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Lv J, Zeng R, Ho MP, D'Souza A, Calamante F. Building a tissue-unbiased brain template of fiber orientation distribution and tractography with multimodal registration. Magn Reson Med 2023; 89:1207-1220. [PMID: 36299169 PMCID: PMC10952616 DOI: 10.1002/mrm.29496] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 09/07/2022] [Accepted: 09/30/2022] [Indexed: 02/02/2023]
Abstract
PURPOSE Brain templates provide an essential standard space for statistical analysis of brain structure and function. Despite recent advances, diffusion MRI still lacks a template of fiber orientation distribution (FOD) and tractography that is unbiased for both white and gray matter. Therefore, we aim to build up a set of such templates for better white-matter analysis and joint structural and functional analysis. METHODS We have developed a multimodal registration method to leverage the complementary information captured by T1 -weighted, T2 -weighted, and diffusion MRI, so that a coherent transformation is generated to register FODs into a common space and average them into a template. Consequently, the anatomically constrained fiber-tracking method was applied to the FOD template to generate a tractography template. Fiber-centered functional connectivity analysis was then performed as an example of the benefits of such an unbiased template. RESULTS Our FOD template preserves fine structural details in white matter and also, importantly, clear folding patterns in the cortex and good contrast in the subcortex. Quantitatively, our templates show better individual-template agreement at the whole-brain scale and segmentation scale. The tractography template aligns well with the gray matter, which led to fiber-centered functional connectivity showing high cross-group consistency. CONCLUSION We have proposed a novel methodology for building a tissue-unbiased FOD and anatomically constrained tractography template based on multimodal registration. Our templates provide a standard space and statistical platform for not only white-matter analysis but also joint structural and functional analysis, therefore filling an important gap in multimodal neuroimage analysis.
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Affiliation(s)
- Jinglei Lv
- School of Biomedical EngineeringThe University of Sydney
SydneyNew South WalesAustralia
- Brain and Mind CentreThe University of SydneySydneyNew South WalesAustralia
| | - Rui Zeng
- School of Biomedical EngineeringThe University of Sydney
SydneyNew South WalesAustralia
- Brain and Mind CentreThe University of SydneySydneyNew South WalesAustralia
| | - Mai Phuong Ho
- School of Biomedical EngineeringThe University of Sydney
SydneyNew South WalesAustralia
| | - Arkiev D'Souza
- Brain and Mind CentreThe University of SydneySydneyNew South WalesAustralia
- Translational Research Collective, Faculty of Medicine and HealthThe University of SydneySydneyNew South WalesAustralia
| | - Fernando Calamante
- School of Biomedical EngineeringThe University of Sydney
SydneyNew South WalesAustralia
- Brain and Mind CentreThe University of SydneySydneyNew South WalesAustralia
- Sydney ImagingThe University of SydneySydneyNew South WalesAustralia
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2
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White matter substrates of functional connectivity dynamics in the human brain. Neuroimage 2022; 258:119391. [PMID: 35716842 DOI: 10.1016/j.neuroimage.2022.119391] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 06/06/2022] [Accepted: 06/09/2022] [Indexed: 11/22/2022] Open
Abstract
The contribution of structural connectivity to functional connectivity dynamics is still far from being elucidated. Herein, we applied track-weighted dynamic functional connectivity (tw-dFC), a model integrating structural, functional, and dynamic connectivity, on high quality diffusion weighted imaging and resting-state fMRI data from two independent repositories. The tw-dFC maps were analyzed using independent component analysis, aiming at identifying spatially independent white matter components which support dynamic changes in functional connectivity. Each component consisted of a spatial map of white matter bundles that show consistent fluctuations in functional connectivity at their endpoints, and a time course representative of such functional activity. These components show high intra-subject, inter-subject, and inter-cohort reproducibility. We provided also converging evidence that functional information about white matter activity derived by this method can capture biologically meaningful features of brain connectivity organization, as well as predict higher-order cognitive performance.
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3
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Pirondini E, Kinany N, Sueur CL, Griffis JC, Shulman GL, Corbetta M, Ville DVD. Post-stroke reorganization of transient brain activity characterizes deficits and recovery of cognitive functions. Neuroimage 2022; 255:119201. [PMID: 35405342 DOI: 10.1016/j.neuroimage.2022.119201] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 03/24/2022] [Accepted: 04/07/2022] [Indexed: 02/06/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) has been widely employed to study stroke pathophysiology. In particular, analyses of fMRI signals at rest were directed at quantifying the impact of stroke on spatial features of brain networks. However, brain networks have intrinsic time features that were, so far, disregarded in these analyses. In consequence, standard fMRI analysis failed to capture temporal imbalance resulting from stroke lesions, hence restricting their ability to reveal the interdependent pathological changes in structural and temporal network features following stroke. Here, we longitudinally analyzed hemodynamic-informed transient activity in a large cohort of stroke patients (n = 103) to assess spatial and temporal changes of brain networks after stroke. Metrics extracted from the hemodynamic-informed transient activity were replicable within- and between-individuals in healthy participants, hence supporting their robustness and their clinical applicability. While large-scale spatial patterns of brain networks were preserved after stroke, their durations were altered, with stroke subjects exhibiting a varied pattern of longer and shorter network activations compared to healthy individuals. Specifically, patients showed a longer duration in the lateral precentral gyrus and anterior cingulum, and a shorter duration in the occipital lobe and in the cerebellum. These temporal alterations were associated with white matter damage in projection and association pathways. Furthermore, they were tied to deficits in specific behavioral domains as restoration of healthy brain dynamics paralleled recovery of cognitive functions (attention, language and spatial memory), but was not significantly correlated to motor recovery. These findings underscore the critical importance of network temporal properties in dissecting the pathophysiology of brain changes after stroke, thus shedding new light on the clinical potential of time-resolved methods for fMRI analysis.
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Affiliation(s)
- Elvira Pirondini
- Department of Radiology and Medical Informatics, University of Geneva; 1211 Geneva, Switzerland; Medical Image Processing Laboratory, Center for Neuroprosthetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL); 1202 Geneva, Switzerland; Department of Physical Medicine and Rehabilitation, University of Pittsburgh; Pittsburgh, PA, USA; Rehabilitation Neural Engineering Laboratories, University of Pittsburgh; Pittsburgh, PA, USA; Department of BioEngineering, University of Pittsburgh; Pittsburgh, PA, USA.
| | - Nawal Kinany
- Department of Radiology and Medical Informatics, University of Geneva; 1211 Geneva, Switzerland; Medical Image Processing Laboratory, Center for Neuroprosthetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL); 1202 Geneva, Switzerland; Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics, Institute of Bioengineerin, Ecole Polytechnique Fédérale de Lausanne (EPFL); 1202 Geneva, Switzerland
| | - Cécile Le Sueur
- Medical Image Processing Laboratory, Center for Neuroprosthetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL); 1202 Geneva, Switzerland
| | - Joseph C Griffis
- Department of Neurology, Washington University School of Medicine, St. Louis; MO, 63110, USA
| | - Gordon L Shulman
- Department of Neurology, Washington University School of Medicine, St. Louis; MO, 63110, USA
| | - Maurizio Corbetta
- Department of Neurology, Washington University School of Medicine, St. Louis; MO, 63110, USA; Department of Radiology, Washington University School of Medicine, St. Louis; MO, 63110, USA; Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis; MO, 63110, USA; Department of Bioengineering, Washington University School of Medicine, St. Louis; MO, 63110, USA; Department of Neuroscience and Padua Neuroscience Center, University of Padua; Padua, Italy; Venetian Institute of Molecular Medicine (VIMM); Padua, Italy
| | - Dimitri Van De Ville
- Department of Radiology and Medical Informatics, University of Geneva; 1211 Geneva, Switzerland; Medical Image Processing Laboratory, Center for Neuroprosthetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL); 1202 Geneva, Switzerland.
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4
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Liang X, Koh CL, Yeh CH, Goodin P, Lamp G, Connelly A, Carey LM. Predicting Post-Stroke Somatosensory Function from Resting-State Functional Connectivity: A Feasibility Study. Brain Sci 2021; 11:brainsci11111388. [PMID: 34827387 PMCID: PMC8615819 DOI: 10.3390/brainsci11111388] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 10/07/2021] [Accepted: 10/18/2021] [Indexed: 12/02/2022] Open
Abstract
Accumulating evidence shows that brain functional deficits may be impacted by damage to remote brain regions. Recent advances in neuroimaging suggest that stroke impairment can be better predicted based on disruption to brain networks rather than from lesion locations or volumes only. Our aim was to explore the feasibility of predicting post-stroke somatosensory function from brain functional connectivity through the application of machine learning techniques. Somatosensory impairment was measured using the Tactile Discrimination Test. Functional connectivity was employed to model the global brain function. Behavioral measures and MRI were collected at the same timepoint. Two machine learning models (linear regression and support vector regression) were chosen to predict somatosensory impairment from disrupted networks. Along with two feature pools (i.e., low-order and high-order functional connectivity, or low-order functional connectivity only) engineered, four predictive models were built and evaluated in the present study. Forty-three chronic stroke survivors participated this study. Results showed that the regression model employing both low-order and high-order functional connectivity can predict outcomes based on correlation coefficient of r = 0.54 (p = 0.0002). A machine learning predictive approach, involving high- and low-order modelling, is feasible for the prediction of residual somatosensory function in stroke patients using functional brain networks.
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Affiliation(s)
- Xiaoyun Liang
- Neurorehabilitation and Recovery, Florey Institute of Neuroscience and Mental Health, Melbourne, VIC 3084, Australia; (C.-L.K.); (P.G.); (G.L.); (L.M.C.)
- Victorian Infant Brain Studies (VIBeS) Group, Murdoch Children’s Research Institute, Melbourne, VIC 3052, Australia
- Correspondence:
| | - Chia-Lin Koh
- Neurorehabilitation and Recovery, Florey Institute of Neuroscience and Mental Health, Melbourne, VIC 3084, Australia; (C.-L.K.); (P.G.); (G.L.); (L.M.C.)
- Department of Occupational Therapy, Social Work and Social Policy, School of Allied Health Human Services and Sport, La Trobe University, Melbourne, VIC 3086, Australia
- Department of Occupational Therapy, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan
| | - Chun-Hung Yeh
- Imaging Division, Florey Institute of Neuroscience and Mental Health, Melbourne, VIC 3084, Australia; (C.-H.Y.); (A.C.)
- Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital, Taoyuan 33302, Taiwan
- Department of Psychiatry, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan 33305, Taiwan
| | - Peter Goodin
- Neurorehabilitation and Recovery, Florey Institute of Neuroscience and Mental Health, Melbourne, VIC 3084, Australia; (C.-L.K.); (P.G.); (G.L.); (L.M.C.)
| | - Gemma Lamp
- Neurorehabilitation and Recovery, Florey Institute of Neuroscience and Mental Health, Melbourne, VIC 3084, Australia; (C.-L.K.); (P.G.); (G.L.); (L.M.C.)
- Department of Psychology and Counselling, School of Psychology and Public Health, La Trobe University, Melbourne, VIC 3086, Australia
| | - Alan Connelly
- Imaging Division, Florey Institute of Neuroscience and Mental Health, Melbourne, VIC 3084, Australia; (C.-H.Y.); (A.C.)
| | - Leeanne M. Carey
- Neurorehabilitation and Recovery, Florey Institute of Neuroscience and Mental Health, Melbourne, VIC 3084, Australia; (C.-L.K.); (P.G.); (G.L.); (L.M.C.)
- Department of Occupational Therapy, Social Work and Social Policy, School of Allied Health Human Services and Sport, La Trobe University, Melbourne, VIC 3086, Australia
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5
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Griffa A, Bommarito G, Assal F, Herrmann FR, Van De Ville D, Allali G. Dynamic functional networks in idiopathic normal pressure hydrocephalus: Alterations and reversibility by CSF tap test. Hum Brain Mapp 2020; 42:1485-1502. [PMID: 33296129 PMCID: PMC7927299 DOI: 10.1002/hbm.25308] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 11/02/2020] [Accepted: 11/26/2020] [Indexed: 12/19/2022] Open
Abstract
Idiopathic Normal Pressure Hydrocephalus (iNPH)—the leading cause of reversible dementia in aging—is characterized by ventriculomegaly and gait, cognitive and urinary impairments. Despite its high prevalence estimated at 6% among the elderlies, iNPH remains underdiagnosed and undertreated due to the lack of iNPH‐specific diagnostic markers and limited understanding of pathophysiological mechanisms. INPH diagnosis is also complicated by the frequent occurrence of comorbidities, the most common one being Alzheimer's disease (AD). Here we investigate the resting‐state functional magnetic resonance imaging dynamics of 26 iNPH patients before and after a CSF tap test, and of 48 normal older adults. Alzheimer's pathology was evaluated by CSF biomarkers. We show that the interactions between the default mode, and the executive‐control, salience and attention networks are impaired in iNPH, explain gait and executive disturbances in patients, and are not driven by AD‐pathology. In particular, AD molecular biomarkers are associated with functional changes distinct from iNPH functional alterations. Finally, we demonstrate a partial normalization of brain dynamics 24 hr after a CSF tap test, indicating functional plasticity mechanisms. We conclude that functional changes involving the default mode cross‐network interactions reflect iNPH pathophysiological mechanisms and track treatment response, possibly contributing to iNPH differential diagnosis and better clinical management.
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Affiliation(s)
- Alessandra Griffa
- Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland.,Institute of Bioengineering, Center of Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland
| | - Giulia Bommarito
- Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland.,Institute of Bioengineering, Center of Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland
| | - Frédéric Assal
- Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - François R Herrmann
- Department of Rehabilitation and Geriatrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, Center of Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland.,Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Gilles Allali
- Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland.,Department of Neurology, Division of Cognitive & Motor Aging, Albert Einstein College of Medicine, Yeshiva University, Bronx, New York, USA
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6
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Fortel I, Korthauer LE, Morrissey Z, Zhan L, Ajilore O, Wolfson O, Driscoll I, Schonfeld D, Leow A. Connectome Signatures of Hyperexcitation in Cognitively Intact Middle-Aged Female APOE-ε4 Carriers. Cereb Cortex 2020; 30:6350-6362. [PMID: 32662517 PMCID: PMC7609923 DOI: 10.1093/cercor/bhaa190] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 05/08/2020] [Accepted: 06/07/2020] [Indexed: 12/20/2022] Open
Abstract
Synaptic dysfunction is hypothesized to be one of the earliest brain changes in Alzheimer's disease, leading to "hyperexcitability" in neuronal circuits. In this study, we evaluated a novel hyperexcitation indicator (HI) for each brain region using a hybrid resting-state structural connectome to probe connectome-level excitation-inhibition balance in cognitively intact middle-aged apolipoprotein E (APOE) ε4 carriers with noncarriers (16 male/22 female in each group). Regression with three-way interactions (sex, age, and APOE-ε4 carrier status) to assess the effect of APOE-ε4 on excitation-inhibition balance within each sex and across an age range of 40-60 years yielded a significant shift toward higher HI in female carriers compared with noncarriers (beginning at 50 years). Hyperexcitation was insignificant in the male group. Further, in female carriers the degree of hyperexcitation exhibited significant positive correlation with working memory performance (evaluated via a virtual Morris Water task) in three regions: the left pars triangularis, left hippocampus, and left isthmus of cingulate gyrus. Increased excitation of memory-related circuits may be evidence of compensatory recruitment of neuronal resources for memory-focused activities. In sum, our results are consistent with known Alzheimer's disease sex differences; in that female APOE-ε4 carriers have globally disrupted excitation-inhibition balance that may confer greater vulnerability to disease neuropathology.
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Affiliation(s)
- Igor Fortel
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Laura E Korthauer
- Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA
- Warren Alpert Medical School, Brown University, Providence, RI 02903, USA
| | - Zachery Morrissey
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Liang Zhan
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Ouri Wolfson
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Ira Driscoll
- Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA
| | - Dan Schonfeld
- Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL 60607 USA
| | - Alex Leow
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
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7
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Tarun A, Behjat H, Bolton T, Abramian D, Van De Ville D. Structural mediation of human brain activity revealed by white-matter interpolation of fMRI. Neuroimage 2020; 213:116718. [PMID: 32184188 DOI: 10.1016/j.neuroimage.2020.116718] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 02/07/2020] [Accepted: 03/05/2020] [Indexed: 12/15/2022] Open
Abstract
Understanding how the anatomy of the human brain constrains and influences the formation of large-scale functional networks remains a fundamental question in neuroscience. Here, given measured brain activity in gray matter, we interpolate these functional signals into the white matter on a structurally-informed high-resolution voxel-level brain grid. The interpolated volumes reflect the underlying anatomical information, revealing white matter structures that mediate the interaction between temporally coherent gray matter regions. Functional connectivity analyses of the interpolated volumes reveal an enriched picture of the default mode network (DMN) and its subcomponents, including the different white matter bundles that are implicated in their formation, thus extending currently known spatial patterns that are limited within the gray matter only. These subcomponents have distinct structure-function patterns, each of which are differentially observed during tasks, demonstrating plausible structural mechanisms for functional switching between task-positive and -negative components. This work opens new avenues for the integration of brain structure and function, and demonstrates the collective mediation of white matter pathways across short and long-distance functional connections.
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Affiliation(s)
- Anjali Tarun
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Geneva, 1202, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, 1202, Switzerland.
| | - Hamid Behjat
- Center for Medical Image Science and Visualization, University of Linköping, Linköping, 58183, Sweden
| | - Thomas Bolton
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Geneva, 1202, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, 1202, Switzerland
| | - David Abramian
- Department of Biomedical Engineering, University of Linköping, Linköping, 58183, Sweden; Department of Biomedical Engineering, Lund University, Lund, 22100, Sweden
| | - Dimitri Van De Ville
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Geneva, 1202, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, 1202, Switzerland
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8
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Combined Use of MRI, fMRIand Cognitive Data for Alzheimer’s Disease: Preliminary Results. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9153156] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
MRI can favor clinical diagnosis providing morphological and functional information of several neurological disorders. This paper deals with the problem of exploiting both data, in a combined way, to develop a tool able to support clinicians in the study and diagnosis of Alzheimer’s Disease (AD). In this work, 69 subjects from the ADNI open database, 33 AD patients and 36 healthy controls, were analyzed. The possible existence of a relationship between brain structure modifications and altered functions between patients and healthy controls was investigated performing a correlation analysis on brain volume, calculated from the MRI image, the clustering coefficient, derived from fRMI acquisitions, and the Mini Mental Score Examination (MMSE). A statistically-significant correlation was found only in four ROIs after Bonferroni’s correction. The correlation analysis alone was still not sufficient to provide a reliable and powerful clinical tool in AD diagnosis however. Therefore, a machine learning strategy was studied by training a set of support vector machine classifiers comparing different features. The use of a unimodal approach led to unsatisfactory results, whereas the multimodal approach, i.e., the synergistic combination of MRI, fMRI, and MMSE features, resulted in an accuracy of 95.65%, a specificity of 97.22%, and a sensibility of 93.93%.
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9
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Finotelli P, Forlim CG, Klock L, Pini A, Bächle J, Stoll L, Giemsa P, Fuchs M, Schoofs N, Montag C, Dulio P, Gallinat J, Kühn S. New Graph-Theoretical-Multimodal Approach Using Temporal and Structural Correlations Reveals Disruption in the Thalamo-Cortical Network in Patients with Schizophrenia. Brain Connect 2019; 9:760-769. [PMID: 31232080 DOI: 10.1089/brain.2018.0654] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Schizophrenia has been understood as a network disease with altered functional and structural connectivity in multiple brain networks compatible to the extremely broad spectrum of psychopathological, cognitive, and behavioral symptoms in this disorder. When building brain networks, functional and structural networks are typically modeled independently: Functional network models are based on temporal correlations among brain regions, whereas structural network models are based on anatomical characteristics. Combining both features may give rise to more realistic and reliable models of brain networks. In this study, we applied a new flexible graph-theoretical-multimodal model called FD (F, the functional connectivity matrix, and D, the structural matrix) to construct brain networks combining functional, structural, and topological information of magnetic resonance imaging (MRI) measurements (structural and resting-state imaging) to patients with schizophrenia (n = 35) and matched healthy individuals (n = 41). As a reference condition, the traditional pure functional connectivity (pFC) analysis was carried out. By using the FD model, we found disrupted connectivity in the thalamo-cortical network in schizophrenic patients, whereas the pFC model failed to extract group differences after multiple comparison correction. We interpret this observation as evidence that the FD model is superior to conventional connectivity analysis, by stressing relevant features of the whole-brain connectivity, including functional, structural, and topological signatures. The FD model can be used in future research to model subtle alterations of functional and structural connectivity, resulting in pronounced clinical syndromes and major psychiatric disorders. Lastly, FD is not limited to the analysis of resting-state functional MRI, and it can be applied to electro-encephalography, magneto-encephalography, etc.
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Affiliation(s)
- Paolo Finotelli
- Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - Caroline Garcia Forlim
- Clinic and Policlinic for Psychiatry and Psychotherapy, University Medical Center Hamburg - Eppendorf, Hamburg, Germany
| | - Leonie Klock
- Clinic and Policlinic for Psychiatry and Psychotherapy, University Medical Center Hamburg - Eppendorf, Hamburg, Germany
| | - Alessia Pini
- Department of Statistical Sciences, Università Cattolica del Sacro Cuore, Milan, Italy
| | - Johanna Bächle
- Department of Psychiatry and Psychotherapy, Charité University Medicine and St. Hedwig-Krankenhaus, Berlin, Germany
| | - Laura Stoll
- Department of Psychiatry and Psychotherapy, Charité University Medicine and St. Hedwig-Krankenhaus, Berlin, Germany
| | - Patrick Giemsa
- Department of Psychiatry and Psychotherapy, Charité University Medicine and St. Hedwig-Krankenhaus, Berlin, Germany
| | - Marie Fuchs
- Department of Psychiatry and Psychotherapy, Charité University Medicine and St. Hedwig-Krankenhaus, Berlin, Germany
| | - Nikola Schoofs
- Department of Psychiatry and Psychotherapy, Charité University Medicine and St. Hedwig-Krankenhaus, Berlin, Germany
| | - Christiane Montag
- Department of Psychiatry and Psychotherapy, Charité University Medicine and St. Hedwig-Krankenhaus, Berlin, Germany
| | - Paolo Dulio
- Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - Jürgen Gallinat
- Clinic and Policlinic for Psychiatry and Psychotherapy, University Medical Center Hamburg - Eppendorf, Hamburg, Germany
| | - Simone Kühn
- Clinic and Policlinic for Psychiatry and Psychotherapy, University Medical Center Hamburg - Eppendorf, Hamburg, Germany
- Lise-Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany
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10
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Zamani A, Mychasiuk R, Semple BD. Determinants of social behavior deficits and recovery after pediatric traumatic brain injury. Exp Neurol 2019; 314:34-45. [PMID: 30653969 DOI: 10.1016/j.expneurol.2019.01.007] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Revised: 12/29/2018] [Accepted: 01/12/2019] [Indexed: 12/15/2022]
Abstract
Traumatic brain injury (TBI) during early childhood is associated with a particularly high risk of developing social behavior impairments, including deficits in social cognition that manifest as reduced social interactions, with profound consequences for the individuals' quality of life. A number of pre-injury, post-injury, and injury-related factors have been identified or hypothesized to determine the extent of social behavior problems after childhood TBI. These include variables associated with the individual themselves (e.g. age, genetics, the injury severity, and extent of white matter damage), proximal environmental factors (e.g. family functioning, parental mental health), and more distal environmental factors (e.g. socioeconomic status, access to resources). In this review, we synthesize the available evidence demonstrating which of these determinants influence risk versus resilience to social behavior deficits after pediatric TBI, drawing upon the available clinical and preclinical literature. Injury-related pathology in neuroanatomical regions associated with social cognition and behaviors will also be described, with a focus on findings from magnetic resonance imaging and diffusion tensor imaging. Finally, study limitations and suggested future directions are highlighted. In summary, while no single variable can alone accurately predict the manifestation of social behavior problems after TBI during early childhood, an increased understanding of how both injury and environmental factors can influence social outcomes provides a useful framework for the development of more effective rehabilitation strategies aiming to optimize recovery for young brain-injured patients.
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Affiliation(s)
- Akram Zamani
- Department of Neuroscience, Monash University, Prahran, VIC, Australia
| | - Richelle Mychasiuk
- Department of Neuroscience, Monash University, Prahran, VIC, Australia; Department of Psychology, University of Calgary, Calgary, AB, Canada
| | - Bridgette D Semple
- Department of Neuroscience, Monash University, Prahran, VIC, Australia; Department of Medicine (Royal Melbourne Hospital), The University of Melbourne, Parkville, VIC, Australia.
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11
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Korthauer LE, Zhan L, Ajilore O, Leow A, Driscoll I. Disrupted topology of the resting state structural connectome in middle-aged APOE ε4 carriers. Neuroimage 2018; 178:295-305. [PMID: 29803958 PMCID: PMC6249680 DOI: 10.1016/j.neuroimage.2018.05.052] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Revised: 05/04/2018] [Accepted: 05/22/2018] [Indexed: 01/08/2023] Open
Abstract
The apolipoprotein E (APOE) ε4 allele is the best characterized genetic risk factor for Alzheimer's disease to date. Older APOE ε4 carriers (aged 60 + years) are known to have disrupted structural and functional connectivity, but less is known about APOE-associated network integrity in middle age. The goal of this study was to characterize APOE-related differences in network topology in middle age, as disentangling the early effects of healthy versus pathological aging may aid early detection of Alzheimer's disease and inform treatments. We performed resting state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) in healthy, cognitively normal, middle-aged adults (age 40-60; N = 76, 38 APOE ε4 carriers). Graph theoretical analysis was used to calculate local and global efficiency of 1) a whole brain rs-fMRI network; 2) a whole brain DTI network; and 3) the resting state structural connectome (rsSC), an integrated functional-structural network derived using functional-by-structural hierarchical (FSH) mapping. Our results indicated no APOE ε4-associated differences in network topology of the rs-fMRI or DTI networks alone. However, ε4 carriers had significantly lower global and local efficiency of the integrated rsSC compared to non-carriers. Furthermore, ε4 carriers were less resilient to targeted node failure of the rsSC, which mimics the neuropathological process of Alzheimer's disease. Collectively, these findings suggest that integrating multiple neuroimaging modalities and employing graph theoretical analysis may reveal network-level vulnerabilities that may serve as biomarkers of age-related cognitive decline in middle age, decades before the onset of overt cognitive impairment.
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Affiliation(s)
- L E Korthauer
- Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, USA; Warren Alpert Medical School, Brown University, Providence, RI, USA.
| | - L Zhan
- Engineering and Technology Department, University of Wisconsin-Stout, Menomonie, WI, USA; Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - O Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
| | - A Leow
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA; Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
| | - I Driscoll
- Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
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Guo H, Liu L, Chen J, Xu Y, Jie X. Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset. Front Neurosci 2017; 11:639. [PMID: 29249926 PMCID: PMC5717514 DOI: 10.3389/fnins.2017.00639] [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] [Received: 05/05/2017] [Accepted: 11/03/2017] [Indexed: 12/22/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) is one of the most useful methods to generate functional connectivity networks of the brain. However, conventional network generation methods ignore dynamic changes of functional connectivity between brain regions. Previous studies proposed constructing high-order functional connectivity networks that consider the time-varying characteristics of functional connectivity, and a clustering method was performed to decrease computational cost. However, random selection of the initial clustering centers and the number of clusters negatively affected classification accuracy, and the network lost neurological interpretability. Here we propose a novel method that introduces the minimum spanning tree method to high-order functional connectivity networks. As an unbiased method, the minimum spanning tree simplifies high-order network structure while preserving its core framework. The dynamic characteristics of time series are not lost with this approach, and the neurological interpretation of the network is guaranteed. Simultaneously, we propose a multi-parameter optimization framework that involves extracting discriminative features from the minimum spanning tree high-order functional connectivity networks. Compared with the conventional methods, our resting-state fMRI classification method based on minimum spanning tree high-order functional connectivity networks greatly improved the diagnostic accuracy for Alzheimer's disease.
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Affiliation(s)
- Hao Guo
- Department of Software Engineering, College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Lei Liu
- Department of Software Engineering, College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Junjie Chen
- Department of Software Engineering, College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Yong Xu
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Xiang Jie
- Department of Software Engineering, College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
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