA.
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25
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Lyu I, Kang H, Woodward ND, Styner MA, Landman BA. Hierarchical spherical deformation for cortical surface registration. Med Image Anal 2019; 57:72-88. [PMID: 31280090 PMCID: PMC6733638 DOI: 10.1016/j.media.2019.06.013] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 04/30/2019] [Accepted: 06/24/2019] [Indexed: 11/30/2022]
Abstract
We present hierarchical spherical deformation for a group-wise shape correspondence to address template selection bias and to minimize registration distortion. In this work, we aim at a continuous and smooth deformation field to guide accurate cortical surface registration. In conventional spherical registration methods, a global rigid alignment and local deformation are independently performed. Motivated by the composition of precession and intrinsic rotation, we simultaneously optimize global rigid rotation and non-rigid local deformation by utilizing spherical harmonics interpolation of local composite rotations in a single framework. To this end, we indirectly encode local displacements by such local composite rotations as functions of spherical locations. Furthermore, we introduce an additional regularization term to the spherical deformation, which maximizes its rigidity while reducing registration distortion. To improve surface registration performance, we employ the second order approximation of the energy function that enables fast convergence of the optimization. In the experiments, we validate our method on healthy normal subjects with manual cortical surface parcellation in registration accuracy and distortion. We show an improved shape correspondence with high accuracy in cortical surface parcellation and significantly low registration distortion in surface area and edge length. In addition to validation, we discuss parameter tuning, optimization, and implementation design with potential acceleration.
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Affiliation(s)
- Ilwoo Lyu
- Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA.
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Neil D Woodward
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Martin A Styner
- Department of Computer Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Psychiatry, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Bennett A Landman
- Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA; Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
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26
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Parvathaneni P, Nath V, McHugo M, Huo Y, Resnick SM, Woodward ND, Landman BA, Lyu I. Improving human cortical sulcal curve labeling in large scale cross-sectional MRI using deep neural networks. J Neurosci Methods 2019; 324:108311. [PMID: 31201823 PMCID: PMC6663093 DOI: 10.1016/j.jneumeth.2019.108311] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 05/24/2019] [Accepted: 06/11/2019] [Indexed: 02/04/2023]
Abstract
BACKGROUND Human cortical primary sulci are relatively stable landmarks and commonly observed across the population. Despite their stability, the primary sulci exhibit phenotypic variability. NEW METHOD We propose a fully automated pipeline that integrates both sulcal curve extraction and labeling. In this study, we use a large normal control population (n = 1424) to train neural networks for accurately labeling the primary sulci. Briefly, we use sulcal curve distance map, surface parcellation, mean curvature and spectral features to delineate their sulcal labels. We evaluate the proposed method with 8 primary sulcal curves in the left and right hemispheres compared to an established multi-atlas curve labeling method. RESULTS Sulcal labels by the proposed method reasonably well agree with manual labeling. The proposed method outperforms the existing multi-atlas curve labeling method. COMPARISON WITH EXISTING METHOD Significantly improved sulcal labeling results are achieved with over 12.5 and 20.6 percent improvement on labeling accuracy in the left and right hemispheres, respectively compared to that of a multi-atlas curve labeling method in eight curves (p≪0.001, two-sample t-test). CONCLUSION The proposed method offers a computationally efficient and robust labeling of major sulci.
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Affiliation(s)
| | - Vishwesh Nath
- Computer Science, Vanderbilt Universitay, Nashville, TN, USA
| | - Maureen McHugo
- Department of Psychiatry and Behavioral Science, Vanderbilt Universitay, Nashville, TN, USA
| | - Yuankai Huo
- Electrical Engineering, Vanderbilt Universitay, Nashville, TN, USA
| | | | - Neil D Woodward
- Department of Psychiatry and Behavioral Science, Vanderbilt Universitay, Nashville, TN, USA
| | - Bennett A Landman
- Electrical Engineering, Vanderbilt Universitay, Nashville, TN, USA; Computer Science, Vanderbilt Universitay, Nashville, TN, USA; Department of Psychiatry and Behavioral Science, Vanderbilt Universitay, Nashville, TN, USA
| | - Ilwoo Lyu
- Computer Science, Vanderbilt Universitay, Nashville, TN, USA.
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27
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Ge F, Li X, Razavi MJ, Chen H, Zhang T, Zhang S, Guo L, Hu X, Wang X, Liu T. Denser Growing Fiber Connections Induce 3-hinge Gyral Folding. Cereb Cortex 2019; 28:1064-1075. [PMID: 28968837 DOI: 10.1093/cercor/bhx227] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Accepted: 08/11/2017] [Indexed: 01/13/2023] Open
Abstract
Recent studies have shown that quantitative description of gyral shape patterns offers a novel window to examine the relationship between brain structure and function. Along this research line, this paper examines a unique and interesting type of cortical gyral region where 3 different gyral crests meet, termed 3-hinge gyral region. We extracted 3-hinge gyral regions in macaque/chimpanzee/human brains, quantified and compared the relevant DTI-derived fiber densities in 3-hinge and 2-hinge gyral regions. Our observations consistently showed that DTI-derived fiber densities in 3-hinge regions are much higher than those in 2-hinge regions. Therefore, we hypothesize that besides the cortical expansion, denser fiber connections can induce the formation of 3-hinge gyri. To examine the biomechanical basis of this hypothesis, we constructed a series of 3-dimensional finite element soft tissue models based on continuum growth theory to investigate fundamental biomechanical mechanisms of consistent 3-hinge gyri formation. Our computational simulation results consistently showed that during gyrification gyral regions with higher concentrations of growing axonal fibers tend to form 3-hinge gyri. Our integrative approach combining neuroimaging data analysis and computational modeling appears effective in probing a plausible theory of 3-hinge gyri formation and providing new insights into structural and functional cortical architectures and their relationship.
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Affiliation(s)
- Fangfei Ge
- School of Automation, Northwestern Polytechnical University, Xi'an, China.,Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
| | - Xiao Li
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Mir Jalil Razavi
- College of Engineering, The University of Georgia, Athens, GA, USA
| | - Hanbo Chen
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, China.,Brain Decoding Research Center, Northwestern Polytechnical University, Xi'an, China
| | - Shu Zhang
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Xiaoping Hu
- Department of Bioengineering, UC Riverside, Riverside, CA, USA
| | - Xianqiao Wang
- College of Engineering, The University of Georgia, Athens, GA, USA
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
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28
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Berchuck SI, Mwanza JC, Warren JL. Diagnosing Glaucoma Progression with Visual Field Data Using a Spatiotemporal Boundary Detection Method. J Am Stat Assoc 2019; 114:1063-1074. [PMID: 31662589 PMCID: PMC6818507 DOI: 10.1080/01621459.2018.1537911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2017] [Revised: 09/23/2018] [Accepted: 10/11/2018] [Indexed: 10/27/2022]
Abstract
Diagnosing glaucoma progression is critical for limiting irreversible vision loss. A common method for assessing glaucoma progression uses a longitudinal series of visual fields (VF) acquired at regular intervals. VF data are characterized by a complex spatiotemporal structure due to the data generating process and ocular anatomy. Thus, advanced statistical methods are needed to make clinical determinations regarding progression status. We introduce a spatiotemporal boundary detection model that allows the underlying anatomy of the optic disc to dictate the spatial structure of the VF data across time. We show that our new method provides novel insight into vision loss that improves diagnosis of glaucoma progression using data from the Vein Pulsation Study Trial in Glaucoma and the Lions Eye Institute trial registry. Simulations are presented, showing the proposed methodology is preferred over existing spatial methods for VF data. Supplementary materials for this article are available online and the method is implemented in the R package womblR.
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Affiliation(s)
- Samuel I Berchuck
- Department of Statistical Science and Forge, Duke University, NC 27708
| | - Jean-Claude Mwanza
- Department of Ophthalmology, University of North Carolina-Chapel Hill (DO, UNC-CH), NC 27517
| | - Joshua L Warren
- Department of Biostatistics, Yale University, New Haven, CT, 06520
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29
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Dong Q, Zhang W, Wu J, Li B, Schron EH, McMahon T, Shi J, Gutman BA, Chen K, Baxter LC, Thompson PM, Reiman EM, Caselli RJ, Wang Y. Applying surface-based hippocampal morphometry to study APOE-E4 allele dose effects in cognitively unimpaired subjects. NEUROIMAGE-CLINICAL 2019; 22:101744. [PMID: 30852398 PMCID: PMC6411498 DOI: 10.1016/j.nicl.2019.101744] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 02/02/2019] [Accepted: 03/02/2019] [Indexed: 11/30/2022]
Abstract
Apolipoprotein E (APOE) e4 is the major genetic risk factor for late-onset Alzheimer's disease (AD). The dose-dependent impact of this allele on hippocampal volumes has been documented, but its influence on general hippocampal morphology in cognitively unimpaired individuals is still elusive. Capitalizing on the study of a large number of cognitively unimpaired late middle aged and older adults with two, one and no APOE-e4 alleles, the current study aims to characterize the ability of our automated surface-based hippocampal morphometry algorithm to distinguish between these three levels of genetic risk for AD and demonstrate its superiority to a commonly used hippocampal volume measurement. We examined the APOE-e4 dose effect on cross-sectional hippocampal morphology analysis in a magnetic resonance imaging (MRI) database of 117 cognitively unimpaired subjects aged between 50 and 85 years (mean = 57.4, SD = 6.3), including 36 heterozygotes (e3/e4), 37 homozygotes (e4/e4) and 44 non-carriers (e3/e3). The proposed automated framework includes hippocampal surface segmentation and reconstruction, higher-order hippocampal surface correspondence computation, and hippocampal surface deformation analysis with multivariate statistics. In our experiments, the surface-based method identified APOE-e4 dose effects on the left hippocampal morphology. Compared to the widely-used hippocampal volume measure, our hippocampal morphometry statistics showed greater statistical power by distinguishing cognitively unimpaired subjects with two, one, and no APOE-e4 alleles. Our findings mirrored previous studies showing that APOE-e4 has a dose effect on the acceleration of brain structure deformities. The results indicated that the proposed surface-based hippocampal morphometry measure is a potential preclinical AD imaging biomarker for cognitively unimpaired individuals. Applied surface-based hippocampal morphometry on cognitively unimpaired subjects. Our study identified APOE-e4 dose effects on cognitively unimpaired subjects. Surface-based hippocampal morphometry outperformed the hippocampal volume measure. Surface-based hippocampal morphometry may be a potential preclinical AD biomarker.
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Affiliation(s)
- Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Wen Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Bolun Li
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | | | - Travis McMahon
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Boris A Gutman
- Armour College of Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | - Leslie C Baxter
- Human Brain Imaging Laboratory, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | | | | | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
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30
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Luo Z, Hou C, Wang L, Hu D. Gender Identification of Human Cortical 3-D Morphology Using Hierarchical Sparsity. Front Hum Neurosci 2019; 13:29. [PMID: 30792634 PMCID: PMC6374327 DOI: 10.3389/fnhum.2019.00029] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2018] [Accepted: 01/21/2019] [Indexed: 12/30/2022] Open
Abstract
Difference exists widely in cognition, behavior and psychopathology between males and females, while the underlying neurobiology is still unclear. As brain structure is the fundament of its function, getting insight into structural brain may help us to better understand the functional mechanism of gender difference. Previous structural studies of gender difference in Magnetic Resonance Imaging (MRI) usually focused on gray matter (GM) concentration and structural connectivity (SC), leaving cortical morphology not characterized properly. In this study a large dataset is used to explore whether cortical three-dimensional (3-D) morphology can offer enough discriminative morphological features to effectively identify gender. Data of all available healthy controls (N = 1113) from the Human Connectome Project (HCP) were utilized. We suggested a multivariate pattern analysis method called Hierarchical Sparse Representation Classifier (HSRC) and got an accuracy of 96.77% for gender identification. Permutation tests were used to testify the reliability of gender discrimination (p < 0.001). Cortical 3-D morphological features within the frontal lobe were found the most important contributors to gender difference of human brain morphology. Moreover, we investigated gender discriminative ability of cortical 3-D morphology in predefined Anatomical Automatic Labeling (AAL) and Resting-State Networks (RSN) templates, and found the superior frontal gyrus the most discriminative in AAL and the default mode network the most discriminative in RSN. Gender difference of surface-based morphology was also discussed. The frontal lobe, as well as the default mode network, was widely reported of gender difference in previous structural and functional MRI studies, which suggested that morphology indeed affect human brain function. Our study indicates that gender can be identified on individual level by using cortical 3-D morphology and offers a new approach for structural MRI research, as well as highlights the importance of gender balance in brain imaging studies.
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Affiliation(s)
- Zhiguo Luo
- College of Mechatronics and Automation, National University of Defense Technology, Changsha, China
| | - Chenping Hou
- College of Science, National University of Defense Technology, Changsha, China
| | - Lubin Wang
- Cognitive and Mental Health Research Center, Beijing Institute of Basic Medical Science, Beijing, China
| | - Dewen Hu
- College of Mechatronics and Automation, National University of Defense Technology, Changsha, China
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31
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Qiao J, Lv Y, Cao C, Wang Z, Li A. Multivariate Deep Learning Classification of Alzheimer's Disease Based on Hierarchical Partner Matching Independent Component Analysis. Front Aging Neurosci 2018; 10:417. [PMID: 30618723 PMCID: PMC6304436 DOI: 10.3389/fnagi.2018.00417] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Accepted: 12/03/2018] [Indexed: 12/11/2022] Open
Abstract
Machine learning and pattern recognition have been widely investigated in order to look for the biomarkers of Alzheimer’s disease (AD). However, most existing methods extract features by seed-based correlation, which not only requires prior information but also ignores the relationship between resting state functional magnetic resonance imaging (rs-fMRI) voxels. In this study, we proposed a deep learning classification framework with multivariate data-driven based feature extraction for automatic diagnosis of AD. Specifically, a three-level hierarchical partner matching independent components analysis (3LHPM-ICA) approach was proposed first in order to address the issues in spatial individual ICA, including the uncertainty of the numbers of components, the randomness of initial values, and the correspondence of ICs of multiple subjects, resulting in stable and reliable ICs which were applied as the intrinsic brain functional connectivity (FC) features. Second, Granger causality (GC) was utilized to infer directional interaction between the ICs that were identified by the 3LHPM-ICA method and extract the effective connectivity features. Finally, a deep learning classification framework was developed to distinguish AD from controls by fusing the functional and effective connectivities. A resting state fMRI dataset containing 34 AD patients and 34 normal controls (NCs) was applied to the multivariate deep learning platform, leading to a classification accuracy of 95.59%, with a sensitivity of 97.06% and a specificity of 94.12% with leave-one-out cross validation (LOOCV). The experimental results demonstrated that the measures of neural connectivities of ICA and GC followed by deep learning classification represented the most powerful methods of distinguishing AD clinical data from NCs, and these aberrant brain connectivities might serve as robust brain biomarkers for AD. This approach also allows for expansion of the methodology to classify other psychiatric disorders.
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Affiliation(s)
- Jianping Qiao
- Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, Institute of Data Science and Technology, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Yingru Lv
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Chongfeng Cao
- Department of Emergency, Jinan Central Hospital Affiliated to Shandong University, Jinan, China
| | - Zhishun Wang
- Department of Psychiatry, Columbia University, New York, NY, United States
| | - Anning Li
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
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32
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Tan M, Qiu A. Multiscale Frame-Based Kernels for Large Deformation Diffeomorphic Metric Mapping. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:2344-2355. [PMID: 29994047 DOI: 10.1109/tmi.2018.2832038] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
We present a set of multiscale frame-based kernels that can be used to construct diffeomorphic transformation in the large deformation diffeomorphic metric mapping (LDDMM) framework. We construct multiscale kernels via compact wavelet frames that are equipped with the hierarchical multiresolution analysis. We show that these kernels under certain conditions can form reproducing kernel Hilbert spaces of smooth velocity fields and hence can be used to generate multiscale diffeomorphic transformation for LDDMM. As a proof of concept, we incorporate these kernels in the LDDMM framework. We show the improvement of whole brain mapping accuracy using the LDDMM with frame-based kernels in comparison to that obtained using the LDDMM with Gaussian kernels. Moreover, we evaluate whole brain mapping accuracy of the LDDMM with frame-based kernels against that obtained from the 14 brain mapping methods given by Klein et al.. Our results suggest that the LDDMM with frame-based kernels has the potential to outperform the 14 brain mapping methods for whole brain mapping.
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33
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Phan TV, Smeets D, Talcott JB, Vandermosten M. Processing of structural neuroimaging data in young children: Bridging the gap between current practice and state-of-the-art methods. Dev Cogn Neurosci 2018; 33:206-223. [PMID: 29033222 PMCID: PMC6969273 DOI: 10.1016/j.dcn.2017.08.009] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 07/28/2017] [Accepted: 08/17/2017] [Indexed: 11/25/2022] Open
Abstract
The structure of the brain is subject to very rapid developmental changes during early childhood. Pediatric studies based on Magnetic Resonance Imaging (MRI) over this age range have recently become more frequent, with the advantage of providing in vivo and non-invasive high-resolution images of the developing brain, toward understanding typical and atypical trajectories. However, it has also been demonstrated that application of currently standard MRI processing methods that have been developed with datasets from adults may not be appropriate for use with pediatric datasets. In this review, we examine the approaches currently used in MRI studies involving young children, including an overview of the rationale for new MRI processing methods that have been designed specifically for pediatric investigations. These methods are mainly related to the use of age-specific or 4D brain atlases, improved methods for quantifying and optimizing image quality, and provision for registration of developmental data obtained with longitudinal designs. The overall goal is to raise awareness of the existence of these methods and the possibilities for implementing them in developmental neuroimaging studies.
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Affiliation(s)
- Thanh Vân Phan
- Experimental Oto-rhino-laryngology, Department Neurosciences, KU Leuven, Leuven, Belgium; icometrix, Research and Development, Leuven, Belgium.
| | - Dirk Smeets
- icometrix, Research and Development, Leuven, Belgium
| | - Joel B Talcott
- Aston Brain Centre, School of Life and Health Sciences, Aston University, Birmingham, United Kingdom
| | - Maaike Vandermosten
- Experimental Oto-rhino-laryngology, Department Neurosciences, KU Leuven, Leuven, Belgium
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34
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Lyu I, Kim SH, Woodward ND, Styner MA, Landman BA. TRACE: A Topological Graph Representation for Automatic Sulcal Curve Extraction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:1653-1663. [PMID: 29969416 PMCID: PMC6889090 DOI: 10.1109/tmi.2017.2787589] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
A proper geometric representation of the cortical regions is a fundamental task for cortical shape analysis and landmark extraction. However, a significant challenge has arisen due to the highly variable, convoluted cortical folding patterns. In this paper, we propose a novel topological graph representation for automatic sulcal curve extraction (TRACE). In practice, the reconstructed surface suffers from noise influences introduced during image acquisition/surface reconstruction. In the presence of noise on the surface, TRACE determines stable sulcal fundic regions by employing the line simplification method that prevents the sulcal folding pattern from being significantly smoothed out. The sulcal curves are then traced over the connected graph in the determined regions by the Dijkstra's shortest path algorithm. For validation, we used the state-of-the-art surface reconstruction pipelines on a reproducibility data set. The experimental results showed higher reproducibility and robustness to noise in TRACE than the existing method (Li et al. 2010) with over 20% relative improvement in error for both surface reconstruction pipelines. In addition, the extracted sulcal curves by TRACE were well-aligned with manually delineated primary sulcal curves. We also provided a choice of parameters to control quality of the extracted sulcal curves and showed the influences of the parameter selection on the resulting curves.
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Affiliation(s)
- Ilwoo Lyu
- Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235 USA
| | - Sun Hyung Kim
- Department of Psychiatry, The University of North Carolina, Chapel Hill, NC 27599, USA
| | - Neil D. Woodward
- Department of Psychiatry, Vanderbilt University, Nashville, TN 37235 USA
| | - Martin A. Styner
- Department of Psychiatry, The University of North Carolina, Chapel Hill, NC 27599, USA
| | - Bennett A. Landman
- Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235 USA
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35
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Tu Y, Wen C, Zhang W, Wu J, Zhang J, Chen K, Caselli RJ, Reiman EM, Reiman EM, Wang Y. Isometry Invariant Shape Descriptors for Abnormality Detection on Brain Surfaces Affected by Alzheimer's Disease. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:427-4631. [PMID: 30440425 PMCID: PMC6241283 DOI: 10.1109/embc.2018.8513129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Alzheimer's disease (AD), a progressive brain disorder, is the most common neurodegenerative disease in older adults. There is a need for brain structural magnetic resonance imaging (MRI) biomarkers to help assess AD progression and intervention effects. Prior research showed that surface based brain imaging features hold great promise as efficient AD biomarkers. However, the complex geometry of cortical surfaces poses a major challenge to defining such a feature that is sensitive in qualification, robust in analysis, and intuitive in visualization. Here we propose a novel isometry invariant shape descriptor for brain morphometry analysis. First, we calculate a global area-preserving mapping from cortical surface to the unit sphere. Based on the mapping, the Beltrami coefficient shape descriptor is calculated. An analysis of average shape descriptors reveals that our detected features are consistent with some previous AD studies where medial temporal lobe volume was identified as an important AD imaging biomarker. We further apply a novel patch-based spherical sparse coding scheme for feature dimension reduction. Later, a support vector machine (SVM) classifier is applied to discriminate 135 amyloid-beta positive persons with the clinical diagnosis of Mild Cognitive Impairment (MCI) from 248 amyloid-beta-negative normal control subjects. The 5-folder cross-validation accuracy is about 81.82\% on the dataset, outperforming some traditional, Freesurfer based, brain surface features. The results show that our shape descriptor is effective in distinguishing dementia due to AD from age-matched normal aging individuals. Our isometry invariant shape descriptors may provide a unique and intuitive way to inspect cortical surface and its morphometry changes.
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36
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Hong C, Choi K, Kachroo Y, Kwon T, Nguyen A, McComb R, Moon W. Evaluation of the 3dMDface system as a tool for soft tissue analysis. Orthod Craniofac Res 2018. [PMID: 28643910 DOI: 10.1111/ocr.12178] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
OBJECTIVES To evaluate the accuracy of three-dimensional stereophotogrammetry by comparing values obtained from direct anthropometry and the 3dMDface system. To achieve a more comprehensive evaluation of the reliability of 3dMD, both linear and surface measurements were examined. SETTING AND SAMPLE POPULATION UCLA Section of Orthodontics. Mannequin head as model for anthropometric measurements. MATERIAL AND METHODS Image acquisition and analysis were carried out on a mannequin head using 16 anthropometric landmarks and 21 measured parameters for linear and surface distances. 3D images using 3dMDface system were made at 0, 1 and 24 hours; 1, 2, 3 and 4 weeks. Error magnitude statistics used include mean absolute difference, standard deviation of error, relative error magnitude and root mean square error. Intra-observer agreement for all measurements was attained. RESULTS Overall mean errors were lower than 1.00 mm for both linear and surface parameter measurements, except in 5 of the 21 measurements. The three longest parameter distances showed increased variation compared to shorter distances. No systematic errors were observed for all performed paired t tests (P<.05). Agreement values between two observers ranged from 0.91 to 0.99. CONCLUSIONS Measurements on a mannequin confirmed the accuracy of all landmarks and parameters analysed in this study using the 3dMDface system. Results indicated that 3dMDface system is an accurate tool for linear and surface measurements, with potentially broad-reaching applications in orthodontics, surgical treatment planning and treatment evaluation.
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Affiliation(s)
- C Hong
- UCLA Department of Orthodontics, Los Angeles, CA, USA
| | - K Choi
- UCLA School of Dentistry, Los Angeles, CA, USA
| | - Y Kachroo
- UCLA Department of Orthodontics, Los Angeles, CA, USA
| | - T Kwon
- UCLA School of Dentistry, Los Angeles, CA, USA
| | - A Nguyen
- UCLA School of Dentistry, Los Angeles, CA, USA
| | - R McComb
- UCLA Department of Orthodontics, Los Angeles, CA, USA
| | - W Moon
- UCLA Department of Orthodontics, Los Angeles, CA, USA
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37
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Parvathaneni P, Lyu I, Huo Y, Blaber J, Hainline AE, Kang H, Woodward ND, Landman BA. Constructing Statistically Unbiased Cortical Surface Templates Using Feature-Space Covariance. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2018; 10574. [PMID: 29887664 DOI: 10.1117/12.2293641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
The choice of surface template plays an important role in cross-sectional subject analyses involving cortical brain surfaces because there is a tendency toward registration bias given variations in inter-individual and inter-group sulcal and gyral patterns. In order to account for the bias and spatial smoothing, we propose a feature-based unbiased average template surface. In contrast to prior approaches, we factor in the sample population covariance and assign weights based on feature information to minimize the influence of covariance in the sampled population. The mean surface is computed by applying the weights obtained from an inverse covariance matrix, which guarantees that multiple representations from similar groups (e.g., involving imaging, demographic, diagnosis information) are down-weighted to yield an unbiased mean in feature space. Results are validated by applying this approach in two different applications. For evaluation, the proposed unbiased weighted surface mean is compared with un-weighted means both qualitatively and quantitatively (mean squared error and absolute relative distance of both the means with baseline). In first application, we validated the stability of the proposed optimal mean on a scan-rescan reproducibility dataset by incrementally adding duplicate subjects. In the second application, we used clinical research data to evaluate the difference between the weighted and unweighted mean when different number of subjects were included in control versus schizophrenia groups. In both cases, the proposed method achieved greater stability that indicated reduced impacts of sampling bias. The weighted mean is built based on covariance information in feature space as opposed to spatial location, thus making this a generic approach to be applicable to any feature of interest.
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Affiliation(s)
| | - Ilwoo Lyu
- Computer Science, Vanderbilt University, Nashville, TN
| | - Yuankai Huo
- Electrical Engineering, Vanderbilt University, Nashville, TN
| | - Justin Blaber
- Electrical Engineering, Vanderbilt University, Nashville, TN
| | | | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University, Nashville, TN.,Center for Quantitative Sciences, Vanderbilt University, Nashville, TN
| | - Neil D Woodward
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University School of Medicine, Nashville, TN
| | - Bennett A Landman
- Electrical Engineering, Vanderbilt University, Nashville, TN.,Computer Science, Vanderbilt University, Nashville, TN.,Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN.,Department of Psychiatry and Behavioral Sciences, Vanderbilt University School of Medicine, Nashville, TN.,Center for Quantitative Sciences, Vanderbilt University, Nashville, TN
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38
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Zhang J, Tu Y, Li Q, Caselli RJ, Thompson PM, Ye J, Wang Y. MULTI-TASK SPARSE SCREENING FOR PREDICTING FUTURE CLINICAL SCORES USING LONGITUDINAL CORTICAL THICKNESS MEASURES. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2018; 2018:1406-1410. [PMID: 30023040 PMCID: PMC6047361 DOI: 10.1109/isbi.2018.8363835] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Cortical thickness estimation performed in-vivo via magnetic resonance imaging (MRI) is an effective measure of brain atrophy in preclinical individuals at high risk for Alzheimer's disease (AD). However, the high dimensionality of individual cortical thickness data coupled with small population samples make it challenging to perform cortical thickness feature selection for AD diagnosis and prognosis. Thus far, there are very few methods that can accurately predict future clinical scores using longitudinal cortical thickness measures. In this paper, we propose an unsupervised dictionary learning algorithm, termed Multi-task Sparse Screening (MSS) that produces improved results over previous methods within this problem domain. Specifically, we formulate and solve a multi-task problem using extracted top-p significant features from the Alzheimer's Disease Neuroimaging Initiative (ADNI) longitudinal data. Empirical studies on publicly available longitudinal data from ADNI dataset (N = 2797) demonstrate improved correlation coefficients and root mean square errors, when compared to other algorithms.
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Affiliation(s)
- Jie Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ
| | - Yanshuai Tu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ
| | - Qingyang Li
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ
| | | | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, Univ. of Southern California, Los Angeles, CA
| | - Jieping Ye
- Department of Electrical Engineering and Computer Science, Univ. of Michigan, Ann Arbor, MI
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ
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39
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Walhovd KB, Fjell AM, Giedd J, Dale AM, Brown TT. Through Thick and Thin: a Need to Reconcile Contradictory Results on Trajectories in Human Cortical Development. Cereb Cortex 2018; 27:1472-1481. [PMID: 28365755 DOI: 10.1093/cercor/bhv301] [Citation(s) in RCA: 117] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Understanding how brain development normally proceeds is a premise of understanding neurodevelopmental disorders. This has sparked a wealth of magnetic resonance imaging (MRI) studies. Unfortunately, they are in marked disagreement on how the cerebral cortex matures. While cortical thickness increases for the first 8-9 years of life have repeatedly been reported, others find continuous cortical thinning from early childhood, at least from age 3 or 4 years. We review these inconsistencies, and discuss possible reasons, including the use of different scanners, recording parameters and analysis tools, and possible effects of variables such as head motion. When tested on the same subsample, 2 popular thickness estimation methods (CIVET and FreeSurfer) both yielded a continuous thickness decrease from 3 years. Importantly, MRI-derived measures of cortical development are merely our best current approximations, hence the term "apparent cortical thickness" may be preferable. We recommend strategies for reaching consensus in the field, including multimodal neuroimaging to measure phenomena using different techniques, for example, the use of T1/T2 ratio, and data sharing to allow replication across analysis methods. As neurodevelopmental origins of early- and late-onset disease are increasingly recognized, resolving inconsistencies in brain maturation trajectories is important.
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Affiliation(s)
- Kristine B Walhovd
- Research Group for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Anders M Fjell
- Research Group for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | | | - Anders M Dale
- Department of Radiology.,Department of Neurosciences
| | - Timothy T Brown
- Department of Neurosciences.,Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, USA
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40
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Global grey matter volume in adult bipolar patients with and without lithium treatment: A meta-analysis. J Affect Disord 2018; 225:599-606. [PMID: 28886501 DOI: 10.1016/j.jad.2017.08.078] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Revised: 07/20/2017] [Accepted: 08/27/2017] [Indexed: 12/12/2022]
Abstract
OBJECTIVE The goal of this meta-analysis was to quantitatively summarize the evidence available on the differences in grey matter volume between lithium-treated and lithium-free bipolar patients. METHODS A systematic search was conducted in Cochrane Central, Embase, MEDLINE, and PsycINFO databases for original peer-reviewed journal articles that reported on global grey matter volume in lithium-medicated and lithium-free bipolar patients. Standard mean difference and Hedges' g were used to calculate effect size in a random-effects model. Risk of publication bias was assessed using Egger's test and quality of evidence was assessed using standard criteria. RESULTS There were 15 studies with a total of 854 patients (368 lithium-medicated, 486 lithium-free) included in the meta-analysis. Global grey matter volume was significantly larger in lithium-treated bipolar patients compared to lithium-free patients (SMD: 0.17, 95% CI: 0.01-0.33; z = 2.11, p = 0.035). Additionally, there was a difference in global grey matter volume between groups in studies that employed semi-automated segmentation methods (SMD: 0.66, 95% CI: 0.01-1.31; z = 1.99, p = 0.047), but no significant difference in studies that used fully-automated segmentation. No publication bias was detected (bias coefficient = - 0.65, p = 0.46). LIMITATIONS Variability in imaging methods and lack of high-quality evidence limits the interpretation of the findings. CONCLUSIONS Results suggest that lithium-treated patients have a greater global grey matter volume than those who were lithium-free. Further study of the relationship between lithium and grey matter volume may elucidate the therapeutic potential of lithium in conditions characterized by abnormal changes in brain structure.
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41
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Luders E, Kurth F, Pigdon L, Conti-Ramsden G, Reilly S, Morgan AT. Atypical Callosal Morphology in Children with Speech Sound Disorder. Neuroscience 2017; 367:211-218. [PMID: 29102664 DOI: 10.1016/j.neuroscience.2017.10.039] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2017] [Revised: 10/25/2017] [Accepted: 10/26/2017] [Indexed: 12/16/2022]
Abstract
Speech sound disorder (SSD) is common, yet its neurobiology is poorly understood. Recent studies indicate atypical structural and functional anomalies either in one hemisphere or both hemispheres, which might be accompanied by alterations in inter-hemispheric connectivity. Indeed, abnormalities of the corpus callosum - the main fiber tract connecting the two hemispheres - have been linked to speech and language deficits in associated disorders, such as stuttering, dyslexia, aphasia, etc. However, there is a dearth of studies examining the corpus callosum in SSD. Here, we investigated whether a sample of 18 children with SSD differed in callosal morphology from 18 typically developing children carefully matched for age. Significantly reduced dimensions of the corpus callosum, particularly in the callosal anterior third, were observed in children with SSD. These findings indicating pronounced callosal aberrations in SSD make an important contribution to an understudied field of research and may suggest that SSD is accompanied by atypical lateralization of speech and language function.
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Affiliation(s)
- Eileen Luders
- Cousins Center for Psychoneuroimmunology, Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, UCLA School of Medicine, Los Angeles, USA; Murdoch Childrens Research Institute, Melbourne, Australia.
| | - Florian Kurth
- Cousins Center for Psychoneuroimmunology, Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, UCLA School of Medicine, Los Angeles, USA
| | - Lauren Pigdon
- Murdoch Childrens Research Institute, Melbourne, Australia
| | - Gina Conti-Ramsden
- Murdoch Childrens Research Institute, Melbourne, Australia; The University of Manchester and Manchester Academic Health Science Centre (MAHSC), Manchester, United Kingdom
| | - Sheena Reilly
- Murdoch Childrens Research Institute, Melbourne, Australia; Menzies Health Institute at Griffith University, Gold Coast, Queensland, Australia
| | - Angela T Morgan
- Murdoch Childrens Research Institute, Melbourne, Australia; University of Melbourne, Melbourne, Australia.
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42
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Daianu M, Mendez MF, Baboyan VG, Jin Y, Melrose RJ, Jimenez EE, Thompson PM. An advanced white matter tract analysis in frontotemporal dementia and early-onset Alzheimer's disease. Brain Imaging Behav 2017; 10:1038-1053. [PMID: 26515192 PMCID: PMC5167220 DOI: 10.1007/s11682-015-9458-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Cortical and subcortical nuclei degenerate in the dementias, but less is known about changes in the white matter tracts that connect them. To better understand white matter changes in behavioral variant frontotemporal dementia (bvFTD) and early-onset Alzheimer’s disease (EOAD), we used a novel approach to extract full 3D profiles of fiber bundles from diffusion-weighted MRI (DWI) and map white matter abnormalities onto detailed models of each pathway. The result is a spatially complex picture of tract-by-tract microstructural changes. Our atlas of tracts for each disease consists of 21 anatomically clustered and recognizable white matter tracts generated from whole-brain tractography in 20 patients with bvFTD, 23 with age-matched EOAD, and 33 healthy elderly controls. To analyze the landscape of white matter abnormalities, we used a point-wise tract correspondence method along the 3D profiles of the tracts and quantified the pathway disruptions using common diffusion metrics – fractional anisotropy, mean, radial, and axial diffusivity. We tested the hypothesis that bvFTD and EOAD are associated with preferential degeneration in specific neural networks. We mapped axonal tract damage that was best detected with mean and radial diffusivity metrics, supporting our network hypothesis, highly statistically significant and more sensitive than widely studied fractional anisotropy reductions. From white matter diffusivity, we identified abnormalities in bvFTD in all 21 tracts of interest but especially in the bilateral uncinate fasciculus, frontal callosum, anterior thalamic radiations, cingulum bundles and left superior longitudinal fasciculus. This network of white matter alterations extends beyond the most commonly studied tracts, showing greater white matter abnormalities in bvFTD versus controls and EOAD patients. In EOAD, network alterations involved more posterior white matter – the parietal sector of the corpus callosum and parahipoccampal cingulum bilaterally. Widespread but distinctive white matter alterations are a key feature of the pathophysiology of these two forms of dementia.
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Affiliation(s)
- Madelaine Daianu
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA.,Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Mario F Mendez
- Behavioral Neurology Program, Department of Neurology, UCLA, Los Angeles, CA, USA
| | - Vatche G Baboyan
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Yan Jin
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Rebecca J Melrose
- Brain, Behavior, and Aging Research Center, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA.,Departments of Psychiatry and Biobehavioral Sciences, UCLA School of Medicine, Los Angeles, CA, USA
| | - Elvira E Jimenez
- Behavioral Neurology Program, Department of Neurology, UCLA, Los Angeles, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA. .,Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA. .,Departments of Neurology, Psychiatry, Radiology, Engineering, Pediatrics, and Ophthalmology, University of Southern California, Los Angeles, CA, USA.
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43
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Seki F, Hikishima K, Komaki Y, Hata J, Uematsu A, Okahara N, Yamamoto M, Shinohara H, Sasaki E, Okano H. Developmental trajectories of macroanatomical structures in common marmoset brain. Neuroscience 2017; 364:143-156. [PMID: 28939259 DOI: 10.1016/j.neuroscience.2017.09.021] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Revised: 08/11/2017] [Accepted: 09/12/2017] [Indexed: 11/17/2022]
Abstract
Morphometry studies of human brain development have revealed characteristics of some growth patterns, such as gray matter (GM) and white matter (WM), but the features that make human neurodevelopment distinct from that in other species remain unclear. Studies of the common marmoset (Callithrix jacchus), a small New World primate, can provide insights into unique features such as cooperative behaviors complementary to those from comparative analyses using mouse and rhesus monkey. In the present study, we analyzed developmental patterns of GM, WM, and cortical regions with volume measurements using longitudinal sample (23 marmosets; 11 male, 12 female) between the ages of one and 30months. Regional analysis using a total of 164 magnetic resonance imaging datasets revealed that GM volume increased before puberty (5.4months), but subsequently declined until adulthood, whereas WM volume increased rapidly before stabilizing around puberty (9.9months). Cortical regions showed similar patterns of increase and decrease, patterns with global GM but differed in the timing of volume peak and degree of decline across regions. The progressive-regressive pattern detected in both global and cortical GM was well correlated to phases of synaptogenesis and synaptic pruning reported in previous marmoset studies. A rapid increase in WM in early development may represent a distinctive aspect of human neurodevelopment. These findings suggest that studies of marmoset brain development can provide valuable comparative information that will facilitate a deeper understanding of human brain growth and neurodevelopmental disorders.
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Affiliation(s)
- Fumiko Seki
- Department of Physiology, Keio University School of Medicine, Shinjuku-ku, Tokyo 160-8582, Japan; Central Institute for Experimental Animals, Kawasaki, Japan; Laboratory for Marmoset Neural Architecture, Brain Science Institute RIKEN, Wako, Japan
| | - Keigo Hikishima
- Department of Physiology, Keio University School of Medicine, Shinjuku-ku, Tokyo 160-8582, Japan; Central Institute for Experimental Animals, Kawasaki, Japan; Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
| | - Yuji Komaki
- Department of Physiology, Keio University School of Medicine, Shinjuku-ku, Tokyo 160-8582, Japan; Central Institute for Experimental Animals, Kawasaki, Japan
| | - Junichi Hata
- Department of Physiology, Keio University School of Medicine, Shinjuku-ku, Tokyo 160-8582, Japan; Central Institute for Experimental Animals, Kawasaki, Japan; Laboratory for Marmoset Neural Architecture, Brain Science Institute RIKEN, Wako, Japan
| | - Akiko Uematsu
- Department of Physiology, Keio University School of Medicine, Shinjuku-ku, Tokyo 160-8582, Japan; Central Institute for Experimental Animals, Kawasaki, Japan; Laboratory for Marmoset Neural Architecture, Brain Science Institute RIKEN, Wako, Japan
| | - Norio Okahara
- Central Institute for Experimental Animals, Kawasaki, Japan
| | | | | | - Erika Sasaki
- Department of Physiology, Keio University School of Medicine, Shinjuku-ku, Tokyo 160-8582, Japan; Central Institute for Experimental Animals, Kawasaki, Japan
| | - Hideyuki Okano
- Department of Physiology, Keio University School of Medicine, Shinjuku-ku, Tokyo 160-8582, Japan; Laboratory for Marmoset Neural Architecture, Brain Science Institute RIKEN, Wako, Japan.
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44
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Tsao S, Gajawelli N, Zhou J, Shi J, Ye J, Wang Y, Leporé N. Feature selective temporal prediction of Alzheimer's disease progression using hippocampus surface morphometry. Brain Behav 2017; 7:e00733. [PMID: 28729939 PMCID: PMC5516607 DOI: 10.1002/brb3.733] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Revised: 04/10/2017] [Accepted: 04/14/2017] [Indexed: 12/14/2022] Open
Abstract
INTRODUCTION Prediction of Alzheimer's disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end, we combine a predictive multi-task machine learning method (cFSGL) with a novel MR-based multivariate morphometric surface map of the hippocampus (mTBM) to predict future cognitive scores of patients. METHODS Previous work has shown that a multi-task learning framework that performs prediction of all future time points simultaneously (cFSGL) can be used to encode both sparsity as well as temporal smoothness. The authors showed that this method is able to predict cognitive outcomes of ADNI subjects using FreeSurfer-based baseline MRI features, MMSE score demographic information and ApoE status. Whilst volumetric information may hold generalized information on brain status, we hypothesized that hippocampus specific information may be more useful in predictive modeling of AD. To this end, we applied a multivariate tensor-based parametric surface analysis method (mTBM) to extract features from the hippocampal surfaces. RESULTS We combined mTBM features with traditional surface features such as middle axis distance, the Jacobian determinant as well as 2 of the Jacobian principal eigenvalues to yield 7 normalized hippocampal surface maps of 300 points each. By combining these 7 × 300 = 2100 features together with the previous ~350 features, we illustrate how this type of sparsifying method can be applied to an entire surface map of the hippocampus that yields a feature space that is 2 orders of magnitude larger than what was previously attempted. CONCLUSIONS By combining the power of the cFSGL multi-task machine learning framework with the addition of AD sensitive mTBM feature maps of the hippocampus surface, we are able to improve the predictive performance of ADAS cognitive scores 6, 12, 24, 36 and 48 months from baseline.
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Affiliation(s)
- Sinchai Tsao
- CIBORG Children's Hospital Los Angeles and University of Southern California Los Angeles CA USA
| | - Niharika Gajawelli
- CIBORG Children's Hospital Los Angeles and University of Southern California Los Angeles CA USA
| | - Jiayu Zhou
- Department of Computer Science and Engineering Michigan State University East Lansing MI USA
| | - Jie Shi
- School of Computing, Informatics and Decision Systems Engineering Arizona State University Phoenix AZ USA
| | - Jieping Ye
- Department of Computational Medicine and Bioinformatics & Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA
| | - Yalin Wang
- School of Computing, Informatics and Decision Systems Engineering Arizona State University Phoenix AZ USA
| | - Natasha Leporé
- CIBORG Children's Hospital Los Angeles and University of Southern California Los Angeles CA USA
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45
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Zhang W, Shi J, Yu J, Zhan L, Thompson PM, Wang Y. Enhancing Diffusion MRI Measures By Integrating Grey and White Matter Morphometry With Hyperbolic Wasserstein Distance. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2017; 2017:520-524. [PMID: 28936280 DOI: 10.1109/isbi.2017.7950574] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In order to improve the preclinical diagnose of Alzheimer's disease (AD), there is a great deal of interest in analyzing the AD related brain structural changes with magnetic resonance image (MRI) analyses. As the major features, variation of the structural connectivity and the cortical surface morphometry provide different views of structural changes to determine whether AD is present on presymptomatic patients. However, the large scale tensor-valued information and relatively low imaging resolution in diffusion MRI (dMRI) have created huge challenges for analysis. In this paper, we propose a novel framework that improves dMRI analysis power by fusing cortical surface morphometry features from structural MRI (sMRI). We first compute the hyperbolic harmonic maps between cortical surfaces with the landmark constraints thus to precisely evaluate surface tensor-based morphometry. Meanwhile, the graph-based analysis of structural connectivity derived from dMRI is conducted. Next, we fuse these two features via the optimal mass transportation (OMT) and eventually the Wasserstein distance (WD) based single image index is computed as a potential clinical multimodality imaging score. We apply our framework to brain images of 20 AD patients and 20 matched healthy controls, randomly chosen from the Alzheimer's Disease Neuroimaging Initiative (AD-NI2) dataset. Our preliminary experimental results of group classification outperformed those of some other single dMRI-based features, such as regional hippocampal volume, mean scores of fractional anisotropy (FA) and mean axial (MD). The novel image fusion pipeline and simple imaging score of structural changes may benefit the preclinical AD and AD prevention research.
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Affiliation(s)
- Wen Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ
| | - Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ
| | - Jun Yu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ
| | - Liang Zhan
- Computer Engineering Program, University of Wisconsin-Stout, Menomonie, WI
| | - Paul M Thompson
- Imaging Genetics Center, Keck School of Medicine, University of Southern California, CA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ
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46
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Wang G, Wang Y. Towards a Holistic Cortical Thickness Descriptor: Heat Kernel-Based Grey Matter Morphology Signatures. Neuroimage 2017; 147:360-380. [PMID: 28033566 PMCID: PMC5303630 DOI: 10.1016/j.neuroimage.2016.12.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Revised: 12/05/2016] [Accepted: 12/07/2016] [Indexed: 11/19/2022] Open
Abstract
In this paper, we propose a heat kernel based regional shape descriptor that may be capable of better exploiting volumetric morphological information than other available methods, thereby improving statistical power on brain magnetic resonance imaging (MRI) analysis. The mechanism of our analysis is driven by the graph spectrum and the heat kernel theory, to capture the volumetric geometry information in the constructed tetrahedral meshes. In order to capture profound brain grey matter shape changes, we first use the volumetric Laplace-Beltrami operator to determine the point pair correspondence between white-grey matter and CSF-grey matter boundary surfaces by computing the streamlines in a tetrahedral mesh. Secondly, we propose multi-scale grey matter morphology signatures to describe the transition probability by random walk between the point pairs, which reflects the inherent geometric characteristics. Thirdly, a point distribution model is applied to reduce the dimensionality of the grey matter morphology signatures and generate the internal structure features. With the sparse linear discriminant analysis, we select a concise morphology feature set with improved classification accuracies. In our experiments, the proposed work outperformed the cortical thickness features computed by FreeSurfer software in the classification of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment, on publicly available data from the Alzheimer's Disease Neuroimaging Initiative. The multi-scale and physics based volumetric structure feature may bring stronger statistical power than some traditional methods for MRI-based grey matter morphology analysis.
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Affiliation(s)
- Gang Wang
- School of Information and Electrical Engineering, Ludong University, Yantai, Shandong 264025, China.
| | - Yalin Wang
- Arizona State University, School of Computing, Informatics, Decision Systems Engineering, 699 S. Mill Avenue, Tempe, AZ 85281, United States.
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47
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Clinical application of apparent diffusion coefficient mapping in voxel-based morphometry in the diagnosis of Alzheimer's disease. Clin Radiol 2017; 72:108-115. [DOI: 10.1016/j.crad.2016.11.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2016] [Accepted: 11/07/2016] [Indexed: 11/19/2022]
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48
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Bae S, Kang I, Lee BC, Jeon Y, Cho HB, Yoon S, Lim SM, Kim J, Lyoo IK, Kim JE, Choi IG. Prefrontal Cortical Thickness Deficit in Detoxified Alcohol-dependent Patients. Exp Neurobiol 2016; 25:333-341. [PMID: 28035184 PMCID: PMC5195819 DOI: 10.5607/en.2016.25.6.333] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2016] [Revised: 12/14/2016] [Accepted: 12/23/2016] [Indexed: 01/30/2023] Open
Abstract
Alcohol dependence is a serious disorder that can be related with a number of potential health-related and social consequences. Cortical thickness measurements would provide important information on the cortical structural alterations in patients with alcohol dependence. Twenty-one patients with alcohol dependence and 22 healthy comparison subjects have been recruited and underwent high-resolution brain magnetic resonance (MR) imaging and clinical assessments. T1-weighted MR images were analyzed using the cortical thickness analysis program. Significantly thinner cortical thickness in patients with alcohol dependence than healthy comparison subjects was noted in the left superior frontal cortical region, correcting for multiple comparisons and adjusting with age and hemispheric average cortical thickness. There was a significant association between thickness in the cluster of the left superior frontal cortex and the duration of alcohol use. The prefrontal cortical region may particularly be vulnerable to chronic alcohol exposure. It is also possible that the pre-existing deficit in this region may have rendered individuals more susceptible to alcohol dependence.
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Affiliation(s)
- Sujin Bae
- Industry Academic Cooperation Foundation, Chung-Ang University, Seoul 06974, Korea
| | - Ilhyang Kang
- Ewha Brain Institute, Ewha Womans University, Seoul 03760, Korea.; Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul 03760, Korea
| | - Boung Chul Lee
- Department of Neuropsychiatry, Hallym University Hangang Sacred Heart Hospital, Seoul 07247, Korea
| | - Yujin Jeon
- Ewha Brain Institute, Ewha Womans University, Seoul 03760, Korea
| | - Han Byul Cho
- The Brain Institute, University of Utah, Salt Lake City, UT 84108, USA
| | - Sujung Yoon
- Ewha Brain Institute, Ewha Womans University, Seoul 03760, Korea.; Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul 03760, Korea
| | - Soo Mee Lim
- Department of Radiology, Ewha Womans University College of Medicine, Seoul 03760, Korea
| | - Jungyoon Kim
- Ewha Brain Institute, Ewha Womans University, Seoul 03760, Korea
| | - In Kyoon Lyoo
- Ewha Brain Institute, Ewha Womans University, Seoul 03760, Korea.; Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul 03760, Korea.; College of Pharmacy, Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea
| | - Jieun E Kim
- Ewha Brain Institute, Ewha Womans University, Seoul 03760, Korea.; Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul 03760, Korea
| | - Ihn-Geun Choi
- Department of Psychiatry, Hallym University Kangnam Sacred Heart Hospital, Seoul 07441, Korea
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49
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Li X, Chen H, Zhang T, Yu X, Jiang X, Li K, Li L, Razavi MJ, Wang X, Hu X, Han J, Guo L, Hu X, Liu T. Commonly preserved and species-specific gyral folding patterns across primate brains. Brain Struct Funct 2016; 222:2127-2141. [PMID: 27796591 DOI: 10.1007/s00429-016-1329-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Accepted: 10/18/2016] [Indexed: 02/05/2023]
Abstract
Cortical folding pattern analysis is very important to understand brain organization and development. Since previous studies mostly focus on human brain cortex, the regularity and variability of cortical folding patterns across primate brains (macaques, chimpanzees and human) remain largely unknown. This paper presents a novel computational framework to identify common or unique gyral folding patterns in macaque, chimpanzee and human brains using magnetic resonance imaging (MRI) data. We quantitatively characterize gyral folding patterns via hinge numbers with cortical surfaces constructed from MRI data, and identify 6 common three-hinge gyral folds that exhibit consistent anatomical locations across these three species as well as 2 unique three hinges in macaque, 6 ones in chimpanzee and 14 ones in human. A novel morphology descriptor is then applied to classify three-hinge gyral folds, and the increasing complexity is identified among the species analyzed. This study may provide novel insights into the regularity and variability of the cerebral cortex from developmental perspective and may potentially facilitate novel neuroimage analyses such as cortical parcellation with correspondences across species in the future.
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Affiliation(s)
- Xiao Li
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Hanbo Chen
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, China.,Brain Decoding Research Center, Northwestern Polytechnical University, Xi'an, China
| | - Xiang Yu
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Xi Jiang
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
| | - Kaiming Li
- Department of Bioengineering, UC Riverside, Riverside, GA, USA.,West China Hospital of Sichuan University, Chengdu, China
| | - Longchuan Li
- Marcus Autism Center, Emory University, Atlanta, GA, USA
| | - Mir Jalil Razavi
- College of Engineering, The University of Georgia, Athens, GA, USA
| | - Xianqiao Wang
- College of Engineering, The University of Georgia, Athens, GA, USA
| | - Xintao Hu
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Xiaoping Hu
- Department of Bioengineering, UC Riverside, Riverside, GA, USA
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
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Shakil S, Magnuson ME, Keilholz SD, Lee CH. Cluster-based analysis for characterizing dynamic functional connectivity. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2014:982-5. [PMID: 25570125 DOI: 10.1109/embc.2014.6943757] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Different regions in the resting brain exhibit non-stationary functional connectivity (FC) over time. In this paper, a simple and efficient framework of clustering the variability in FC of a rat's brain at rest is proposed. This clustering process reveals areas that are always connected with a chosen region, called seed voxel, along with the areas exhibiting variability in the FC. This addresses an issue common to most dynamic FC analysis techniques, which is the assumption that the spatial extent of a given network remains constant over time. We increase the voxel size and reduce the spatial resolution to analyze variable FC of the whole resting brain. We hypothesize that the adjacent voxels in resting state functional magnetic resonance imaging (rsfMRI), just as in task-based fMRI, exhibit similar intensities, so they can be averaged to obtain larger voxels without any significant loss of information. Sliding window correlation is used to compute variable patterns of the rat's whole brain FC with the seed voxel in the sensorimotor cortex. These patterns are grouped based on their spatial similarities using binary transformed feature vectors in k-means clustering, not only revealing the variable and nonvariable portions of FC in the resting brain but also detecting the extent of the variability of these patterns.
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