1
|
Smith JL, Ahluwalia V, Gore RK, Allen JW. Eagle-449: A volumetric, whole-brain compilation of brain atlases for vestibular functional MRI research. Sci Data 2023; 10:29. [PMID: 36641517 PMCID: PMC9840609 DOI: 10.1038/s41597-023-01938-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 01/04/2023] [Indexed: 01/15/2023] Open
Abstract
Human vestibular processing involves distributed networks of cortical and subcortical regions which perform sensory and multimodal integrative functions. These functional hubs are also interconnected with areas subserving cognitive, affective, and body-representative domains. Analysis of these diverse components of the vestibular and vestibular-associated networks, and synthesis of their holistic functioning, is therefore vital to our understanding of the genesis of vestibular dysfunctions and aid treatment development. Novel neuroimaging methodologies, including functional and structural connectivity analyses, have provided important contributions in this area, but often require the use of atlases which are comprised of well-defined a priori regions of interest. Investigating vestibular dysfunction requires a more detailed atlas that encompasses cortical, subcortical, cerebellar, and brainstem regions. The present paper represents an effort to establish a compilation of existing, peer-reviewed brain atlases which collectively afford comprehensive coverage of these regions while explicitly focusing on vestibular substrates. It is expected that this compilation will be iteratively improved with additional contributions from researchers in the field.
Collapse
Affiliation(s)
- Jeremy L Smith
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Vishwadeep Ahluwalia
- Georgia Institute of Technology, Atlanta, Georgia, USA
- GSU/GT Center for Advanced Brain Imaging, Atlanta, Georgia, USA
| | - Russell K Gore
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
- Shepherd Center, Atlanta, Georgia, USA
| | - Jason W Allen
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia, USA.
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA.
- Department of Neurology, Emory University School of Medicine, Atlanta, Georgia, USA.
| |
Collapse
|
2
|
Wang Q, Wang Y, Xu W, Chen X, Li X, Li Q, Li H. Corresponding anatomical of the macaque superior parietal lobule areas 5 (PE) subdivision reveal similar connectivity patterns with humans. Front Neurosci 2022; 16:964310. [PMID: 36267237 PMCID: PMC9577089 DOI: 10.3389/fnins.2022.964310] [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: 06/15/2022] [Accepted: 08/26/2022] [Indexed: 11/25/2022] Open
Abstract
Using the animal brain as a cross-species tool for human brain research based on imaging features can provide more potential to reveal comprehensive human brain analysis. Previous studies have shown that human Brodmann area 5 (BA5) and macaque PE are homologous regions. They are both involved in processes depth and direction information during the touch process in the arm movement. However, recent studies show that both BA5 and PE are not homogeneous. According to the cytoarchitecture, BA5 is subdivided into three different subregions, and PE can be subdivided into PEl, PEla, and PEm. The species homologous relationship among the subregions is not clear between BA5 and PE. At the same time, the subdivision of PE based on the anatomical connection of white matter fiber bundles needs more verification. This research subdivided the PE of macaques based on the anatomical connection of white matter fiber bundles. Two PE subregions are defined based on probabilistic fiber tracking, one on the anterior side and the other on the dorsal side. Finally, the research draws connectivity fingerprints with predefined homologous target areas for the BA5 and PE subregions to reveal the characteristics of structure and functions and gives the homologous correspondence identified.
Collapse
|
3
|
Zhao Y, Gao Y, Li M, Anderson AW, Ding Z, Gore JC. Functional Parcellation of Human Brain Using Localized Topo-Connectivity Mapping. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2670-2680. [PMID: 35442885 PMCID: PMC9844109 DOI: 10.1109/tmi.2022.3168888] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The analysis of connectivity between parcellated regions of cortex provides insights into the functional architecture of the brain at a systems level. However, the derivation of functional structures from voxel-wise analyses at finer scales remains a challenge. We propose a novel method, called localized topo-connectivity mapping with singular-value-decomposition-informed filtering (or filtered LTM), to identify and characterize voxel-wise functional structures in the human brain from resting-state fMRI data. Here we describe its mathematical formulation and provide a proof-of-concept using simulated data that allow an intuitive interpretation of the results of filtered LTM. The algorithm has also been applied to 7T fMRI data acquired as part of the Human Connectome Project to generate group-average LTM images. Generally, most of the functional structures revealed by LTM images agree in the boundaries with anatomical structures identified by T1-weighted images and fractional anisotropy maps derived from diffusion MRI. In addition, the LTM images also reveal subtle functional variations that are not apparent in the anatomical structures. To assess the performance of LTM images, the subcortical region and occipital white matter were separately parcellated. Statistical tests were performed to demonstrate that the synchronies of fMRI signals in LTM-derived functional parcels are significantly larger than those with geometric perturbations. Overall, the filtered LTM approach can serve as a tool to investigate the functional organization of the brain at the scale of individual voxels as measured in fMRI.
Collapse
|
4
|
Bielski K, Adamus S, Kolada E, Rączaszek-Leonardi J, Szatkowska I. Parcellation of the human amygdala using recurrence quantification analysis. Neuroimage 2020; 227:117644. [PMID: 33338610 DOI: 10.1016/j.neuroimage.2020.117644] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 11/26/2020] [Accepted: 12/03/2020] [Indexed: 01/05/2023] Open
Abstract
Several previous attempts have been made to divide the human amygdala into smaller subregions based on the unique functional properties of the subregions. Although these attempts have provided valuable insight into the functional heterogeneity in this structure, the possibility that spatial patterns of functional characteristics can quickly change over time has rarely been considered in previous studies. In the present study, we explicitly account for the dynamic nature of amygdala activity. Our goal was not only to develop another parcellation method but also to augment existing methods with novel information about amygdala subdivisions. We performed state-specific amygdala parcellation using resting-state fMRI (rsfMRI) data and recurrence quantification analysis (RQA). RsfMRI data from 102 subjects were acquired with a 3T Trio Siemens scanner. We analyzed values of several RQA measures across all voxels in the amygdala and found two amygdala subdivisions, the ventrolateral (VL) and dorsomedial (DM) subdivisions, that differ with respect to one of the RQA measures, Shannon's entropy of diagonal lines. Compared to the DM subdivision, the VL subdivision can be characterized by a higher value of entropy. The results suggest that VL activity is determined and influenced by more brain structures than is DM activity. To assess the biological validity of the obtained subdivisions, we compared them with histological atlases and currently available parcellations based on structural connectivity patterns (Anatomy Probability Maps) and cytoarchitectonic features (SPM Anatomy toolbox). Moreover, we examined their cortical and subcortical functional connectivity. The obtained results are similar to those previously reported on parcellation performed on the basis of structural connectivity patterns. Functional connectivity analysis revealed that the VL subdivision has strong connections to several cortical areas, whereas the DM subdivision is mainly connected to subcortical regions. This finding suggests that the VL subdivision corresponds to the basolateral subdivision of the amygdala (BLA), while the DM subdivision has some characteristics typical of the centromedial amygdala (CMA). The similarity in functional connectivity patterns between the VL subdivision and BLA, as well as between the DM subdivision and CMA, confirm the utility of our parcellation method. Overall, the study shows that parcellation based on BOLD signal dynamics is a powerful tool for identifying distinct functional systems within the amygdala. This tool might be useful for future research on functional brain organization.
Collapse
Affiliation(s)
- Krzysztof Bielski
- Laboratory of Emotions Neurobiology, BRAINCITY - Centre of Excellence for Neural Plasticity and Brain Disorders, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Sylwia Adamus
- Laboratory of Emotions Neurobiology, BRAINCITY - Centre of Excellence for Neural Plasticity and Brain Disorders, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Emilia Kolada
- Laboratory of Emotions Neurobiology, BRAINCITY - Centre of Excellence for Neural Plasticity and Brain Disorders, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | | | - Iwona Szatkowska
- Laboratory of Emotions Neurobiology, BRAINCITY - Centre of Excellence for Neural Plasticity and Brain Disorders, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland.
| |
Collapse
|
5
|
Jayakar R, Tone EB, Crosson B, Turner JA, Anderson PL, Phan KL, Klumpp H. Amygdala volume and social anxiety symptom severity: Does segmentation technique matter? Psychiatry Res Neuroimaging 2020; 295:111006. [PMID: 31760338 PMCID: PMC6982531 DOI: 10.1016/j.pscychresns.2019.111006] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2019] [Revised: 11/03/2019] [Accepted: 11/04/2019] [Indexed: 12/19/2022]
Abstract
The amygdala factors prominently in neurobiological models of social anxiety (SA), yet amygdala volume findings regarding SA have been inconsistent and largely focused on case-control characterization. One source of discrepant findings could be variability in volumetric techniques. Therefore, we compared amygdala volumes derived via an automated technique (Freesurfer) against a manually corrected approach, also involving Freesurfer. Additionally, we tested whether the relationship between volume and SA symptom severity would differ across volumetric techniques. We pooled participants (n = 76) from archival studies. SA severity was assessed with the Liebowitz Social Anxiety Scale; scores ranged from non-clinical to clinical levels. Freesurfer produced significantly larger amygdalar volumes for participants with poor image quality. Even after excluding such participants, paired sample t-tests showed Freesurfer's boundaries produced significantly larger amygdalar volumes than manually corrected ones, bilaterally. Yet, intra-class correlation coefficients between the two methods were high, which suggests that Freesurfer's over-estimation of amygdala volume was systemic. Regardless of segmentation technique, volumes were not associated with SA symptom severity. Potentially, amygdala sub-regions may yield clearer patterns regarding SA symptoms. Further, our study underscores the importance of image quality for segmentation of the amygdala, and image quality may be particularly valuable when examining anatomical data for subtle inter-individual differences.
Collapse
Affiliation(s)
- Reema Jayakar
- Department of Psychology, Georgia State University, Atlanta, GA 30303, USA.
| | - Erin B Tone
- Department of Psychology, Georgia State University, Atlanta, GA 30303, USA.
| | - Bruce Crosson
- Department of Psychology, Georgia State University, Atlanta, GA 30303, USA; Center for Visual and Neurocognitive Rehabilitation, Atlanta VA Medical Center, Decatur, GA 30033, USA; Department of Neurology, Emory University, Atlanta, GA 30329, USA.
| | - Jessica A Turner
- Department of Psychology, Georgia State University, Atlanta, GA 30303, USA.
| | - Page L Anderson
- Department of Psychology, Georgia State University, Atlanta, GA 30303, USA.
| | - K Luan Phan
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, USA.
| | - Heide Klumpp
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA.
| |
Collapse
|
6
|
Prediction of the Clinical Severity of Progressive Supranuclear Palsy by Diffusion Tensor Imaging. J Clin Med 2019; 9:jcm9010040. [PMID: 31878122 PMCID: PMC7020078 DOI: 10.3390/jcm9010040] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 12/14/2019] [Accepted: 12/15/2019] [Indexed: 01/06/2023] Open
Abstract
Progressive supranuclear palsy (PSP) is characterized by a rapid and progressive clinical course. A timely and objective image-based evaluation of disease severity before standard clinical assessments might increase the diagnostic confidence of the neurologist. We sought to investigate whether features from diffusion tensor imaging of the entire brain with a machine learning algorithm, rather than a few pathogenically involved regions, may predict the clinical severity of PSP. Fifty-three patients who met the diagnostic criteria for probable PSP were subjected to diffusion tensor imaging. Of them, 15 underwent follow-up imaging. Clinical severity was assessed by the neurological examinations. Mean diffusivity and fractional anisotropy maps were spatially co-registered, normalized, and parcellated into 246 brain regions from the human Brainnetome atlas. The predictors of clinical severity from a stepwise linear regression model were determined after feature reduction by the least absolute shrinkage and selection operator. Performance estimates were obtained using bootstrapping, cross-validation, and through application of the model in the patients who underwent repeated imaging. The algorithm confidently predicts the clinical severity of PSP at the individual level (adjusted R2: 0.739 and 0.892, p < 0.001). The machine learning algorithm for selection of diffusion tensor imaging-based features is accurate in predicting motor subscale of unified Parkinson’s disease rating scale and postural instability and gait disturbance of PSP.
Collapse
|
7
|
Corresponding anatomical and coactivation architecture of the human precuneus showing similar connectivity patterns with macaques. Neuroimage 2019; 200:562-574. [DOI: 10.1016/j.neuroimage.2019.07.001] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 06/03/2019] [Accepted: 07/01/2019] [Indexed: 12/22/2022] Open
|
8
|
Abstract
A defining aspect of brain organization is its spatial heterogeneity, which gives rise to multiple topographies at different scales. Brain parcellation - defining distinct partitions in the brain, be they areas or networks that comprise multiple discontinuous but closely interacting regions - is thus fundamental for understanding brain organization and function. The past decade has seen an explosion of in vivo MRI-based approaches to identify and parcellate the brain on the basis of a wealth of different features, ranging from local properties of brain tissue to long-range connectivity patterns, in addition to structural and functional markers. Given the high diversity of these various approaches, assessing the convergence and divergence among these ensuing maps is a challenge. Inter-individual variability adds to this challenge but also provides new opportunities when coupled with cross-species and developmental parcellation studies.
Collapse
|
9
|
Wang L, Yu L, Wu F, Wu H, Wang J. Altered whole brain functional connectivity pattern homogeneity in medication-free major depressive disorder. J Affect Disord 2019; 253:18-25. [PMID: 31009844 DOI: 10.1016/j.jad.2019.04.040] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 03/11/2019] [Accepted: 04/07/2019] [Indexed: 11/29/2022]
Abstract
BACKGROUND Many previous studies have revealed abnormal functional connectivity patterns between brain areas underlying the onset of major depressive disorder (MDD) using resting-state functional magnetic resonance imaging (rs-fMRI). However, how to exactly characterize the voxel-wise whole brain functional connectivity pattern changes in MDD remains unclear, which will provide more convincing evidence for localizing the exactly functional connectivity abnormality in MDD. METHODS In this study, we employed our newly developed whole brain functional connectivity homogeneity (FcHo) method to identify the voxel-wise changes of functional connectivity patterns in 27 medication-free MDD patients and 34 gender-, age-, and education level-matched healthy controls (HC). Furthermore, seed-based functional connectivity analysis was then used to identify the alteration of corresponding functional connectivity. RESULTS Significantly decreased FcHo values in right ventral anterior insula (INS) and medial prefrontal cortex (MPFC) were identified in MDD patients. The ensuing functional connectivity analyses identified decreased functional connectivity between MPFC and left angular gyrus (AG) in MDD patients. Moreover, both decreased FcHo values in INS, MPFC and functional connectivity between MPFC and left AG showed significant negative correlations with Hamilton depression rating scale (HDRS) scores. The FcHo values in INS were also negatively correlated with disease duration. Finally, meta-analysis based functional characterization found that these brain areas are mainly involved in emotion, theory of mind and reward processing. CONCLUSIONS Our findings revealed abnormal whole brain FcHo in INS and MPFC and functional interactions between MPFC and AG in MDD and suggested that dysfunctions of INS and MPFC play an important role in the neuropathology of MDD.
Collapse
Affiliation(s)
- Lijie Wang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
| | - Lin Yu
- The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou 510370, China
| | - Fengchun Wu
- The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Huawang Wu
- The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China.
| | - Jiaojian Wang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
| |
Collapse
|
10
|
Wang C, Ng B, Garbi R. Multimodal Brain Parcellation Based on Functional and Anatomical Connectivity. Brain Connect 2018; 8:604-617. [PMID: 30499336 DOI: 10.1089/brain.2017.0576] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Brain parcellation is often a prerequisite for network analysis due to the statistical challenges, computational burdens, and interpretation difficulties arising from the high dimensionality of neuroimaging data. Predominant approaches are largely unimodal with functional magnetic resonance imaging (fMRI) being the primary modality used. These approaches thus neglect other brain attributes that relate to brain organization. In this paper, we propose an approach for integrating fMRI and diffusion MRI (dMRI) data. Our approach introduces a nonlinear mapping between the connectivity values of two modalities, and adaptively balances their weighting based on their voxel-wise test-retest reliability. An efficient region level extension that additionally incorporates structural information on gyri and sulci is further presented. To validate, we compare multimodal parcellations with unimodal parcellations and existing atlases on the Human Connectome Project data. We show that multimodal parcellations achieve higher reproducibility, comparable/higher functional homogeneity, and comparable/higher leftout data likelihood. The boundaries of multimodal parcels are observed to align to those based on cyto-architecture, and subnetworks extracted from multimodal parcels matched well with established brain systems. Our results thus show that multimodal information improves brain parcellation.
Collapse
Affiliation(s)
- Chendi Wang
- University of British Columbia, Electrical and Computer Engineering , ICICS x421-2366 Main Mall , Vancouver, British Columbia, Canada , V6T 1Z4 ;
| | - Bernard Ng
- University of British Columbia, Department of Statistics , Vancouver, British Columbia, Canada ;
| | - Rafeef Garbi
- University of British Columbia, Electrical and Computer Engineering, Vancouver, British Columbia, Canada ;
| |
Collapse
|
11
|
Zhang L, Wu H, Xu J, Shang J. Abnormal Global Functional Connectivity Patterns in Medication-Free Major Depressive Disorder. Front Neurosci 2018; 12:692. [PMID: 30356761 PMCID: PMC6189368 DOI: 10.3389/fnins.2018.00692] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2018] [Accepted: 09/18/2018] [Indexed: 01/15/2023] Open
Abstract
Mounting studies have applied resting-state functional magnetic resonance imaging (rs-fMRI) to study major depressive disorder (MDD) and have identified abnormal functional activities. However, how the global functional connectivity patterns change in MDD is still unknown. Using rs-fMRI, we investigated the alterations of global resting-state functional connectivity (RSFC) patterns in MDD using weighted global brain connectivity (wGBC) method. First, a whole brain voxel-wise wGBC map was calculated for 23 MDD patients and 34 healthy controls. Two-sample t-tests were applied to compare the wGBC and RSFC maps and the significant level was set at p < 0.05, cluster-level correction with voxel-level p < 0.001. MDD patients showed significantly decreased wGBC in left temporal pole (TP) and increased wGBC in right parahippocampus (PHC). Subsequent RSFC analyses showed decreased functional interaction between TP and right posterior superior temporal cortex and increased functional interaction between PHC and right inferior frontal gyrus in MDD patients. These results revealed the abnormal global FC patterns and its corresponding disrupted functional connectivity in MDD. Our findings present new evidence for the functional interruption in MDD.
Collapse
Affiliation(s)
- Lu Zhang
- Lab of Learning Sciences, Graduate School of Education, Peking University, Beijing, China
| | - Huawang Wu
- The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Hui'ai Hospital), Guangzhou, China
| | - Jinping Xu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Junjie Shang
- Lab of Learning Sciences, Graduate School of Education, Peking University, Beijing, China
| |
Collapse
|
12
|
Alhazmi FH, Beaton D, Abdi H. Semantically defined subdomains of functional neuroimaging literature and their corresponding brain regions. Hum Brain Mapp 2018; 39:2764-2776. [PMID: 29575246 PMCID: PMC6866474 DOI: 10.1002/hbm.24038] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2017] [Revised: 02/17/2018] [Accepted: 03/04/2018] [Indexed: 11/10/2022] Open
Abstract
The functional neuroimaging literature has become increasingly complex and thus difficult to navigate. This complexity arises from the rate at which new studies are published and from the terminology that varies widely from study-to-study and even more so from discipline-to-discipline. One way to investigate and manage this problem is to build a "semantic space" that maps the different vocabulary used in functional neuroimaging literature. Such a semantic space will also help identify the primary research domains of neuroimaging and their most commonly reported brain regions. In this work, we analyzed the multivariate semantic structure of abstracts in Neurosynth and found that there are six primary domains of the functional neuroimaging literature, each with their own preferred reported brain regions. Our analyses also highlight possible semantic sources of reported brain regions within and across domains because some research topics (e.g., memory disorders, substance use disorder) use heterogeneous terminology. Furthermore, we highlight the growth and decline of the primary domains over time. Finally, we note that our techniques and results form the basis of a "recommendation engine" that could help readers better navigate the neuroimaging literature.
Collapse
Affiliation(s)
- Fahd H. Alhazmi
- School of Behavioral and Brain SciencesThe University of Texas at Dallas, MS: GR4.1, 800 West Campbell RdRichardsonTexas75080
| | - Derek Beaton
- Rotman Research Institute, Baycrest Health Sciences, 3560 Bathurst StreetTorontoOntarioM6A 2E1Canada
| | - Hervé Abdi
- School of Behavioral and Brain SciencesThe University of Texas at Dallas, MS: GR4.1, 800 West Campbell RdRichardsonTexas75080
| |
Collapse
|
13
|
Wang L, Xu J, Wang C, Wang J. Whole Brain Functional Connectivity Pattern Homogeneity Mapping. Front Hum Neurosci 2018; 12:164. [PMID: 29740305 PMCID: PMC5928195 DOI: 10.3389/fnhum.2018.00164] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Accepted: 04/09/2018] [Indexed: 01/13/2023] Open
Abstract
Mounting studies have demonstrated that brain functions are determined by its external functional connectivity patterns. However, how to characterize the voxel-wise similarity of whole brain functional connectivity pattern is still largely unknown. In this study, we introduced a new method called functional connectivity homogeneity (FcHo) to delineate the voxel-wise similarity of whole brain functional connectivity patterns. FcHo was defined by measuring the whole brain functional connectivity patterns similarity of a given voxel with its nearest 26 neighbors using Kendall's coefficient concordance (KCC). The robustness of this method was tested in four independent datasets selected from a large repository of MRI. Furthermore, FcHo mapping results were further validated using the nearest 18 and six neighbors and intra-subject reproducibility with each subject scanned two times. We also compared FcHo distribution patterns with local regional homogeneity (ReHo) to identify the similarity and differences of the two methods. Finally, FcHo method was used to identify the differences of whole brain functional connectivity patterns between professional Chinese chess players and novices to test its application. FcHo mapping consistently revealed that the high FcHo was mainly distributed in association cortex including parietal lobe, frontal lobe, occipital lobe and default mode network (DMN) related areas, whereas the low FcHo was mainly found in unimodal cortex including primary visual cortex, sensorimotor cortex, paracentral lobule and supplementary motor area. These results were further supported by analyses of the nearest 18 and six neighbors and intra-subject similarity. Moreover, FcHo showed both similar and different whole brain distribution patterns compared to ReHo. Finally, we demonstrated that FcHo can effectively identify the whole brain functional connectivity pattern differences between professional Chinese chess players and novices. Our findings indicated that FcHo is a reliable method to delineate the whole brain functional connectivity pattern similarity and may provide a new way to study the functional organization and to reveal neuropathological basis for brain disorders.
Collapse
Affiliation(s)
- Lijie Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jinping Xu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Chao Wang
- College of Psychology and Sociology, Shenzhen University, Shenzhen, China
| | - Jiaojian Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| |
Collapse
|
14
|
Salehi M, Karbasi A, Shen X, Scheinost D, Constable RT. An exemplar-based approach to individualized parcellation reveals the need for sex specific functional networks. Neuroimage 2018; 170:54-67. [PMID: 28882628 PMCID: PMC5905726 DOI: 10.1016/j.neuroimage.2017.08.068] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Revised: 06/09/2017] [Accepted: 08/24/2017] [Indexed: 01/09/2023] Open
Abstract
Recent work with functional connectivity data has led to significant progress in understanding the functional organization of the brain. While the majority of the literature has focused on group-level parcellation approaches, there is ample evidence that the brain varies in both structure and function across individuals. In this work, we introduce a parcellation technique that incorporates delineation of functional networks both at the individual- and group-level. The proposed technique deploys the notion of "submodularity" to jointly parcellate the cerebral cortex while establishing an inclusive correspondence between the individualized functional networks. Using this parcellation technique, we successfully established a cross-validated predictive model that predicts individuals' sex, solely based on the parcellation schemes (i.e. the node-to-network assignment vectors). The sex prediction finding illustrates that individualized parcellation of functional networks can reveal subgroups in a population and suggests that the use of a global network parcellation may overlook fundamental differences in network organization. This is a particularly important point to consider in studies comparing patients versus controls or even patient subgroups. Network organization may differ between individuals and global configurations should not be assumed. This approach to the individualized study of functional organization in the brain has many implications for both neuroscience and clinical applications.
Collapse
Affiliation(s)
- Mehraveh Salehi
- Department of Electrical Engineering, Yale University, New Haven, CT, USA; Yale Institute for Network Science, Yale University, New Haven, CT, USA.
| | - Amin Karbasi
- Department of Electrical Engineering, Yale University, New Haven, CT, USA; Yale Institute for Network Science, Yale University, New Haven, CT, USA
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| |
Collapse
|
15
|
Gibbard CR, Ren J, Skuse DH, Clayden JD, Clark CA. Structural connectivity of the amygdala in young adults with autism spectrum disorder. Hum Brain Mapp 2018; 39:1270-1282. [PMID: 29265723 PMCID: PMC5838552 DOI: 10.1002/hbm.23915] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Revised: 11/30/2017] [Accepted: 12/01/2017] [Indexed: 01/11/2023] Open
Abstract
Autism spectrum disorder (ASD) is characterized by impairments in social cognition, a function associated with the amygdala. Subdivisions of the amygdala have been identified which show specificity of structure, connectivity, and function. Little is known about amygdala connectivity in ASD. The aim of this study was to investigate the microstructural properties of amygdala-cortical connections and their association with ASD behaviours, and whether connectivity of specific amygdala subregions is associated with particular ASD traits. The brains of 51 high-functioning young adults (25 with ASD; 26 controls) were scanned using MRI. Amygdala volume was measured, and amygdala-cortical connectivity estimated using probabilistic tractography. An iterative 'winner takes all' algorithm was used to parcellate the amygdala based on its primary cortical connections. Measures of amygdala connectivity were correlated with clinical scores. In comparison with controls, amygdala volume was greater in ASD (F(1,94) = 4.19; p = .04). In white matter (WM) tracts connecting the right amygdala to the right cortex, ASD subjects showed increased mean diffusivity (t = 2.35; p = .05), which correlated with the severity of emotion recognition deficits (rho = -0.53; p = .01). Following amygdala parcellation, in ASD subjects reduced fractional anisotropy in WM connecting the left amygdala to the temporal cortex was associated with with greater attention switching impairment (rho = -0.61; p = .02). This study demonstrates that both amygdala volume and the microstructure of connections between the amygdala and the cortex are altered in ASD. Findings indicate that the microstructure of right amygdala WM tracts are associated with overall ASD severity, but that investigation of amygdala subregions can identify more specific associations.
Collapse
Affiliation(s)
- Clare R. Gibbard
- Developmental Imaging and Biophysics Section, UCL Great Ormond Street Institute of Child Health, 30 Guilford StreetLondonWC1N 1EHUnited Kingdom
| | - Juejing Ren
- Behavioural Sciences UnitUCL Great Ormond Street Institute of Child Health, 30 Guilford StreetLondonWC1N 1EHUnited Kingdom
| | - David H. Skuse
- Behavioural Sciences UnitUCL Great Ormond Street Institute of Child Health, 30 Guilford StreetLondonWC1N 1EHUnited Kingdom
| | - Jonathan D. Clayden
- Developmental Imaging and Biophysics Section, UCL Great Ormond Street Institute of Child Health, 30 Guilford StreetLondonWC1N 1EHUnited Kingdom
| | - Chris A. Clark
- Developmental Imaging and Biophysics Section, UCL Great Ormond Street Institute of Child Health, 30 Guilford StreetLondonWC1N 1EHUnited Kingdom
| |
Collapse
|
16
|
Preti MG, Van De Ville D. Dynamics of functional connectivity at high spatial resolution reveal long-range interactions and fine-scale organization. Sci Rep 2017; 7:12773. [PMID: 28986564 PMCID: PMC5630612 DOI: 10.1038/s41598-017-12993-1] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Accepted: 09/14/2017] [Indexed: 12/18/2022] Open
Abstract
Dynamic functional connectivity (dFC) derived from resting-state functional magnetic resonance imaging sheds light onto moment-to-moment reconfigurations of large-scale functional brain networks. Due to computational limits, connectivity is typically computed using pre-defined atlases, a non-trivial choice that might influence results. Here, we leverage new computational methods to retrieve dFC at the voxel level in terms of dominant patterns of fluctuations, and demonstrate that this new representation is informative to derive meaningful brain parcellations, capturing both long-range interactions and fine-scale local organization. Specifically, voxelwise dFC dominant patterns were captured through eigenvector centrality followed by clustering across time/subjects to yield most representative dominant patterns (RDPs). Voxel-wise labeling according to positive/negative contributions to RDPs, led to 37 unique labels identifying strikingly symmetric dFC long-range patterns. These included 449 contiguous regions, defining a fine-scale parcellation consistent with known cortical/subcortical subdivisions. Our contribution provides an alternative to obtain a whole-brain parcellation that is for the first time driven by voxel-level dFC and bridges the gap between voxel-based approaches and graph theoretical analysis.
Collapse
Affiliation(s)
- Maria Giulia Preti
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
| | - Dimitri Van De Ville
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| |
Collapse
|
17
|
Li H, Fan L, Zhuo J, Wang J, Zhang Y, Yang Z, Jiang T. ATPP: A Pipeline for Automatic Tractography-Based Brain Parcellation. Front Neuroinform 2017; 11:35. [PMID: 28611620 PMCID: PMC5447055 DOI: 10.3389/fninf.2017.00035] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Accepted: 05/15/2017] [Indexed: 12/18/2022] Open
Abstract
There is a longstanding effort to parcellate brain into areas based on micro-structural, macro-structural, or connectional features, forming various brain atlases. Among them, connectivity-based parcellation gains much emphasis, especially with the considerable progress of multimodal magnetic resonance imaging in the past two decades. The Brainnetome Atlas published recently is such an atlas that follows the framework of connectivity-based parcellation. However, in the construction of the atlas, the deluge of high resolution multimodal MRI data and time-consuming computation poses challenges and there is still short of publically available tools dedicated to parcellation. In this paper, we present an integrated open source pipeline (https://www.nitrc.org/projects/atpp), named Automatic Tractography-based Parcellation Pipeline (ATPP) to realize the framework of parcellation with automatic processing and massive parallel computing. ATPP is developed to have a powerful and flexible command line version, taking multiple regions of interest as input, as well as a user-friendly graphical user interface version for parcellating single region of interest. We demonstrate the two versions by parcellating two brain regions, left precentral gyrus and middle frontal gyrus, on two independent datasets. In addition, ATPP has been successfully utilized and fully validated in a variety of brain regions and the human Brainnetome Atlas, showing the capacity to greatly facilitate brain parcellation.
Collapse
Affiliation(s)
- Hai Li
- Brainnetome Center, Institute of Automation, Chinese Academy of SciencesBeijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of SciencesBeijing, China.,University of Chinese Academy of SciencesBeijing, China
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of SciencesBeijing, China.,University of Chinese Academy of SciencesBeijing, China
| | - Junjie Zhuo
- Brainnetome Center, Institute of Automation, Chinese Academy of SciencesBeijing, China
| | - Jiaojian Wang
- Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengdu, China
| | - Yu Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of SciencesBeijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of SciencesBeijing, China
| | - Zhengyi Yang
- Brainnetome Center, Institute of Automation, Chinese Academy of SciencesBeijing, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of SciencesBeijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of SciencesBeijing, China.,University of Chinese Academy of SciencesBeijing, China.,Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengdu, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of SciencesBeijing, China.,Queensland Brain Institute, The University of QueenslandBrisbane, QLD, Australia
| |
Collapse
|
18
|
Fan L, Li H, Zhuo J, Zhang Y, Wang J, Chen L, Yang Z, Chu C, Xie S, Laird AR, Fox PT, Eickhoff SB, Yu C, Jiang T. The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture. Cereb Cortex 2016; 26:3508-26. [PMID: 27230218 PMCID: PMC4961028 DOI: 10.1093/cercor/bhw157] [Citation(s) in RCA: 1596] [Impact Index Per Article: 199.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
The human brain atlases that allow correlating brain anatomy with psychological and cognitive functions are in transition from ex vivo histology-based printed atlases to digital brain maps providing multimodal in vivo information. Many current human brain atlases cover only specific structures, lack fine-grained parcellations, and fail to provide functionally important connectivity information. Using noninvasive multimodal neuroimaging techniques, we designed a connectivity-based parcellation framework that identifies the subdivisions of the entire human brain, revealing the in vivo connectivity architecture. The resulting human Brainnetome Atlas, with 210 cortical and 36 subcortical subregions, provides a fine-grained, cross-validated atlas and contains information on both anatomical and functional connections. Additionally, we further mapped the delineated structures to mental processes by reference to the BrainMap database. It thus provides an objective and stable starting point from which to explore the complex relationships between structure, connectivity, and function, and eventually improves understanding of how the human brain works. The human Brainnetome Atlas will be made freely available for download at http://atlas.brainnetome.org, so that whole brain parcellations, connections, and functional data will be readily available for researchers to use in their investigations into healthy and pathological states.
Collapse
Affiliation(s)
| | - Hai Li
- Brainnetome Center National Laboratory of Pattern Recognition and
| | - Junjie Zhuo
- Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 625014, China
| | - Yu Zhang
- Brainnetome Center National Laboratory of Pattern Recognition and
| | - Jiaojian Wang
- Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 625014, China
| | - Liangfu Chen
- Brainnetome Center National Laboratory of Pattern Recognition and
| | - Zhengyi Yang
- Brainnetome Center National Laboratory of Pattern Recognition and
| | - Congying Chu
- Brainnetome Center National Laboratory of Pattern Recognition and
| | - Sangma Xie
- Brainnetome Center National Laboratory of Pattern Recognition and
| | - Angela R Laird
- Department of Physics, Florida International University, Miami, FL, USA
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center, San Antonio, TX, USA
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-1), Research Centre Juelich, Juelich 52425, Germany Institute for Clinical Neuroscience and Medical Psychology, Heinrich-Heine-University Düsseldorf, Düsseldorf 40225, Germany
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Tianzi Jiang
- Brainnetome Center National Laboratory of Pattern Recognition and CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 625014, China The Queensland Brain Institute, University of Queensland, Brisbane, QLD 4072, Australia
| |
Collapse
|