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Lv Z, Li J, Yao L, Guo X. Predicting resting-state brain functional connectivity from the structural connectome using the heat diffusion model: a multiple-timescale fusion method. J Neural Eng 2024; 21:026041. [PMID: 38565132 DOI: 10.1088/1741-2552/ad39a6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 04/02/2024] [Indexed: 04/04/2024]
Abstract
Objective.Understanding the intricate relationship between structural connectivity (SC) and functional connectivity (FC) is pivotal for understanding the complexities of the human brain. To explore this relationship, the heat diffusion model (HDM) was utilized to predict FC from SC. However, previous studies using the HDM have typically predicted FC at a critical time scale in the heat kernel equation, overlooking the dynamic nature of the diffusion process and providing an incomplete representation of the predicted FC.Approach.In this study, we propose an alternative approach based on the HDM. First, we introduced a multiple-timescale fusion method to capture the dynamic features of the diffusion process. Additionally, to enhance the smoothness of the predicted FC values, we employed the Wavelet reconstruction method to maintain local consistency and remove noise. Moreover, to provide a more accurate representation of the relationship between SC and FC, we calculated the linear transformation between the smoothed FC and the empirical FC.Main results.We conducted extensive experiments in two independent datasets. By fusing different time scales in the diffusion process for predicting FC, the proposed method demonstrated higher predictive correlation compared with method considering only critical time points (Singlescale). Furthermore, compared with other existing methods, the proposed method achieved the highest predictive correlations of 0.6939±0.0079 and 0.7302±0.0117 on the two datasets respectively. We observed that the visual network at the network level and the parietal lobe at the lobe level exhibited the highest predictive correlations, indicating that the functional activity in these regions may be closely related to the direct diffusion of information between brain regions.Significance.The multiple-timescale fusion method proposed in this study provides insights into the dynamic aspects of the diffusion process, contributing to a deeper understanding of how brain structure gives rise to brain function.
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Affiliation(s)
- Zhengyuan Lv
- School of Artificial Intelligence, Beijing Normal University, Beijing 100875, People's Republic of China
| | - Jingming Li
- School of Artificial Intelligence, Beijing Normal University, Beijing 100875, People's Republic of China
| | - Li Yao
- School of Artificial Intelligence, Beijing Normal University, Beijing 100875, People's Republic of China
| | - Xiaojuan Guo
- School of Artificial Intelligence, Beijing Normal University, Beijing 100875, People's Republic of China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, People's Republic of China
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Learning multi-agent coordination through connectivity-driven communication. Mach Learn 2022. [DOI: 10.1007/s10994-022-06286-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
AbstractIn artificial multi-agent systems, the ability to learn collaborative policies is predicated upon the agents’ communication skills: they must be able to encode the information received from the environment and learn how to share it with other agents as required by the task at hand. We present a deep reinforcement learning approach, Connectivity Driven Communication (CDC), that facilitates the emergence of multi-agent collaborative behaviour only through experience. The agents are modelled as nodes of a weighted graph whose state-dependent edges encode pair-wise messages that can be exchanged. We introduce a graph-dependent attention mechanisms that controls how the agents’ incoming messages are weighted. This mechanism takes into full account the current state of the system as represented by the graph, and builds upon a diffusion process that captures how the information flows on the graph. The graph topology is not assumed to be known a priori, but depends dynamically on the agents’ observations, and is learnt concurrently with the attention mechanism and policy in an end-to-end fashion. Our empirical results show that CDC is able to learn effective collaborative policies and can over-perform competing learning algorithms on cooperative navigation tasks.
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Huang W, Hu W, Zhang P, Wang J, Jiang Y, Ma L, Zheng Y, Zhang J. Early Changes in the White Matter Microstructure and Connectome Underlie Cognitive Deficit and Depression Symptoms After Mild Traumatic Brain Injury. Front Neurol 2022; 13:880902. [PMID: 35847204 PMCID: PMC9279564 DOI: 10.3389/fneur.2022.880902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 06/01/2022] [Indexed: 11/18/2022] Open
Abstract
Cognitive and emotional impairments are frequent among patients with mild traumatic brain injury (mTBI) and may reflect alterations in the brain structural properties. The relationship between microstructural changes and cognitive and emotional deficits remains unclear in patients with mTBI at the acute stage. The purpose of this study was to analyze the alterations in white matter microstructure and connectome of patients with mTBI within 7 days after injury and investigate whether they are related to the clinical questionnaires. A total of 79 subjects (42 mTBI and 37 healthy controls) underwent neuropsychological assessment and diffusion-tensor MRI scan. The microstructure and connectome of white matter were characterized by tract-based spatial statistics (TBSSs) and graph theory approaches, respectively. Mini-mental state examination (MMSE) and self-rating depression scale (SDS) were used to evaluate the cognitive function and depressive symptoms of all the subjects. Patients with mTBI revealed early increases of fractional anisotropy in most areas compared with the healthy controls. Graph theory analyses showed that patients with mTBI had increased nodal shortest path length, along with decreased nodal degree centrality and nodal efficiency, mainly located in the bilateral temporal lobe and right middle occipital gyrus. Moreover, lower nodal shortest path length and higher nodal efficiency of the right middle occipital gyrus were associated with higher SDS scores. Significantly, the strength of the rich club connection in the mTBI group decreased and was associated with the MMSE. Our study demonstrated that the neuroanatomical alterations of mTBI in the acute stage might be an initial step of damage leading to cognitive deficits and depression symptoms, and arguably, these occur due to distinct mechanisms.
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Affiliation(s)
- Wenjing Huang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Wanjun Hu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Pengfei Zhang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Jun Wang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Yanli Jiang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Laiyang Ma
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Yu Zheng
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Jing Zhang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
- *Correspondence: Jing Zhang
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Thomas PJ, Leow A, Klumpp H, Phan KL, Ajilore O. Network Diffusion Embedding Reveals Transdiagnostic Subnetwork Disruption and Potential Treatment Targets in Internalizing Psychopathologies. Cereb Cortex 2022; 32:1823-1839. [PMID: 34521109 PMCID: PMC9070362 DOI: 10.1093/cercor/bhab314] [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: 05/18/2021] [Revised: 08/03/2021] [Accepted: 08/04/2021] [Indexed: 11/14/2022] Open
Abstract
Network diffusion models are a common and powerful way to study the propagation of information through a complex system and they offer straightforward approaches for studying multimodal brain network data. We developed an analytic framework to identify brain subnetworks with perturbed information diffusion capacity using the structural basis that best maps to resting state functional connectivity and applied it towards a heterogeneous dataset of internalizing psychopathologies (IPs), a set of psychiatric conditions in which similar brain network deficits are found across the swath of the disorders, but a unifying neuropathological substrate for transdiagnostic symptom expression is currently unknown. This research provides preliminary evidence of a transdiagnostic brain subnetwork deficit characterized by information diffusion impairment of the right area 8BM, a key brain region involved in organizing a broad spectrum of cognitive tasks, which may underlie previously reported dysfunction of multiple brain circuits in the IPs. We also demonstrate that models of neuromodulation involving targeting this brain region normalize IP diffusion dynamics towards those of healthy controls. These analyses provide a framework for multimodal methods that identify both brain subnetworks with disrupted information diffusion and potential targets of these subnetworks for therapeutic neuromodulatory intervention based on previously well-characterized methodology.
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Affiliation(s)
- Paul J Thomas
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Alex Leow
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Heide Klumpp
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - K Luan Phan
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH 43210, USA
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
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5
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Schirmer MD, Venkataraman A, Rekik I, Kim M, Mostofsky SH, Nebel MB, Rosch K, Seymour K, Crocetti D, Irzan H, Hütel M, Ourselin S, Marlow N, Melbourne A, Levchenko E, Zhou S, Kunda M, Lu H, Dvornek NC, Zhuang J, Pinto G, Samal S, Zhang J, Bernal-Rusiel JL, Pienaar R, Chung AW. Neuropsychiatric disease classification using functional connectomics - results of the connectomics in neuroimaging transfer learning challenge. Med Image Anal 2021; 70:101972. [PMID: 33677261 DOI: 10.1016/j.media.2021.101972] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 11/25/2020] [Accepted: 01/11/2021] [Indexed: 01/26/2023]
Abstract
Large, open-source datasets, such as the Human Connectome Project and the Autism Brain Imaging Data Exchange, have spurred the development of new and increasingly powerful machine learning approaches for brain connectomics. However, one key question remains: are we capturing biologically relevant and generalizable information about the brain, or are we simply overfitting to the data? To answer this, we organized a scientific challenge, the Connectomics in NeuroImaging Transfer Learning Challenge (CNI-TLC), held in conjunction with MICCAI 2019. CNI-TLC included two classification tasks: (1) diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) within a pre-adolescent cohort; and (2) transference of the ADHD model to a related cohort of Autism Spectrum Disorder (ASD) patients with an ADHD comorbidity. In total, 240 resting-state fMRI (rsfMRI) time series averaged according to three standard parcellation atlases, along with clinical diagnosis, were released for training and validation (120 neurotypical controls and 120 ADHD). We also provided Challenge participants with demographic information of age, sex, IQ, and handedness. The second set of 100 subjects (50 neurotypical controls, 25 ADHD, and 25 ASD with ADHD comorbidity) was used for testing. Classification methodologies were submitted in a standardized format as containerized Docker images through ChRIS, an open-source image analysis platform. Utilizing an inclusive approach, we ranked the methods based on 16 metrics: accuracy, area under the curve, F1-score, false discovery rate, false negative rate, false omission rate, false positive rate, geometric mean, informedness, markedness, Matthew's correlation coefficient, negative predictive value, optimized precision, precision, sensitivity, and specificity. The final rank was calculated using the rank product for each participant across all measures. Furthermore, we assessed the calibration curves of each methodology. Five participants submitted their method for evaluation, with one outperforming all other methods in both ADHD and ASD classification. However, further improvements are still needed to reach the clinical translation of functional connectomics. We have kept the CNI-TLC open as a publicly available resource for developing and validating new classification methodologies in the field of connectomics.
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Affiliation(s)
- Markus D Schirmer
- Massachusetts General Hospital, Harvard Medical School, Boston, USA; German Center for Neurodegenerative Diseases, Bonn, Germany; Clinic for Neuroradiology, University Hospital Bonn, Germany; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA.
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Islem Rekik
- BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; School of Science and Engineering, Computing, University of Dundee, UK; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Minjeong Kim
- Department of Computer Science, University of North Carolina at Greensboro, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Stewart H Mostofsky
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, USA; Department of Neurology, Johns Hopkins School of Medicine, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, USA; Department of Neurology, Johns Hopkins School of Medicine, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Keri Rosch
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA; Department of Radiology, Boston Children's Hospital,Harvard Medical School, Boston, MA, USA
| | - Karen Seymour
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Deana Crocetti
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Hassna Irzan
- Department of Medical Physics and Biomedical Engineering, University College London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, UK; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Michael Hütel
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Neil Marlow
- Institute for Women's Health, University College London, UK; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Andrew Melbourne
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK; Department of Medical Physics and Biomedical Engineering, University College London, UK; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Egor Levchenko
- Institute for Cognitive Neuroscience, Higher School of Economics, Moscow, Russia; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Shuo Zhou
- Department of Computer Science, The University of Sheffield, Sheffield, UK; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Mwiza Kunda
- Department of Computer Science, The University of Sheffield, Sheffield, UK; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Haiping Lu
- Department of Computer Science, The University of Sheffield, Sheffield, UK; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Nicha C Dvornek
- Department of Radiology & Biomedical Imaging, Yale University, New Haven, CT, USA; Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Juntang Zhuang
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Gideon Pinto
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Sandip Samal
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Jennings Zhang
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Jorge L Bernal-Rusiel
- Teradyte LLC, Coral Gables, FL, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Rudolph Pienaar
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Boston Children's Hospital,Harvard Medical School, Boston, MA, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Ai Wern Chung
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA.
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6
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Iandolo R, Semprini M, Buccelli S, Barban F, Zhao M, Samogin J, Bonassi G, Avanzino L, Mantini D, Chiappalone M. Small-World Propensity Reveals the Frequency Specificity of Resting State Networks. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2020; 1:57-64. [PMID: 35402950 PMCID: PMC8979624 DOI: 10.1109/ojemb.2020.2965323] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 12/23/2019] [Accepted: 12/27/2019] [Indexed: 12/02/2022] Open
Abstract
Goal: Functional connectivity (FC) is an important indicator of the brain's state in different conditions, such as rest/task or health/pathology. Here we used high-density electroencephalography coupled to source reconstruction to assess frequency-specific changes of FC during resting state. Specifically, we computed the Small-World Propensity (SWP) index to characterize network small-world architecture across frequencies. Methods: We collected resting state data from healthy participants and built connectivity matrices maintaining the heterogeneity of connection strengths. For a subsample of participants, we also investigated whether the SWP captured FC changes after the execution of a working memory (WM) task. Results: We found that SWP demonstrated a selective increase in the alpha and low beta bands. Moreover, SWP was modulated by a cognitive task and showed increased values in the bands entrained by the WM task. Conclusions: SWP is a valid metric to characterize the frequency-specific behavior of resting state networks.
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Affiliation(s)
- Riccardo Iandolo
- Rehab TechnologiesIstituto Italiano di Tecnologia 16163 Genova Italy
| | - Marianna Semprini
- Rehab TechnologiesIstituto Italiano di Tecnologia 16163 Genova Italy
| | - Stefano Buccelli
- Rehab TechnologiesIstituto Italiano di Tecnologia 16163 Genova Italy
- Rehab TechnologiesIstituto Italiano di Tecnologia 16163 Genova Italy
| | - Federico Barban
- Rehab TechnologiesIstituto Italiano di Tecnologia 16163 Genova Italy
- Department of Informatics, Bioengineering, Robotics and systems Engineering (DIBRIS)University of Genova Genova Italy
| | - Mingqi Zhao
- Research Center for Motor Control and NeuroplasticityKatholieke Universiteit Leuven 3001 Leuven Belgium
| | - Jessica Samogin
- Research Center for Motor Control and NeuroplasticityKatholieke Universiteit Leuven 3001 Leuven Belgium
| | - Gaia Bonassi
- Department of Experimental Medicine, Section of Human PhysiologyUniversity of Genova 16132 Genova Italy
| | - Laura Avanzino
- Department of Experimental Medicine, Section of Human PhysiologyUniversity of Genova 16132 Genova Italy
- IRCCS San Martino Hospital 16132 Genova Italy
| | - Dante Mantini
- Research Center for Motor Control and NeuroplasticityKatholieke Universiteit Leuven 3001 Leuven Belgium
- IRCSS San Camillo Hospital 30126 Venice Lido Italy
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7
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Longitudinal structural connectomic and rich-club analysis in adolescent mTBI reveals persistent, distributed brain alterations acutely through to one year post-injury. Sci Rep 2019; 9:18833. [PMID: 31827105 PMCID: PMC6906376 DOI: 10.1038/s41598-019-54950-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 11/20/2019] [Indexed: 12/28/2022] Open
Abstract
The diffuse nature of mild traumatic brain injury (mTBI) impacts brain white-matter pathways with potentially long-term consequences, even after initial symptoms have resolved. To understand post-mTBI recovery in adolescents, longitudinal studies are needed to determine the interplay between highly individualised recovery trajectories and ongoing development. To capture the distributed nature of mTBI and recovery, we employ connectomes to probe the brain’s structural organisation. We present a diffusion MRI study on adolescent mTBI subjects scanned one day, two weeks and one year after injury with controls. Longitudinal global network changes over time suggests an altered and more ‘diffuse’ network topology post-injury (specifically lower transitivity and global efficiency). Stratifying the connectome by its back-bone, known as the ‘rich-club’, these network changes were driven by the ‘peripheral’ local subnetwork by way of increased network density, fractional anisotropy and decreased diffusivities. This increased structural integrity of the local subnetwork may be to compensate for an injured network, or it may be robust to mTBI and is exhibiting a normal developmental trend. The rich-club also revealed lower diffusivities over time with controls, potentially indicative of longer-term structural ramifications. Our results show evolving, diffuse alterations in adolescent mTBI connectomes beginning acutely and continuing to one year.
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Zhang D, Huang J, Jie B, Du J, Tu L, Liu M. Ordinal Pattern: A New Descriptor for Brain Connectivity Networks. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:1711-1722. [PMID: 29969421 DOI: 10.1109/tmi.2018.2798500] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Brain connectivity networks based on magnetic resonance imaging (MRI) or functional MRI (fMRI) data provide a straightforward way to quantify the structural or functional systems of the brain. Currently, there are several network descriptors developed for representing and analyzing brain connectivity networks. However, most of them are designed for unweighted networks, regardless of the valuable weight information of edges, or do not take advantage of the ordinal relationship of weighted edges (even though they are designed for weighted networks). In this paper, we propose a new network descriptor (i.e., ordinal pattern that contains a sequence of weighted edges) for brain connectivity network analysis. Compared with previous network properties, the proposed ordinal patterns cannot only take advantage of the weight information of edges but also explicitly model the ordinal relationship of weighted edges in brain connectivity networks. We further develop an ordinal pattern-based learning framework for brain disease diagnosis using resting-state fMRI data. Specifically, we first construct a set of brain functional connectivity networks, where each network is corresponding to a particular subject. We then develop an algorithm to identify ordinal patterns that frequently appear in brain connectivity networks of patients and normal controls. We further perform discriminative ordinal pattern selection and extract feature representations for subjects based on the selected ordinal patterns, followed by a learning model for automated brain disease diagnosis. Experimental results on both Alzheimer's Disease Neuroimaging Initiative and attention deficit hyperactivity disorder-200 data sets demonstrate that our method outperforms the several state-of-the-art approaches in the tasks of disease classification and clinical score regression.
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9
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Enguix V, Ding Y, Lodygensky GA. Recent advances in preclinical and clinical multimodal MR in the newborn brain. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2018; 292:149-154. [PMID: 29731237 DOI: 10.1016/j.jmr.2018.04.017] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Revised: 04/24/2018] [Accepted: 04/28/2018] [Indexed: 05/20/2023]
Abstract
Aside from injury identification, MRI of the newborn brain has given us insight into cortical and white matter development, identified windows of vulnerabilities, enabled the introduction of therapeutic hypothermia which has become the standard of care in neonatal asphyxia, and is fostering leapfrogging discoveries in the field of neuro-genetics. This article reviews the main advances in recent years in newborn brain imaging both in preclinical and clinical research.
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Affiliation(s)
- Vicente Enguix
- Department of Pediatrics, University of Montréal, Research Center, CHU Sainte-Justine, Montréal, Québec, Canada
| | - Yang Ding
- Department of Pediatrics, University of Montréal, Research Center, CHU Sainte-Justine, Montréal, Québec, Canada
| | - Gregory A Lodygensky
- Department of Pediatrics, University of Montréal, Research Center, CHU Sainte-Justine, Montréal, Québec, Canada; Department of Pharmacology and Physiology, Université de Montréal, Montréal, Québec, Canada.
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10
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Bastiani M, Andersson JLR, Cordero-Grande L, Murgasova M, Hutter J, Price AN, Makropoulos A, Fitzgibbon SP, Hughes E, Rueckert D, Victor S, Rutherford M, Edwards AD, Smith SM, Tournier JD, Hajnal JV, Jbabdi S, Sotiropoulos SN. Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project. Neuroimage 2018; 185:750-763. [PMID: 29852283 PMCID: PMC6299258 DOI: 10.1016/j.neuroimage.2018.05.064] [Citation(s) in RCA: 90] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Revised: 05/25/2018] [Accepted: 05/26/2018] [Indexed: 12/29/2022] Open
Abstract
The developing Human Connectome Project is set to create and make available to the scientific community a 4-dimensional map of functional and structural cerebral connectivity from 20 to 44 weeks post-menstrual age, to allow exploration of the genetic and environmental influences on brain development, and the relation between connectivity and neurocognitive function. A large set of multi-modal MRI data from fetuses and newborn infants is currently being acquired, along with genetic, clinical and developmental information. In this overview, we describe the neonatal diffusion MRI (dMRI) image processing pipeline and the structural connectivity aspect of the project. Neonatal dMRI data poses specific challenges, and standard analysis techniques used for adult data are not directly applicable. We have developed a processing pipeline that deals directly with neonatal-specific issues, such as severe motion and motion-related artefacts, small brain sizes, high brain water content and reduced anisotropy. This pipeline allows automated analysis of in-vivo dMRI data, probes tissue microstructure, reconstructs a number of major white matter tracts, and includes an automated quality control framework that identifies processing issues or inconsistencies. We here describe the pipeline and present an exemplar analysis of data from 140 infants imaged at 38–44 weeks post-menstrual age. A comprehensive and automated pipeline to consistently analyse neonatal dMRI data. Optimised motion and distortions correction to address newborn specific challenges. The automated QC framework allows to detect issues and to quantify data quality. Automated white matter segmentation allows to extract tract-specific masks. Preliminary data analysis of 140 infants imaged at 38–44 weeks post-menstrual age.
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Affiliation(s)
- Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK.
| | - Jesper L R Andersson
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | | | | | - Jana Hutter
- Centre for the Developing Brain, King's College London, UK
| | | | | | - Sean P Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Emer Hughes
- Centre for the Developing Brain, King's College London, UK
| | | | - Suresh Victor
- Centre for the Developing Brain, King's College London, UK
| | | | | | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | | | | | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Stamatios N Sotiropoulos
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK
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Batalle D, Edwards AD, O'Muircheartaigh J. Annual Research Review: Not just a small adult brain: understanding later neurodevelopment through imaging the neonatal brain. J Child Psychol Psychiatry 2018; 59:350-371. [PMID: 29105061 PMCID: PMC5900873 DOI: 10.1111/jcpp.12838] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/04/2017] [Indexed: 12/27/2022]
Abstract
BACKGROUND There has been a recent proliferation in neuroimaging research focusing on brain development in the prenatal, neonatal and very early childhood brain. Early brain injury and preterm birth are associated with increased risk of neurodevelopmental disorders, indicating the importance of this early period for later outcome. SCOPE AND METHODOLOGY Although using a wide range of different methodologies and investigating diverse samples, the common aim of many of these studies has been to both track normative development and investigate deviations in this development to predict behavioural, cognitive and neurological function in childhood. Here we review structural and functional neuroimaging studies investigating the developing brain. We focus on practical and technical complexities of studying this early age range and discuss how neuroimaging techniques have been successfully applied to investigate later neurodevelopmental outcome. CONCLUSIONS Neuroimaging markers of later outcome still have surprisingly low predictive power and their specificity to individual neurodevelopmental disorders is still under question. However, the field is still young, and substantial challenges to both acquiring and modeling neonatal data are being met.
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Affiliation(s)
- Dafnis Batalle
- Centre for the Developing BrainSchool of Imaging Sciences & Biomedical EngineeringKing's College LondonLondonUK
| | - A. David Edwards
- Centre for the Developing BrainSchool of Imaging Sciences & Biomedical EngineeringKing's College LondonLondonUK
| | - Jonathan O'Muircheartaigh
- Centre for the Developing BrainSchool of Imaging Sciences & Biomedical EngineeringKing's College LondonLondonUK
- Department of NeuroimagingInstitute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
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Opris I, Chang S, Noga BR. What Is the Evidence for Inter-laminar Integration in a Prefrontal Cortical Minicolumn? Front Neuroanat 2017; 11:116. [PMID: 29311848 PMCID: PMC5735117 DOI: 10.3389/fnana.2017.00116] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Accepted: 11/27/2017] [Indexed: 12/25/2022] Open
Abstract
The objective of this perspective article is to examine columnar inter-laminar integration during the executive control of behavior. The integration hypothesis posits that perceptual and behavioral signals are integrated within the prefrontal cortical inter-laminar microcircuits. Inter-laminar minicolumnar activity previously recorded from the dorsolateral prefrontal cortex (dlPFC) of nonhuman primates, trained in a visual delay match-to-sample (DMS) task, was re-assessed from an integrative perspective. Biomorphic multielectrode arrays (MEAs) played a unique role in the in vivo recording of columnar cell firing in the dlPFC layers 2/3 and 5/6. Several integrative aspects stem from these experiments: 1. Functional integration of perceptual and behavioral signals across cortical layers during executive control. The integrative effect of dlPFC minicolumns was shown by: (i) increased correlated firing on correct vs. error trials; (ii) decreased correlated firing when the number of non-matching images increased; and (iii) similar spatial firing preference across cortical-striatal cells during spatial-trials, and less on object-trials. 2. Causal relations to integration of cognitive signals by the minicolumnar turbo-engines. The inter-laminar integration between the perceptual and executive circuits was facilitated by stimulating the infra-granular layers with firing patterns obtained from supra-granular layers that enhanced spatial preference of percent correct performance on spatial trials. 3. Integration across hierarchical levels of the brain. The integration of intention signals (visual spatial, direction) with movement preparation (timing, velocity) in striatum and with the motor command and posture in midbrain is also discussed. These findings provide evidence for inter-laminar integration of executive control signals within brain's prefrontal cortical microcircuits.
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Affiliation(s)
- Ioan Opris
- The Miami Project to Cure Paralysis, Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Stephano Chang
- The Miami Project to Cure Paralysis, Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Brian R. Noga
- The Miami Project to Cure Paralysis, Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL, United States
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Monti RP, Lorenz R, Hellyer P, Leech R, Anagnostopoulos C, Montana G. Decoding Time-Varying Functional Connectivity Networks via Linear Graph Embedding Methods. Front Comput Neurosci 2017; 11:14. [PMID: 28373838 PMCID: PMC5357637 DOI: 10.3389/fncom.2017.00014] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2016] [Accepted: 02/27/2017] [Indexed: 01/24/2023] Open
Abstract
An exciting avenue of neuroscientific research involves quantifying the time-varying properties of functional connectivity networks. As a result, many methods have been proposed to estimate the dynamic properties of such networks. However, one of the challenges associated with such methods involves the interpretation and visualization of high-dimensional, dynamic networks. In this work, we employ graph embedding algorithms to provide low-dimensional vector representations of networks, thus facilitating traditional objectives such as visualization, interpretation and classification. We focus on linear graph embedding methods based on principal component analysis and regularized linear discriminant analysis. The proposed graph embedding methods are validated through a series of simulations and applied to fMRI data from the Human Connectome Project.
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Affiliation(s)
- Ricardo P Monti
- Department of Mathematics, Imperial College London London, UK
| | - Romy Lorenz
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Imperial College London, The Hammersmith HospitalLondon, UK; Department of Bioengineering, Imperial College LondonLondon, UK
| | - Peter Hellyer
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Imperial College London, The Hammersmith HospitalLondon, UK; Department of Bioengineering, Imperial College LondonLondon, UK; Center for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College LondonLondon, UK
| | - Robert Leech
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Imperial College London, The Hammersmith Hospital London, UK
| | | | - Giovanni Montana
- Department of Mathematics, Imperial College LondonLondon, UK; Department of Biomedical Engineering, King's College London, St Thomas' HospitalLondon, UK
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