1
|
Jin S, Wang J, He Y. The brain network hub degeneration in Alzheimer's disease. BIOPHYSICS REPORTS 2024; 10:213-229. [PMID: 39281195 PMCID: PMC11399886 DOI: 10.52601/bpr.2024.230025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 04/26/2024] [Indexed: 09/18/2024] Open
Abstract
Alzheimer's disease (AD) has been conceptualized as a syndrome of brain network dysfunction. Recent imaging connectomics studies have provided unprecedented opportunities to map structural and functional brain networks in AD. By reviewing molecular, imaging, and computational modeling studies, we have shown that highly connected brain hubs are primarily distributed in the medial and lateral prefrontal, parietal, and temporal regions in healthy individuals and that the hubs are selectively and severely affected in AD as manifested by increased amyloid-beta deposition and regional atrophy, hypo-metabolism, and connectivity dysfunction. Furthermore, AD-related hub degeneration depends on the imaging modality with the most notable degeneration in the medial temporal hubs for morphological covariance networks, the prefrontal hubs for structural white matter networks, and in the medial parietal hubs for functional networks. Finally, the AD-related hub degeneration shows metabolic, molecular, and genetic correlates. Collectively, we conclude that the brain-network-hub-degeneration framework is promising to elucidate the biological mechanisms of network dysfunction in AD, which provides valuable information on potential diagnostic biomarkers and promising therapeutic targets for the disease.
Collapse
Affiliation(s)
- Suhui Jin
- Institute for Brain Research and Rehabilitation, Guangzhou 510631, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, Guangzhou 510631, China
- Guangdong Key Laboratory of Mental Health and Cognitive Science, Guangzhou 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou 510631, China
| | - Yong He
- IDG/McGovern Institute for Brain Research, Beijing 100875, China
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
| |
Collapse
|
2
|
Mai N, Wu Y, Zhong X, Chen B, Zhang M, Peng Q, Ning Y. Increasing variance of rich-club nodes distribution in early onset depression according to dynamic network. Brain Imaging Behav 2024; 18:662-674. [PMID: 38349505 DOI: 10.1007/s11682-023-00848-5] [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] [Accepted: 12/23/2023] [Indexed: 07/04/2024]
Abstract
Early onset depression (EOD) and late onset depression (LOD) are thought to have different pathogeneses, but lack of pathological evidence. In the current study we describe the dynamic rich-club properties of patients with EOD and LOD to address this question indirectly. We recruited 82 patients with late life depression (EOD 40, LOD 42) and 90 healthy controls. Memory, executive function and processing speed were measured, and resting-stage functional MRI was performed with all participants. We constructed a dynamic functional connectivity network and carried out rich-club and modularity analyses. Normalized mutual information (NMI) was applied to describe the variance in rich-club nodes distribution and partitioning. The NMI coefficient of rich club nodes distribution among the three groups was the lowest in the EOD patients (F = 4.298; P = 0.0151, FDR = 0.0231), which was positively correlated with rich-club connectivity (R = 0.886, P < 0.001) and negatively correlated with memory (R = -0.347, P = 0.038) in the EOD group. In the LOD patients, non-rich-club connectivity was positively correlated with memory (R = 0.353, P = 0.030 and R = 0.420, P = 0.009). Furthermore, local connectivity was positively correlated with processing speed in the LOD patients (R = 0.374, P = 0.021). The modular partition was different between the EOD patients and the HCs (P = 0.0013 < 0.05/3). The temporal instability of rich-club nodes was found in the EOD patients, but not the LOD patients, supporting the hypothesis that EOD and LOD result from different pathogenesis, and showing that the instability of the rich-club nodes across time might disrupt rich-club connectivity.
Collapse
Affiliation(s)
- Naikeng Mai
- Department of Neurology, the Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangdong, Guangzhou, China
| | - Yujie Wu
- School of Education Science, Guangdong Polytechnic Normal University, Guangdong, Guangzhou, China
| | - Xiaomei Zhong
- Geriatric Neuroscience center, the Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangdong, Guangzhou, China
| | - Ben Chen
- Geriatric Neuroscience center, the Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangdong, Guangzhou, China
| | - Min Zhang
- Geriatric Neuroscience center, the Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangdong, Guangzhou, China
| | - Qi Peng
- Geriatric Neuroscience center, the Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangdong, Guangzhou, China
| | - Yuping Ning
- Geriatric Neuroscience center, the Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangdong, Guangzhou, China.
| |
Collapse
|
3
|
Spalek K, Coynel D, de Quervain D, Milnik A. Sex-dependent differences in connectivity patterns are related to episodic memory recall. Hum Brain Mapp 2023; 44:5612-5623. [PMID: 37647201 PMCID: PMC10619411 DOI: 10.1002/hbm.26465] [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: 04/03/2023] [Revised: 07/12/2023] [Accepted: 08/08/2023] [Indexed: 09/01/2023] Open
Abstract
Previous studies have shown that females typically outperform males on episodic memory tasks. In this study, we investigated if (1) there are differences between males and females in their connectome characteristics, (2) if these connectivity patterns are associated with memory performance, and (3) if these brain connectome characteristics contribute to the differences in episodic memory performance between sexes. In a sample of 655 healthy young subjects (n = 391 females; n = 264 males), we derived brain network characteristics from diffusion-weighted imaging (DWI) data using models of crossing fibers within each voxel of the brain and probabilistic tractography (graph strength, shortest path length, global efficiency, and weighted transitivity). Group differences were analysed with linear models and mediation analyses were used to explore how connectivity patterns might relate to sex-dependent differences in memory performance. Our results show significant sex-dependent differences in weighted transitivity (d = 0.42), with males showing higher values. Further, we observed a negative association between weighted transitivity and memory performance (r = -0.12). Finally, these distinct connectome characteristics partially mediated the observed differences in memory performance (effect size of the indirect effect r = 0.02). Our findings indicate a higher interconnectedness in females compared to males. Additionally, we demonstrate that the sex-dependent differences in episodic memory performance can be partially explained by the differences in this connectome measure. These results further underscore the importance of sex-dependent differences in brain connectivity and their impact on cognitive function.
Collapse
Affiliation(s)
- Klara Spalek
- Division of Cognitive NeuroscienceDepartment of BiomedicineUniversity of BaselBaselSwitzerland
- Division of Molecular NeuroscienceDepartment of BiomedicineUniversity of BaselBaselSwitzerland
- Hoekzema Lab, Adult PsychiatryUniversity Medical Centre AmsterdamAmsterdamNetherlands
| | - David Coynel
- Division of Cognitive NeuroscienceDepartment of BiomedicineUniversity of BaselBaselSwitzerland
- Research Cluster Molecular and Cognitive NeurosciencesUniversity of BaselBaselSwitzerland
| | - Dominique de Quervain
- Division of Cognitive NeuroscienceDepartment of BiomedicineUniversity of BaselBaselSwitzerland
- Research Cluster Molecular and Cognitive NeurosciencesUniversity of BaselBaselSwitzerland
- Psychiatric University Clinics, University of BaselBaselSwitzerland
| | - Annette Milnik
- Division of Cognitive NeuroscienceDepartment of BiomedicineUniversity of BaselBaselSwitzerland
- Division of Molecular NeuroscienceDepartment of BiomedicineUniversity of BaselBaselSwitzerland
- Psychiatric University Clinics, University of BaselBaselSwitzerland
| |
Collapse
|
4
|
De Benedictis A, Rossi-Espagnet MC, de Palma L, Sarubbo S, Marras CE. Structural networking of the developing brain: from maturation to neurosurgical implications. Front Neuroanat 2023; 17:1242757. [PMID: 38099209 PMCID: PMC10719860 DOI: 10.3389/fnana.2023.1242757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 11/09/2023] [Indexed: 12/17/2023] Open
Abstract
Modern neuroscience agrees that neurological processing emerges from the multimodal interaction among multiple cortical and subcortical neuronal hubs, connected at short and long distance by white matter, to form a largely integrated and dynamic network, called the brain "connectome." The final architecture of these circuits results from a complex, continuous, and highly protracted development process of several axonal pathways that constitute the anatomical substrate of neuronal interactions. Awareness of the network organization of the central nervous system is crucial not only to understand the basis of children's neurological development, but also it may be of special interest to improve the quality of neurosurgical treatments of many pediatric diseases. Although there are a flourishing number of neuroimaging studies of the connectome, a comprehensive vision linking this research to neurosurgical practice is still lacking in the current pediatric literature. The goal of this review is to contribute to bridging this gap. In the first part, we summarize the main current knowledge concerning brain network maturation and its involvement in different aspects of normal neurocognitive development as well as in the pathophysiology of specific diseases. The final section is devoted to identifying possible implications of this knowledge in the neurosurgical field, especially in epilepsy and tumor surgery, and to discuss promising perspectives for future investigations.
Collapse
Affiliation(s)
| | | | - Luca de Palma
- Clinical and Experimental Neurology, Bambino Gesù Children’s Hospital, Rome, Italy
| | - Silvio Sarubbo
- Department of Neurosurgery, Santa Chiara Hospital, Azienda Provinciale per i Servizi Sanitari (APSS), Trento, Italy
| | | |
Collapse
|
5
|
Giersch A, Laprévote V. Perceptual Functioning. Curr Top Behav Neurosci 2023; 63:79-113. [PMID: 36306053 DOI: 10.1007/7854_2022_393] [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: 06/16/2023]
Abstract
Perceptual disorders are not part of the diagnosis criteria for schizophrenia. Yet, a considerable amount of work has been conducted, especially on visual perception abnormalities, and there is little doubt that visual perception is altered in patients. There are several reasons why such perturbations are of interest in this pathology. They are observed during the prodromal phase of psychosis, they are related to the pathophysiology (clinical disorganization, disorders of the sense of self), and they are associated with neuronal connectivity disorders. Perturbations occur at different levels of processing and likely affect how patients interact and adapt to their surroundings. The literature has become very large, and here we try to summarize different models that have guided the exploration of perception in patients. We also illustrate several lines of research by showing how perception has been investigated and by discussing the interpretation of the results. In addition to discussing domains such as contrast sensitivity, masking, and visual grouping, we develop more recent fields like processing at the level of the retina, and the timing of perception.
Collapse
Affiliation(s)
- Anne Giersch
- University of Strasbourg, INSERM U1114, Centre Hospitalier Régional Universitaire de Strasbourg, Strasbourg, France.
| | - Vincent Laprévote
- University of Strasbourg, INSERM U1114, Centre Hospitalier Régional Universitaire de Strasbourg, Strasbourg, France
- CLIP Centre de Liaison et d'Intervention Précoce, Centre Psychothérapique de Nancy, Laxou, France
- Faculté de Médecine, Université de Lorraine, Vandoeuvre-lès-Nancy, France
| |
Collapse
|
6
|
Liu N, Liu Q, Yang Z, Xu J, Fu G, Zhou Y, Li H, Wang Y, Liu L, Qian Q. Different functional alteration in attention-deficit/hyperactivity disorder across developmental age groups: A meta-analysis and an independent validation of resting-state functional connectivity studies. CNS Neurosci Ther 2022; 29:60-69. [PMID: 36468409 PMCID: PMC9804052 DOI: 10.1111/cns.14032] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 03/21/2022] [Accepted: 10/09/2022] [Indexed: 12/09/2022] Open
Abstract
BACKGROUND Attention-deficit/hyperactivity disorder (ADHD) is a highly complex and heterogeneous disorder. Abnormal brain connectivity in ADHD might be influenced by developmental ages which might lead to the lacking of significant spatial convergence across studies. However, the developmental patterns and mechanisms of ADHD brain connectivity remain to be fully uncovered. METHODS In the present study, we searched PubMed, Scopus, Web of Science, and Embase for seed-based whole-brain resting-state functional connectivity studies of ADHD published through October 12th, 2020. The seeds meeting inclusion criteria were categorized into the cortex group and subcortex group, as previous studies suggested that the cortex and subcortex have different temporal patterns of development. Activation likelihood estimation meta-analysis was performed to investigate the abnormal connectivity in different age groups (all-age group, younger: <12 years, older: ≥12 years). Moreover, significant convergence of reported foci was used as seeds for validation with our independent dataset. RESULTS As with previous studies, scarce results were found in the all-age group. However, we found that the younger group consistently exhibited hyper-connectivity between different parts of the cortex and left middle frontal gyrus, and hypo-connectivity between different parts of the cortex and left putamen/pallidus/amygdala. Whereas, the older group (mainly for adults) showed hyper-connectivity between the cortex and right precuneus/sub-gyral/cingulate gyrus. Besides, the abnormal cortico-cortical and cortico-subcortical functional connectivity in children, and the abnormal cortico-cortical functional connectivity in adults were verified in our independent dataset. CONCLUSION Our study emphasizes the importance of developmental age effects on the study of brain networks in ADHD. Further, we proposed that cortico-cortical and cortico-subcortical connectivity might play an important role in the pathophysiology of children with ADHD, while abnormal cortico-cortical connections were more important for adults with ADHD. This work provided a potential new insight to understand the neurodevelopmental mechanisms and possible clinical application of ADHD.
Collapse
Affiliation(s)
- Ningning Liu
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
| | - Qianrong Liu
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
| | - Ziqi Yang
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
| | - Jie Xu
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Magnetic Resonance Imaging Center, Center for Brain Disorders and Cognitive Sciences, Shenzhen UniversityShenzhenChina,Department of Biomedical Sciences of Cells & Systems, Section Cognitive Neuroscience, University Medical Center Groningen, University of GroningenGroningenThe Netherlands
| | - Guanghui Fu
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
| | - Yi Zhou
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
| | - Haimei Li
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
| | - Yufeng Wang
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
| | - Lu Liu
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
| | - Qiujin Qian
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
| |
Collapse
|
7
|
La Corte E, Ordóñez-Rubiano EG, Paiva WS, Johnson JM, Serrao G. Editorial: Current state of the art of human brain white matter: From structural and functional connectivity to neurosurgical applications. Front Neurol 2022; 13:1068212. [DOI: 10.3389/fneur.2022.1068212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 11/01/2022] [Indexed: 11/23/2022] Open
|
8
|
Liu M, Amey RC, Backer RA, Simon JP, Forbes CE. Behavioral Studies Using Large-Scale Brain Networks – Methods and Validations. Front Hum Neurosci 2022; 16:875201. [PMID: 35782044 PMCID: PMC9244405 DOI: 10.3389/fnhum.2022.875201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 05/17/2022] [Indexed: 11/13/2022] Open
Abstract
Mapping human behaviors to brain activity has become a key focus in modern cognitive neuroscience. As methods such as functional MRI (fMRI) advance cognitive scientists show an increasing interest in investigating neural activity in terms of functional connectivity and brain networks, rather than activation in a single brain region. Due to the noisy nature of neural activity, determining how behaviors are associated with specific neural signals is not well-established. Previous research has suggested graph theory techniques as a solution. Graph theory provides an opportunity to interpret human behaviors in terms of the topological organization of brain network architecture. Graph theory-based approaches, however, only scratch the surface of what neural connections relate to human behavior. Recently, the development of data-driven methods, e.g., machine learning and deep learning approaches, provide a new perspective to study the relationship between brain networks and human behaviors across the whole brain, expanding upon past literatures. In this review, we sought to revisit these data-driven approaches to facilitate our understanding of neural mechanisms and build models of human behaviors. We start with the popular graph theory approach and then discuss other data-driven approaches such as connectome-based predictive modeling, multivariate pattern analysis, network dynamic modeling, and deep learning techniques that quantify meaningful networks and connectivity related to cognition and behaviors. Importantly, for each topic, we discuss the pros and cons of the methods in addition to providing examples using our own data for each technique to describe how these methods can be applied to real-world neuroimaging data.
Collapse
Affiliation(s)
- Mengting Liu
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China
- Mengting Liu,
| | - Rachel C. Amey
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, United States
- *Correspondence: Rachel C. Amey,
| | - Robert A. Backer
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, United States
| | - Julia P. Simon
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Chad E. Forbes
- Department of Psychology, Florida Atlantic University, Boca Raton, FL, United States
| |
Collapse
|
9
|
Hall SA, Bell RP, Gadde S, Towe SL, Nadeem MT, McCann PS, Song AW, Meade CS. Strengthened and posterior-shifted structural rich-club organization in people who use cocaine. Drug Alcohol Depend 2022; 235:109436. [PMID: 35413558 PMCID: PMC9948276 DOI: 10.1016/j.drugalcdep.2022.109436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 03/18/2022] [Accepted: 03/30/2022] [Indexed: 02/04/2023]
Abstract
BACKGROUND People with cocaine use disorder (CUD) often have abnormal cognitive function and brain structure. Cognition is supported by brain networks that typically have characteristics like rich-club organization, which is a group of regions that are highly connected across the brain and to each other, and small worldness, which is a balance between local and long-distance connections. However, it is unknown whether there are abnormalities in structural brain network connectivity of CUD. METHODS Using diffusion-weighted imaging, we measured structural connectivity in 37 people with CUD and 38 age-matched controls. We identified differences in rich-club organization and whether such differences related to small worldness and behavior. We also tested whether rich-club reorganization was associated with caudate and putamen structural connectivity due to the relevance of the dopamine system to cocaine use. RESULTS People with CUD had a higher normalized rich-club coefficient than controls, more edges connecting rich-club nodes to each other and to non-rich-club nodes, and fewer edges connecting non-rich-club nodes. Rich-club nodes were shifted posterior and lateral. Rich-club reorganization was related to lower clustered connectivity around individual nodes found in CUD, to increased impulsivity, and to a decrease in caudate connectivity. CONCLUSIONS These findings are consistent with previous work showing increased rich-club connectivity in conditions associated with a hypofunctional dopamine system. The posterior shift in rich-club nodes in CUD suggests that the structural connectivity of posterior regions may be more impacted than previously recognized in models based on brain function and morphology.
Collapse
Affiliation(s)
- Shana A Hall
- Duke University School of Medicine, Department of Psychiatry and Behavioral Sciences, Campus Box 102848, Durham, NC 27710, USA
| | - Ryan P Bell
- Duke University School of Medicine, Department of Psychiatry and Behavioral Sciences, Campus Box 102848, Durham, NC 27710, USA
| | - Syam Gadde
- Brain Imaging and Analysis Center, Duke University Medical Center, Campus Box 3918, Durham, NC 27710, USA
| | - Sheri L Towe
- Duke University School of Medicine, Department of Psychiatry and Behavioral Sciences, Campus Box 102848, Durham, NC 27710, USA
| | - Muhammad Tauseef Nadeem
- Duke University School of Medicine, Department of Psychiatry and Behavioral Sciences, Campus Box 102848, Durham, NC 27710, USA
| | - Peter S McCann
- Duke University Hospital, 2301 Erwin Rd, Durham, NC 27710, USA
| | - Allen W Song
- Brain Imaging and Analysis Center, Duke University Medical Center, Campus Box 3918, Durham, NC 27710, USA
| | - Christina S Meade
- Duke University School of Medicine, Department of Psychiatry and Behavioral Sciences, Campus Box 102848, Durham, NC 27710, USA; Brain Imaging and Analysis Center, Duke University Medical Center, Campus Box 3918, Durham, NC 27710, USA.
| |
Collapse
|
10
|
The brainstem connectome database. Sci Data 2022; 9:168. [PMID: 35414055 PMCID: PMC9005652 DOI: 10.1038/s41597-022-01219-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 02/25/2022] [Indexed: 11/29/2022] Open
Abstract
Connectivity data of the nervous system and subdivisions, such as the brainstem, cerebral cortex and subcortical nuclei, are necessary to understand connectional structures, predict effects of connectional disorders and simulate network dynamics. For that purpose, a database was built and analyzed which comprises all known directed and weighted connections within the rat brainstem. A longterm metastudy of original research publications describing tract tracing results form the foundation of the brainstem connectome (BC) database which can be analyzed directly in the framework neuroVIISAS. The BC database can be accessed directly by connectivity tables, a web-based tool and the framework. Analysis of global and local network properties, a motif analysis, and a community analysis of the brainstem connectome provides insight into its network organization. For example, we found that BC is a scale-free network with a small-world connectivity. The Louvain modularity and weighted stochastic block matching resulted in partially matching of functions and connectivity. BC modeling was performed to demonstrate signal propagation through the somatosensory pathway which is affected in Multiple sclerosis. Measurement(s) | brainstem | Technology Type(s) | tract tracing metastudy | Factor Type(s) | brain region | Sample Characteristic - Organism | Rattus rattus | Sample Characteristic - Environment | Experimental setup | Sample Characteristic - Location | Germany |
Collapse
|
11
|
Rossini PM, Miraglia F, Vecchio F, Di Iorio R, Iodice F, Cotelli M. General principles of brain electromagnetic rhythmic oscillations and implications for neuroplasticity. HANDBOOK OF CLINICAL NEUROLOGY 2022; 184:221-237. [PMID: 35034737 DOI: 10.1016/b978-0-12-819410-2.00012-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Neuro-plasticity describes the ability of the brain in achieving novel functions, either by transforming its internal connectivity, or by changing the elements of which it is made, meaning that, only those changes, that affect both structural and functional aspects of the system, can be defined as "plastic." The concept of plasticity can be applied to molecular as well as to environmental events that can be recognized as the basic mechanism by which our brain reacts to the internal and external stimuli. When considering brain plasticity within a clinical context-that is the process linked with changes of brain functions following a lesion- the term "reorganization" is somewhat synonymous, referring to the specific types of structural/functional modifications observed as axonal sprouting, long-term synaptic potentiation/inhibition or to the plasticity related genomic responses. Furthermore, brain rewires during maturation, and aging thus maintaining a remarkable learning capacity, allowing it to acquire a wide range of skills, from motor actions to complex abstract reasoning, in a lifelong expression. In this review, the contribution on the "neuroplasticity" topic coming from advanced analysis of EEG rhythms is put forward.
Collapse
Affiliation(s)
- Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy.
| | - Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
| | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy; Department of Technical and Applied Sciences, eCampus University, Novedrate (Como), Italy
| | | | - Francesco Iodice
- Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
| | - Maria Cotelli
- Neuropsychology Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| |
Collapse
|
12
|
Yun JY, Kim YK. Graph theory approach for the structural-functional brain connectome of depression. Prog Neuropsychopharmacol Biol Psychiatry 2021; 111:110401. [PMID: 34265367 DOI: 10.1016/j.pnpbp.2021.110401] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 06/30/2021] [Accepted: 07/07/2021] [Indexed: 01/22/2023]
Abstract
To decipher the organizational styles of neural underpinning in major depressive disorder (MDD), the current article reviewed recent neuroimaging studies (published during 2015-2020) that applied graph theory approach to the diffusion tensor imaging data or functional brain activation data acquired during task-free resting state. The global network organization of resting-state functional connectivity network in MDD were diverse according to the onset age and medication status. Intra-modular functional connections were weaker in MDD compared to healthy controls (HC) for default mode and limbic networks. Weaker local graph metrics of default mode, frontoparietal, and salience network components in MDD compared to HC were also found. On the contrary, brain regions comprising the limbic, sensorimotor, and subcortical networks showed higher local graph metrics in MDD compared to HC. For the brain white matter-based structural connectivity network, the global network organization was comparable to HC in adult MDD but was attenuated in late-life depression. Local graph metrics of limbic, salience, default-mode, subcortical, insular, and frontoparietal network components in structural connectome were affected from the severity of depressive symptoms, burden of perceived stress, and treatment effects. Collectively, the current review illustrated changed global network organization of structural and functional brain connectomes in MDD compared to HC and were varied according to the onset age and medication status. Intra-modular functional connectivity within the default mode and limbic networks were weaker in MDD compared to HC. Local graph metrics of structural connectome for MDD reflected severity of depressive symptom and perceived stress, and were also changed after treatments. Further studies that explore the graph metrics-based neural correlates of clinical features, cognitive styles, treatment response and prognosis in MDD are required.
Collapse
Affiliation(s)
- Je-Yeon Yun
- Seoul National University Hospital, Seoul, Republic of Korea; Yeongeon Student Support Center, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Yong-Ku Kim
- Department of Psychiatry, College of Medicine, Korea University, Seoul, South Korea
| |
Collapse
|
13
|
Bhargava A, Arnold AP, Bangasser DA, Denton KM, Gupta A, Hilliard Krause LM, Mayer EA, McCarthy M, Miller WL, Raznahan A, Verma R. Considering Sex as a Biological Variable in Basic and Clinical Studies: An Endocrine Society Scientific Statement. Endocr Rev 2021; 42:219-258. [PMID: 33704446 PMCID: PMC8348944 DOI: 10.1210/endrev/bnaa034] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Indexed: 02/08/2023]
Abstract
In May 2014, the National Institutes of Health (NIH) stated its intent to "require applicants to consider sex as a biological variable (SABV) in the design and analysis of NIH-funded research involving animals and cells." Since then, proposed research plans that include animals routinely state that both sexes/genders will be used; however, in many instances, researchers and reviewers are at a loss about the issue of sex differences. Moreover, the terms sex and gender are used interchangeably by many researchers, further complicating the issue. In addition, the sex or gender of the researcher might influence study outcomes, especially those concerning behavioral studies, in both animals and humans. The act of observation may change the outcome (the "observer effect") and any experimental manipulation, no matter how well-controlled, is subject to it. This is nowhere more applicable than in physiology and behavior. The sex of established cultured cell lines is another issue, in addition to aneuploidy; chromosomal numbers can change as cells are passaged. Additionally, culture medium contains steroids, growth hormone, and insulin that might influence expression of various genes. These issues often are not taken into account, determined, or even considered. Issues pertaining to the "sex" of cultured cells are beyond the scope of this Statement. However, we will discuss the factors that influence sex and gender in both basic research (that using animal models) and clinical research (that involving human subjects), as well as in some areas of science where sex differences are routinely studied. Sex differences in baseline physiology and associated mechanisms form the foundation for understanding sex differences in diseases pathology, treatments, and outcomes. The purpose of this Statement is to highlight lessons learned, caveats, and what to consider when evaluating data pertaining to sex differences, using 3 areas of research as examples; it is not intended to serve as a guideline for research design.
Collapse
Affiliation(s)
- Aditi Bhargava
- Center for Reproductive Sciences, San Francisco, CA, USA
- Department of Obstetrics and Gynecology, University of California, San Francisco, CA, USA
| | - Arthur P Arnold
- Department of Integrative Biology & Physiology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Debra A Bangasser
- Department of Psychology and Neuroscience Program, Temple University, Philadelphia, PA, USA
| | - Kate M Denton
- Cardiovascular Disease Program, Monash Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, Victoria, Australia
| | - Arpana Gupta
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, Division of Digestive Diseases, University of California, Los Angeles, Los Angeles, CA, USA
| | - Lucinda M Hilliard Krause
- Cardiovascular Disease Program, Monash Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, Victoria, Australia
| | - Emeran A Mayer
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, Division of Digestive Diseases, University of California, Los Angeles, Los Angeles, CA, USA
| | - Margaret McCarthy
- Department of Pharmacology and Program in Neuroscience, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Walter L Miller
- Center for Reproductive Sciences, San Francisco, CA, USA
- Department of Pediatrics, University of California, San Francisco, CA, USA
| | - Armin Raznahan
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institutes of Mental Health, Intramural Research Program, Bethesda, MD, USA
| | - Ragini Verma
- Diffusion and Connectomics In Precision Healthcare Research (DiCIPHR) lab, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| |
Collapse
|
14
|
Vanes LD, Dolan RJ. Transdiagnostic neuroimaging markers of psychiatric risk: A narrative review. NEUROIMAGE-CLINICAL 2021; 30:102634. [PMID: 33780864 PMCID: PMC8022867 DOI: 10.1016/j.nicl.2021.102634] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 03/03/2021] [Accepted: 03/12/2021] [Indexed: 02/07/2023]
Abstract
We review the literature on neural correlates of a general psychopathology factor General psychopathology relates to structural and functional neurodevelopment Disrupted network connectivity maturation may underlie psychiatric vulnerability
Several decades of neuroimaging research in psychiatry have shed light on structural and functional neural abnormalities associated with individual psychiatric disorders. However, there is increasing evidence for substantial overlap in the patterns of neural dysfunction seen across disorders, suggesting that risk for psychiatric illness may be shared across diagnostic boundaries. Gaining insights on the existence of shared neural mechanisms which may transdiagnostically underlie psychopathology is important for psychiatric research in order to tease apart the unique and common aspects of different disorders, but also clinically, so as to help identify individuals early on who may be biologically vulnerable to psychiatric disorder in general. In this narrative review, we first evaluate recent studies investigating the functional and structural neural correlates of a general psychopathology factor, which is thought to reflect the shared variance across common mental health symptoms and therefore index psychiatric vulnerability. We then link insights from this research to existing meta-analytic evidence for shared patterns of neural dysfunction across categorical psychiatric disorders. We conclude by providing an integrative account of vulnerability to mental illness, whereby delayed or disrupted maturation of large-scale networks (particularly default-mode, executive, and sensorimotor networks), and more generally between-network connectivity, results in a compromised ability to integrate and switch between internally and externally focused tasks.
Collapse
Affiliation(s)
- Lucy D Vanes
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, King's College London, United Kingdom.
| | - Raymond J Dolan
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
| |
Collapse
|
15
|
Liloia D, Mancuso L, Uddin LQ, Costa T, Nani A, Keller R, Manuello J, Duca S, Cauda F. Gray matter abnormalities follow non-random patterns of co-alteration in autism: Meta-connectomic evidence. Neuroimage Clin 2021; 30:102583. [PMID: 33618237 PMCID: PMC7903137 DOI: 10.1016/j.nicl.2021.102583] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 12/15/2020] [Accepted: 01/30/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by atypical brain anatomy and connectivity. Graph-theoretical methods have mainly been applied to detect altered patterns of white matter tracts and functional brain activation in individuals with ASD. The network topology of gray matter (GM) abnormalities in ASD remains relatively unexplored. METHODS An innovative meta-connectomic analysis on voxel-based morphometry data (45 experiments, 1,786 subjects with ASD) was performed in order to investigate whether GM variations can develop in a distinct pattern of co-alteration across the brain. This pattern was then compared with normative profiles of structural and genetic co-expression maps. Graph measures of centrality and clustering were also applied to identify brain areas with the highest topological hierarchy and core sub-graph components within the co-alteration network observed in ASD. RESULTS Individuals with ASD exhibit a distinctive and topologically defined pattern of GM co-alteration that moderately follows the structural connectivity constraints. This was not observed with respect to the pattern of genetic co-expression. Hub regions of the co-alteration network were mainly left-lateralized, encompassing the precuneus, ventral anterior cingulate, and middle occipital gyrus. Regions of the default mode network appear to be central in the topology of co-alterations. CONCLUSION These findings shed new light on the pathobiology of ASD, suggesting a network-level dysfunction among spatially distributed GM regions. At the same time, this study supports pathoconnectomics as an insightful approach to better understand neuropsychiatric disorders.
Collapse
Affiliation(s)
- Donato Liloia
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Lorenzo Mancuso
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL, USA; Neuroscience Program, University of Miami Miller School of Medicine, Miami, FL, USA.
| | - Tommaso Costa
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy.
| | - Andrea Nani
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Roberto Keller
- Adult Autism Center, DSM Local Health Unit, ASL TO, Turin, Italy.
| | - Jordi Manuello
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Sergio Duca
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Franco Cauda
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy.
| |
Collapse
|
16
|
Fauchon C, Meunier D, Faillenot I, Pomares FB, Bastuji H, Garcia-Larrea L, Peyron R. The Modular Organization of Pain Brain Networks: An fMRI Graph Analysis Informed by Intracranial EEG. Cereb Cortex Commun 2020; 1:tgaa088. [PMID: 34296144 PMCID: PMC8152828 DOI: 10.1093/texcom/tgaa088] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 10/05/2020] [Accepted: 11/16/2020] [Indexed: 11/14/2022] Open
Abstract
Intracranial EEG (iEEG) studies have suggested that the conscious perception of pain builds up from successive contributions of brain networks in less than 1 s. However, the functional organization of cortico-subcortical connections at the multisecond time scale, and its accordance with iEEG models, remains unknown. Here, we used graph theory with modular analysis of fMRI data from 60 healthy participants experiencing noxious heat stimuli, of whom 36 also received audio stimulation. Brain connectivity during pain was organized in four modules matching those identified through iEEG, namely: 1) sensorimotor (SM), 2) medial fronto-cingulo-parietal (default mode-like), 3) posterior parietal-latero-frontal (central executive-like), and 4) amygdalo-hippocampal (limbic). Intrinsic overlaps existed between the pain and audio conditions in high-order areas, but also pain-specific higher small-worldness and connectivity within the sensorimotor module. Neocortical modules were interrelated via “connector hubs” in dorsolateral frontal, posterior parietal, and anterior insular cortices, the antero-insular connector being most predominant during pain. These findings provide a mechanistic picture of the brain networks architecture and support fractal-like similarities between the micro-and macrotemporal dynamics associated with pain. The anterior insula appears to play an essential role in information integration, possibly by determining priorities for the processing of information and subsequent entrance into other points of the brain connectome.
Collapse
Affiliation(s)
- Camille Fauchon
- Central Integration of Pain in Humans (NeuroPain-lab), Inserm U1028, CNRS UMR5292, Lyon Neuroscience Research Center, Bron 69500, France.,University Jean Monnet, Saint-Étienne 42100, France
| | - David Meunier
- Central Integration of Pain in Humans (NeuroPain-lab), Inserm U1028, CNRS UMR5292, Lyon Neuroscience Research Center, Bron 69500, France.,Aix Marseille Université, CNRS, INT (Institute of Neuroscience de la Timone), Marseille 13005 France
| | - Isabelle Faillenot
- Central Integration of Pain in Humans (NeuroPain-lab), Inserm U1028, CNRS UMR5292, Lyon Neuroscience Research Center, Bron 69500, France.,University Jean Monnet, Saint-Étienne 42100, France
| | - Florence B Pomares
- Central Integration of Pain in Humans (NeuroPain-lab), Inserm U1028, CNRS UMR5292, Lyon Neuroscience Research Center, Bron 69500, France.,Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, QC H3W 1W6, Canada
| | - Hélène Bastuji
- Central Integration of Pain in Humans (NeuroPain-lab), Inserm U1028, CNRS UMR5292, Lyon Neuroscience Research Center, Bron 69500, France.,University Claude Bernard Lyon 1, Villeurbanne 69100, France.,Hospices Civils de Lyon, Lyon 69002, France
| | - Luis Garcia-Larrea
- Central Integration of Pain in Humans (NeuroPain-lab), Inserm U1028, CNRS UMR5292, Lyon Neuroscience Research Center, Bron 69500, France.,University Claude Bernard Lyon 1, Villeurbanne 69100, France
| | - Roland Peyron
- Central Integration of Pain in Humans (NeuroPain-lab), Inserm U1028, CNRS UMR5292, Lyon Neuroscience Research Center, Bron 69500, France.,University Jean Monnet, Saint-Étienne 42100, France.,Service de Neurologie et Centre de la Douleur du CHU de St-Etienne, St-Etienne 42055, France
| |
Collapse
|
17
|
Nani A, Manuello J, Mancuso L, Liloia D, Costa T, Vercelli A, Duca S, Cauda F. The pathoconnectivity network analysis of the insular cortex: A morphometric fingerprinting. Neuroimage 2020; 225:117481. [PMID: 33122115 DOI: 10.1016/j.neuroimage.2020.117481] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 10/14/2020] [Accepted: 10/19/2020] [Indexed: 12/14/2022] Open
Abstract
Brain disorders tend to impact on many different regions in a typical way: alterations do not spread randomly; rather, they seem to follow specific patterns of propagation that show a strong overlap between different pathologies. The insular cortex is one of the brain areas more involved in this phenomenon, as it seems to be altered by a wide range of brain diseases. On these grounds we thoroughly investigated the impact of brain disorders on the insular cortices analyzing the patterns of their structural co-alteration. We therefore investigated, applying a network analysis approach to meta-analytic data, 1) what pattern of gray matter alteration is associated with each of the insular cortex parcels; 2) whether or not this pattern correlates and overlaps with its functional meta-analytic connectivity; and, 3) the behavioral profile related to each insular co-alteration pattern. All the analyses were repeated considering two solutions: one with two clusters and another with three. Our study confirmed that the insular cortex is one of the most altered cerebral regions among the cortical areas, and exhibits a dense network of co-alteration including a prevalence of cortical rather than sub-cortical brain regions. Regions of the frontal lobe are the most involved, while occipital lobe is the less affected. Furthermore, the co-alteration and co-activation patterns greatly overlap each other. These findings provide significant evidence that alterations caused by brain disorders are likely to be distributed according to the logic of network architecture, in which brain hubs lie at the center of networks composed of co-altered areas. For the first time, we shed light on existing differences between insula sub-regions even in the pathoconnectivity domain.
Collapse
Affiliation(s)
- Andrea Nani
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Via Verdi, 10, Turin 10124, Italy
| | - Jordi Manuello
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Via Verdi, 10, Turin 10124, Italy
| | - Lorenzo Mancuso
- FOCUS Lab, Department of Psychology, University of Turin, Via Verdi, 10, Turin 10124, Italy
| | - Donato Liloia
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Via Verdi, 10, Turin 10124, Italy
| | - Tommaso Costa
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Via Verdi, 10, Turin 10124, Italy.
| | - Alessandro Vercelli
- Neuroscience Institute of Turin, Turin, Italy; Neuroscience Institute Cavalieri Ottolenghi, Turin, Italy; Department of Neuroscience, University of Turin, Turin, Italy
| | - Sergio Duca
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Via Verdi, 10, Turin 10124, Italy
| | - Franco Cauda
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Via Verdi, 10, Turin 10124, Italy; Neuroscience Institute of Turin, Turin, Italy
| |
Collapse
|
18
|
Rossini PM, Miraglia F, Alù F, Cotelli M, Ferreri F, Di Iorio R, Iodice F, Vecchio F. Neurophysiological Hallmarks of Neurodegenerative Cognitive Decline: The Study of Brain Connectivity as A Biomarker of Early Dementia. J Pers Med 2020; 10:E34. [PMID: 32365890 PMCID: PMC7354555 DOI: 10.3390/jpm10020034] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 04/26/2020] [Accepted: 04/27/2020] [Indexed: 02/07/2023] Open
Abstract
Neurodegenerative processes of various types of dementia start years before symptoms, but the presence of a "neural reserve", which continuously feeds and supports neuroplastic mechanisms, helps the aging brain to preserve most of its functions within the "normality" frame. Mild cognitive impairment (MCI) is an intermediate stage between dementia and normal brain aging. About 50% of MCI subjects are already in a stage that is prodromal-to-dementia and during the following 3 to 5 years will develop clinically evident symptoms, while the other 50% remains at MCI or returns to normal. If the risk factors favoring degenerative mechanisms are modified during early stages (i.e., in the prodromal), the degenerative process and the loss of abilities in daily living activities will be delayed. It is therefore extremely important to have biomarkers able to identify-in association with neuropsychological tests-prodromal-to-dementia MCI subjects as early as possible. MCI is a large (i.e., several million in EU) and substantially healthy population; therefore, biomarkers should be financially affordable, largely available and non-invasive, but still accurate in their diagnostic prediction. Neurodegeneration initially affects synaptic transmission and brain connectivity; methods exploring them would represent a 1st line screening. Neurophysiological techniques able to evaluate mechanisms of synaptic function and brain connectivity are attracting general interest and are described here. Results are quite encouraging and suggest that by the application of artificial intelligence (i.e., learning-machine), neurophysiological techniques represent valid biomarkers for screening campaigns of the MCI population.
Collapse
Affiliation(s)
- Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Pisana, 00167 Rome, Italy; (F.M.); (F.A.); (F.I.); (F.V.)
| | - Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Pisana, 00167 Rome, Italy; (F.M.); (F.A.); (F.I.); (F.V.)
| | - Francesca Alù
- Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Pisana, 00167 Rome, Italy; (F.M.); (F.A.); (F.I.); (F.V.)
| | - Maria Cotelli
- Neuropsychology Unit, IRCCS Istituto Centro San Giovanni di DioFatebenefratelli, 25125 Brescia, Italy;
| | - Florinda Ferreri
- Department of Neuroscience, Unit of Neurology and Neurophysiology, University of Padua, 35100 Padua, Italy;
- Department of Clinical Neurophysiology, Kuopio University Hospital, University of Eastern Finland, 70100 Kuopio, Finland
| | - Riccardo Di Iorio
- Neurology Unit, IRCCS Polyclinic A. Gemelli Foundation, 00168 Rome, Italy;
| | - Francesco Iodice
- Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Pisana, 00167 Rome, Italy; (F.M.); (F.A.); (F.I.); (F.V.)
- Neurology Unit, IRCCS Polyclinic A. Gemelli Foundation, 00168 Rome, Italy;
| | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Pisana, 00167 Rome, Italy; (F.M.); (F.A.); (F.I.); (F.V.)
| |
Collapse
|
19
|
Rossini P, Di Iorio R, Bentivoglio M, Bertini G, Ferreri F, Gerloff C, Ilmoniemi R, Miraglia F, Nitsche M, Pestilli F, Rosanova M, Shirota Y, Tesoriero C, Ugawa Y, Vecchio F, Ziemann U, Hallett M. Methods for analysis of brain connectivity: An IFCN-sponsored review. Clin Neurophysiol 2019; 130:1833-1858. [DOI: 10.1016/j.clinph.2019.06.006] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 05/08/2019] [Accepted: 06/18/2019] [Indexed: 01/05/2023]
|
20
|
Chiang FL, Wang Q, Yu FF, Romero RS, Huang SY, Fox PM, Tantiwongkosi B, Fox PT. Localised grey matter atrophy in multiple sclerosis is network-based: a coordinate-based meta-analysis. Clin Radiol 2019; 74:816.e19-816.e28. [PMID: 31421864 PMCID: PMC6757337 DOI: 10.1016/j.crad.2019.07.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 07/10/2019] [Indexed: 11/24/2022]
Abstract
AIM To test the network degeneration hypothesis in multiple sclerosis (MS) with a two-stage coordinate-based meta-analysis by: (1) characterising regional selectivity of grey matter (GM) atrophy and (2) testing for functional connectivity involving these regions. MATERIALS AND METHODS Meta-analytic sources included 33 journal articles (1,666 MS patients and 1,269 healthy controls) with coordinate-based results from voxel-based morphometry analysis demonstrating GM atrophy. Mass univariate and multivariate coordinate-based meta-analyses were performed to identify a convergent pattern of GM atrophy and determine inter-regional co-activation (as a surrogate of functional connectivity), with anatomical likelihood estimation and functional meta-analytic connectivity modelling, respectively. RESULTS Localised GM atrophy was demonstrated in the thalamus, putamen, caudate, sensorimotor cortex, insula, superior temporal gyrus, and cingulate gyrus. This convergent pattern of atrophy displayed significant inter-regional functional co-activations. CONCLUSION In MS, GM atrophy was regionally selective, and these regions were functionally connected. The meta-analytic model-based results of this study are intended to guide future development of quantitative neuroimaging markers for diagnosis, evaluating disease progression, and monitoring treatment response.
Collapse
Affiliation(s)
- F L Chiang
- Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
| | - Q Wang
- Department of Neurology, Beijing Tian Tan Hospital, Capital Medical University, Beijing, China
| | - F F Yu
- Division of Neuroradiology, Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - R S Romero
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - S Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Division of Neuroradiology, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - P M Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - B Tantiwongkosi
- Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - P T Fox
- Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; South Texas Veterans Health Care System, San Antonio, TX, USA.
| |
Collapse
|
21
|
Schwanke S, Jenssen J, Eipert P, Schmitt O. Towards Differential Connectomics with NeuroVIISAS. Neuroinformatics 2019; 17:163-179. [PMID: 30014279 DOI: 10.1007/s12021-018-9389-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The comparison of connectomes is an essential step to identify changes in structural and functional neuronal networks. However, the connectomes themselves as well as the comparisons of connectomes could be manifold. In most applications, comparisons of connectomes are applied to specific sets of data. In many studies collections of scripts are applied optimized for certain species (non-generic approaches) or diseases (control versus disease group connectomes). These collections of scripts have a limited functionality which do not support functional and topographic mappings of connectomes (hemispherical asymmetries, peripheral nervous system). The platform-independent and generic neuroVIISAS framework is built to circumvent limitations that come with variants of nomenclatures, connectivity lists and connectional hierarchies as well as restrictions to structural connectome analyses. A new analytical module is introduced into the framework to compare different types of connectomes and different representations of the same connectome within a unique software environment. As an example a differential analysis of the partial connectome of the laboratory rat that is based on virus tract tracing with the same regions of non-virus tract tracing has been performed. A relatively large connectional coherence between the two different techniques was found. However, some detected connections are described by virus tract-tracing only.
Collapse
Affiliation(s)
- Sebastian Schwanke
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057, Rostock, Germany
| | - Jörg Jenssen
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057, Rostock, Germany
| | - Peter Eipert
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057, Rostock, Germany
| | - Oliver Schmitt
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057, Rostock, Germany.
| |
Collapse
|
22
|
Bertolini G, La Corte E, Aquino D, Greco E, Rossini Z, Cardia A, Nicolosi F, Bauer D, Bruzzone MG, Ferroli P, Serrao G. Real-Time Ex-Vivo Magnetic Resonance Image-Guided Dissection of Human Brain White Matter: A Proof-of-Principle Study. World Neurosurg 2019; 125:198-206. [PMID: 30743041 DOI: 10.1016/j.wneu.2019.01.196] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 01/21/2019] [Accepted: 01/22/2019] [Indexed: 11/16/2022]
Abstract
OBJECTIVE Modern neuroanatomic education should be based on interdisciplinary methods that allow an understanding of the cerebral circuitry, which is at the base of the structural connectivity. Ex-vivo MRI-guided dissection is an essential method for developing and refining the knowledge of complex 3-dimensional brain anatomy and the mutual relationships between structures and architecture of the white matter bundles. The aim of this technical note is to present a new and innovative method of studying human brain white matter. METHODS Four adult human cerebral hemispheres were prepared according to the Klinger's method. T1-weighted and T2-weighted and fluid attenuated inversion recovery images were obtained with a 3T magnetic resonance machine. The dissection was performed in a dedicated neurosurgical laboratory equipped with a microscope and an electromagnetic neuronavigation system that guided the whole white matter dissection. RESULTS Gyri and sulci morphology were studied in detail. The relations between superficial and inner structures were observed before and after the dissection. Gray matter was carefully removed with blunt dissectors, and the U-fibers were exposed. Afterwards, deeper association and projection fibers, such as the arcuate fasciculus, superior and inferior longitudinal fasciculus, corona radiata, extreme and external capsule, claustrum, anterior commissure, and internal capsule were visualized under high magnification. The neuronavigation system was crucial for continuously checking the whole dissection procedure to avoid any accidental excision of fibers. CONCLUSION Image-guided neuronavigated dissection can significantly improve the quality of white matter dissection and represents a valid tool for learning the 3-dimensional anatomy of the human brain tracts.
Collapse
Affiliation(s)
- Giacomo Bertolini
- Department of Health Sciences, University of Milan, Milan, Italy; Department of Neurosurgery, Foundation IRCCS Neurological Institute "Carlo Besta", Milan, Italy.
| | - Emanuele La Corte
- Department of Health Sciences, University of Milan, Milan, Italy; Department of Neurosurgery, Foundation IRCCS Neurological Institute "Carlo Besta", Milan, Italy
| | - Domenico Aquino
- Department of Neuroradiology, Foundation IRCCS Neurological Institute "Carlo Besta", Milan, Italy
| | - Elena Greco
- Department of Health Sciences, University of Milan, Milan, Italy
| | - Zefferino Rossini
- Department of Neurosurgery, Humanitas Clinical and Research Center, Rozzano, Italy
| | - Andrea Cardia
- Department of Neurosurgery, Humanitas Clinical and Research Center, Rozzano, Italy
| | - Federico Nicolosi
- Department of Neurosurgery, Humanitas Clinical and Research Center, Rozzano, Italy
| | - Dario Bauer
- Department of Health Sciences, University of Milan, Milan, Italy
| | - Maria Grazia Bruzzone
- Department of Neuroradiology, Foundation IRCCS Neurological Institute "Carlo Besta", Milan, Italy
| | - Paolo Ferroli
- Department of Neurosurgery, Foundation IRCCS Neurological Institute "Carlo Besta", Milan, Italy
| | - Graziano Serrao
- Department of Health Sciences, University of Milan, Milan, Italy
| |
Collapse
|
23
|
Tozzi A. The multidimensional brain. Phys Life Rev 2019; 31:86-103. [PMID: 30661792 DOI: 10.1016/j.plrev.2018.12.004] [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: 12/10/2017] [Revised: 05/17/2018] [Accepted: 12/27/2018] [Indexed: 01/24/2023]
Abstract
Brain activity takes place in three spatial-plus time dimensions. This rather obvious claim has been recently questioned by papers that, taking into account the big data outburst and novel available computational tools, are starting to unveil a more intricate state of affairs. Indeed, various brain activities and their correlated mental functions can be assessed in terms of trajectories embedded in phase spaces of dimensions higher than the canonical ones. In this review, I show how further dimensions may not just represent a convenient methodological tool that allows a better mathematical treatment of otherwise elusive cortical activities, but may also reflect genuine functional or anatomical relationships among real nervous functions. I then describe how to extract hidden multidimensional information from real or artificial neurodata series, and make clear how our mind dilutes, rather than concentrates as currently believed, inputs coming from the environment. Finally, I argue that the principle "the higher the dimension, the greater the information" may explain the occurrence of mental activities and elucidate the mechanisms of human diseases associated with dimensionality reduction.
Collapse
Affiliation(s)
- Arturo Tozzi
- Center for Nonlinear Science, University of North Texas, 1155 Union Circle, #311427 Denton, TX 76203-5017, USA.
| |
Collapse
|
24
|
Abstract
Current conceptions on the fundamental mechanisms underlying brain functioning in normal conditions and pathological states are considered. The author emphasizes that a great progress in the field has been achieved after the implementation of neuroimaging methods in clinical practice. During the last time, attention is drawn to the connections between separate neurons as well as between different brain regions. Functional specialization and functional integration of different brain regions are key concepts of the higher brain function organization. The significance of the resting state of the brain, which is in fact the active process, is analyzed. The state of cerebral functions determines internal processes in the neuronal tissue. Different aspects of energy metabolism determining the normal functioning of cerebral structures are considered. The expediency of using the drugs influencing the energy metabolism, one of which is 2-ethyl-6-methyl-3-hydroxypyridine succinate (mexidol), is highlighted.
Collapse
Affiliation(s)
- I V Damulin
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| |
Collapse
|
25
|
Coelho CAO, Ferreira TL, Kramer-Soares JC, Sato JR, Oliveira MGM. Network supporting contextual fear learning after dorsal hippocampal damage has increased dependence on retrosplenial cortex. PLoS Comput Biol 2018; 14:e1006207. [PMID: 30086129 PMCID: PMC6097702 DOI: 10.1371/journal.pcbi.1006207] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 08/17/2018] [Accepted: 05/15/2018] [Indexed: 01/06/2023] Open
Abstract
Hippocampal damage results in profound retrograde, but no anterograde amnesia in contextual fear conditioning (CFC). Although the content learned in the latter have been discussed, alternative regions supporting CFC learning were seldom proposed and never empirically addressed. Here, we employed network analysis of pCREB expression quantified from brain slices of rats with dorsal hippocampal lesion (dHPC) after undergoing CFC session. Using inter-regional correlations of pCREB-positive nuclei between brain regions, we modelled functional networks using different thresholds. The dHPC network showed small-world topology, equivalent to SHAM (control) network. However, diverging hubs were identified in each network. In a direct comparison, hubs in both networks showed consistently higher centrality values compared to the other network. Further, the distribution of correlation coefficients was different between the groups, with most significantly stronger correlation coefficients belonging to the SHAM network. These results suggest that dHPC network engaged in CFC learning is partially different, and engage alternative hubs. We next tested if pre-training lesions of dHPC and one of the new dHPC network hubs (perirhinal, Per; or disgranular retrosplenial, RSC, cortices) would impair CFC. Only dHPC-RSC, but not dHPC-Per, impaired CFC. Interestingly, only RSC showed a consistently higher centrality in the dHPC network, suggesting that the increased centrality reflects an increased functional dependence on RSC. Our results provide evidence that, without hippocampus, the RSC, an anatomically central region in the medial temporal lobe memory system might support CFC learning and memory.
Collapse
Affiliation(s)
- Cesar A. O. Coelho
- Departamento de Psicobiologia, Universidade Federal de São Paulo - UNIFESP, São Paulo, São Paulo, Brazil
| | - Tatiana L. Ferreira
- Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, UFABC, São Bernardo do Campo, São Paulo, Brazil
| | - Juliana C. Kramer-Soares
- Departamento de Psicobiologia, Universidade Federal de São Paulo - UNIFESP, São Paulo, São Paulo, Brazil
| | - João R. Sato
- Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, UFABC, São Bernardo do Campo, São Paulo, Brazil
| | - Maria Gabriela M. Oliveira
- Departamento de Psicobiologia, Universidade Federal de São Paulo - UNIFESP, São Paulo, São Paulo, Brazil
| |
Collapse
|
26
|
Kenzie ES, Parks EL, Bigler ED, Wright DW, Lim MM, Chesnutt JC, Hawryluk GWJ, Gordon W, Wakeland W. The Dynamics of Concussion: Mapping Pathophysiology, Persistence, and Recovery With Causal-Loop Diagramming. Front Neurol 2018; 9:203. [PMID: 29670568 PMCID: PMC5893805 DOI: 10.3389/fneur.2018.00203] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Accepted: 03/14/2018] [Indexed: 12/21/2022] Open
Abstract
Despite increasing public awareness and a growing body of literature on the subject of concussion, or mild traumatic brain injury, an urgent need still exists for reliable diagnostic measures, clinical care guidelines, and effective treatments for the condition. Complexity and heterogeneity complicate research efforts and indicate the need for innovative approaches to synthesize current knowledge in order to improve clinical outcomes. Methods from the interdisciplinary field of systems science, including models of complex systems, have been increasingly applied to biomedical applications and show promise for generating insight for traumatic brain injury. The current study uses causal-loop diagramming to visualize relationships between factors influencing the pathophysiology and recovery trajectories of concussive injury, including persistence of symptoms and deficits. The primary output is a series of preliminary systems maps detailing feedback loops, intrinsic dynamics, exogenous drivers, and hubs across several scales, from micro-level cellular processes to social influences. Key system features, such as the role of specific restorative feedback processes and cross-scale connections, are examined and discussed in the context of recovery trajectories. This systems approach integrates research findings across disciplines and allows components to be considered in relation to larger system influences, which enables the identification of research gaps, supports classification efforts, and provides a framework for interdisciplinary collaboration and communication-all strides that would benefit diagnosis, prognosis, and treatment in the clinic.
Collapse
Affiliation(s)
- Erin S. Kenzie
- Systems Science Program, Portland State University, Portland, OR, United States
| | - Elle L. Parks
- Systems Science Program, Portland State University, Portland, OR, United States
| | - Erin D. Bigler
- Department of Psychology and Neuroscience Center, Brigham Young University, Provo, UT, United States
| | - David W. Wright
- Department of Emergency Medicine, Emory University School of Medicine, Atlanta, GA, United States
| | - Miranda M. Lim
- Sleep Disorders Clinic, Division of Hospital and Specialty Medicine, Research Service, VA Portland Health Care System, Portland, OR, United States
- Departments of Neurology, Medicine, and Behavioral Neuroscience, Oregon Institute of Occupational Health Sciences, Oregon Health & Science University, Portland, OR, United States
| | - James C. Chesnutt
- TBI/Concussion Program, Orthopedics & Rehabilitation, Neurology and Family Medicine, Oregon Health & Science University, Portland, OR, United States
| | | | - Wayne Gordon
- Department of Rehabilitation Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Wayne Wakeland
- Systems Science Program, Portland State University, Portland, OR, United States
| |
Collapse
|
27
|
Chase HW, Loriemi P, Wensing T, Eickhoff SB, Nickl-Jockschat T. Meta-analytic evidence for altered mesolimbic responses to reward in schizophrenia. Hum Brain Mapp 2018; 39:2917-2928. [PMID: 29573046 DOI: 10.1002/hbm.24049] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Revised: 01/25/2018] [Accepted: 03/08/2018] [Indexed: 11/08/2022] Open
Abstract
Dysfunction of reward-related neural circuitry in schizophrenia (SCZ) has been widely reported, and may provide insight into the motivational and cognitive disturbances that characterize the disorder. Although previous meta-analyses of reward learning paradigms in SCZ have been performed, a meta-analysis of whole-brain coordinate maps in SCZ alone has not been conducted. In this study, we performed an activation likelihood estimate (ALE) meta-analysis, and performed a follow-up analysis of functional connectivity and functional decoding of identified regions. We report several salient findings that extend prior work in this area. First, an alteration in reward-related activation was observed in the right ventral striatum, but this was not solely driven by hypoactivation in the SCZ group compared to healthy controls. Second, the region was characterized by functional connectivity primarily with the lateral prefrontal cortex and pre-supplementary motor area (preSMA), as well as subcortical regions such as the thalamus which show structural deficits in SCZ. Finally, although the meta-analysis showed no regions outside the ventral striatum to be significantly altered, regions with higher functional connectivity with the ventral striatum showed a greater number of subthreshold foci. Together, these findings confirm the alteration of ventral striatal function in SCZ, but suggest that a network-based approach may assist future analysis of the functional underpinnings of the disorder.
Collapse
Affiliation(s)
- Henry W Chase
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Polina Loriemi
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany.,Juelich Aachen Research Alliance - Translational Brain Medicine, Aachen, Germany
| | - Tobias Wensing
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany.,Juelich Aachen Research Alliance - Translational Brain Medicine, Aachen, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany.,Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Thomas Nickl-Jockschat
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany.,Juelich Aachen Research Alliance - Translational Brain Medicine, Aachen, Germany.,Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, USA.,Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| |
Collapse
|
28
|
Kotkowski E, Price LR, Mickle Fox P, Vanasse TJ, Fox PT. The hippocampal network model: A transdiagnostic metaconnectomic approach. NEUROIMAGE-CLINICAL 2018; 18:115-129. [PMID: 29387529 PMCID: PMC5789756 DOI: 10.1016/j.nicl.2018.01.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Revised: 01/05/2018] [Accepted: 01/06/2018] [Indexed: 12/14/2022]
Abstract
Purpose The hippocampus plays a central role in cognitive and affective processes and is commonly implicated in neurodegenerative diseases. Our study aimed to identify and describe a hippocampal network model (HNM) using trans-diagnostic MRI data from the BrainMap® database. We used meta-analysis to test the network degeneration hypothesis (NDH) (Seeley et al., 2009) by identifying structural and functional covariance in this hippocampal network. Methods To generate our network model, we used BrainMap's VBM database to perform a region-to-whole-brain (RtWB) meta-analysis of 269 VBM experiments from 165 published studies across a range of 38 psychiatric and neurological diseases reporting hippocampal gray matter density alterations. This step identified 11 significant gray matter foci, or nodes. We subsequently used meta-analytic connectivity modeling (MACM) to define edges of structural covariance between nodes from VBM data as well as functional covariance using the functional task-activation database, also from BrainMap. Finally, we applied a correlation analysis using Pearson's r to assess the similarities and differences between the structural and functional covariance models. Key findings Our hippocampal RtWB meta-analysis reported consistent and significant structural covariance in 11 key regions. The subsequent structural and functional MACMs showed a strong correlation between HNM nodes with a significant structural-functional covariance correlation of r = .377 (p = .000049). Significance This novel method of studying network covariance using VBM and functional meta-analytic techniques allows for the identification of generalizable patterns of functional and structural abnormalities pertaining to the hippocampus. In accordance with the NDH, this framework could have major implications in studying and predicting spatial disease patterns using network-based assays. We derived regions that structurally co-vary with the hippocampus in a network model using a transdiagnostic meta-analytic approach. We used meta-analytic connectivity mapping to assess inter-regional connectivity from BrainMap's structural and functional databases. We tested the network degeneration hypothesis by identifying network correlations between structural and functional networks.
Collapse
Affiliation(s)
- Eithan Kotkowski
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
| | - Larry R Price
- Department of Mathematics, Texas State University, San Marcos, TX, USA; College of Education, Texas State University, San Marcos, TX, USA
| | - P Mickle Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Thomas J Vanasse
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Department of Psychiatry, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Institute for Neuroscience & Neurotechnology, Shenzhen University, Shenzen, China
| |
Collapse
|
29
|
Cauda F, Costa T, Nani A, Fava L, Palermo S, Bianco F, Duca S, Tatu K, Keller R. Are schizophrenia, autistic, and obsessive spectrum disorders dissociable on the basis of neuroimaging morphological findings?: A voxel-based meta-analysis. Autism Res 2017; 10:1079-1095. [PMID: 28339164 DOI: 10.1002/aur.1759] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Revised: 01/09/2017] [Accepted: 01/10/2017] [Indexed: 12/30/2022]
Abstract
Schizophrenia spectrum disorder (SCZD), autism spectrum disorder (ASD), and obsessive-compulsive spectrum disorder (OCSD) are considered as three separate psychiatric conditions with, supposedly, different brain alterations patterns. From a neuroimaging perspective, this meta-analytic study aimed to address whether this nosographical differentiation is actually supported by different brain patterns of gray matter (GM) or white matter (WM) morphological alterations. We explored two possibilities: (a) to find out whether GM alterations are specific for SCZD, ASD, and OCSD; and (b) to associate the identified brain alteration patterns with cognitive dysfunctions by means of an analysis of lesion decoding. Our analysis reveals that these psychiatric spectra do not present clear distinctive patterns of alterations; rather, they all tend to be distributed in two alteration clusters. Cluster 1, which is more specific for SCZD, includes the anterior insular, anterior cingulate cortex, ventromedial prefrontal cortex, and frontopolar areas, which are parts of the cognitive control system. Cluster 2, which is more specific for OCSD, presents occipital, temporal, and parietal alteration patterns with the involvement of sensorimotor, premotor, visual, and lingual areas, thus forming a network that is more associated with the auditory-visual, auditory, premotor visual somatic functions. In turn, ASD appears to be uniformly distributed in the two clusters. The three spectra share a significant set of alterations. Our new approach promises to provide insight into the understanding of psychiatric conditions under the aspect of a common neurobiological substrate, possibly related to neuroinflammation during brain development. Autism Res 2017. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. Autism Res 2017, 10: 1079-1095. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Franco Cauda
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.,Focus Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Tommaso Costa
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Andrea Nani
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.,Focus Lab, Department of Psychology, University of Turin, Turin, Italy.,Department of Science, University of Eastern Piedmont, Italy.,Michael Trimble Psychiatric Research Group, University of Birmingham and BSMHFT, Birmingham, UK
| | - Luciano Fava
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.,Focus Lab, Department of Psychology, University of Turin, Turin, Italy.,Department of Science, University of Eastern Piedmont, Italy
| | - Sara Palermo
- Department of Neuroscience, University of Turin, Turin, Italy
| | - Francesca Bianco
- Adult Autism Center, Local Health Unit DSM ASL TO2, Turin, Italy
| | - Sergio Duca
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.,Focus Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Karina Tatu
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.,Focus Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Roberto Keller
- Adult Autism Center, Local Health Unit DSM ASL TO2, Turin, Italy
| |
Collapse
|
30
|
Gao M, Zhang D, Wang Z, Liang B, Cai Y, Gao Z, Li J, Chang S, Jiao B, Huang R, Liu M. Mental rotation task specifically modulates functional connectivity strength of intrinsic brain activity in low frequency domains: A maximum uncertainty linear discriminant analysis. Behav Brain Res 2016; 320:233-243. [PMID: 28011171 DOI: 10.1016/j.bbr.2016.12.017] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 12/12/2016] [Accepted: 12/15/2016] [Indexed: 01/12/2023]
Abstract
Neuroimaging studies have highlighted that intrinsic brain activity is modified to implement task demands. However, the relation between mental rotation and intrinsic brain activity remains unclear. To answer this question, we collected functional MRI (fMRI) data from 30 healthy participants in two mental rotation task periods (1st-task state, 2nd-task state) and two rest periods before (pre-task resting state) and after the task (post-task resting state) respectively. By combining the spatial independent component analysis (ICA) and voxel-wise functional connectivity strength (FCS), we identified FCS maps of 10 brain resting state networks (RSNs) within six different bands (i.e., 0-0.05, 0.05-0.1, 0.1-0.15, 0.15-0.2, 0.2-0.25, and 0.01-0.08Hz) corresponding to the four states for each subject. The maximum uncertainty linear discriminant analysis (MLDA) method showed that the FCS within the low frequency bandwidth of 0.05-0.1Hz could effectively classify the mental rotation task state from pre-/post-task resting states but failed to discriminate the pre- and post-task resting states. Discriminative FCSs were observed in the cognitive executive-control network (central executive and attention) and the imagery-based internal mental manipulation network (default mode, primary sensorimotor, and primary visual). Imagery manipulation is a stable mental element of mental rotation, and the involvement of executive control is dependent on the degree of task familiarity. Together, the present study provides evidence that mental rotation task specifically modifies intrinsic brain activity to complement cognitive demands, which provides further insight into the neural basis of mental rotation manipulation.
Collapse
Affiliation(s)
- Mengxia Gao
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, China
| | - Delong Zhang
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, China
| | - Zengjian Wang
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, China
| | - Bishan Liang
- College of Education, Guangdong Polytechnic Normal University, China
| | - Yuxuan Cai
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, China
| | - Zhenni Gao
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, China
| | - Junchao Li
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, China
| | - Song Chang
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, China
| | - Bingqing Jiao
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, China
| | - Ruiwang Huang
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, China.
| | - Ming Liu
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, China.
| |
Collapse
|
31
|
Abstract
Somatic mosaicism refers to the fact that cells within an organism have different genomes. It is now clear that somatic mosaicism occurs in all brains and that somatic mutations in a subset of cells can cause various rare neurodevelopmental disorders. However, for most individuals, the extent and consequences of somatic mosaicism are largely unknown. The complexity and unique features of the brain suggest that somatic mosaicism can play an important role in behavior and cognition. Here we review recent manuscripts showing instances of somatic mosaicism in the brain and estimating its extent and possible biological consequences. The consequences of somatic mosaicism span vast dimensions -from a single-locus variant, to genes and gene networks, to cells, to the interactions of the mosaic cells via neural networks affecting behavior and cognition. We highlight how systems biology approaches are particularly well suited for the complex emerging field of brain somatic mosaicism.
Collapse
Affiliation(s)
| | | | - Fred H Gage
- Salk Institute for Biological Studies, La Jolla, CA, USA
| |
Collapse
|
32
|
Eickhoff SB, Laird AR, Fox PM, Lancaster JL, Fox PT. Implementation errors in the GingerALE Software: Description and recommendations. Hum Brain Mapp 2016; 38:7-11. [PMID: 27511454 DOI: 10.1002/hbm.23342] [Citation(s) in RCA: 177] [Impact Index Per Article: 22.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2016] [Accepted: 07/29/2016] [Indexed: 01/17/2023] Open
Abstract
Neuroscience imaging is a burgeoning, highly sophisticated field the growth of which has been fostered by grant-funded, freely distributed software libraries that perform voxel-wise analyses in anatomically standardized three-dimensional space on multi-subject, whole-brain, primary datasets. Despite the ongoing advances made using these non-commercial computational tools, the replicability of individual studies is an acknowledged limitation. Coordinate-based meta-analysis offers a practical solution to this limitation and, consequently, plays an important role in filtering and consolidating the enormous corpus of functional and structural neuroimaging results reported in the peer-reviewed literature. In both primary data and meta-analytic neuroimaging analyses, correction for multiple comparisons is a complex but critical step for ensuring statistical rigor. Reports of errors in multiple-comparison corrections in primary-data analyses have recently appeared. Here, we report two such errors in GingerALE, a widely used, US National Institutes of Health (NIH)-funded, freely distributed software package for coordinate-based meta-analysis. These errors have given rise to published reports with more liberal statistical inferences than were specified by the authors. The intent of this technical report is threefold. First, we inform authors who used GingerALE of these errors so that they can take appropriate actions including re-analyses and corrective publications. Second, we seek to exemplify and promote an open approach to error management. Third, we discuss the implications of these and similar errors in a scientific environment dependent on third-party software. Hum Brain Mapp 38:7-11, 2017. © 2016 Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Germany.,Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Germany
| | - Angela R Laird
- Department of Physics, Florida International University, Miami, Florida
| | - P Mickle Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, Texas
| | - Jack L Lancaster
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, Texas.,Department of Radiology, University of Texas Health Science Center at San Antonio, Florida
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, Texas.,Department of Radiology, University of Texas Health Science Center at San Antonio, Florida.,South Texas Veterans Health Care System, San Antonio, Texas
| |
Collapse
|
33
|
Murphy E, Benítez-Burraco A. Language deficits in schizophrenia and autism as related oscillatory connectomopathies: An evolutionary account. Neurosci Biobehav Rev 2016; 83:742-764. [PMID: 27475632 DOI: 10.1016/j.neubiorev.2016.07.029] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Revised: 06/23/2016] [Accepted: 07/25/2016] [Indexed: 01/28/2023]
Abstract
Schizophrenia (SZ) and autism spectrum disorders (ASD) are characterised by marked language deficits, but it is not clear how these arise from gene mutations associated with the disorders. Our goal is to narrow the gap between SZ and ASD and, ultimately, give support to the view that they represent abnormal (but related) ontogenetic itineraries for the human faculty of language. We will focus on the distinctive oscillatory profiles of the SZ and ASD brains, in turn using these insights to refine our understanding of how the brain implements linguistic computations by exploring a novel model of linguistic feature-set composition. We will argue that brain rhythms constitute the best route to interpreting language deficits in both conditions and mapping them to neural dysfunction and risk alleles of the genes. Importantly, candidate genes for SZ and ASD are overrepresented among the gene sets believed to be important for language evolution. This translational effort may help develop an understanding of the aetiology of SZ and ASD and their high prevalence among modern populations.
Collapse
Affiliation(s)
- Elliot Murphy
- Division of Psychology and Language Sciences, University College London, London, United Kingdom.
| | | |
Collapse
|
34
|
Mišić B, Sporns O. From regions to connections and networks: new bridges between brain and behavior. Curr Opin Neurobiol 2016; 40:1-7. [PMID: 27209150 DOI: 10.1016/j.conb.2016.05.003] [Citation(s) in RCA: 155] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Accepted: 05/04/2016] [Indexed: 12/11/2022]
Abstract
Connections and interactions among distributed brain areas are increasingly recognized as the basis for cognitive operations and a diverse repertoire of behaviors. Analytic advances have allowed for brain connectivity to be represented and quantified at multiple levels: from single connections to communities and networks. This review traces the trajectory of network neuroscience, focusing on how connectivity patterns can be related to cognition and behavior. As recent initiatives for open science provide access to imaging and phenotypic data with great detail and depth, we argue that approaches capable of directly modeling multivariate relationships between brain and behavior will become increasingly important in the field.
Collapse
Affiliation(s)
- Bratislav Mišić
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada H3A 2B4; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Network Science Institute, Indiana University, Bloomington, IN 47405, USA.
| |
Collapse
|
35
|
Mišić B, Betzel RF, de Reus MA, van den Heuvel MP, Berman MG, McIntosh AR, Sporns O. Network-Level Structure-Function Relationships in Human Neocortex. Cereb Cortex 2016; 26:3285-96. [PMID: 27102654 PMCID: PMC4898678 DOI: 10.1093/cercor/bhw089] [Citation(s) in RCA: 181] [Impact Index Per Article: 22.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
The dynamics of spontaneous fluctuations in neural activity are shaped by underlying patterns of anatomical connectivity. While numerous studies have demonstrated edge-wise correspondence between structural and functional connections, much less is known about how large-scale coherent functional network patterns emerge from the topology of structural networks. In the present study, we deploy a multivariate statistical technique, partial least squares, to investigate the association between spatially extended structural networks and functional networks. We find multiple statistically robust patterns, reflecting reliable combinations of structural and functional subnetworks that are optimally associated with one another. Importantly, these patterns generally do not show a one-to-one correspondence between structural and functional edges, but are instead distributed and heterogeneous, with many functional relationships arising from nonoverlapping sets of anatomical connections. We also find that structural connections between high-degree hubs are disproportionately represented, suggesting that these connections are particularly important in establishing coherent functional networks. Altogether, these results demonstrate that the network organization of the cerebral cortex supports the emergence of diverse functional network configurations that often diverge from the underlying anatomical substrate.
Collapse
Affiliation(s)
- Bratislav Mišić
- McConnel Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montréal, QC, H3A 2B4, Canada Department of Psychological and Brain Sciences
| | - Richard F Betzel
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Marcel A de Reus
- Brain Center Rudolf Magnus, UMC Utrecht, Utrecht, 3508 GA, The Netherlands
| | | | - Marc G Berman
- Department of Psychology, University of Chicago, Chicago, IL, 60637, USA
| | - Anthony R McIntosh
- Rotman Research Institute, Baycrest Centre, Toronto, ON, M6A 2E1, Canada
| | - Olaf Sporns
- Department of Psychological and Brain Sciences Indiana University Network Science Institute, Indiana University, Bloomington, IN, 47405, USA
| |
Collapse
|