1
|
Chen X, Ai C, Liu Z, Wang G. Neuroimaging studies of resting-state functional magnetic resonance imaging in eating disorders. BMC Med Imaging 2024; 24:265. [PMID: 39375605 PMCID: PMC11460144 DOI: 10.1186/s12880-024-01432-z] [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: 02/17/2024] [Accepted: 09/18/2024] [Indexed: 10/09/2024] Open
Abstract
Eating disorders (EDs), including anorexia nervosa (AN), bulimia nervosa (BN), binge-eating disorder (BED), and pica, are psychobehavioral conditions characterized by abnormal eating behaviors and an excessive preoccupation with weight and body shape. This review examines changes in brain regions and functional connectivity in ED patients over the past decade (2013-2023) using resting-state functional magnetic resonance imaging (rs-fMRI). Key findings highlight alterations in brain networks such as the default mode network (DMN), central executive network (CEN), and emotion regulation network (ERN). In individuals with AN, there is reduced functional connectivity in areas associated with facial information processing and social cognition, alongside increased connectivity in regions linked to sensory stimulation, aesthetic judgment, and social anxiety. Conversely, BED patients show diminished connectivity in the dorsal anterior cingulate cortex within the salience network and increased connectivity in the posterior cingulate cortex and medial prefrontal cortex within the DMN. These findings suggest that rs-fMRI could serve as a valuable biomarker for assessing brain function and predicting treatment outcomes in EDs, paving the way for personalized therapeutic strategies.
Collapse
Affiliation(s)
- Xiong Chen
- Capital Medical University, Beijing Anding Hospital, Beijing Key Laboratory of Diagnosis and Treatment of Mental Disorders, National Clinical Medical Research Center for Mental Disorders, Beijing, 100088, China
- Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, China
| | - Chunqi Ai
- Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, China
| | - Zhongchun Liu
- RenMin Hospital of Wuhan University, Wuhan, 430060, China
| | - Gang Wang
- Capital Medical University, Beijing Anding Hospital, Beijing Key Laboratory of Diagnosis and Treatment of Mental Disorders, National Clinical Medical Research Center for Mental Disorders, Beijing, 100088, China.
| |
Collapse
|
2
|
Hadi Z, Mahmud M, Seemungal BM. Brain Mechanisms Explaining Postural Imbalance in Traumatic Brain Injury: A Systematic Review. Brain Connect 2024; 14:144-177. [PMID: 38343363 DOI: 10.1089/brain.2023.0064] [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] [Indexed: 03/27/2024] Open
Abstract
Introduction: Persisting imbalance and falls in community-dwelling traumatic brain injury (TBI) survivors are linked to reduced long-term survival. However, a detailed understanding of the impact of TBI upon the brain mechanisms mediating imbalance is lacking. To understand the state of the art concerning the brain mechanisms mediating imbalance in TBI, we performed a systematic review of the literature. Methods: PubMed, Web of Science, and Scopus were searched and peer-reviewed research articles in humans, with any severity of TBI (mild, moderate, severe, or concussion), which linked a postural balance assessment (objective or subjective) with brain imaging (through computed tomography, T1-weighted imaging, functional magnetic resonance imaging [fMRI], resting-state fMRI, diffusion tensor imaging, magnetic resonance spectroscopy, single-photon emission computed tomography, electroencephalography, magnetoencephalography, near-infrared spectroscopy, and evoked potentials) were included. Out of 1940 articles, 60 were retrieved and screened, and 25 articles fulfilling inclusion criteria were included. Results: The most consistent finding was the link between imbalance and the cerebellum; however, the regions within the cerebellum were inconsistent. Discussion: The lack of consistent findings could reflect that imbalance in TBI is due to a widespread brain network dysfunction, as opposed to focal cortical damage. The inconsistency in the reported findings may also be attributed to heterogeneity of methodology, including data analytical techniques, small sample sizes, and choice of control groups. Future studies should include a detailed clinical phenotyping of vestibular function in TBI patients to account for the confounding effect of peripheral vestibular disorders on imbalance and brain imaging.
Collapse
Affiliation(s)
- Zaeem Hadi
- Centre for Vestibular Neurology, Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Mohammad Mahmud
- Centre for Vestibular Neurology, Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Barry M Seemungal
- Centre for Vestibular Neurology, Department of Brain Sciences, Imperial College London, London, United Kingdom
| |
Collapse
|
3
|
Jimenez-Marin A, Diez I, Erramuzpe A, Stramaglia S, Bonifazi P, Cortes JM. Open datasets and code for multi-scale relations on structure, function and neuro-genetics in the human brain. Sci Data 2024; 11:256. [PMID: 38424112 PMCID: PMC10904384 DOI: 10.1038/s41597-024-03060-2] [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: 08/10/2023] [Accepted: 02/12/2024] [Indexed: 03/02/2024] Open
Abstract
The human brain is an extremely complex network of structural and functional connections that operate at multiple spatial and temporal scales. Investigating the relationship between these multi-scale connections is critical to advancing our comprehension of brain function and disorders. However, accurately predicting structural connectivity from its functional counterpart remains a challenging pursuit. One of the major impediments is the lack of public repositories that integrate structural and functional networks at diverse resolutions, in conjunction with modular transcriptomic profiles, which are essential for comprehensive biological interpretation. To mitigate this limitation, our contribution encompasses the provision of an open-access dataset consisting of derivative matrices of functional and structural connectivity across multiple scales, accompanied by code that facilitates the investigation of their interrelations. We also provide additional resources focused on neuro-genetic associations of module-level network metrics, which present promising opportunities to further advance research in the field of network neuroscience, particularly concerning brain disorders.
Collapse
Affiliation(s)
- Antonio Jimenez-Marin
- Computational Neuroimaging Lab, Biobizkaia HRI, Barakaldo, Spain
- Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Ibai Diez
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, United States of America
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, United States of America
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, United States of America
| | - Asier Erramuzpe
- Computational Neuroimaging Lab, Biobizkaia HRI, Barakaldo, Spain
- IKERBASQUE Basque Foundation for Science, Bilbao, Spain
| | - Sebastiano Stramaglia
- Dipartamento Interateneo di Fisica, Universita Degli Studi di Bari Aldo Moro, INFN, Bari, Italy
| | - Paolo Bonifazi
- Computational Neuroimaging Lab, Biobizkaia HRI, Barakaldo, Spain
- IKERBASQUE Basque Foundation for Science, Bilbao, Spain
| | - Jesus M Cortes
- Computational Neuroimaging Lab, Biobizkaia HRI, Barakaldo, Spain.
- IKERBASQUE Basque Foundation for Science, Bilbao, Spain.
- Department of Cell Biology and Histology, University of the Basque Country (UPV/EHU), Leioa, Spain.
| |
Collapse
|
4
|
Bickart KC, Olsen A, Dennis EL, Babikian T, Hoffman AN, Snyder A, Sheridan CA, Fischer JT, Giza CC, Choe MC, Asarnow RF. Frontoamygdala hyperconnectivity predicts affective dysregulation in adolescent moderate-severe TBI. FRONTIERS IN REHABILITATION SCIENCES 2023; 3:1064215. [PMID: 36684686 PMCID: PMC9845889 DOI: 10.3389/fresc.2022.1064215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 11/29/2022] [Indexed: 01/06/2023]
Abstract
In survivors of moderate to severe traumatic brain injury (msTBI), affective disruptions often remain underdetected and undertreated, in part due to poor understanding of the underlying neural mechanisms. We hypothesized that limbic circuits are integral to affective dysregulation in msTBI. To test this, we studied 19 adolescents with msTBI 17 months post-injury (TBI: M age 15.6, 5 females) as well as 44 matched healthy controls (HC: M age 16.4, 21 females). We leveraged two previously identified, large-scale resting-state (rsfMRI) networks of the amygdala to determine whether connectivity strength correlated with affective problems in the adolescents with msTBI. We found that distinct amygdala networks differentially predicted externalizing and internalizing behavioral problems in patients with msTBI. Specifically, patients with the highest medial amygdala connectivity were rated by parents as having greater externalizing behavioral problems measured on the BRIEF and CBCL, but not cognitive problems. The most correlated voxels in that network localize to the rostral anterior cingulate (rACC) and posterior cingulate (PCC) cortices, predicting 48% of the variance in externalizing problems. Alternatively, patients with the highest ventrolateral amygdala connectivity were rated by parents as having greater internalizing behavioral problems measured on the CBCL, but not cognitive problems. The most correlated voxels in that network localize to the ventromedial prefrontal cortex (vmPFC), predicting 57% of the variance in internalizing problems. Both findings were independent of potential confounds including ratings of TBI severity, time since injury, lesion burden based on acute imaging, demographic variables, and other non-amygdalar rsfMRI metrics (e.g., rACC to PCC connectivity), as well as macro- and microstructural measures of limbic circuitry (e.g., amygdala volume and uncinate fasciculus fractional anisotropy). Supporting the clinical significance of these findings, patients with msTBI had significantly greater externalizing problem ratings than healthy control participants and all the brain-behavior findings were specific to the msTBI group in that no similar correlations were found in the healthy control participants. Taken together, frontoamygdala pathways may underlie chronic dysregulation of behavior and mood in patients with msTBI. Future work will focus on neuromodulation techniques to directly affect frontoamygdala pathways with the aim to mitigate such dysregulation problems.
Collapse
Affiliation(s)
- Kevin C. Bickart
- BrainSPORT, Department of Neurosurgery, UCLA, Los Angeles, CA, United States,Department of Neurology, UCLA, Los Angeles, CA, United States,Correspondence: Kevin C. Bickart
| | - Alexander Olsen
- Department of Psychiatry and Biobehavioral Sciences, UCLA, Los Angeles, CA, United States,Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway,Department of Physical Medicine and Rehabilitation, St. Olavs Hospital, University Hospital, Trondheim, Norway
| | - Emily L. Dennis
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, United States
| | - Talin Babikian
- BrainSPORT, Department of Neurosurgery, UCLA, Los Angeles, CA, United States,Department of Psychiatry and Biobehavioral Sciences, UCLA, Los Angeles, CA, United States
| | - Ann N. Hoffman
- BrainSPORT, Department of Neurosurgery, UCLA, Los Angeles, CA, United States
| | - Aliyah Snyder
- BrainSPORT, Department of Neurosurgery, UCLA, Los Angeles, CA, United States,Department of Psychiatry and Biobehavioral Sciences, UCLA, Los Angeles, CA, United States
| | - Christopher A. Sheridan
- Wake Forest School of Medicine, Radiology Informatics and Image Processing Laboratory, Winston-Salem, NC, United States,Wake Forest School of Medicine, Department of Radiology, Section of Neuroradiology, Winston-Salem, NC, United States
| | - Jesse T. Fischer
- BrainSPORT, Department of Neurosurgery, UCLA, Los Angeles, CA, United States,Department of Psychiatry and Biobehavioral Sciences, UCLA, Los Angeles, CA, United States
| | - Christopher C. Giza
- BrainSPORT, Department of Neurosurgery, UCLA, Los Angeles, CA, United States,UCLA Mattel Children's Hospital, Department of Pediatrics, Division of Neurology, Los Angeles, CA, United States
| | - Meeryo C. Choe
- BrainSPORT, Department of Neurosurgery, UCLA, Los Angeles, CA, United States,UCLA Mattel Children's Hospital, Department of Pediatrics, Division of Neurology, Los Angeles, CA, United States
| | - Robert F. Asarnow
- BrainSPORT, Department of Neurosurgery, UCLA, Los Angeles, CA, United States,Department of Psychiatry and Biobehavioral Sciences, UCLA, Los Angeles, CA, United States
| |
Collapse
|
5
|
Gatica M, E. Rosas F, A. M. Mediano P, Diez I, P. Swinnen S, Orio P, Cofré R, M. Cortes J. High-order functional redundancy in ageing explained via alterations in the connectome in a whole-brain model. PLoS Comput Biol 2022; 18:e1010431. [PMID: 36054198 PMCID: PMC9477425 DOI: 10.1371/journal.pcbi.1010431] [Citation(s) in RCA: 8] [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: 10/01/2021] [Revised: 09/15/2022] [Accepted: 07/23/2022] [Indexed: 12/02/2022] Open
Abstract
The human brain generates a rich repertoire of spatio-temporal activity patterns, which support a wide variety of motor and cognitive functions. These patterns of activity change with age in a multi-factorial manner. One of these factors is the variations in the brain's connectomics that occurs along the lifespan. However, the precise relationship between high-order functional interactions and connnectomics, as well as their variations with age are largely unknown, in part due to the absence of mechanistic models that can efficiently map brain connnectomics to functional connectivity in aging. To investigate this issue, we have built a neurobiologically-realistic whole-brain computational model using both anatomical and functional MRI data from 161 participants ranging from 10 to 80 years old. We show that the differences in high-order functional interactions between age groups can be largely explained by variations in the connectome. Based on this finding, we propose a simple neurodegeneration model that is representative of normal physiological aging. As such, when applied to connectomes of young participant it reproduces the age-variations that occur in the high-order structure of the functional data. Overall, these results begin to disentangle the mechanisms by which structural changes in the connectome lead to functional differences in the ageing brain. Our model can also serve as a starting point for modeling more complex forms of pathological ageing or cognitive deficits.
Collapse
Affiliation(s)
- Marilyn Gatica
- Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile
- Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Fernando E. Rosas
- Centre for Psychedelic Research, Department of Brain Science, Imperial College London, London, United Kingdom
- Data Science Institute, Imperial College London, London, United Kingdom
- Center for Complexity Science, Imperial College London, London, United Kingdom
- Department of Informatics, University of Sussex, Brighton, United Kingdom
| | - Pedro A. M. Mediano
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
- Department of Psychology, Queen Mary University of London, London, United Kingdom
| | - Ibai Diez
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Stephan P. Swinnen
- Research Center for Movement Control and Neuroplasticity, Department of Movement Sciences, KU Leuven, Leuven, Belgium
- KU Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
| | - Patricio Orio
- Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile
- Instituto de Neurociencias, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Rodrigo Cofré
- CIMFAV-Ingemat, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso, Chile
- Department of Integrative and Computational Neuroscience, Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique, Gif-sur-Yvette, France
| | - Jesus M. Cortes
- Neuroimaging Lab, Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain
- IKERBASQUE: The Basque Foundation for Science, Bilbao, Spain
- Department of Cell Biology and Histology, University of the Basque Country, Leioa, Spain
| |
Collapse
|
6
|
Fernandez-Iriondo I, Jimenez-Marin A, Sierra B, Aginako N, Bonifazi P, Cortes JM. Brain Mapping of Behavioral Domains Using Multi-Scale Networks and Canonical Correlation Analysis. Front Neurosci 2022; 16:889725. [PMID: 35801180 PMCID: PMC9255673 DOI: 10.3389/fnins.2022.889725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 05/27/2022] [Indexed: 11/13/2022] Open
Abstract
Simultaneous mapping of multiple behavioral domains into brain networks remains a major challenge. Here, we shed some light on this problem by employing a combination of machine learning, structural and functional brain networks at different spatial resolutions (also known as scales), together with performance scores across multiple neurobehavioral domains, including sensation, motor skills, and cognition. Provided by the Human Connectome Project, we make use of three cohorts: 640 participants for model training, 160 subjects for validation, and 200 subjects for model performance testing thus enhancing prediction generalization. Our modeling consists of two main stages, namely dimensionality reduction in brain network features at multiple scales, followed by canonical correlation analysis, which determines an optimal linear combination of connectivity features to predict multiple behavioral performance scores. To assess the differences in the predictive power of each modality, we separately applied three different strategies: structural unimodal, functional unimodal, and multimodal, that is, structural in combination with functional features of the brain network. Our results show that the multimodal association outperforms any of the unimodal analyses. Then, to answer which human brain structures were most involved in predicting multiple behavioral scores, we simulated different synthetic scenarios in which in each case we completely deleted a brain structure or a complete resting state network, and recalculated performance in its absence. In deletions, we found critical structures to affect performance when predicting single behavioral domains, but this occurred in a lesser manner for prediction of multi-domain behavior. Overall, our results confirm that although there are synergistic contributions between brain structure and function that enhance behavioral prediction, brain networks may also be mutually redundant in predicting multidomain behavior, such that even after deletion of a structure, the connectivity of the others can compensate for its lack in predicting behavior.
Collapse
Affiliation(s)
- Izaro Fernandez-Iriondo
- Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), San Sebastian, Spain
- Computational Neuroimaging Lab, BioCruces-Bizkaia Health Research Institute, Barakaldo, Spain
- Doctoral Programme in Informatics Engineering, University of the Basque Country (UPV/EHU), San Sebastian, Spain
| | - Antonio Jimenez-Marin
- Computational Neuroimaging Lab, BioCruces-Bizkaia Health Research Institute, Barakaldo, Spain
- Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Basilio Sierra
- Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), San Sebastian, Spain
| | - Naiara Aginako
- Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), San Sebastian, Spain
| | - Paolo Bonifazi
- Computational Neuroimaging Lab, BioCruces-Bizkaia Health Research Institute, Barakaldo, Spain
- IKERBASQUE: The Basque Foundation for Science, Bilbao, Spain
| | - Jesus M. Cortes
- Computational Neuroimaging Lab, BioCruces-Bizkaia Health Research Institute, Barakaldo, Spain
- IKERBASQUE: The Basque Foundation for Science, Bilbao, Spain
- Department of Cell Biology and Histology, University of the Basque Country (UPV/EHU), Leioa, Spain
| |
Collapse
|
7
|
Botchway E, Kooper CC, Pouwels PJW, Bruining H, Engelen M, Oosterlaan J, Königs M. Resting-state network organisation in children with traumatic brain injury. Cortex 2022; 154:89-104. [PMID: 35763900 DOI: 10.1016/j.cortex.2022.05.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 04/15/2022] [Accepted: 05/23/2022] [Indexed: 11/03/2022]
Abstract
Children with traumatic brain injury are at risk of neurocognitive and behavioural impairment. Although there is evidence for abnormal brain activity in resting-state networks after TBI, the role of resting-state network organisation in paediatric TBI outcome remains poorly understood. This study is the first to investigate the impact of paediatric TBI on resting-state network organisation using graph theory, and its relevance for functional outcome. Participants were 8-14 years and included children with (i) mild TBI and risk factors for complicated TBI (mildRF+, n = 20), (ii) moderate/severe TBI (n = 15), and (iii) trauma control injuries (n = 27). Children underwent resting-state functional magnetic resonance imaging (fMRI), neurocognitive testing, and behavioural assessment at 2.8 years post-injury. Graph theory was applied to fMRI timeseries to evaluate the impact of TBI on global and local organisation of the resting-state network, and relevance for neurocognitive and behavioural functioning. Children with TBI showed atypical global network organisation as compared to the trauma control group, reflected by lower modularity (mildRF + TBI and moderate/severe TBI), higher smallworldness (mildRF + TBI) and lower assortativity (moderate/severe TBI ps < .04, Cohen's ds: > .6). Regarding local network organisation, the relative importance of hub regions in the network did not differ between groups. Regression analyses showed relationships between global as well as local network parameters with neurocognitive functioning (i.e., working memory, memory encoding; R2 = 23.3 - 38.5%) and behavioural functioning (i.e., externalising problems, R2 = 36.1%). Findings indicate the impact of TBI on global functional network organisation, and the relevance of both global and local network organisation for long-term neurocognitive and behavioural outcome after paediatric TBI. The results suggest potential prognostic value of resting-state network organisation for outcome after paediatric TBI.
Collapse
Affiliation(s)
- Edith Botchway
- School of Psychology, Faculty of Health at the Deakin University, Burwood, Australia
| | - Cece C Kooper
- Emma Children's Hospital, Amsterdam UMC location University of Amsterdam, Department of Pediatrics, Emma Neuroscience Group, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Reproduction and Development Research Institute, Amsterdam, the Netherlands; Amsterdam Neuroscience Research Institute, Amsterdam, the Netherlands.
| | - Petra J W Pouwels
- Amsterdam Neuroscience Research Institute, Amsterdam, the Netherlands; Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Boelelaan 1117, Amsterdam, the Netherlands
| | - Hilgo Bruining
- Amsterdam Reproduction and Development Research Institute, Amsterdam, the Netherlands; Amsterdam Neuroscience Research Institute, Amsterdam, the Netherlands; Emma Children's Hospital, Amsterdam UMC location Vrije Universiteit Amsterdam, N=You Centre, Amsterdam, the Netherlands
| | - Marc Engelen
- Emma Children's Hospital, Amsterdam UMC location University of Amsterdam, Department of Pediatric Neurology, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Leukodystrophy Center, Amsterdam, the Netherlands
| | - Jaap Oosterlaan
- Amsterdam Reproduction and Development Research Institute, Amsterdam, the Netherlands; Emma Children's Hospital, Amsterdam UMC location University of Amsterdam, Department of Pediatrics, Emma Children's Hospital Amsterdam UMC Follow-Me program & Emma Neuroscience Group, Meibergdreef 9, Amsterdam, the Netherlands
| | - Marsh Königs
- Amsterdam Reproduction and Development Research Institute, Amsterdam, the Netherlands; Emma Children's Hospital, Amsterdam UMC location University of Amsterdam, Department of Pediatrics, Emma Children's Hospital Amsterdam UMC Follow-Me program & Emma Neuroscience Group, Meibergdreef 9, Amsterdam, the Netherlands
| |
Collapse
|
8
|
|
9
|
Shenoy Handiru V, Alivar A, Hoxha A, Saleh S, Suviseshamuthu ES, Yue GH, Allexandre D. Graph-theoretical analysis of EEG functional connectivity during balance perturbation in traumatic brain injury: A pilot study. Hum Brain Mapp 2021; 42:4427-4447. [PMID: 34312933 PMCID: PMC8410544 DOI: 10.1002/hbm.25554] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 05/08/2021] [Accepted: 05/27/2021] [Indexed: 12/13/2022] Open
Abstract
Traumatic brain injury (TBI) often results in balance impairment, increasing the risk of falls, and the chances of further injuries. However, the underlying neural mechanisms of postural control after TBI are not well understood. To this end, we conducted a pilot study to explore the neural mechanisms of unpredictable balance perturbations in 17 chronic TBI participants and 15 matched healthy controls (HC) using the EEG, MRI, and diffusion tensor imaging (DTI) data. As quantitative measures of the functional integration and segregation of the brain networks during the postural task, we computed the global graph-theoretic network measures (global efficiency and modularity) of brain functional connectivity derived from source-space EEG in different frequency bands. We observed that the TBI group showed a lower balance performance as measured by the center of pressure displacement during the task, and the Berg Balance Scale (BBS). They also showed reduced brain activation and connectivity during the balance task. Furthermore, the decrease in brain network segregation in alpha-band from baseline to task was smaller in TBI than HC. The DTI findings revealed widespread structural damage. In terms of the neural correlates, we observed a distinct role played by different frequency bands: theta-band modularity during the task was negatively correlated with the BBS in the TBI group; lower beta-band network connectivity was associated with the reduction in white matter structural integrity. Our future studies will focus on how postural training will modulate the functional brain networks in TBI.
Collapse
Affiliation(s)
- Vikram Shenoy Handiru
- Center for Mobility and Rehabilitation Engineering Research, Kessler Foundation, West Orange, New Jersey, USA.,Department of Physical Medicine and Rehabilitation, Rutgers University New Jersey Medical School, Newark, New Jersey, USA
| | - Alaleh Alivar
- Center for Mobility and Rehabilitation Engineering Research, Kessler Foundation, West Orange, New Jersey, USA.,Department of Physical Medicine and Rehabilitation, Rutgers University New Jersey Medical School, Newark, New Jersey, USA
| | - Armand Hoxha
- Center for Mobility and Rehabilitation Engineering Research, Kessler Foundation, West Orange, New Jersey, USA
| | - Soha Saleh
- Center for Mobility and Rehabilitation Engineering Research, Kessler Foundation, West Orange, New Jersey, USA.,Department of Physical Medicine and Rehabilitation, Rutgers University New Jersey Medical School, Newark, New Jersey, USA
| | - Easter S Suviseshamuthu
- Center for Mobility and Rehabilitation Engineering Research, Kessler Foundation, West Orange, New Jersey, USA.,Department of Physical Medicine and Rehabilitation, Rutgers University New Jersey Medical School, Newark, New Jersey, USA
| | - Guang H Yue
- Center for Mobility and Rehabilitation Engineering Research, Kessler Foundation, West Orange, New Jersey, USA.,Department of Physical Medicine and Rehabilitation, Rutgers University New Jersey Medical School, Newark, New Jersey, USA
| | - Didier Allexandre
- Center for Mobility and Rehabilitation Engineering Research, Kessler Foundation, West Orange, New Jersey, USA.,Department of Physical Medicine and Rehabilitation, Rutgers University New Jersey Medical School, Newark, New Jersey, USA
| |
Collapse
|
10
|
Gatica M, Cofré R, Mediano PAM, Rosas FE, Orio P, Diez I, Swinnen SP, Cortes JM. High-Order Interdependencies in the Aging Brain. Brain Connect 2021; 11:734-744. [PMID: 33858199 DOI: 10.1089/brain.2020.0982] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Background: Brain interdependencies can be studied from either a structural/anatomical perspective ("structural connectivity") or by considering statistical interdependencies ("functional connectivity" [FC]). Interestingly, while structural connectivity is by definition pairwise (white-matter fibers project from one region to another), FC is not. However, most FC analyses only focus on pairwise statistics and they neglect higher order interactions. A promising tool to study high-order interdependencies is the recently proposed O-Information, which can quantify the intrinsic statistical synergy and the redundancy in groups of three or more interacting variables. Methods: We analyzed functional magnetic resonance imaging (fMRI) data obtained at rest from 164 healthy subjects with ages ranging in 10 to 80 years and used O-Information to investigate how high-order statistical interdependencies are affected by age. Results: Older participants (from 60 to 80 years old) exhibited a higher predominance of redundant dependencies compared with younger participants, an effect that seems to be pervasive as it is evident for all orders of interaction. In addition, while there is strong heterogeneity across brain regions, we found a "redundancy core" constituted by the prefrontal and motor cortices in which redundancy was evident at all the interaction orders studied. Discussion: High-order interdependencies in fMRI data reveal a dominant redundancy in functions such as working memory, executive, and motor functions. Our methodology can be used for a broad range of applications, and the corresponding code is freely available.
Collapse
Affiliation(s)
- Marilyn Gatica
- Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile.,Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Rodrigo Cofré
- CIMFAV-Ingemat, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso, Chile
| | - Pedro A M Mediano
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - Fernando E Rosas
- Centre for Psychedelic Research, Department of Brain Science, Imperial College London, London, United Kingdom.,Data Science Institute, Imperial College London, London, United Kingdom.,Centre for Complexity Science, Imperial College London, London, United Kingdom
| | - Patricio Orio
- Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile.,Instituto de Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Ibai Diez
- Department of Radiology, Gordon Center for Medical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA.,Neurology Department, Harvard Medical School, Boston, Massachusetts, USA.,Neurotechnology Laboratory, Tecnalia Health Department, Derio, Spain
| | - Stephan P Swinnen
- Research Center for Movement Control and Neuroplasticity, Department of Movement Sciences, KU Leuven, Leuven, Belgium.,Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
| | - Jesus M Cortes
- Computational Neuroimaging Lab, Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain.,IKERBASQUE: The Basque Foundation for Science, Bilbao, Spain.,Department of Cell Biology and Histology, University of the Basque Country, Leioa, Spain
| |
Collapse
|
11
|
Cao M, Luo Y, Wu Z, Mazzola CA, Catania L, Alvarez TL, Halperin JM, Biswal B, Li X. Topological Aberrance of Structural Brain Network Provides Quantitative Substrates of Post-Traumatic Brain Injury Attention Deficits in Children. Brain Connect 2021; 11:651-662. [PMID: 33765837 DOI: 10.1089/brain.2020.0866] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: Traumatic brain injury (TBI)-induced attention deficits are among the most common long-term cognitive consequences in children. Most of the existing studies attempting to understand the neuropathological underpinnings of cognitive and behavioral impairments in TBI have utilized heterogeneous samples and resulted in inconsistent findings. The current research proposed to investigate topological properties of the structural brain network in children with TBI and their relationship with post-TBI attention problems in a more homogeneous subgroup of children who had severe post-TBI attention deficits (TBI-A). Materials and Methods: A total of 31 children with TBI-A and 35 group-matched controls were involved in the study. Diffusion tensor imaging-based probabilistic tractography and graph theoretical techniques were used to construct the structural brain network in each subject. Network topological properties were calculated in both global level and regional (nodal) level. Between-group comparisons among the topological network measures and analyses for searching brain-behavioral were all corrected for multiple comparisons using Bonferroni method. Results: Compared with controls, the TBI-A group showed significantly higher nodal local efficiency and nodal clustering coefficient in left inferior frontal gyrus and right transverse temporal gyrus, whereas significantly lower nodal clustering coefficient in left supramarginal gyrus and lower nodal local efficiency in left parahippocampal gyrus. The temporal lobe topological alterations were significantly associated with the post-TBI inattentive and hyperactive symptoms in the TBI-A group. Conclusion: The results suggest that TBI-related structural re-modularity in the white matter subnetworks associated with temporal lobe may play a critical role in the onset of severe post-TBI attention deficits in children. These findings provide valuable input for understanding the neurobiological substrates of post-TBI attention deficits, and have the potential to serve as quantitatively measurable criteria guiding the development of more timely and tailored strategies for diagnoses and treatments to the affected individuals. Impact statement This study provides a new insight into the neurobiological substrates associated with post-traumatic brain injury attention deficits (TBI-A) in children, by evaluating topological alterations of the structural brain network. The results demonstrated that relative to group-matched controls, the children with TBI-A had significantly altered nodal local efficiency and nodal clustering coefficient in temporal lobe, which strongly linked to elevated inattentive and hyperactive symptoms in the TBI-A group. These findings suggested that white matter structural re-modularity in subnetworks associated with temporal lobe may serve as quantitatively measurable biomarkers for early prediction and diagnosis of post-TBI attention deficits in children.
Collapse
Affiliation(s)
- Meng Cao
- Department of Biomedical Engineering and New Jersey Institute of Technology, Newark, New Jersey, USA
| | - Yuyang Luo
- Department of Biomedical Engineering and New Jersey Institute of Technology, Newark, New Jersey, USA
| | - Ziyan Wu
- Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
| | | | - Lori Catania
- North Jersey Neurodevelopmental Center, North Haledon, New Jersey, USA
| | - Tara L Alvarez
- Department of Biomedical Engineering and New Jersey Institute of Technology, Newark, New Jersey, USA
| | - Jeffrey M Halperin
- Department of Psychology, Queens College, City University of New York, New York, New York, USA
| | - Bharat Biswal
- Department of Biomedical Engineering and New Jersey Institute of Technology, Newark, New Jersey, USA
| | - Xiaobo Li
- Department of Biomedical Engineering and New Jersey Institute of Technology, Newark, New Jersey, USA.,Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
| |
Collapse
|
12
|
Cognitive deficits and rehabilitation mechanisms in mild traumatic brain injury patients revealed by EEG connectivity markers. Clin Neurophysiol 2021; 132:554-567. [PMID: 33453686 DOI: 10.1016/j.clinph.2020.11.034] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 10/13/2020] [Accepted: 11/16/2020] [Indexed: 11/22/2022]
Abstract
OBJECTIVE To explore the multiple specific biomarkers and cognitive compensatory mechanisms of mild traumatic brain injury (mTBI) patients at recovery stage. METHODS The experiment was performed in two sections. In Section I, using event-related potential, event-related oscillation and spatial phase-synchronization, we explored neural dynamics in 24 volunteered healthy controls (HC) and 38 patients at least 6 months post-mTBI (19 with epidural hematoma, EDH; 19 with subdural hematoma, SDH) during a Go/NoGo task. In Section II, according to the neuropsychological scales, patients were divided into sub-groups to assess these electroencephalography (EEG) indicators in identifying different rehabilitation outcomes of mTBI. RESULTS In Section I, mean amplitudes of NoGo-P3 and P3d were decreased in mTBI patients relative to HC, and NoGo-theta power in the non-injured hemisphere was decreased in SDH patients only. In Section II, patients with chronic neuropsychological defects exhibited more serious impairments of intra-hemispheric connectivity, whereas inter-hemispheric centro-parietal and frontal connectivity were enhanced in response to lesions. CONCLUSIONS EEG distinguished mTBI patients from healthy controls, and estimated different rehabilitation outcomes of mTBI. The centro-parietal and frontal connectivity are the main compensatory mechanism for the recovery of mTBI patients. SIGNIFICANCE EEG measurements and network connectivity can track recovery process and mechanism of mTBI.
Collapse
|
13
|
Jimenez-Marin A, Rivera D, Boado V, Diez I, Labayen F, Garrido I, Ramos-Usuga D, Benito-Sánchez I, Rasero J, Cabrera-Zubizarreta A, Gabilondo I, Stramaglia S, Arango-Lasprilla JC, Cortes JM. Brain connectivity and cognitive functioning in individuals six months after multiorgan failure. Neuroimage Clin 2019; 25:102137. [PMID: 31931402 PMCID: PMC6957787 DOI: 10.1016/j.nicl.2019.102137] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 12/03/2019] [Accepted: 12/21/2019] [Indexed: 01/05/2023]
Abstract
Multiorgan failure (MOF) is a life-threating condition that affects two or more systems of organs not involved in the disorder that motivates admission to an Intensive Care Unit (ICU). Patients who survive MOF frequently present long-term functional, neurological, cognitive, and psychiatric sequelae. However, the changes to the brain that explain such symptoms remain unclear. OBJECTIVE To determine brain connectivity and cognitive functioning differences between a group of MOF patients six months after ICU discharge and healthy controls (HC). METHODS 22 MOF patients and 22 HC matched by age, sex, and years of education were recruited. Both groups were administered a 3T magnetic resonance imaging (MRI), including structural T1 and functional BOLD, as well as a comprehensive neuropsychological evaluation that included tests of learning and memory, speed of information processing and attention, executive function, visual constructional abilities, and language. Voxel-based morphometry was used to analyses T1 images. For the functional data at rest, functional connectivity (FC) analyses were performed. RESULTS There were no significant differences in structural imaging and neuropsychological performance between groups, even though patients with MOF performed worse in all the cognitive tests. Functional neuroimaging in the default mode network (DMN) showed hyper-connectivity towards sensory-motor, cerebellum, and visual networks. DMN connectivity had a significant association with the severity of MOF during ICU stay and with the neuropsychological scores in tests of attention and visual constructional abilities. CONCLUSIONS In MOF patients without structural brain injury, DMN connectivity six months after ICU discharge is associated with MOF severity and neuropsychological impairment, which supports the use of resting-state functional MRI as a potential tool to predict the onset of long-term cognitive deficits in these patients. Similar to what occurs at the onset of other pathologies, the observed hyper-connectivity might suggest network re-adaptation following MOF.
Collapse
Affiliation(s)
- Antonio Jimenez-Marin
- Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain; Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Diego Rivera
- Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain
| | - Victoria Boado
- Intensive Care Unit. Cruces University Hospital, Barakaldo, Spain
| | - Ibai Diez
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Neurotechnology Laboratory, Tecnalia Health Department, Derio, Spain
| | - Fermin Labayen
- Intensive Care Unit. Cruces University Hospital, Barakaldo, Spain
| | - Irati Garrido
- Intensive Care Unit. Cruces University Hospital, Barakaldo, Spain
| | - Daniela Ramos-Usuga
- Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Itziar Benito-Sánchez
- Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain; Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Javier Rasero
- Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain
| | | | - Iñigo Gabilondo
- Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain; IKERBASQUE, The Basque Foundation for Science, Bilbao, Spain
| | - Sebastiano Stramaglia
- Dipartamento Interateneo di Fisica, Universita di Bari, and INFN, Sezione di Bari, Italy
| | - Juan Carlos Arango-Lasprilla
- Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain; IKERBASQUE, The Basque Foundation for Science, Bilbao, Spain; Department of Cell Biology and Histology, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Jesus M Cortes
- Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain; IKERBASQUE, The Basque Foundation for Science, Bilbao, Spain; Department of Cell Biology and Histology, University of the Basque Country (UPV/EHU), Leioa, Spain.
| |
Collapse
|
14
|
beim Graben P, Jimenez-Marin A, Diez I, Cortes JM, Desroches M, Rodrigues S. Metastable Resting State Brain Dynamics. Front Comput Neurosci 2019; 13:62. [PMID: 31551744 PMCID: PMC6743347 DOI: 10.3389/fncom.2019.00062] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 08/23/2019] [Indexed: 12/26/2022] Open
Abstract
Metastability refers to the fact that the state of a dynamical system spends a large amount of time in a restricted region of its available phase space before a transition takes place, bringing the system into another state from where it might recur into the previous one. beim Graben and Hutt (2013) suggested to use the recurrence plot (RP) technique introduced by Eckmann et al. (1987) for the segmentation of system's trajectories into metastable states using recurrence grammars. Here, we apply this recurrence structure analysis (RSA) for the first time to resting-state brain dynamics obtained from functional magnetic resonance imaging (fMRI). Brain regions are defined according to the brain hierarchical atlas (BHA) developed by Diez et al. (2015), and as a consequence, regions present high-connectivity in both structure (obtained from diffusion tensor imaging) and function (from the blood-level dependent-oxygenation-BOLD-signal). Remarkably, regions observed by Diez et al. were completely time-invariant. Here, in order to compare this static picture with the metastable systems dynamics obtained from the RSA segmentation, we determine the number of metastable states as a measure of complexity for all subjects and for region numbers varying from 3 to 100. We find RSA convergence toward an optimal segmentation of 40 metastable states for normalized BOLD signals, averaged over BHA modules. Next, we build a bistable dynamics at population level by pooling 30 subjects after Hausdorff clustering. In link with this finding, we reflect on the different modeling frameworks that can allow for such scenarios: heteroclinic dynamics, dynamics with riddled basins of attraction, multiple-timescale dynamics. Finally, we characterize the metastable states both functionally and structurally, using templates for resting state networks (RSNs) and the automated anatomical labeling (AAL) atlas, respectively.
Collapse
Affiliation(s)
- Peter beim Graben
- Communication Engineering, Institute of Electrical Engineering and Information Science, Brandenburg University of Technology Cottbus – Senftenberg, Cottbus, Germany
| | - Antonio Jimenez-Marin
- Computational Neuroimaging Lab, BioCruces-Bizkaia Health Research Institute, Barakaldo, Spain
| | - Ibai Diez
- Department of Radiology, Gordon Center for Medical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, MA, United States
- Neurology Department, Harvard Medical School, Boston, MA, United States
- Neurotechnology Laboratory, Tecnalia Health Department, Derio, Spain
| | - Jesus M. Cortes
- Computational Neuroimaging Lab, BioCruces-Bizkaia Health Research Institute, Barakaldo, Spain
- Ikerbasque - the Basque Foundation for Science, Bilbao, Spain
- Department of Cell Biology and Histology, University of the Basque Country, Leioa, Spain
| | - Mathieu Desroches
- MathNeuro Team, Inria Sophia Antipolis Méditerranée, Valbonne, France
- Université Côte d'Azur, Nice, France
| | - Serafim Rodrigues
- Ikerbasque - the Basque Foundation for Science, Bilbao, Spain
- Mathematical, Computational and Experimental Neuroscience, Basque Center for Applied Mathematics, Bilbao, Spain
| |
Collapse
|
15
|
Wang X, Seguin C, Zalesky A, Wong WW, Chu WCW, Tong RKY. Synchronization lag in post stroke: relation to motor function and structural connectivity. Netw Neurosci 2019; 3:1121-1140. [PMID: 31637341 PMCID: PMC6777982 DOI: 10.1162/netn_a_00105] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2019] [Accepted: 07/25/2019] [Indexed: 12/20/2022] Open
Abstract
Stroke is characterized by delays in the resting-state hemodynamic response, resulting in synchronization lag in neural activity between brain regions. However, the structural basis of this lag remains unclear. In this study, we used resting-state functional MRI (rs-fMRI) to characterize synchronization lag profiles between homotopic regions in 15 individuals (14 males, 1 female) with brain lesions consequent to stroke as well as a group of healthy comparison individuals. We tested whether the network communication efficiency of each individual's structural brain network (connectome) could explain interindividual and interregional variation in synchronization lag profiles. To this end, connectomes were mapped using diffusion MRI data, and communication measures were evaluated under two schemes: shortest paths and navigation. We found that interindividual variation in synchronization lags was inversely associated with communication efficiency under both schemes. Interregional variation in lag was related to navigation efficiency and navigation distance, reflecting its dependence on both distance and structural constraints. Moreover, severity of motor deficits significantly correlated with average synchronization lag in stroke. Our results provide a structural basis for the delay of information transfer between homotopic regions inferred from rs-fMRI and provide insight into the clinical significance of structural-functional relationships in stroke individuals.
Collapse
Affiliation(s)
- Xin Wang
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Melbourne, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Melbourne, Australia
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Australia
| | - Wan-wa Wong
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Winnie Chiu-wing Chu
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China
| | - Raymond Kai-yu Tong
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China
| |
Collapse
|
16
|
Sta Maria NS, Sargolzaei S, Prins ML, Dennis EL, Asarnow RF, Hovda DA, Harris NG, Giza CC. Bridging the gap: Mechanisms of plasticity and repair after pediatric TBI. Exp Neurol 2019; 318:78-91. [PMID: 31055004 DOI: 10.1016/j.expneurol.2019.04.016] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2018] [Revised: 03/09/2019] [Accepted: 04/25/2019] [Indexed: 01/25/2023]
Abstract
Traumatic brain injury is the leading cause of death and disability in the United States, and may be associated with long lasting impairments into adulthood. The multitude of ongoing neurobiological processes that occur during brain maturation confer both considerable vulnerability to TBI but may also provide adaptability and potential for recovery. This review will examine and synthesize our current understanding of developmental neurobiology in the context of pediatric TBI. Delineating this biology will facilitate more targeted initial care, mechanism-based therapeutic interventions and better long-term prognostication and follow-up.
Collapse
Affiliation(s)
- Naomi S Sta Maria
- Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, University of Southern California, 1501 San Pablo Street, ZNI115, Los Angeles, CA 90033, United States of America.
| | - Saman Sargolzaei
- UCLA Brain Injury Research Center, Department of Neurosurgery, University of California at Los Angeles, Box 956901, 300 Stein Plaza, Ste 562, 5th Floor, Los Angeles, CA 90095-6901, United States of America.
| | - Mayumi L Prins
- UCLA Brain Injury Research Center, Department of Neurosurgery, University of California at Los Angeles, Box 956901, 300 Stein Plaza, Ste 562, 5th Floor, Los Angeles, CA 90095-6901, United States of America; Steve Tisch BrainSPORT Program, University of California at Los Angeles, Los Angeles, CA, United States of America.
| | - Emily L Dennis
- Brigham and Women's Hospital/Harvard University and Department of Psychology, Stanford University, 1249 Boylston Street, Boston, MA 02215, United States of America.
| | - Robert F Asarnow
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Box 951759, 760 Westwood Plaza, 48-240C Semel Institute, Los Angeles, CA 90095-1759, United States of America.
| | - David A Hovda
- UCLA Brain Injury Research Center, Department of Neurosurgery, University of California at Los Angeles, Box 956901, 300 Stein Plaza, Ste 562, 5th Floor, Los Angeles, CA 90095-6901, United States of America; Department of Medical and Molecular Pharmacology, University of California at Los Angeles, Box 956901, 300 Stein Plaza, Ste 562 & Semel 18-228A, Los Angeles, CA 90095-6901, United States of America.
| | - Neil G Harris
- UCLA Brain Injury Research Center, Department of Neurosurgery, University of California at Los Angeles, Box 956901, 300 Stein Plaza, Ste 562, 5th Floor, Los Angeles, CA 90095-6901, United States of America; Intellectual Development and Disabilities Research Center, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, United States of America.
| | - Christopher C Giza
- UCLA Brain Injury Research Center, Department of Neurosurgery, University of California at Los Angeles, Box 956901, 300 Stein Plaza, Ste 562, 5th Floor, Los Angeles, CA 90095-6901, United States of America; Steve Tisch BrainSPORT Program, University of California at Los Angeles, Los Angeles, CA, United States of America; Division of Pediatric Neurology, Mattel Children's Hospital - UCLA, Los Angeles, CA, United States of America.
| |
Collapse
|
17
|
Camino-Pontes B, Diez I, Jimenez-Marin A, Rasero J, Erramuzpe A, Bonifazi P, Stramaglia S, Swinnen S, Cortes JM. Interaction Information Along Lifespan of the Resting Brain Dynamics Reveals a Major Redundant Role of the Default Mode Network. ENTROPY 2018; 20:e20100742. [PMID: 33265831 PMCID: PMC7512305 DOI: 10.3390/e20100742] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 09/07/2018] [Accepted: 09/24/2018] [Indexed: 01/06/2023]
Abstract
Interaction Information (II) generalizes the univariate Shannon entropy to triplets of variables, allowing the detection of redundant (R) or synergetic (S) interactions in dynamical networks. Here, we calculated II from functional magnetic resonance imaging data and asked whether R or S vary across brain regions and along lifespan. Preserved along lifespan, we found high overlapping between the pattern of high R and the default mode network, whereas high values of S were overlapping with different cognitive domains, such as spatial and temporal memory, emotion processing and motor skills. Moreover, we have found a robust balance between R and S among different age intervals, indicating informational compensatory mechanisms in brain networks.
Collapse
Affiliation(s)
- Borja Camino-Pontes
- Computational Neuroimaging Lab, Biocruces Health Research Institute, 48903 Barakaldo, Spain
| | - Ibai Diez
- Functional Neurology Research Group, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
- Gordon Center, Department of Nuclear Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
- Neurotechnology Laboratory, Tecnalia Health Department, 48160 Derio, Spain
| | - Antonio Jimenez-Marin
- Computational Neuroimaging Lab, Biocruces Health Research Institute, 48903 Barakaldo, Spain
| | - Javier Rasero
- Computational Neuroimaging Lab, Biocruces Health Research Institute, 48903 Barakaldo, Spain
| | - Asier Erramuzpe
- Computational Neuroimaging Lab, Biocruces Health Research Institute, 48903 Barakaldo, Spain
| | - Paolo Bonifazi
- Computational Neuroimaging Lab, Biocruces Health Research Institute, 48903 Barakaldo, Spain
- IKERBASQUE: The Basque Foundation for Science, 48013 Bilbao, Spain
| | | | - Stephan Swinnen
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, 3001 Leuven, Belgium
- Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | - Jesus M. Cortes
- Computational Neuroimaging Lab, Biocruces Health Research Institute, 48903 Barakaldo, Spain
- IKERBASQUE: The Basque Foundation for Science, 48013 Bilbao, Spain
- Department of Cell Biology and Histology, University of the Basque Country, 48940 Leioa, Spain
- Correspondence: ; Tel.: +34-94600600 (ext. 5199)
| |
Collapse
|
18
|
Bonifazi P, Erramuzpe A, Diez I, Gabilondo I, Boisgontier MP, Pauwels L, Stramaglia S, Swinnen SP, Cortes JM. Structure-function multi-scale connectomics reveals a major role of the fronto-striato-thalamic circuit in brain aging. Hum Brain Mapp 2018; 39:4663-4677. [PMID: 30004604 DOI: 10.1002/hbm.24312] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Revised: 06/27/2018] [Accepted: 06/28/2018] [Indexed: 12/15/2022] Open
Abstract
Physiological aging affects brain structure and function impacting morphology, connectivity, and performance. However, whether some brain connectivity metrics might reflect the age of an individual is still unclear. Here, we collected brain images from healthy participants (N = 155) ranging from 10 to 80 years to build functional (resting state) and structural (tractography) connectivity matrices, both data sets combined to obtain different connectivity features. We then calculated the brain connectome age-an age estimator resulting from a multi-scale methodology applied to the structure-function connectome, and compared it to the chronological age (ChA). Our results were twofold. First, we found that aging widely affects the connectivity of multiple structures, such as anterior cingulate and medial prefrontal cortices, basal ganglia, thalamus, insula, cingulum, hippocampus, parahippocampus, occipital cortex, fusiform, precuneus, and temporal pole. Second, we found that the connectivity between basal ganglia and thalamus to frontal areas, also known as the fronto-striato-thalamic (FST) circuit, makes the major contribution to age estimation. In conclusion, our results highlight the key role played by the FST circuit in the process of healthy aging. Notably, the same methodology can be generally applied to identify the structural-functional connectivity patterns correlating to other biomarkers than ChA.
Collapse
Affiliation(s)
- Paolo Bonifazi
- Biocruces Health Research Institute, Barakaldo, Spain.,IKERBASQUE: The Basque Foundation for Science, Bilbao, Spain
| | | | - Ibai Diez
- Biocruces Health Research Institute, Barakaldo, Spain
| | | | - Matthieu P Boisgontier
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
| | - Lisa Pauwels
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
| | - Sebastiano Stramaglia
- Dipartimento Interateneo di Fisica, Universita di Bari, and INFN, Sezione di Bari, Italy
| | - Stephan P Swinnen
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Leuven, Belgium.,Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
| | - Jesus M Cortes
- Biocruces Health Research Institute, Barakaldo, Spain.,IKERBASQUE: The Basque Foundation for Science, Bilbao, Spain.,Department of Cell Biology and Histology, University of the Basque Country, Leioa, Spain
| |
Collapse
|
19
|
Dennis EL, Babikian T, Giza CC, Thompson PM, Asarnow RF. Neuroimaging of the Injured Pediatric Brain: Methods and New Lessons. Neuroscientist 2018; 24:652-670. [PMID: 29488436 DOI: 10.1177/1073858418759489] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Traumatic brain injury (TBI) is a significant public health problem in the United States, especially for children and adolescents. Current epidemiological data estimate over 600,000 patients younger than 20 years are treated for TBI in emergency rooms annually. While many patients experience a full recovery, for others there can be long-lasting cognitive, neurological, psychological, and behavioral disruptions. TBI in youth can disrupt ongoing brain development and create added family stress during a formative period. The neuroimaging methods used to assess brain injury improve each year, providing researchers a more detailed characterization of the injury and recovery process. In this review, we cover current imaging methods used to quantify brain disruption post-injury, including structural magnetic resonance imaging (MRI), diffusion MRI, functional MRI, resting state fMRI, and magnetic resonance spectroscopy (MRS), with brief coverage of other methods, including electroencephalography (EEG), single-photon emission computed tomography (SPECT), and positron emission tomography (PET). We include studies focusing on pediatric moderate-severe TBI from 2 months post-injury and beyond. While the morbidity of pediatric TBI is considerable, continuing advances in imaging methods have the potential to identify new treatment targets that can lead to significant improvements in outcome.
Collapse
Affiliation(s)
- Emily L Dennis
- 1 Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University Southern California, Marina del Rey, CA, USA
| | - Talin Babikian
- 2 Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA.,3 UCLA Brain Injury Research Center, Department of Neurosurgery and Division of Pediatric Neurology, Mattel Children's Hospital, Los Angeles, CA, USA.,4 UCLA Steve Tisch BrainSPORT Program, Los Angeles, CA, USA
| | - Christopher C Giza
- 3 UCLA Brain Injury Research Center, Department of Neurosurgery and Division of Pediatric Neurology, Mattel Children's Hospital, Los Angeles, CA, USA.,4 UCLA Steve Tisch BrainSPORT Program, Los Angeles, CA, USA.,5 Brain Research Institute, University of California, Los Angeles, CA, USA
| | - Paul M Thompson
- 1 Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University Southern California, Marina del Rey, CA, USA.,6 Departments of Neurology, Pediatrics, Psychiatry, Radiology, Engineering, and Ophthalmology, University of Southern California, Los Angeles, CA, USA
| | - Robert F Asarnow
- 2 Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA.,4 UCLA Steve Tisch BrainSPORT Program, Los Angeles, CA, USA.,5 Brain Research Institute, University of California, Los Angeles, CA, USA.,7 Department of Psychology, University of California, Los Angeles, CA, USA
| |
Collapse
|
20
|
Rasero J, Alonso-Montes C, Diez I, Olabarrieta-Landa L, Remaki L, Escudero I, Mateos B, Bonifazi P, Fernandez M, Arango-Lasprilla JC, Stramaglia S, Cortes JM. Group-Level Progressive Alterations in Brain Connectivity Patterns Revealed by Diffusion-Tensor Brain Networks across Severity Stages in Alzheimer's Disease. Front Aging Neurosci 2017; 9:215. [PMID: 28736521 PMCID: PMC5500648 DOI: 10.3389/fnagi.2017.00215] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Accepted: 06/20/2017] [Indexed: 01/22/2023] Open
Abstract
Alzheimer's disease (AD) is a chronically progressive neurodegenerative disease highly correlated to aging. Whether AD originates by targeting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. Here, we aim to provide an answer to this question at the group-level by looking at differences in diffusion-tensor brain networks. In particular, making use of data from Alzheimer's Disease Neuroimaging Initiative (ADNI), four different groups were defined (all of them matched by age, sex and education level): G1 (N1 = 36, healthy control subjects, Control), G2 (N2 = 36, early mild cognitive impairment, EMCI), G3 (N3 = 36, late mild cognitive impairment, LMCI) and G4 (N4 = 36, AD). Diffusion-tensor brain networks were compared across three disease stages: stage I (Control vs. EMCI), stage II (Control vs. LMCI) and stage III (Control vs. AD). The group comparison was performed using the multivariate distance matrix regression analysis, a technique that was born in genomics and was recently proposed to handle brain functional networks, but here applied to diffusion-tensor data. The results were threefold: First, no significant differences were found in stage I. Second, significant differences were found in stage II in the connectivity pattern of a subnetwork strongly associated to memory function (including part of the hippocampus, amygdala, entorhinal cortex, fusiform gyrus, inferior and middle temporal gyrus, parahippocampal gyrus and temporal pole). Third, a widespread disconnection across the entire AD brain was found in stage III, affecting more strongly the same memory subnetwork appearing in stage II, plus the other new subnetworks, including the default mode network, medial visual network, frontoparietal regions and striatum. Our results are consistent with a scenario where progressive alterations of connectivity arise as the disease severity increases and provide the brain areas possibly involved in such a degenerative process. Further studies applying the same strategy to longitudinal data are needed to fully confirm this scenario.
Collapse
Affiliation(s)
- Javier Rasero
- Dipartimento Interateneo di Fisica, Istituto Nazionale di Fisica Nucleare, Universita degli Studi di BariBari, Italy
- Biocruces Health Research InstituteBarakaldo, Spain
| | | | - Ibai Diez
- Biocruces Health Research InstituteBarakaldo, Spain
| | | | | | - Iñaki Escudero
- Biocruces Health Research InstituteBarakaldo, Spain
- Radiology Service, Cruces University HospitalBarakaldo, Spain
| | - Beatriz Mateos
- Biocruces Health Research InstituteBarakaldo, Spain
- Radiology Service, Cruces University HospitalBarakaldo, Spain
| | - Paolo Bonifazi
- Biocruces Health Research InstituteBarakaldo, Spain
- IKERBASQUE: The Basque Foundation for ScienceBilbao, Spain
| | - Manuel Fernandez
- Biocruces Health Research InstituteBarakaldo, Spain
- Neurology Service, Cruces University HospitalBarakaldo, Spain
| | | | - Sebastiano Stramaglia
- Dipartimento Interateneo di Fisica, Istituto Nazionale di Fisica Nucleare, Universita degli Studi di BariBari, Italy
- Basque Center for Applied MathematicsBilbao, Spain
| | - Jesus M. Cortes
- Biocruces Health Research InstituteBarakaldo, Spain
- IKERBASQUE: The Basque Foundation for ScienceBilbao, Spain
- Department of Cell Biology and Histology, University of the Basque CountryLeioa, Spain
| | | |
Collapse
|