1
|
The impact of aging on human brain network target controllability. Brain Struct Funct 2022; 227:3001-3015. [DOI: 10.1007/s00429-022-02584-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 10/09/2022] [Indexed: 11/27/2022]
|
2
|
Statistical and Machine Learning Link Selection Methods for Brain Functional Networks: Review and Comparison. Brain Sci 2021; 11:brainsci11060735. [PMID: 34073098 PMCID: PMC8227272 DOI: 10.3390/brainsci11060735] [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: 04/22/2021] [Revised: 05/24/2021] [Accepted: 05/28/2021] [Indexed: 11/28/2022] Open
Abstract
Network-based representations have introduced a revolution in neuroscience, expanding the understanding of the brain from the activity of individual regions to the interactions between them. This augmented network view comes at the cost of high dimensionality, which hinders both our capacity of deciphering the main mechanisms behind pathologies, and the significance of any statistical and/or machine learning task used in processing this data. A link selection method, allowing to remove irrelevant connections in a given scenario, is an obvious solution that provides improved utilization of these network representations. In this contribution we review a large set of statistical and machine learning link selection methods and evaluate them on real brain functional networks. Results indicate that most methods perform in a qualitatively similar way, with NBS (Network Based Statistics) winning in terms of quantity of retained information, AnovaNet in terms of stability and ExT (Extra Trees) in terms of lower computational cost. While machine learning methods are conceptually more complex than statistical ones, they do not yield a clear advantage. At the same time, the high heterogeneity in the set of links retained by each method suggests that they are offering complementary views to the data. The implications of these results in neuroscience tasks are finally discussed.
Collapse
|
3
|
Nuzzi D, Pellicoro M, Angelini L, Marinazzo D, Stramaglia S. Synergistic information in a dynamical model implemented on the human structural connectome reveals spatially distinct associations with age. Netw Neurosci 2020; 4:910-924. [PMID: 33615096 PMCID: PMC7888489 DOI: 10.1162/netn_a_00146] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 05/08/2020] [Indexed: 11/24/2022] Open
Abstract
We implement the dynamical Ising model on the large-scale architecture of white matter connections of healthy subjects in the age range 4-85 years, and analyze the dynamics in terms of the synergy, a quantity measuring the extent to which the joint state of pairs of variables is projected onto the dynamics of a target one. We find that the amount of synergy in explaining the dynamics of the hubs of the structural connectivity (in terms of degree strength) peaks before the critical temperature, and can thus be considered as a precursor of a critical transition. Conversely, the greatest amount of synergy goes into explaining the dynamics of more central nodes. We also find that the aging of structural connectivity is associated with significant changes in the simulated dynamics: There are brain regions whose synergy decreases with age, in particular the frontal pole, the subcallosal area, and the supplementary motor area; these areas could then be more likely to show a decline in terms of the capability to perform higher order computation (if structural connectivity was the sole variable). On the other hand, several regions in the temporal cortex show a positive correlation with age in the first 30 years of life, that is, during brain maturation.
Collapse
Affiliation(s)
- Davide Nuzzi
- Dipartimento Interateneo di Fisica, Universitá degli Studi Aldo Moro, Bari and INFN, Bari, Italy
| | - Mario Pellicoro
- Dipartimento Interateneo di Fisica, Universitá degli Studi Aldo Moro, Bari and INFN, Bari, Italy
| | - Leonardo Angelini
- Dipartimento Interateneo di Fisica, Universitá degli Studi Aldo Moro, Bari and INFN, Bari, Italy
| | | | - Sebastiano Stramaglia
- Dipartimento Interateneo di Fisica, Universitá degli Studi Aldo Moro, Bari and INFN, Bari, Italy
- Center of Innovative Technologies for Signal Detection and Processing (TIRES), Universitá degli Studi Aldo Moro, Bari, Italy
| |
Collapse
|
4
|
MRI quality control for the Italian Neuroimaging Network Initiative: moving towards big data in multiple sclerosis. J Neurol 2019; 266:2848-2858. [PMID: 31422457 DOI: 10.1007/s00415-019-09509-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 08/12/2019] [Accepted: 08/13/2019] [Indexed: 01/19/2023]
Abstract
The Italian Neuroimaging Network Initiative (INNI) supports the creation of a repository, where MRI, clinical, and neuropsychological data from multiple sclerosis (MS) patients and healthy controls are collected from Italian Research Centers with internationally recognized expertise in MRI applied to MS. However, multicenter MRI data integration needs standardization and quality control (QC). This study aimed to implement quantitative measures for characterizing the standardization and quality of MRI collected within INNI. MRI scans of 423 MS patients, including 3D T1- and T2-weighted, were obtained from INNI repository (from Centers A, B, C, and D). QC measures were implemented to characterize: (1) head positioning relative to the magnet isocenter; (2) intensity inhomogeneity; (3) relative image contrast between brain tissues; and (4) image artefacts. Centers A and D showed the most accurate subject positioning within the MR scanner (median z-offsets = - 2.6 ± 1.7 cm and - 1.1 ± 2 cm). A low, but significantly different, intensity inhomogeneity on 3D T1-weighted MRI was found between all centers (p < 0.05), except for Centers A and C that showed comparable image bias fields. Center D showed the highest relative contrast between gray and normal appearing white matter (NAWM) on 3D T1-weighed MRI (0.63 ± 0.04), while Center B showed the highest relative contrast between NAWM and MS lesions on FLAIR (0.21 ± 0.06). Image artefacts were mainly due to brain movement (60%) and ghosting (35%). The implemented QC procedure ensured systematic data quality assessment within INNI, thus making available a huge amount of high-quality MRI to better investigate pathophysiological substrates and validate novel MRI biomarkers in MS.
Collapse
|
5
|
Kopetzky SJ, Butz-Ostendorf M. From Matrices to Knowledge: Using Semantic Networks to Annotate the Connectome. Front Neuroanat 2018; 12:111. [PMID: 30581382 PMCID: PMC6292998 DOI: 10.3389/fnana.2018.00111] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Accepted: 11/23/2018] [Indexed: 11/18/2022] Open
Abstract
The connectome is regarded as the key to brain function in health and disease. Structural and functional neuroimaging enables us to measure brain connectivity in the living human brain. The field of connectomics describes the connectome as a mathematical graph with its connection strengths being represented by connectivity matrices. Graph theory algorithms are used to assess the integrity of the graph as a whole and to reveal brain network biomarkers for brain diseases; however, the faulty wiring of single connections or subnetworks as the structural correlate for neurological or mental diseases remains elusive. We describe a novel approach to represent the knowledge of human brain connectivity by a semantic network – a formalism frequently used in knowledge management to describe the semantic relations between objects. In our novel approach, objects are brain areas and connectivity is modeled as semantic relations among them. The semantic network turns the graph of the connectome into an explicit knowledge base about which brain areas are interconnected. Moreover, this approach can semantically enrich the measured connectivity of an individual subject by the semantic context from ontologies, brain atlases and molecular biological databases. Integrating all measurements and facts into one unified feature space enables cross-modal comparisons and analyses. We used a query mechanism for semantic networks to extract functional, structural and transcriptome networks. We found that in general higher structural and functional connectivity go along with a lower differential gene expression among connected brain areas; however, subcortical motor areas and limbic structures turned out to have a localized high differential gene expression while being strongly connected. In an additional explorative use case, we could show a localized high availability of fkbp5, gmeb1, and gmeb2 genes at a connection hub of temporo-limbic brain networks. Fkbp5 is known for having a role in stress-related psychiatric disorders, while gmeb1 and gmeb2 encode for modulator proteins of the glucocorticoid receptor, a key receptor in the hormonal stress system. Semantic networks tremendously ease working with multimodal neuroimaging and neurogenetics data and may reveal relevant coincidences between transcriptome and connectome networks.
Collapse
|
6
|
Battiston F, Guillon J, Chavez M, Latora V, De Vico Fallani F. Multiplex core-periphery organization of the human connectome. J R Soc Interface 2018; 15:20180514. [PMID: 30209045 PMCID: PMC6170773 DOI: 10.1098/rsif.2018.0514] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Accepted: 08/16/2018] [Indexed: 01/16/2023] Open
Abstract
What is the core of the human brain is a fundamental question that has been mainly addressed by studying the anatomical connections between differently specialized areas, thus neglecting the possible contributions from their functional interactions. While many methods are available to identify the core of a network when connections between nodes are all of the same type, a principled approach to define the core when multiple types of connectivity are allowed is still lacking. Here, we introduce a general framework to define and extract the core-periphery structure of multi-layer networks by explicitly taking into account the connectivity patterns at each layer. We first validate our algorithm on synthetic networks of different size and density, and with tunable overlap between the cores at different layers. We then use our method to merge information from structural and functional brain networks, obtaining in this way an integrated description of the core of the human connectome. Results confirm the role of the main known cortical and subcortical hubs, but also suggest the presence of new areas in the sensori-motor cortex that are crucial for intrinsic brain functioning. Taken together these findings provide fresh evidence on a fundamental question in modern neuroscience and offer new opportunities to explore the mesoscale properties of multimodal brain networks.
Collapse
Affiliation(s)
- Federico Battiston
- Inria Paris, Aramis project-team, 75013 Paris, France
- CNRS, Sorbonne Universites, UPMC Univ Paris 06, Inserm, Institut du cerveau et la moelle epiniere (ICM), Hopital Pitie-Salpetriere, 75013 Paris, France
- School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, UK
| | - Jeremy Guillon
- Inria Paris, Aramis project-team, 75013 Paris, France
- CNRS, Sorbonne Universites, UPMC Univ Paris 06, Inserm, Institut du cerveau et la moelle epiniere (ICM), Hopital Pitie-Salpetriere, 75013 Paris, France
| | - Mario Chavez
- CNRS, Sorbonne Universites, UPMC Univ Paris 06, Inserm, Institut du cerveau et la moelle epiniere (ICM), Hopital Pitie-Salpetriere, 75013 Paris, France
| | - Vito Latora
- School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, UK
- Dipartimento di Fisica ed Astronomia, Università di Catania and INFN, 95123 Catania, Italy
| | - Fabrizio De Vico Fallani
- Inria Paris, Aramis project-team, 75013 Paris, France
- CNRS, Sorbonne Universites, UPMC Univ Paris 06, Inserm, Institut du cerveau et la moelle epiniere (ICM), Hopital Pitie-Salpetriere, 75013 Paris, France
| |
Collapse
|
7
|
Battiston F, Nicosia V, Chavez M, Latora V. Multilayer motif analysis of brain networks. CHAOS (WOODBURY, N.Y.) 2017; 27:047404. [PMID: 28456158 DOI: 10.1063/1.4979282] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In the last decade, network science has shed new light both on the structural (anatomical) and on the functional (correlations in the activity) connectivity among the different areas of the human brain. The analysis of brain networks has made possible to detect the central areas of a neural system and to identify its building blocks by looking at overabundant small subgraphs, known as motifs. However, network analysis of the brain has so far mainly focused on anatomical and functional networks as separate entities. The recently developed mathematical framework of multi-layer networks allows us to perform an analysis of the human brain where the structural and functional layers are considered together. In this work, we describe how to classify the subgraphs of a multiplex network, and we extend the motif analysis to networks with an arbitrary number of layers. We then extract multi-layer motifs in brain networks of healthy subjects by considering networks with two layers, anatomical and functional, respectively, obtained from diffusion and functional magnetic resonance imaging. Results indicate that subgraphs in which the presence of a physical connection between brain areas (links at the structural layer) coexists with a non-trivial positive correlation in their activities are statistically overabundant. Finally, we investigate the existence of a reinforcement mechanism between the two layers by looking at how the probability to find a link in one layer depends on the intensity of the connection in the other one. Showing that functional connectivity is non-trivially constrained by the underlying anatomical network, our work contributes to a better understanding of the interplay between the structure and function in the human brain.
Collapse
Affiliation(s)
- Federico Battiston
- School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom
| | - Vincenzo Nicosia
- School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom
| | - Mario Chavez
- CNRS-UMR-7225, Hôpital Pitié Salpêtrière, 75013 Paris, France
| | - Vito Latora
- School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom
| |
Collapse
|
8
|
Hull JV, Dokovna LB, Jacokes ZJ, Torgerson CM, Irimia A, Van Horn JD. Resting-State Functional Connectivity in Autism Spectrum Disorders: A Review. Front Psychiatry 2017; 7:205. [PMID: 28101064 PMCID: PMC5209637 DOI: 10.3389/fpsyt.2016.00205] [Citation(s) in RCA: 265] [Impact Index Per Article: 37.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 12/13/2016] [Indexed: 11/18/2022] Open
Abstract
Ongoing debate exists within the resting-state functional MRI (fMRI) literature over how intrinsic connectivity is altered in the autistic brain, with reports of general over-connectivity, under-connectivity, and/or a combination of both. Classifying autism using brain connectivity is complicated by the heterogeneous nature of the condition, allowing for the possibility of widely variable connectivity patterns among individuals with the disorder. Further differences in reported results may be attributable to the age and sex of participants included, designs of the resting-state scan, and to the analysis technique used to evaluate the data. This review systematically examines the resting-state fMRI autism literature to date and compares studies in an attempt to draw overall conclusions that are presently challenging. We also propose future direction for rs-fMRI use to categorize individuals with autism spectrum disorder, serve as a possible diagnostic tool, and best utilize data-sharing initiatives.
Collapse
Affiliation(s)
- Jocelyn V. Hull
- Laboratory of Neuro Imaging (LONI), The Institute for Neuroimaging and Informatics (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Lisa B. Dokovna
- Laboratory of Neuro Imaging (LONI), The Institute for Neuroimaging and Informatics (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Zachary J. Jacokes
- Laboratory of Neuro Imaging (LONI), The Institute for Neuroimaging and Informatics (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Carinna M. Torgerson
- Laboratory of Neuro Imaging (LONI), The Institute for Neuroimaging and Informatics (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | - John Darrell Van Horn
- Laboratory of Neuro Imaging (LONI), The Institute for Neuroimaging and Informatics (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| |
Collapse
|
9
|
Bellec P, Chu C, Chouinard-Decorte F, Benhajali Y, Margulies DS, Craddock RC. The Neuro Bureau ADHD-200 Preprocessed repository. Neuroimage 2016; 144:275-286. [PMID: 27423255 DOI: 10.1016/j.neuroimage.2016.06.034] [Citation(s) in RCA: 102] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2015] [Revised: 05/28/2016] [Accepted: 06/17/2016] [Indexed: 12/17/2022] Open
Abstract
In 2011, the "ADHD-200 Global Competition" was held with the aim of identifying biomarkers of attention-deficit/hyperactivity disorder from resting-state functional magnetic resonance imaging (rs-fMRI) and structural MRI (s-MRI) data collected on 973 individuals. Statisticians and computer scientists were potentially the most qualified for the machine learning aspect of the competition, but generally lacked the specialized skills to implement the necessary steps of data preparation for rs-fMRI. Realizing this barrier to entry, the Neuro Bureau prospectively collaborated with all competitors by preprocessing the data and sharing these results at the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) (http://www.nitrc.org/frs/?group_id=383). This "ADHD-200 Preprocessed" release included multiple analytical pipelines to cater to different philosophies of data analysis. The processed derivatives included denoised and registered 4D fMRI volumes, regional time series extracted from brain parcellations, maps of 10 intrinsic connectivity networks, fractional amplitude of low frequency fluctuation, and regional homogeneity, along with grey matter density maps. The data was used by several teams who competed in the ADHD-200 Global Competition, including the winning entry by a group of biostaticians. To the best of our knowledge, the ADHD-200 Preprocessed release was the first large public resource of preprocessed resting-state fMRI and structural MRI data, and remains to this day the only resource featuring a battery of alternative processing paths.
Collapse
Affiliation(s)
- Pierre Bellec
- The Neuro Bureau, Germany; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Canada; Département d'Informatique et de Recherche Opérationnelle, Université de Montréal, Montréal, Canada.
| | - Carlton Chu
- The Neuro Bureau, Germany; Google DeepMind, London, UK.
| | - François Chouinard-Decorte
- The Neuro Bureau, Germany; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Canada.
| | - Yassine Benhajali
- The Neuro Bureau, Germany; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Canada; Département d'Anthropologie, Université de Montréal, Montréal, Canada.
| | - Daniel S Margulies
- The Neuro Bureau, Germany; Max Planck Research Group for Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - R Cameron Craddock
- The Neuro Bureau, Germany; Computational Neuroimaging Laboratory, Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Center for the Developing Brain, Child Mind Institute, New York, NY, USA.
| |
Collapse
|