1
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Forbes CE. On the neural networks of self and other bias and their role in emergent social interactions. Cortex 2024; 177:113-129. [PMID: 38848651 DOI: 10.1016/j.cortex.2024.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 02/09/2024] [Accepted: 05/14/2024] [Indexed: 06/09/2024]
Abstract
Extensive research has documented the brain networks that play an integral role in bias, or the alteration and filtration of information processing in a manner that fundamentally favors an individual. The roots of bias, whether self- or other-oriented, are a complex constellation of neural and psychological processes that start at the most fundamental levels of sensory processing. From the millisecond information is received in the brain it is filtered at various levels and through various brain networks in relation to extant intrinsic activity to provide individuals with a perception of reality that complements and satisfies the conscious perceptions they have for themselves and the cultures in which they were reared. The products of these interactions, in turn, are dynamically altered by the introduction of others, be they friends or strangers who are similar or different in socially meaningful ways. While much is known about the various ways that basic biases alter specific aspects of neural function to support various forms of bias, the breadth and scope of the phenomenon remains entirely unclear. The purpose of this review is to examine the brain networks that shape (i.e., bias) the self-concept and how interactions with similar (ingroup) compared to dissimilar (outgroup) others alter these network (and subsequent interpersonal) interactions in fundamental ways. Throughout, focus is placed on an emerging understanding of the brain as a complex system, which suggests that many of these network interactions likely occur on a non-linear scale that blurs the lines between network hierarchies.
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Affiliation(s)
- Chad E Forbes
- Social Neuroscience Laboratory, Department of Psychology, Florida Atlantic University, Boca Raton, FL, USA; Florida Atlantic University Stiles-Nicholson Brain Institute, USA.
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2
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Wei B, Huang X, Ji Y, Fu WW, Cheng Q, Shu BL, Huang QY, Chai H, Zhou L, Yuan HY, Wu XR. Analyzing the topological properties of resting-state brain function network connectivity based on graph theoretical methods in patients with high myopia. BMC Ophthalmol 2024; 24:315. [PMID: 39075405 PMCID: PMC11287926 DOI: 10.1186/s12886-024-03592-6] [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: 04/05/2024] [Accepted: 07/24/2024] [Indexed: 07/31/2024] Open
Abstract
AIM Recent imaging studies have found significant abnormalities in the brain's functional or structural connectivity among patients with high myopia (HM), indicating a heightened risk of cognitive impairment and other behavioral changes. However, there is a lack of research on the topological characteristics and connectivity changes of the functional networks in HM patients. In this study, we employed graph theoretical analysis to investigate the topological structure and regional connectivity of the brain function network in HM patients. METHODS We conducted rs-fMRI scans on 82 individuals with HM and 59 healthy controls (HC), ensuring that the two groups were matched for age and education level. Through graph theoretical analysis, we studied the topological structure of whole-brain functional networks among participants, exploring the topological properties and differences between the two groups. RESULTS In the range of 0.05 to 0.50 of sparsity, both groups demonstrated a small-world architecture of the brain network. Compared to the control group, HM patients showed significantly lower values of normalized clustering coefficient (γ) (P = 0.0101) and small-worldness (σ) (P = 0.0168). Additionally, the HM group showed lower nodal centrality in the right Amygdala (P < 0.001, Bonferroni-corrected). Notably, there is an increase in functional connectivity (FC) between the saliency network (SN) and Sensorimotor Network (SMN) in the HM group, while the strength of FC between the basal ganglia is relatively weaker (P < 0.01). CONCLUSION HM Patients exhibit reduced small-world characteristics in their brain networks, with significant drops in γ and σ values indicating weakened global interregional information transfer ability. Not only that, the topological properties of the amygdala nodes in HM patients significantly decline, indicating dysfunction within the brain network. In addition, there are abnormalities in the FC between the SN, SMN, and basal ganglia networks in HM patients, which is related to attention regulation, motor impairment, emotions, and cognitive performance. These findings may provide a new mechanism for central pathology in HM patients.
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Affiliation(s)
- Bin Wei
- Department of Ophthalmology, Jiangxi Medical College, Nanchang University, The 1st Affiliated Hospital, Nanchang, Jiangxi, People's Republic of China
| | - Xin Huang
- Department of Ophthalmology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
| | - Yu Ji
- Department of Ophthalmology, Jiangxi Medical College, Nanchang University, The 1st Affiliated Hospital, Nanchang, Jiangxi, People's Republic of China
| | - Wen-Wen Fu
- Department of Ophthalmology, Jiangxi Medical College, Nanchang University, The 1st Affiliated Hospital, Nanchang, Jiangxi, People's Republic of China
| | - Qi Cheng
- Department of Ophthalmology, Jiangxi Medical College, Nanchang University, The 1st Affiliated Hospital, Nanchang, Jiangxi, People's Republic of China
| | - Ben-Liang Shu
- Department of Ophthalmology, Jiangxi Medical College, Nanchang University, The 1st Affiliated Hospital, Nanchang, Jiangxi, People's Republic of China
| | - Qin-Yi Huang
- Department of Ophthalmology, Jiangxi Medical College, Nanchang University, The 1st Affiliated Hospital, Nanchang, Jiangxi, People's Republic of China
| | - Hua Chai
- Department of Ophthalmology, Jiangxi Medical College, Nanchang University, The 1st Affiliated Hospital, Nanchang, Jiangxi, People's Republic of China
| | - Lin Zhou
- Department of Ophthalmology, Jiangxi Medical College, Nanchang University, The 1st Affiliated Hospital, Nanchang, Jiangxi, People's Republic of China
| | - Hao-Yu Yuan
- Department of Ophthalmology, Jiangxi Medical College, Nanchang University, The 1st Affiliated Hospital, Nanchang, Jiangxi, People's Republic of China
| | - Xiao-Rong Wu
- Department of Ophthalmology, Jiangxi Medical College, Nanchang University, The 1st Affiliated Hospital, Nanchang, Jiangxi, People's Republic of China.
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Najar D, Dichev J, Stoyanov D. Towards New Methodology for Cross-Validation of Clinical Evaluation Scales and Functional MRI in Psychiatry. J Clin Med 2024; 13:4363. [DOI: 10.3390/jcm13154363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/01/2024] Open
Abstract
Objective biomarkers have been a critical challenge for the field of psychiatry, where diagnostic, prognostic, and theranostic assessments are still based on subjective narratives. Psychopathology operates with idiographic knowledge and subjective evaluations incorporated into clinical assessment inventories, but is considered to be a medical discipline and, as such, uses medical intervention methods (e.g., pharmacological, ECT; rTMS; tDCS) and, therefore, is supposed to operate with the language and methods of nomothetic networks. The idiographic assessments are provisionally “quantified” into “structured clinical scales” to in some way resemble nomothetic measures. Instead of fostering data merging and integration, this approach further encapsulates the clinical psychiatric methods, as all other biological tests (molecular, neuroimaging) are performed separately, only after the clinical assessment has provided diagnosis. Translational cross-validation of clinical assessment instruments and fMRI is an attempt to address the gap. The aim of this approach is to investigate whether there exist common and specific neural circuits, which underpin differential item responses to clinical self-rating scales during fMRI sessions in patients suffering from the two main spectra of mental disorders: schizophrenia and major depression. The current status of this research program and future implications to promote the development of psychiatry as a medical discipline are discussed.
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Affiliation(s)
- Diyana Najar
- Faculty of Medicine, Medical University, 4002 Plovdiv, Bulgaria
| | - Julian Dichev
- Faculty of Medicine, Medical University, 4002 Plovdiv, Bulgaria
| | - Drozdstoy Stoyanov
- Department of Psychiatry, Medical University Plovdiv, 4000 Plovdiv, Bulgaria
- Research Institute & Strategic Research and Innovation Program for the Development of MU-PLOVDIV–(SRIPD-MUP), European Union-NextGenerationEU, 4002 Plovdiv, Bulgaria
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4
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Zhang X, Cheng X, Chen J, Sun J, Yang X, Li W, Chen L, Mao Y, Liu Y, Zeng X, Ye B, Yang C, Li X, Cao L. Distinct global brain connectivity alterations in depressed adolescents with subthreshold mania and the relationship with processing speed: Evidence from sBEAD Cohort. J Affect Disord 2024; 357:97-106. [PMID: 38657768 DOI: 10.1016/j.jad.2024.04.063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 04/06/2024] [Accepted: 04/15/2024] [Indexed: 04/26/2024]
Abstract
BACKGROUND Bipolar disorder (BD) is a progressive condition. Investigating the neuroimaging mechanisms in depressed adolescents with subthreshold mania (SubMD) facilitates the early identification of BD. However, the global brain connectivity (GBC) patterns in SubMD patients, as well as the relationship with processing speed before the onset of full-blown BD, remain unclear. METHODS The study involved 72 SubMD, 77 depressed adolescents without subthreshold mania (nSubMD), and 69 gender- and age-matched healthy adolescents (HCs). All patients underwent a clinical follow-up ranging from six to twelve months. We calculated the voxel-based graph theory analysis of the GBC map and conducted the TMT-A test to measure the processing speed. RESULTS Compared to HCs and nSubMD, SubMD patients displayed distinctive GBC index patterns: GBC index decreased in the right Medial Superior Frontal Gyrus (SFGmed.R)/Superior Frontal Gyrus (SFG) while increased in the right Precuneus and left Postcentral Gyrus. Both patient groups showed increased GBC index in the right Inferior Temporal Gyrus. An increased GBC value in the right Supplementary Motor Area was exclusively observed in the nSubMD-group. There were opposite changes in the GBC index in SFGmed.R/SFG between two patient groups, with an AUC of 0.727. Additionally, GBC values in SFGmed.R/SFG exhibited a positive correlation with TMT-A scores in SubMD-group. LIMITATIONS Relatively shorter follow-up duration, medications confounding, and modest sample size. CONCLUSION These findings suggest that adolescents with subthreshold BD have specific impairments patterns at the whole brain connectivity level associated with processing speed impairments, providing insights into early identification and intervention strategies for BD.
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Affiliation(s)
- Xiaofei Zhang
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong province 510000, PR China
| | - Xiaofang Cheng
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Jianshan Chen
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Jiaqi Sun
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Xiaoyong Yang
- Department of Psychiatry, Guangzhou Medical University, Guangdong province 510300, PR China
| | - Weiming Li
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Lei Chen
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Yimiao Mao
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Yutong Liu
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Xuanlin Zeng
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Biyu Ye
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Chanjuan Yang
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Xuan Li
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China.
| | - Liping Cao
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China.
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Calazans MAA, Ferreira FABS, Santos FAN, Madeiro F, Lima JB. Machine Learning and Graph Signal Processing Applied to Healthcare: A Review. Bioengineering (Basel) 2024; 11:671. [PMID: 39061753 PMCID: PMC11273494 DOI: 10.3390/bioengineering11070671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 06/20/2024] [Accepted: 06/26/2024] [Indexed: 07/28/2024] Open
Abstract
Signal processing is a very useful field of study in the interpretation of signals in many everyday applications. In the case of applications with time-varying signals, one possibility is to consider them as graphs, so graph theory arises, which extends classical methods to the non-Euclidean domain. In addition, machine learning techniques have been widely used in pattern recognition activities in a wide variety of tasks, including health sciences. The objective of this work is to identify and analyze the papers in the literature that address the use of machine learning applied to graph signal processing in health sciences. A search was performed in four databases (Science Direct, IEEE Xplore, ACM, and MDPI), using search strings to identify papers that are in the scope of this review. Finally, 45 papers were included in the analysis, the first being published in 2015, which indicates an emerging area. Among the gaps found, we can mention the need for better clinical interpretability of the results obtained in the papers, that is not to restrict the results or conclusions simply to performance metrics. In addition, a possible research direction is the use of new transforms. It is also important to make new public datasets available that can be used to train the models.
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Affiliation(s)
| | - Felipe A. B. S. Ferreira
- Unidade Acadêmica do Cabo de Santo Agostinho, Universidade Federal Rural de Pernambuco, Cabo de Santo Agostinho 54518-430, Brazil;
| | - Fernando A. N. Santos
- Institute for Advanced Studies, Universiteit van Amsterdam, 1012 WP Amsterdam, The Netherlands;
| | - Francisco Madeiro
- Escola Politécnica de Pernambuco, Universidade de Pernambuco, Recife 50720-001, Brazil;
| | - Juliano B. Lima
- Centro de Tecnologia e Geociências, Universidade Federal de Pernambuco, Recife 50670-901, Brazil;
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Page D, Buchanan CR, Moodie JE, Harris MA, Taylor A, Valdés Hernández M, Muñoz Maniega S, Corley J, Bastin ME, Wardlaw JM, Russ TC, Deary IJ, Cox SR. Examining the neurostructural architecture of intelligence: The Lothian Birth Cohort 1936 study. Cortex 2024; 178:269-286. [PMID: 39067180 DOI: 10.1016/j.cortex.2024.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 05/10/2024] [Accepted: 06/05/2024] [Indexed: 07/30/2024]
Abstract
Examining underlying neurostructural correlates of specific cognitive abilities is practically and theoretically complicated by the existence of the positive manifold (all cognitive tests positively correlate): if a brain structure is associated with a cognitive task, how much of this is uniquely related to the cognitive domain, and how much is due to covariance with all other tests across domains (captured by general cognitive functioning, also known as general intelligence, or 'g')? We quantitatively address this question by examining associations between brain structural and diffusion MRI measures (global tissue volumes, white matter hyperintensities, global white matter diffusion fractional anisotropy and mean diffusivity, and FreeSurfer processed vertex-wise cortical volumes, smoothed at 20mm fwhm) with g and cognitive domains (processing speed, crystallised ability, memory, visuospatial ability). The cognitive domains were modelled using confirmatory factor analysis to derive both hierarchical and bifactor solutions using 13 cognitive tests in 697 participants from the Lothian Birth Cohort 1936 study (mean age 72.5 years; SD = .7). Associations between the extracted cognitive factor scores for each domain and g were computed for each brain measure covarying for age, sex and intracranial volume, and corrected for false discovery rate. There were a range of significant associations between cognitive domains and global MRI brain structural measures (r range .008 to .269, p < .05). Regions implicated by vertex-wise regional cortical volume included a widespread number of medial and lateral areas of the frontal, temporal and parietal lobes. However, at both global and regional level, much of the domain-MRI associations were shared (statistically accounted for by g). Removing g-related variance from cognitive domains attenuated association magnitudes with global brain MRI measures by 27.9-59.7% (M = 46.2%), with only processing speed retaining all significant associations. At the regional cortical level, g appeared to account for the majority (range 22.1-88.4%; M = 52.8% across cognitive domains) of regional domain-specific associations. Crystallised and memory domains had almost no unique cortical correlates, whereas processing speed and visuospatial ability retained limited cortical volumetric associations. The greatest spatial overlaps across cognitive domains (as denoted by g) were present in the medial and lateral temporal, lateral parietal and lateral frontal areas.
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Affiliation(s)
- Danielle Page
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, UK
| | - Colin R Buchanan
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, UK
| | - Joanna E Moodie
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, UK
| | - Mathew A Harris
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, UK
| | - Adele Taylor
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, UK
| | - Maria Valdés Hernández
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, UK; Division of Neuroimaging Sciences and Row Fogo Centre for Small Vessel Diseases Research, Centre for Clinical Brain Sciences, University of Edinburgh, UK; UK Dementia Research Institute Centre at the University of Edinburgh, UK
| | - Susana Muñoz Maniega
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, UK; Division of Neuroimaging Sciences and Row Fogo Centre for Small Vessel Diseases Research, Centre for Clinical Brain Sciences, University of Edinburgh, UK; UK Dementia Research Institute Centre at the University of Edinburgh, UK
| | - Janie Corley
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, UK
| | - Mark E Bastin
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, UK
| | - Joanna M Wardlaw
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, UK; Division of Neuroimaging Sciences and Row Fogo Centre for Small Vessel Diseases Research, Centre for Clinical Brain Sciences, University of Edinburgh, UK; UK Dementia Research Institute Centre at the University of Edinburgh, UK
| | - Tom C Russ
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, UK; Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, UK; Alzheimer Scotland Dementia Research Centre, University of Edinburgh, UK
| | - Ian J Deary
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, UK
| | - Simon R Cox
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, UK.
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Pini L, Lombardi G, Sansone G, Gaiola M, Padovan M, Volpin F, Denaro L, Corbetta M, Salvalaggio A. Indirect functional connectivity does not predict overall survival in glioblastoma. Neurobiol Dis 2024; 196:106521. [PMID: 38697575 DOI: 10.1016/j.nbd.2024.106521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/14/2024] [Accepted: 04/29/2024] [Indexed: 05/05/2024] Open
Abstract
BACKGROUND Lesion network mapping (LNM) is a popular framework to assess clinical syndromes following brain injury. The classical approach involves embedding lesions from patients into a normative functional connectome and using the corresponding functional maps as proxies for disconnections. However, previous studies indicated limited predictive power of this approach in behavioral deficits. We hypothesized similarly low predictiveness for overall survival (OS) in glioblastoma (GBM). METHODS A retrospective dataset of patients with GBM was included (n = 99). Lesion masks were registered in the normative space to compute disconnectivity maps. The brain functional normative connectome consisted in data from 173 healthy subjects obtained from the Human Connectome Project. A modified version of the LNM was then applied to core regions of GBM masks. Linear regression, classification, and principal component (PCA) analyses were conducted to explore the relationship between disconnectivity and OS. OS was considered both as continuous and categorical (low, intermediate, and high survival) variable. RESULTS The results revealed no significant associations between OS and network disconnection strength when analyzed at both voxel-wise and classification levels. Moreover, patients stratified into different OS groups did not exhibit significant differences in network connectivity patterns. The spatial similarity among the first PCA of network maps for each OS group suggested a lack of distinctive network patterns associated with survival duration. CONCLUSIONS Compared with indirect structural measures, functional indirect mapping does not provide significant predictive power for OS in patients with GBM. These findings are consistent with previous research that demonstrated the limitations of indirect functional measures in predicting clinical outcomes, underscoring the need for more comprehensive methodologies and a deeper understanding of the factors influencing clinical outcomes in this challenging disease.
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Affiliation(s)
- Lorenzo Pini
- Padova Neuroscience Center, University of Padova, Italy
| | - Giuseppe Lombardi
- Department of Oncology, Oncology 1, Veneto Institute of Oncology IOV-IRCCS, Padova, Italy
| | - Giulio Sansone
- Departments of Neuroscience, University of Padova, Italy
| | - Matteo Gaiola
- Departments of Neuroscience, University of Padova, Italy
| | - Marta Padovan
- Department of Oncology, Oncology 1, Veneto Institute of Oncology IOV-IRCCS, Padova, Italy
| | - Francesco Volpin
- Division of Neurosurgery, Azienda Ospedaliera Università di Padova, Padova, Italy
| | - Luca Denaro
- Departments of Neuroscience, University of Padova, Italy
| | - Maurizio Corbetta
- Padova Neuroscience Center, University of Padova, Italy; Departments of Neuroscience, University of Padova, Italy; Veneto institute of Molecular Medicine (VIMM), Padova, Italy
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Forrer S, Delavari F, Sandini C, Rafi H, Preti MG, Van De Ville D, Eliez S. Longitudinal Analysis of Brain Function-Structure Dependencies in 22q11.2 Deletion Syndrome and Psychotic Symptoms. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00141-1. [PMID: 38849032 DOI: 10.1016/j.bpsc.2024.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 05/03/2024] [Accepted: 05/19/2024] [Indexed: 06/09/2024]
Abstract
BACKGROUND Compared with conventional unimodal analysis, understanding how brain function and structure relate to one another opens a new biologically relevant assessment of neural mechanisms. However, how function-structure dependencies (FSDs) evolve throughout typical and abnormal neurodevelopment remains elusive. The 22q11.2 deletion syndrome (22q11.2DS) offers an important opportunity to study the development of FSDs and their specific association with the pathophysiology of psychosis. METHODS Previously, we used graph signal processing to combine brain activity and structural connectivity measures in adults, quantifying FSD. Here, we combined FSD with longitudinal multivariate partial least squares correlation to evaluate FSD alterations across groups and among patients with and without mild to moderate positive psychotic symptoms. We assessed 391 longitudinally repeated resting-state functional and diffusion-weighted magnetic resonance images from 194 healthy control participants and 197 deletion carriers (ages 7-34 years, data collected over a span of 12 years). RESULTS Compared with control participants, patients with 22q11.2DS showed a persistent developmental offset from childhood, with regions of hyper- and hypocoupling across the brain. Additionally, a second deviating developmental pattern showed an exacerbation during adolescence, presenting hypocoupling in the frontal and cingulate cortices and hypercoupling in temporal regions for patients with 22q11.2DS. Interestingly, the observed aggravation during adolescence was strongly driven by the group with positive psychotic symptoms. CONCLUSIONS These results confirm a central role of altered FSD maturation in the emergence of psychotic symptoms in 22q11.2DS during adolescence. The FSD deviations precede the onset of psychotic episodes and thus offer a potential early indication for behavioral interventions in individuals at risk.
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Affiliation(s)
- Silas Forrer
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland; Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
| | - Farnaz Delavari
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland; Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Corrado Sandini
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland
| | - Halima Rafi
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland; Developmental Clinical Psychology Research Unit, University of Geneva Faculty of Psychology and Educational Sciences, Geneva, Switzerland
| | - Maria Giulia Preti
- Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland; CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Dimitri Van De Ville
- Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland; CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Stephan Eliez
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland; Department of Genetic Medicine and Development, University of Geneva School of Medicine, Geneva, Switzerland
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Nairuz T, Sangwoo-Cho, Lee JH. Photobiomodulation Therapy on Brain: Pioneering an Innovative Approach to Revolutionize Cognitive Dynamics. Cells 2024; 13:966. [PMID: 38891098 PMCID: PMC11171912 DOI: 10.3390/cells13110966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2024] [Revised: 05/30/2024] [Accepted: 05/30/2024] [Indexed: 06/21/2024] Open
Abstract
Photobiomodulation (PBM) therapy on the brain employs red to near-infrared (NIR) light to treat various neurological and psychological disorders. The mechanism involves the activation of cytochrome c oxidase in the mitochondrial respiratory chain, thereby enhancing ATP synthesis. Additionally, light absorption by ion channels triggers the release of calcium ions, instigating the activation of transcription factors and subsequent gene expression. This cascade of events not only augments neuronal metabolic capacity but also orchestrates anti-oxidant, anti-inflammatory, and anti-apoptotic responses, fostering neurogenesis and synaptogenesis. It shows promise for treating conditions like dementia, stroke, brain trauma, Parkinson's disease, and depression, even enhancing cognitive functions in healthy individuals and eliciting growing interest within the medical community. However, delivering sufficient light to the brain through transcranial approaches poses a significant challenge due to its limited penetration into tissue, prompting an exploration of alternative delivery methods such as intracranial and intranasal approaches. This comprehensive review aims to explore the mechanisms through which PBM exerts its effects on the brain and provide a summary of notable preclinical investigations and clinical trials conducted on various brain disorders, highlighting PBM's potential as a therapeutic modality capable of effectively impeding disease progression within the organism-a task often elusive with conventional pharmacological interventions.
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Affiliation(s)
| | | | - Jong-Ha Lee
- Department of Biomedical Engineering, Keimyung University, Daegu 42601, Republic of Korea; (T.N.); (S.-C.)
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10
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Li F, Zhang S, Jiang L, Duan K, Feng R, Zhang Y, Zhang G, Zhang Y, Li P, Yao D, Xie J, Xu W, Xu P. Recognition of autism spectrum disorder in children based on electroencephalogram network topology. Cogn Neurodyn 2024; 18:1033-1045. [PMID: 38826670 PMCID: PMC11143134 DOI: 10.1007/s11571-023-09962-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 02/24/2023] [Accepted: 03/17/2023] [Indexed: 06/04/2024] Open
Abstract
Although our knowledge of autism spectrum disorder (ASD) has been deepened, the accurate diagnosis of ASD from normal individuals is still left behind. In this study, we proposed to apply the spatial pattern of the network topology (SPN) to identify children with ASD from normal ones. Based on two independent batches of electroencephalogram datasets collected separately, the accurate recognition of ASD from normal children was achieved by applying the proposed SPN features. Since decreased long-range connectivity was identified for children with ASD, the SPN features extracted from the distinctive topological architecture between two groups in the first dataset were used to validate the capacity of SPN in classifying ASD, and the SPN features achieved the highest accuracy of 92.31%, which outperformed the other features e.g., power spectrum density (84.62%), network properties (76.92%), and sample entropy (73.08%). Moreover, within the second dataset, by using the model trained in the first dataset, the SPN also acquired the highest sensitivity in recognizing ASD, when compared to the other features. These results consistently illustrated that the functional brain network, especially the intrinsic spatial network topology, might be the potential biomarker for the diagnosis of ASD.
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Affiliation(s)
- Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731 China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
- Research Unit of NeuroInformation, 2019RU035, Chinese Academy of Medical Sciences, Chengdu, China
| | - Shu Zhang
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Lin Jiang
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Keyi Duan
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Rui Feng
- Rainbow Biotechnology Co., Ltd., Chengdu, 610041 China
| | - Yingli Zhang
- Rainbow Biotechnology Co., Ltd., Chengdu, 610041 China
| | - Gao Zhang
- The Preston Robert Tisch Brain Tumor Center, Department of Neurosurgery, Department of Pathology, Duke University School of Medicine, Durham, NC 27710 USA
| | - Yangsong Zhang
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
- School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, 621010 China
| | - Peiyang Li
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, 400065 China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731 China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
- Research Unit of NeuroInformation, 2019RU035, Chinese Academy of Medical Sciences, Chengdu, China
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001 China
| | - Jiang Xie
- Chengdu Third People’s Hospital, Affiliated Hospital of Southwest JiaoTong University Medical School, Chengdu, 610031 China
| | - Wenming Xu
- Department of Obstetrics/Gynecology, Joint Laboratory of Reproductive Medicine (SCU-CUHK), Key Laboratory of Obstetric, Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, 610041 China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731 China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
- Research Unit of NeuroInformation, 2019RU035, Chinese Academy of Medical Sciences, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, 610042 China
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11
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Sibilia F, Jost-Mousseau C, Banaschewski T, Barker GJ, Büchel C, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Ittermann B, Martinot JL, Martinot MLP, Artiges E, Nees F, Orfanos DP, Poustka L, Millenet S, Fröhner JH, Smolka MN, Walter H, Whelan R, Schumann G, Bokde AL. The relationship between negative life events and cortical structural connectivity in adolescents. IBRO Neurosci Rep 2024; 16:201-210. [PMID: 38348392 PMCID: PMC10859284 DOI: 10.1016/j.ibneur.2024.01.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 01/27/2024] [Indexed: 02/15/2024] Open
Abstract
Adolescence is a crucial period for physical and psychological development. The impact of negative life events represents a risk factor for the onset of neuropsychiatric disorders. This study aims to investigate the relationship between negative life events and structural brain connectivity, considering both graph theory and connectivity strength. A group (n = 487) of adolescents from the IMAGEN Consortium was divided into Low and High Stress groups. Brain networks were extracted at an individual level, based on morphological similarity between grey matter regions with regions defined using an atlas-based region of interest (ROI) approach. Between-group comparisons were performed with global and local graph theory measures in a range of sparsity levels. The analysis was also performed in a larger sample of adolescents (n = 976) to examine linear correlations between stress level and network measures. Connectivity strength differences were investigated with network-based statistics. Negative life events were not found to be a factor influencing global network measures at any sparsity level. At local network level, between-group differences were found in centrality measures of the left somato-motor network (a decrease of betweenness centrality was seen at sparsity 5%), of the bilateral central visual and the left dorsal attention network (increase of degree at sparsity 10% at sparsity 30% respectively). Network-based statistics analysis showed an increase in connectivity strength in the High stress group in edges connecting the dorsal attention, limbic and salience networks. This study suggests negative life events alone do not alter structural connectivity globally, but they are associated to connectivity properties in areas involved in emotion and attention.
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Affiliation(s)
- Francesca Sibilia
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Coline Jost-Mousseau
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
- Paris Institute of Technology for Life, Food and Environmental Sciences, Paris, France
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Gareth J. Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
| | - Christian Büchel
- University Medical Centre Hamburg-Eppendorf, House W34, 3.OG, Martinistr. 52, 20246, Hamburg, Germany
| | - Sylvane Desrivières
- Medical Research Council - Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
| | - Herta Flor
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131 Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, 05405 Burlington, VT, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Andreas Heinz
- Charité – Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany
| | - Bernd Ittermann
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Saclay, University Paris Descartes – Sorbonne Paris Cité; and Maison de Solenn, Paris, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Saclay, University Paris Descartes; and AP-HP.Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Descartes - Sorbonne Paris Cité; and Psychiatry Department 91G16, Orsay Hospital, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
| | | | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, 37075, Göttingen, Germany
| | - Sabina Millenet
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Juliane H. Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Michael N. Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Henrik Walter
- Charité – Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Gunter Schumann
- Medical Research Council - Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
| | - Arun L.W. Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - IMAGEN Consortium
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
- Paris Institute of Technology for Life, Food and Environmental Sciences, Paris, France
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
- University Medical Centre Hamburg-Eppendorf, House W34, 3.OG, Martinistr. 52, 20246, Hamburg, Germany
- Medical Research Council - Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131 Mannheim, Germany
- NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
- Departments of Psychiatry and Psychology, University of Vermont, 05405 Burlington, VT, USA
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
- Charité – Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Saclay, University Paris Descartes – Sorbonne Paris Cité; and Maison de Solenn, Paris, France
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Saclay, University Paris Descartes; and AP-HP.Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Descartes - Sorbonne Paris Cité; and Psychiatry Department 91G16, Orsay Hospital, France
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, 37075, Göttingen, Germany
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
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12
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Gutiérrez-de Pablo V, Poza J, Maturana-Candelas A, Rodríguez-González V, Tola-Arribas MÁ, Cano M, Hoshi H, Shigihara Y, Hornero R, Gómez C. Exploring the disruptions of the neurophysiological organization in Alzheimer's disease: An integrative approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 250:108197. [PMID: 38688139 DOI: 10.1016/j.cmpb.2024.108197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 12/20/2023] [Accepted: 04/21/2024] [Indexed: 05/02/2024]
Abstract
BACKGROUND AND OBJECTIVE Alzheimer's disease (AD) is a neurological disorder that impairs brain functions associated with cognition, memory, and behavior. Noninvasive neurophysiological techniques like magnetoencephalography (MEG) and electroencephalography (EEG) have shown promise in reflecting brain changes related to AD. These techniques are usually assessed at two levels: local activation (spectral, nonlinear, and dynamic properties) and global synchronization (functional connectivity, frequency-dependent network, and multiplex network organization characteristics). Nonetheless, the understanding of the organization formed by the existing relationships between these levels, henceforth named neurophysiological organization, remains unexplored. This work aims to assess the alterations AD causes in the resting-state neurophysiological organization. METHODS To that end, three datasets from healthy controls (HC) and patients with dementia due to AD were considered: MEG database (55 HC and 87 patients with AD), EEG1 database (51 HC and 100 patients with AD), and EEG2 database (45 HC and 82 patients with AD). To explore the alterations induced by AD in the relationships between several features extracted from M/EEG data, association networks (ANs) were computed. ANs are graphs, useful to quantify and visualize the intricate relationships between multiple features. RESULTS Our results suggested a disruption in the neurophysiological organization of patients with AD, exhibiting a greater inclination towards the local activation level; and a significant decrease in the complexity and diversity of the ANs (p-value ¡ 0.05, Mann-Whitney U-test, Bonferroni correction). This effect might be due to a shift of the neurophysiological organization towards more regular configurations, which may increase its vulnerability. Moreover, our findings support the crucial role played by the local activation level in maintaining the stability of the neurophysiological organization. Classification performance exhibited accuracy values of 83.91%, 73.68%, and 72.65% for MEG, EEG1, and EEG2 databases, respectively. CONCLUSION This study introduces a novel, valuable methodology able to integrate parameters characterize different properties of the brain activity and to explore the intricate organization of the neurophysiological organization at different levels. It was noted that AD increases susceptibility to changes in functional neural organization, suggesting a greater ease in the development of severe impairments. Therefore, ANs could facilitate a deeper comprehension of the complex interactions in brain function from a global standpoint.
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Affiliation(s)
- Víctor Gutiérrez-de Pablo
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain.
| | - Jesús Poza
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
| | - Aarón Maturana-Candelas
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain
| | - Víctor Rodríguez-González
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain
| | - Miguel Ángel Tola-Arribas
- CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain; Department of Neurology, Río Hortega University Hospital, Valladolid, Spain
| | - Mónica Cano
- Department of Clinical Neurophysiology, Río Hortega University Hospital, Valladolid, Spain
| | - Hideyuki Hoshi
- Precision Medicine Centre, Hokuto Hospital, Obihiro, Japan
| | | | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
| | - Carlos Gómez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain
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13
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Liu Q, Davey D, Jimmy J, Ajilore O, Klumpp H. Network Analysis of Behavioral Activation/Inhibition Systems and Brain Volume in Individuals With and Without Major Depressive Disorder or Social Anxiety Disorder. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:551-560. [PMID: 37659443 PMCID: PMC10904669 DOI: 10.1016/j.bpsc.2023.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 08/11/2023] [Accepted: 08/13/2023] [Indexed: 09/04/2023]
Abstract
BACKGROUND Social anxiety disorder (SAD) and major depressive disorder (MDD) are characterized by behavioral abnormalities in motivational systems, namely the behavioral inhibition system (BIS) and behavioral activation system (BAS). Limited studies indicate brain volume in regions that support emotion, learning/memory, reward, and cognitive functions relate to BIS/BAS. To increase understanding of BIS/BAS, the current study used a network approach. METHODS Patients with SAD (n = 59), patients with MDD (n = 64), and healthy control participants (n = 36) completed a BIS/BAS questionnaire and structural magnetic resonance imaging scans; volumetric regions of interest comprised cortical and limbic structures based on previous BIS/BAS studies. A Bayesian Gaussian graphical model was used for each diagnostic group, and groups were compared. Among network metrics, bridge centrality was of primary interest. Analysis of variance evaluated BIS/BAS behaviors between groups. RESULTS Bridge centrality showed hippocampus positively related to BAS, but not to BIS, in the MDD group; no findings were observed in the SAD or control groups. Yet, network density (i.e., overall strength of relationships between variables) and degree centrality (i.e., overall relationship between one variable to all other variables) showed that cortical (e.g., precuneus, medial orbitofrontal) and subcortical (e.g., amygdala, hippocampus) regions differed between diagnostic groups. Analysis of variance results showed BAS was lower in the MDD/SAD groups compared with the control group, while BIS was higher in the SAD group relative to the MDD group, which in turn was higher than the control group. CONCLUSIONS Preliminary findings indicate that network-level aberrations may underlie motivational abnormalities in MDD and SAD. Evidence of BIS/BAS differences builds on previous work that points to shared and distinct motivational differences in internalizing psychopathologies.
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Affiliation(s)
- Qimin Liu
- Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois
| | - Delaney Davey
- Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois.
| | - Jagan Jimmy
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, Ohio
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois
| | - Heide Klumpp
- Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois
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14
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Balasubramanian G, Kanagasabai A, Veezhinathan M, Mohan J. Brain connectivity dynamics during listening to music and potential impact on task performance. Cogn Neurodyn 2024; 18:829-845. [PMID: 38826657 PMCID: PMC11143124 DOI: 10.1007/s11571-023-09948-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 01/17/2023] [Accepted: 02/16/2023] [Indexed: 03/06/2023] Open
Abstract
To analyze brain connectivity dynamics during listening to music and estimate the potential impact on task performance. Fifteen participants (13 males and 2 females) participated in this study based on their interest in Indian classical music. Measurements of the influence of Indian music on task performance were obtained by assessing brain activation using EEG signals. Brain connectivity analysis was performed to visualize the connections between brain regions under various experimental conditions. Visual Go/No Go Stimuli was used to evaluate visual spatial attention during operation by evaluating misses, committed errors, and reaction times. In Task 1 (listening to music only), it was reported that there was a change in the positions of the electrodes (F3, F7) located in the left frontal lobe. The energy of the relative beta component was significantly higher only at F7 during task 1 (p = 0.005). Event-related desynchronization alpha and theta synchronization were significant (p = 0.005) at all electrode sites in the bilateral frontal lobes (F3, F4, F7 and F8) while listening to music and performing tasks (task 2). When the task without music (task 3) was performed, the energy of the relative alpha component was significantly higher at the Fp2 electrode position (p = 0.005). It is noteworthy that the energy of the theta component was significantly lower at the location of the Fp2 electrode (p = 0.005). The frontal asymmetry index score measures were significantly high at F4/F3 and F8/F7 during task 1. The connectivity map of theta synchronization showed a robust association between Fp2 and F8 which was in turn connected to P4 and O2 during Task 2. Results indicated an increased omission and commission errors during Task 3.
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Affiliation(s)
| | - Adalarasu Kanagasabai
- School of Electrical and Electronics Engineering, SASTRA Deemed to be University, Thanjavur, Tamil Nadu India
| | - Mahesh Veezhinathan
- Department of Biomedical Engineering, SSN College of Engineering, Chennai, Tamil Nadu India
| | - Jagannath Mohan
- Department of Biomedical Engineering, SSN College of Engineering, Chennai, Tamil Nadu India
- School of Electronics Engineering, Vellore Institute of Technology (VIT), Chennai, Tamil Nadu India
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15
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Murphy C, Thibeault V, Allard A, Desrosiers P. Duality between predictability and reconstructability in complex systems. Nat Commun 2024; 15:4478. [PMID: 38796449 PMCID: PMC11127975 DOI: 10.1038/s41467-024-48020-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 04/15/2024] [Indexed: 05/28/2024] Open
Abstract
Predicting the evolution of a large system of units using its structure of interaction is a fundamental problem in complex system theory. And so is the problem of reconstructing the structure of interaction from temporal observations. Here, we find an intricate relationship between predictability and reconstructability using an information-theoretical point of view. We use the mutual information between a random graph and a stochastic process evolving on this random graph to quantify their codependence. Then, we show how the uncertainty coefficients, which are intimately related to that mutual information, quantify our ability to reconstruct a graph from an observed time series, and our ability to predict the evolution of a process from the structure of its interactions. We provide analytical calculations of the uncertainty coefficients for many different systems, including continuous deterministic systems, and describe a numerical procedure when exact calculations are intractable. Interestingly, we find that predictability and reconstructability, even though closely connected by the mutual information, can behave differently, even in a dual manner. We prove how such duality universally emerges when changing the number of steps in the process. Finally, we provide evidence that predictability-reconstruction dualities may exist in dynamical processes on real networks close to criticality.
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Affiliation(s)
- Charles Murphy
- Département de physique, de génie physique et d'optique, Université Laval, Québec, QC, G1V 0A6, Canada.
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Québec, QC, G1V 0A6, Canada.
| | - Vincent Thibeault
- Département de physique, de génie physique et d'optique, Université Laval, Québec, QC, G1V 0A6, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Québec, QC, G1V 0A6, Canada
| | - Antoine Allard
- Département de physique, de génie physique et d'optique, Université Laval, Québec, QC, G1V 0A6, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Québec, QC, G1V 0A6, Canada
| | - Patrick Desrosiers
- Département de physique, de génie physique et d'optique, Université Laval, Québec, QC, G1V 0A6, Canada.
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Québec, QC, G1V 0A6, Canada.
- Centre de recherche CERVO, Québec, QC, G1J 2G3, Canada.
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16
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Yang L, Peng J, Zhang L, Zhang F, Wu J, Zhang X, Pang J, Jiang Y. Advanced Diffusion Tensor Imaging in White Matter Injury After Subarachnoid Hemorrhage. World Neurosurg 2024; 189:77-88. [PMID: 38789033 DOI: 10.1016/j.wneu.2024.05.107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 05/16/2024] [Indexed: 05/26/2024]
Abstract
Subarachnoid hemorrhage (SAH) is recognized as an especially severe stroke variant, notorious for its high mortality and long-term disability rates, in addition to a range of both immediate and enduring neurologic impacts. Over half of the SAH survivors experience varying degrees of neurologic disorders, with many enduring chronic neuropsychiatric conditions. Due to the limitations of traditional imaging techniques in depicting subtle changes within brain tissues posthemorrhage, the accurate detection and diagnosis of white matter (WM) injuries are complicated. Against this backdrop, diffusion tensor imaging (DTI) has emerged as a promising biomarker for structural imaging, renowned for its enhanced sensitivity in identifying axonal damage. This capability positions DTI as an invaluable tool for forming precise and expedient prognoses for SAH survivors. This study synthesizes an assessment of DTI for the diagnosis and prognosis of neurologic dysfunctions in patients with SAH, emphasizing the notable changes observed in DTI metrics and their association with potential pathophysiological processes. Despite challenges associated with scanning technology differences and data processing, DTI demonstrates significant clinical potential for early diagnosis of cognitive impairments following SAH and monitoring therapeutic effects. Future research requires the development of highly standardized imaging paradigms to enhance diagnostic accuracy and devise targeted therapeutic strategies for SAH patients. In sum, DTI technology not only augments our understanding of the impact of SAH but also may offer new avenues for improving patient prognoses.
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Affiliation(s)
- Lei Yang
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China; Laboratory of Neurological Diseases and Brain Function, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Jianhua Peng
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China; Laboratory of Neurological Diseases and Brain Function, The Affiliated Hospital of Southwest Medical University, Luzhou, China; Academician (Expert) Workstation of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Lifang Zhang
- Institute of Brain Science, Southwest Medical University, Luzhou, China; Sichuan Clinical Research Center for Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Fan Zhang
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China; Laboratory of Neurological Diseases and Brain Function, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Jinpeng Wu
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China; Laboratory of Neurological Diseases and Brain Function, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xianhui Zhang
- Academician (Expert) Workstation of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Jinwei Pang
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yong Jiang
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China; Laboratory of Neurological Diseases and Brain Function, The Affiliated Hospital of Southwest Medical University, Luzhou, China; Institute of Brain Science, Southwest Medical University, Luzhou, China; Sichuan Clinical Research Center for Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
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17
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Borne A, Lemaitre C, Bulteau C, Baciu M, Perrone-Bertolotti M. Unveiling the cognitive network organization through cognitive performance. Sci Rep 2024; 14:11645. [PMID: 38773246 PMCID: PMC11109237 DOI: 10.1038/s41598-024-62234-5] [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] [Received: 11/17/2023] [Accepted: 05/15/2024] [Indexed: 05/23/2024] Open
Abstract
The evaluation of cognitive functions interactions has become increasingly implemented in the cognition exploration. In the present study, we propose to examine the organization of the cognitive network in healthy participants through the analysis of behavioral performances in several cognitive domains. Specifically, we aim to explore cognitive interactions profiles, in terms of cognitive network, and as a function of participants' handedness. To this end, we proposed several behavioral tasks evaluating language, memory, executive functions, and social cognition performances in 175 young healthy right-handed and left-handed participants and we analyzed cognitive scores, from a network perspective, using graph theory. Our results highlight the existence of intricate interactions between cognitive functions both within and beyond the same cognitive domain. Language functions are interrelated with executive functions and memory in healthy cognitive functioning and assume a central role in the cognitive network. Interestingly, for similar high performance, our findings unveiled differential organizations within the cognitive network between right-handed and left-handed participants, with variations observed both at a global and nodal level. This original integrative network approach to the study of cognition provides new insights into cognitive interactions and modulations. It allows a more global understanding and consideration of cognitive functioning, from which complex behaviors emerge.
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Affiliation(s)
- A Borne
- Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LPNC, 38000, Grenoble, France
| | - C Lemaitre
- Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LPNC, 38000, Grenoble, France
| | - C Bulteau
- Service de Neurochirurgie Pédiatrique, Hôpital Fondation Adolphe de Rothschild, 75019, Paris, France
- MC2 Lab, Institut de Psychologie, Université de Paris-Cité, 92100, Boulogne-Billancourt, France
| | - M Baciu
- Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LPNC, 38000, Grenoble, France
| | - M Perrone-Bertolotti
- Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LPNC, 38000, Grenoble, France.
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18
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Wang HE, Triebkorn P, Breyton M, Dollomaja B, Lemarechal JD, Petkoski S, Sorrentino P, Depannemaecker D, Hashemi M, Jirsa VK. Virtual brain twins: from basic neuroscience to clinical use. Natl Sci Rev 2024; 11:nwae079. [PMID: 38698901 PMCID: PMC11065363 DOI: 10.1093/nsr/nwae079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 02/05/2024] [Accepted: 02/20/2024] [Indexed: 05/05/2024] Open
Abstract
Virtual brain twins are personalized, generative and adaptive brain models based on data from an individual's brain for scientific and clinical use. After a description of the key elements of virtual brain twins, we present the standard model for personalized whole-brain network models. The personalization is accomplished using a subject's brain imaging data by three means: (1) assemble cortical and subcortical areas in the subject-specific brain space; (2) directly map connectivity into the brain models, which can be generalized to other parameters; and (3) estimate relevant parameters through model inversion, typically using probabilistic machine learning. We present the use of personalized whole-brain network models in healthy ageing and five clinical diseases: epilepsy, Alzheimer's disease, multiple sclerosis, Parkinson's disease and psychiatric disorders. Specifically, we introduce spatial masks for relevant parameters and demonstrate their use based on the physiological and pathophysiological hypotheses. Finally, we pinpoint the key challenges and future directions.
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Affiliation(s)
- Huifang E Wang
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Paul Triebkorn
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Martin Breyton
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
- Service de Pharmacologie Clinique et Pharmacosurveillance, AP–HM, Marseille, 13005, France
| | - Borana Dollomaja
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Jean-Didier Lemarechal
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Spase Petkoski
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Pierpaolo Sorrentino
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Damien Depannemaecker
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Meysam Hashemi
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Viktor K Jirsa
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
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19
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Yu D, Wu Y, Ding Q, Li T, Jia Y. Emergence of phase clusters and coexisting states reveals the structure-function relationship. Phys Rev E 2024; 109:054312. [PMID: 38907474 DOI: 10.1103/physreve.109.054312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Accepted: 04/30/2024] [Indexed: 06/24/2024]
Abstract
The Brain Connectome Project has made significant strides in uncovering the structural connections within the brain on various levels. This has led to the question of how brain structure and function are related. Our research explores this relationship in an adaptive neural network in which synaptic conductance between neurons follows spike-time synaptic plasticity rules. By adjusting the plasticity boundary, the network exhibits diverse collective behaviors, including phase synchronization, phase locking, hierarchical synchronization (phase clusters), and coexisting states. Using graph theory, we found that hierarchical synchronization is related to the community structure, while coexisting states are related to the hierarchical self-organizing and core-periphery structure. The network evolves into several tightly connected modules, with sparsely intermodule connections resulting in the formation of phase clusters. In addition, the hierarchical self-organizing structure facilitates the emergence of coexisting states. The coexistence state promotes the evolution of the core-periphery structure. Our results point towards the equivalence between function and structure, with function emerging from structure, and structure being influenced by function in a complex dynamic process.
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20
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Liu B, Mao Z, Yan X, Yang H, Xu J, Feng Z, Zhang Y, Yu X. Structural network topologies are associated with deep brain stimulation outcomes in Meige syndrome. Neurotherapeutics 2024; 21:e00367. [PMID: 38679556 DOI: 10.1016/j.neurot.2024.e00367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 04/18/2024] [Accepted: 04/19/2024] [Indexed: 05/01/2024] Open
Abstract
Deep brain stimulation (DBS) is an effective therapy for Meige syndrome (MS). However, the DBS efficacy varies across MS patients and the factors contributing to the variable responses remain enigmatic. We aim to explain the difference in DBS efficacy from a network perspective. We collected preoperative T1-weighted MRI images of 76 MS patients who received DBS in our center. According to the symptomatic improvement rates, all MS patients were divided into two groups: the high improvement group (HIG) and the low improvement group (LIG). We constructed group-level structural covariance networks in each group and compared the graph-based topological properties and interregional connections between groups. Subsequent functional annotation and correlation analyses were also conducted. The results indicated that HIG showed a higher clustering coefficient, longer characteristic path length, lower small-world index, and lower global efficiency compared with LIG. Different nodal betweennesses and degrees between groups were mainly identified in the precuneus, sensorimotor cortex, and subcortical nuclei, among which the gray matter volume of the left precentral gyrus and left thalamus were positively correlated with the symptomatic improvement rates. Moreover, HIG had enhanced interregional connections within the somatomotor network and between the somatomotor network and default-mode network relative to LIG. We concluded that the high and low DBS responders have notable differences in large-scale network architectures. Our study sheds light on the structural network underpinnings of varying DBS responses in MS patients.
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Affiliation(s)
- Bin Liu
- Medical School of Chinese PLA, Beijing, 100853, China; Department of Neurosurgery, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, China
| | - Zhiqi Mao
- Department of Neurosurgery, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, China
| | - Xinyuan Yan
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Hang Yang
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Junpeng Xu
- Medical School of Chinese PLA, Beijing, 100853, China; Department of Neurosurgery, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, China
| | - Zhebin Feng
- Medical School of Chinese PLA, Beijing, 100853, China; Department of Neurosurgery, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, China
| | - Yanyang Zhang
- Department of Neurosurgery, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, China.
| | - Xinguang Yu
- Department of Neurosurgery, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, China.
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21
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Mamat M, Wang Z, Jin L, He K, Li L, Chen Y. Beyond nodes and edges: a bibliometric analysis on graph theory and neuroimaging modalities. Front Neurosci 2024; 18:1373264. [PMID: 38716254 PMCID: PMC11074400 DOI: 10.3389/fnins.2024.1373264] [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: 01/25/2024] [Accepted: 04/08/2024] [Indexed: 07/18/2024] Open
Abstract
Understanding the intricate architecture of the brain through the lens of graph theory and advanced neuroimaging techniques has become increasingly pivotal in unraveling the complexities of neural networks. This bibliometric analysis explores the evolving landscape of brain research by focusing on the intersection of graph theoretical approaches, neuroanatomy, and diverse neuroimaging modalities. A systematic search strategy was used that resulted in the retrieval of a comprehensive dataset of articles and reviews. Using CiteSpace and VOSviewer, a detailed scientometric analysis was conducted that revealed emerging trends, key research clusters, and influential contributions within this multidisciplinary domain. Our review highlights the growing synergy between graph theory methodologies and neuroimaging modalities, reflecting the evolving paradigms shaping our understanding of brain networks. This study offers comprehensive insight into brain network research, emphasizing growth patterns, pivotal contributions, and global collaborative networks, thus serving as a valuable resource for researchers and institutions navigating this interdisciplinary landscape.
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Affiliation(s)
- Makliya Mamat
- School of Basic Medical Sciences, Health Science Center, Ningbo University, Ningbo, China
| | - Ziyan Wang
- School of Basic Medical Sciences, Health Science Center, Ningbo University, Ningbo, China
| | - Ling Jin
- School of Basic Medical Sciences, Health Science Center, Ningbo University, Ningbo, China
| | - Kailong He
- School of Basic Medical Sciences, Health Science Center, Ningbo University, Ningbo, China
| | - Lin Li
- Department of Human Anatomy, Nanjing Medical University, Nanjing, China
| | - Yiyong Chen
- School of Basic Medical Sciences, Health Science Center, Ningbo University, Ningbo, China
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22
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Wang Y, Long H, Bo T, Zheng J. Residual graph transformer for autism spectrum disorder prediction. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 247:108065. [PMID: 38428249 DOI: 10.1016/j.cmpb.2024.108065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 01/28/2024] [Accepted: 02/06/2024] [Indexed: 03/03/2024]
Abstract
Brain functional connectivity (FC) based on resting-state functional magnetic resonance imaging (rs-fMRI) has been in vogue to predict Autism Spectrum Disorder (ASD), which is a neuropsychiatric disease up the plight of locating latent biomarkers for clinical diagnosis. Albeit massive endeavors have been made, most studies are fed up with several chronic issues, such as the intractability of harnessing the interaction flourishing within brain regions, the astriction of representation due to vanishing gradient within deeper network architecture, and the poor interpretability leading to unpersuasive diagnosis. To ameliorate these issues, a FC-learned Residual Graph Transformer Network, namely RGTNet, is proposed. Specifically, we design a Graph Encoder to extract temporal-related features with long-range dependencies, from which interpretable FC matrices would be modeled. Besides, the residual trick is introduced to deepen the GCN architecture, thereby learning the higher-level information. Moreover, a novel Graph Sparse Fitting followed by weighted aggregation is proposed to ease dimensionality explosion. Empirically, the results on two types of ABIDE data sets demonstrate the meliority of RGTNet. Notably, the achieved ACC metric reaches 73.4%, overwhelming most competitors with merely 70.9% on the AAL atlas using a five-fold cross-validation policy. Moreover, the investigated biomarkers concord closely with the authoritative medical knowledge, paving a viable way for ASD-clinical diagnosis. Our code is available at https://github.com/CodeGoat24/RGTNet.
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Affiliation(s)
- Yibin Wang
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China.
| | - Haixia Long
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China
| | - Tao Bo
- Scientific Center, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan 250021, Shandong, China
| | - Jianwei Zheng
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China.
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23
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Qian P, Manubens-Gil L, Jiang S, Peng H. Non-homogenous axonal bouton distribution in whole-brain single-cell neuronal networks. Cell Rep 2024; 43:113871. [PMID: 38451816 DOI: 10.1016/j.celrep.2024.113871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 01/08/2024] [Accepted: 02/09/2024] [Indexed: 03/09/2024] Open
Abstract
We examined the distribution of pre-synaptic contacts in axons of mouse neurons and constructed whole-brain single-cell neuronal networks using an extensive dataset of 1,891 fully reconstructed neurons. We found that bouton locations were not homogeneous throughout the axon and among brain regions. As our algorithm was able to generate whole-brain single-cell connectivity matrices from full morphology reconstruction datasets, we further found that non-homogeneous bouton locations have a significant impact on network wiring, including degree distribution, triad census, and community structure. By perturbing neuronal morphology, we further explored the link between anatomical details and network topology. In our in silico exploration, we found that dendritic and axonal tree span would have the greatest impact on network wiring, followed by synaptic contact deletion. Our results suggest that neuroanatomical details must be carefully addressed in studies of whole-brain networks at the single-cell level.
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Affiliation(s)
- Penghao Qian
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, State Key Laboratory of Digital Medical Engineering, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China; School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Linus Manubens-Gil
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, State Key Laboratory of Digital Medical Engineering, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China.
| | - Shengdian Jiang
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, State Key Laboratory of Digital Medical Engineering, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China; School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Hanchuan Peng
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, State Key Laboratory of Digital Medical Engineering, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China.
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24
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Kashyap R, Holla B, Bhattacharjee S, Sharma E, Mehta UM, Vaidya N, Bharath RD, Murthy P, Basu D, Nanjayya SB, Singh RL, Lourembam R, Chakrabarti A, Kartik K, Kalyanram K, Kumaran K, Krishnaveni G, Krishna M, Kuriyan R, Kurpad SS, Desrivieres S, Purushottam M, Barker G, Orfanos DP, Hickman M, Heron J, Toledano M, Schumann G, Benegal V. Childhood adversities characterize the heterogeneity in the brain pattern of individuals during neurodevelopment. Psychol Med 2024:1-13. [PMID: 38509831 DOI: 10.1017/s0033291724000710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/22/2024]
Abstract
BACKGROUND Several factors shape the neurodevelopmental trajectory. A key area of focus in neurodevelopmental research is to estimate the factors that have maximal influence on the brain and can tip the balance from typical to atypical development. METHODS Utilizing a dissimilarity maximization algorithm on the dynamic mode decomposition (DMD) of the resting state functional MRI data, we classified subjects from the cVEDA neurodevelopmental cohort (n = 987, aged 6-23 years) into homogeneously patterned DMD (representing typical development in 809 subjects) and heterogeneously patterned DMD (indicative of atypical development in 178 subjects). RESULTS Significant DMD differences were primarily identified in the default mode network (DMN) regions across these groups (p < 0.05, Bonferroni corrected). While the groups were comparable in cognitive performance, the atypical group had more frequent exposure to adversities and faced higher abuses (p < 0.05, Bonferroni corrected). Upon evaluating brain-behavior correlations, we found that correlation patterns between adversity and DMN dynamic modes exhibited age-dependent variations for atypical subjects, hinting at differential utilization of the DMN due to chronic adversities. CONCLUSION Adversities (particularly abuse) maximally influence the DMN during neurodevelopment and lead to the failure in the development of a coherent DMN system. While DMN's integrity is preserved in typical development, the age-dependent variability in atypically developing individuals is contrasting. The flexibility of DMN might be a compensatory mechanism to protect an individual in an abusive environment. However, such adaptability might deprive the neural system of the faculties of normal functioning and may incur long-term effects on the psyche.
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Affiliation(s)
- Rajan Kashyap
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Bharath Holla
- Department of Integrative Medicine, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Sagarika Bhattacharjee
- Department of Neurophysiology, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Eesha Sharma
- Department of Child and Adolescent Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Urvakhsh Meherwan Mehta
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Nilakshi Vaidya
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, PONS Centre, Charité Mental Health, Germany
- Department of Psychiatry, Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Rose Dawn Bharath
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Pratima Murthy
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Debashish Basu
- Department of Psychiatry, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | | | | | - Roshan Lourembam
- Department of Psychiatry, Regional Institute of Medical Sciences, Imphal, India
| | - Amit Chakrabarti
- Division of Mental Health, ICMR-Centre for Ageing and Mental Health, Kolkata, India
| | - Kamakshi Kartik
- Rishi Valley Rural Health Centre, Madanapalle, Chittoor, India
| | | | - Kalyanaraman Kumaran
- Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India
- MRC Lifecourse Epidemiology Unit, University of Southampton, UK
| | - Ghattu Krishnaveni
- Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India
| | - Murali Krishna
- Health Equity Cluster, Institute of Public Health, Bangalore, India
| | - Rebecca Kuriyan
- Division of Nutrition, St John's Research Institute, Bengaluru, India
| | - Sunita Simon Kurpad
- Department of Psychiatry & Department of Medical Ethics, St John's Research Institute, Bengaluru, India
| | - Sylvane Desrivieres
- SGDP Centre, Institute of Psychology, Psychiatry & Neuroscience, King's College London, London, UK
| | - Meera Purushottam
- Molecular Genetics Laboratory, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Gareth Barker
- Department of Neuroimaging, Institute of Psychology, Psychiatry & Neuroscience, King's College London, London, UK
| | | | - Matthew Hickman
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jon Heron
- Center for Public Health, Bristol Medical School, University of Bristol, Bristol, UK
| | - Mireille Toledano
- MRC Centre for Environment and Health, School of Public Health, Imperial College, London, UK
| | - Gunter Schumann
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, PONS Centre, Charité Mental Health, Germany
- PONS Centre, Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
| | - Vivek Benegal
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
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25
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Cain JY, Evarts JI, Yu JS, Bagheri N. Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae131. [PMID: 38444088 PMCID: PMC10957516 DOI: 10.1093/bioinformatics/btae131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 02/08/2024] [Accepted: 03/01/2024] [Indexed: 03/07/2024]
Abstract
MOTIVATION Emergent biological dynamics derive from the evolution of lower-level spatial and temporal processes. A long-standing challenge for scientists and engineers is identifying simple low-level rules that give rise to complex higher-level dynamics. High-resolution biological data acquisition enables this identification and has evolved at a rapid pace for both experimental and computational approaches. Simultaneously harnessing the resolution and managing the expense of emerging technologies-e.g. live cell imaging, scRNAseq, agent-based models-requires a deeper understanding of how spatial and temporal axes impact biological systems. Effective emulation is a promising solution to manage the expense of increasingly complex high-resolution computational models. In this research, we focus on the emulation of a tumor microenvironment agent-based model to examine the relationship between spatial and temporal environment features, and emergent tumor properties. RESULTS Despite significant feature engineering, we find limited predictive capacity of tumor properties from initial system representations. However, incorporating temporal information derived from intermediate simulation states dramatically improves the predictive performance of machine learning models. We train a deep-learning emulator on intermediate simulation states and observe promising enhancements over emulators trained solely on initial conditions. Our results underscore the importance of incorporating temporal information in the evaluation of spatio-temporal emergent behavior. Nevertheless, the emulators exhibit inconsistent performance, suggesting that the underlying model characterizes unique cell populations dynamics that are not easily replaced. AVAILABILITY AND IMPLEMENTATION All source codes for the agent-based model, emulation, and analyses are publicly available at the corresponding DOIs: 10.5281/zenodo.10622155, 10.5281/zenodo.10611675, 10.5281/zenodo.10621244, respectively.
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Affiliation(s)
- Jason Y Cain
- Department of Chemical Engineering, University of Washington, Seattle, WA 98195, United States
| | - Jacob I Evarts
- Department of Biology, University of Washington, Seattle, WA 98195, United States
| | - Jessica S Yu
- Department of Biology, University of Washington, Seattle, WA 98195, United States
| | - Neda Bagheri
- Department of Chemical Engineering, University of Washington, Seattle, WA 98195, United States
- Department of Biology, University of Washington, Seattle, WA 98195, United States
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26
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Mecklenbrauck F, Gruber M, Siestrup S, Zahedi A, Grotegerd D, Mauritz M, Trempler I, Dannlowski U, Schubotz RI. The significance of structural rich club hubs for the processing of hierarchical stimuli. Hum Brain Mapp 2024; 45:e26543. [PMID: 38069537 PMCID: PMC10915744 DOI: 10.1002/hbm.26543] [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: 06/09/2023] [Revised: 10/17/2023] [Accepted: 11/09/2023] [Indexed: 03/07/2024] Open
Abstract
The brain's structural network follows a hierarchy that is described as rich club (RC) organization, with RC hubs forming the well-interconnected top of this hierarchy. In this study, we tested whether RC hubs are involved in the processing of hierarchically higher structures in stimulus sequences. Moreover, we explored the role of previously suggested cortical gradients along anterior-posterior and medial-lateral axes throughout the frontal cortex. To this end, we conducted a functional magnetic resonance imaging (fMRI) experiment and presented participants with blocks of digit sequences that were structured on different hierarchically nested levels. We additionally collected diffusion weighted imaging data of the same subjects to identify RC hubs. This classification then served as the basis for a region of interest analysis of the fMRI data. Moreover, we determined structural network centrality measures in areas that were found as activation clusters in the whole-brain fMRI analysis. Our findings support the previously found anterior and medial shift for processing hierarchically higher structures of stimuli. Additionally, we found that the processing of hierarchically higher structures of the stimulus structure engages RC hubs more than for lower levels. Areas involved in the functional processing of hierarchically higher structures were also more likely to be part of the structural RC and were furthermore more central to the structural network. In summary, our results highlight the potential role of the structural RC organization in shaping the cortical processing hierarchy.
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Affiliation(s)
- Falko Mecklenbrauck
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Marius Gruber
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
- Department for Psychiatry, Psychosomatic Medicine and PsychotherapyUniversity Hospital Frankfurt, Goethe UniversityFrankfurtGermany
| | - Sophie Siestrup
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Anoushiravan Zahedi
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Dominik Grotegerd
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Marco Mauritz
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
- Institute for Computational and Applied MathematicsUniversity of MünsterMünsterGermany
| | - Ima Trempler
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Udo Dannlowski
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Ricarda I. Schubotz
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
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Huang Q, Yang Y, Qi N, Guan Y, Zhao J, Hua F, Ren S, Xie F. Coupling Between Human Brain Cortical Thickness and Glucose Metabolism from Regional to Connective Level: A Positron Emission Tomography/Magnetic Resonance Imaging Study. Brain Connect 2024; 14:122-129. [PMID: 38308482 DOI: 10.1089/brain.2023.0070] [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: 02/04/2024] Open
Abstract
Background: Balance between brain structure and function is implicated in aging and many brain disorders. This study aimed to investigate the coupling between brain structure and function using 18F-fludeoxyglucose positron emission tomography (PET)/magnetic resonance imaging (MRI). Methods: One hundred thirty-eight subjects who underwent brain 18F-FDG PET/MRI were recruited. The structural and functional coupling at the regional level was explored by calculating within-subject Spearman's correlation between glucose metabolism (GluM) and cortical thickness (CTh) across the cortex for each subject, which was then correlated with age to explore its physiological effects. Then, subjects were divided into groups of middle-aged and young adults and older adults (OAs); structural connectivity (SC) based on CTh and functional connectivity (FC) based on GluM were constructed for the two groups, respectively, followed by exploring the connective-level structural and functional coupling on SC and FC matrices. The global and local efficiency values of the brain SC and FC were also evaluated. Results: Of the subjects, 97.83% exhibited a significant negative correlation between regional CTh and GluM (r = -0.24 to -0.71, p < 0.05, FDR correction), and this CTh-GluM correlation was negatively correlated with age (R = -0.35, p < 0.001). For connectivity matrices, many regions showed positive correlation between SC and FC, especially in the OA group. Besides, FC exhibited denser connections than SC, resulting in both higher global and local efficiency, but lower global efficiency when the network size was corrected. Conclusions: This study found couplings between CTh and GluM at both regional and connective levels, which reflected the aging progress, and might provide new insight into brain disorders. Impact statement The intricate interplay between brain structures and functions plays a pivotal role in unraveling the complexities inherent in the aging process and the pathogenesis of neurological disorders. This study revealed that 97.83% subjects showed negative correlation between the brain's regional cortical thickness and glucose metabolism, while at the connective level, many regions showed positive correlations between structural and functional connectivity. The observed coupling at the regional and connective levels reflected physiological progress, such as aging, and provides insights into the brain mechanisms and potential implications for the diagnosis and treatment of brain disorders.
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Affiliation(s)
- Qi Huang
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Yihong Yang
- Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Na Qi
- Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yihui Guan
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jun Zhao
- Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Fengchun Hua
- Department of Nuclear Medicine, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Shuhua Ren
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Fang Xie
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, Shanghai, China
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Fan L, Li Y, Zhao X, Huang ZG, Liu T, Wang J. Dynamic nonreversibility view of intrinsic brain organization and brain dynamic analysis of repetitive transcranial magnitude stimulation. Cereb Cortex 2024; 34:bhae098. [PMID: 38494890 DOI: 10.1093/cercor/bhae098] [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: 01/14/2024] [Revised: 02/20/2024] [Accepted: 02/21/2024] [Indexed: 03/19/2024] Open
Abstract
Intrinsic neural activities are characterized as endless spontaneous fluctuation over multiple time scales. However, how the intrinsic brain organization changes over time under local perturbation remains an open question. By means of statistical physics, we proposed an approach to capture whole-brain dynamics based on estimating time-varying nonreversibility and k-means clustering of dynamic varying nonreversibility patterns. We first used synthetic fMRI to investigate the effects of window parameters on the temporal variability of varying nonreversibility. Second, using real test-retest fMRI data, we examined the reproducibility, reliability, biological, and physiological correlation of the varying nonreversibility substates. Finally, using repetitive transcranial magnetic stimulation-fMRI data, we investigated the modulation effects of repetitive transcranial magnetic stimulation on varying nonreversibility substate dynamics. The results show that: (i) as window length increased, the varying nonreversibility variance decreased, while the sliding step almost did not alter it; (ii) the global high varying nonreversibility states and low varying nonreversibility states were reproducible across multiple datasets and different window lengths; and (iii) there were increased low varying nonreversibility states and decreased high varying nonreversibility states when the left frontal lobe was stimulated, but not the occipital lobe. Taken together, these results provide a thermodynamic equilibrium perspective of intrinsic brain organization and reorganization under local perturbation.
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Affiliation(s)
- Liming Fan
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Institute of Health and Rehabilitation Science, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Youjun Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Institute of Health and Rehabilitation Science, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Xingjian Zhao
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Institute of Health and Rehabilitation Science, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Zi-Gang Huang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Institute of Health and Rehabilitation Science, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Tian Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Institute of Health and Rehabilitation Science, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Jue Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Institute of Health and Rehabilitation Science, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
- The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, China
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29
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Jauny G, Mijalkov M, Canal-Garcia A, Volpe G, Pereira J, Eustache F, Hinault T. Linking structural and functional changes during aging using multilayer brain network analysis. Commun Biol 2024; 7:239. [PMID: 38418523 PMCID: PMC10902297 DOI: 10.1038/s42003-024-05927-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 02/16/2024] [Indexed: 03/01/2024] Open
Abstract
Brain structure and function are intimately linked, however this association remains poorly understood and the complexity of this relationship has remained understudied. Healthy aging is characterised by heterogenous levels of structural integrity changes that influence functional network dynamics. Here, we use the multilayer brain network analysis on structural (diffusion weighted imaging) and functional (magnetoencephalography) data from the Cam-CAN database. We found that the level of similarity of connectivity patterns between brain structure and function in the parietal and temporal regions (alpha frequency band) is associated with cognitive performance in healthy older individuals. These results highlight the impact of structural connectivity changes on the reorganisation of functional connectivity associated with the preservation of cognitive function, and provide a mechanistic understanding of the concepts of brain maintenance and compensation with aging. Investigation of the link between structure and function could thus represent a new marker of individual variability, and of pathological changes.
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Affiliation(s)
- Gwendolyn Jauny
- Normandie Univ, UNICAEN, PSL Université Paris, EPHE, Inserm, U1077, CHU de Caen, Centre Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France
| | - Mite Mijalkov
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Anna Canal-Garcia
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Giovanni Volpe
- Department of Physics, Goteborg University, Goteborg, Sweden
| | - Joana Pereira
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Francis Eustache
- Normandie Univ, UNICAEN, PSL Université Paris, EPHE, Inserm, U1077, CHU de Caen, Centre Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France
| | - Thomas Hinault
- Normandie Univ, UNICAEN, PSL Université Paris, EPHE, Inserm, U1077, CHU de Caen, Centre Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France.
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30
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Pollmann A, Sasso R, Bates K, Fuhrmann D. Making Connections: Neurodevelopmental Changes in Brain Connectivity After Adverse Experiences in Early Adolescence. J Neurosci 2024; 44:e0991232023. [PMID: 38124022 PMCID: PMC10883609 DOI: 10.1523/jneurosci.0991-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 10/31/2023] [Accepted: 11/01/2023] [Indexed: 12/23/2023] Open
Abstract
Adverse childhood experiences have been linked to detrimental mental health outcomes in adulthood. This study investigates a potential neurodevelopmental pathway between adversity and mental health outcomes: brain connectivity. We used data from the prospective, longitudinal Adolescent Brain Cognitive Development (ABCD) study (N ≍ 12.000, participants aged 9-13 years, male and female) and assessed structural brain connectivity using fractional anisotropy (FA) of white matter tracts. The adverse experiences modeled included family conflict and traumatic experiences. K-means clustering and latent basis growth models were used to determine subgroups based on total levels and trajectories of brain connectivity. Multinomial regression was used to determine associations between cluster membership and adverse experiences. The results showed that higher family conflict was associated with higher FA levels across brain tracts (e.g., t (3) = -3.81, β = -0.09, p bonf = 0.003) and within the corpus callosum (CC), fornix, and anterior thalamic radiations (ATR). A decreasing FA trajectory across two brain imaging timepoints was linked to lower socioeconomic status and neighborhood safety. Socioeconomic status was related to FA across brain tracts (e.g., t (3) = 3.44, β = 0.10, p bonf = 0.01), the CC and the ATR. Neighborhood safety was associated with FA in the Fornix and ATR (e.g., t (1) = 3.48, β = 0.09, p bonf = 0.01). There is a complex and multifaceted relationship between adverse experiences and brain development, where adverse experiences during early adolescence are related to brain connectivity. These findings underscore the importance of studying adverse experiences beyond early childhood to understand lifespan developmental outcomes.
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Affiliation(s)
- Ayla Pollmann
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, United Kingdom
| | - Remo Sasso
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, United Kingdom
| | - Kathryn Bates
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, United Kingdom
| | - Delia Fuhrmann
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, United Kingdom
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31
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Pang X, Liang X, Chang W, Lv Z, Zhao J, Wu P, Li X, Wei W, Zheng J. The role of the thalamus in modular functional networks in temporal lobe epilepsy with cognitive impairment. CNS Neurosci Ther 2024; 30:e14345. [PMID: 37424152 PMCID: PMC10848054 DOI: 10.1111/cns.14345] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 06/04/2023] [Accepted: 06/27/2023] [Indexed: 07/11/2023] Open
Abstract
OBJECTIVE Cognitive deficit is common in patients with temporal lobe epilepsy (TLE). Here, we aimed to investigate the modular architecture of functional networks associated with distinct cognitive states in TLE patients together with the role of the thalamus in modular networks. METHODS Resting-state functional magnetic resonance imaging scans were acquired from 53 TLE patients and 37 matched healthy controls. All patients received the Montreal Cognitive Assessment test and accordingly were divided into TLE patients with normal cognition (TLE-CN, n = 35) and TLE patients with cognitive impairment (TLE-CI, n = 18) groups. The modular properties of functional networks were calculated and compared including global modularity Q, modular segregation index, intramodular connections, and intermodular connections. Thalamic subdivisions corresponding to the modular networks were generated by applying a 'winner-take-all' strategy before analyzing the modular properties (participation coefficient and within-module degree z-score) of each thalamic subdivision to assess the contribution of the thalamus to modular functional networks. Relationships between network properties and cognitive performance were then further explored. RESULTS Both TLE-CN and TLE-CI patients showed lower global modularity, as well as lower modular segregation index values for the ventral attention network and the default mode network. However, different patterns of intramodular and intermodular connections existed for different cognitive states. In addition, both TLE-CN and TLE-CI patients exhibited anomalous modular properties of functional thalamic subdivisions, with TLE-CI patients presenting a broader range of abnormalities. Cognitive performance in TLE-CI patients was not related to the modular properties of functional network but rather to the modular properties of functional thalamic subdivisions. CONCLUSIONS The thalamus plays a prominent role in modular networks and potentially represents a key neural mechanism underlying cognitive impairment in TLE.
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Affiliation(s)
- Xiaomin Pang
- Department of NeurologyGuangxi Medical University First Affiliated HospitalNanningChina
| | - Xiulin Liang
- Department of NeurologyGuangxi Medical University First Affiliated HospitalNanningChina
| | - Weiwei Chang
- Department of NeurologyGuangxi Medical University First Affiliated HospitalNanningChina
| | - Zongxia Lv
- Department of NeurologyGuangxi Medical University First Affiliated HospitalNanningChina
| | - Jingyuan Zhao
- Department of NeurologyGuangxi Medical University First Affiliated HospitalNanningChina
| | - Peirong Wu
- Department of NeurologyGuangxi Medical University First Affiliated HospitalNanningChina
| | - Xinrong Li
- Department of NeurologyGuangxi Medical University First Affiliated HospitalNanningChina
| | - Wutong Wei
- Department of NeurologyGuangxi Medical University First Affiliated HospitalNanningChina
| | - Jinou Zheng
- Department of NeurologyGuangxi Medical University First Affiliated HospitalNanningChina
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32
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Suárez LE, Mihalik A, Milisav F, Marshall K, Li M, Vértes PE, Lajoie G, Misic B. Connectome-based reservoir computing with the conn2res toolbox. Nat Commun 2024; 15:656. [PMID: 38253577 PMCID: PMC10803782 DOI: 10.1038/s41467-024-44900-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 01/09/2024] [Indexed: 01/24/2024] Open
Abstract
The connection patterns of neural circuits form a complex network. How signaling in these circuits manifests as complex cognition and adaptive behaviour remains the central question in neuroscience. Concomitant advances in connectomics and artificial intelligence open fundamentally new opportunities to understand how connection patterns shape computational capacity in biological brain networks. Reservoir computing is a versatile paradigm that uses high-dimensional, nonlinear dynamical systems to perform computations and approximate cognitive functions. Here we present conn2res: an open-source Python toolbox for implementing biological neural networks as artificial neural networks. conn2res is modular, allowing arbitrary network architecture and dynamics to be imposed. The toolbox allows researchers to input connectomes reconstructed using multiple techniques, from tract tracing to noninvasive diffusion imaging, and to impose multiple dynamical systems, from spiking neurons to memristive dynamics. The versatility of the conn2res toolbox allows us to ask new questions at the confluence of neuroscience and artificial intelligence. By reconceptualizing function as computation, conn2res sets the stage for a more mechanistic understanding of structure-function relationships in brain networks.
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Affiliation(s)
- Laura E Suárez
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
- Mila, Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Agoston Mihalik
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Filip Milisav
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Kenji Marshall
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Mingze Li
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
- Mila, Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Petra E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Guillaume Lajoie
- Mila, Quebec Artificial Intelligence Institute, Montreal, QC, Canada
- Department of Mathematics and Statistics, Université de Montréal, Montreal, QC, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada.
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33
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Xu H, Li J, Huang H, Yin B, Li DD. Abnormal developmental of structural covariance networks in young adults with heavy cannabis use: a 3-year follow-up study. Transl Psychiatry 2024; 14:45. [PMID: 38245512 PMCID: PMC10799944 DOI: 10.1038/s41398-024-02764-8] [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: 07/20/2023] [Revised: 01/05/2024] [Accepted: 01/10/2024] [Indexed: 01/22/2024] Open
Abstract
Heavy cannabis use (HCU) exerts adverse effects on the brain. Structural covariance networks (SCNs) that illustrate coordinated regional maturation patterns are extensively employed to examine abnormalities in brain structure. Nevertheless, the unexplored aspect remains the developmental alterations of SCNs in young adults with HCU for three years, from the baseline (BL) to the 3-year follow-up (FU). These changes demonstrate dynamic development and hold potential as biomarkers. A total of 20 young adults with HCU and 22 matched controls were recruited. All participants underwent magnetic resonance imaging (MRI) scans at both the BL and FU and were evaluated using clinical measures. Both groups used cortical thickness (CT) and cortical surface area (CSA) to construct structural covariance matrices. Subsequently, global and nodal network measures of SCNs were computed based on these matrices. Regarding global network measures, the BL assessment revealed significant deviations in small-worldness and local efficiency of CT and CSA in young adults with HCU compared to controls. However, no significant differences between the two groups were observed at the FU evaluation. Young adults with HCU displayed changes in nodal network measures across various brain regions during the transition from BL to FU. These alterations included abnormal nodal degree, nodal efficiency, and nodal betweenness in widespread areas such as the entorhinal cortex, superior frontal gyrus, and parahippocampal cortex. These findings suggest that the topography of CT and CSA plays a role in the typical structural covariance topology of the brain. Furthermore, these results indicate the effect of HCU on the developmental changes of SCNs in young adults.
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Affiliation(s)
- Hui Xu
- School of Mental Health, Wenzhou Medical University, Wenzhou, 325035, China.
- The Affiliated Kangning Hospital of Wenzhou Medical University, Zhejiang Provincial Clinical Research Center for Mental Disorder, Wenzhou, 325007, China.
| | - Jiahao Li
- Department of Neurosurgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
- Department of Neurology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China
| | - Huan Huang
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Bo Yin
- Department of Neurosurgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Dan-Dong Li
- Department of Neurosurgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China.
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Bröhl T, Rings T, Pukropski J, von Wrede R, Lehnertz K. The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 3:1338864. [PMID: 38293249 PMCID: PMC10825060 DOI: 10.3389/fnetp.2023.1338864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 12/19/2023] [Indexed: 02/01/2024]
Abstract
Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.
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Affiliation(s)
- Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Thorsten Rings
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Jan Pukropski
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
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35
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Zhou Y, Zhu Y, Ye H, Jiang W, Zhang Y, Kong Y, Yuan Y. Abnormal changes of dynamic topological characteristics in patients with major depressive disorder. J Affect Disord 2024; 345:349-357. [PMID: 37884195 DOI: 10.1016/j.jad.2023.10.143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 10/20/2023] [Accepted: 10/23/2023] [Indexed: 10/28/2023]
Abstract
BACKGROUND Most studies have detected abnormalities of static topological characteristics in major depressive disorder (MDD). However, whether dynamic alternations in brain topology are influenced by MDD remains unknown. METHODS An approach was proposed to capture the dynamic topological characteristics with sliding-window and graph theory for a large data sample from the REST-meta-MDD project. RESULTS It was shown that patients with MDD were characterized by decreased nodal efficiency of the left orbitofrontal cortex. The temporal variability of topological characteristics was focused on the left opercular part of inferior frontal gyrus, and the right part of middle frontal gyrus, inferior parietal gyrus, precuneus and thalamus. LIMITATIONS Future studies need larger and diverse samples to explore the relationship between dynamic topological network characteristics and MDD symptoms. CONCLUSIONS The results support that the altered dynamic topology in cortex of frontal and parietal lobes and thalamus during resting-state activity may be involved in the neuropathological mechanism of MDD.
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Affiliation(s)
- Yue Zhou
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, China
| | - Yihui Zhu
- Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu Province 210096, China
| | - Hongting Ye
- Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu Province 210096, China
| | - Wenhao Jiang
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, China
| | - Yubo Zhang
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, China
| | - Youyong Kong
- Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu Province 210096, China.
| | - Yonggui Yuan
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, China; Jiangsu Provincial Key Laboratory of Critical Care Medicine, Southeast University, Nanjing 210009, China.
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36
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Nwaigwe D, Carboni L, Mermillod M, Achard S, Dojat M. Graph-based methods coupled with specific distributional distances for adversarial attack detection. Neural Netw 2024; 169:11-19. [PMID: 37852166 DOI: 10.1016/j.neunet.2023.10.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/26/2023] [Accepted: 10/06/2023] [Indexed: 10/20/2023]
Abstract
Artificial neural networks are prone to being fooled by carefully perturbed inputs which cause an egregious misclassification. These adversarial attacks have been the focus of extensive research. Likewise, there has been an abundance of research in ways to detect and defend against them. We introduce a novel approach of detection and interpretation of adversarial attacks from a graph perspective. For an input image, we compute an associated sparse graph using the layer-wise relevance propagation algorithm (Bach et al., 2015). Specifically, we only keep edges of the neural network with the highest relevance values. Three quantities are then computed from the graph which are then compared against those computed from the training set. The result of the comparison is a classification of the image as benign or adversarial. To make the comparison, two classification methods are introduced: (1) an explicit formula based on Wasserstein distance applied to the degree of node and (2) a logistic regression. Both classification methods produce strong results which lead us to believe that a graph-based interpretation of adversarial attacks is valuable.
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Affiliation(s)
- Dwight Nwaigwe
- Univ. Grenoble Alpes, Inria, CNRS, LJK, Grenoble, France; Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, GIN, Grenoble, France
| | - Lucrezia Carboni
- Univ. Grenoble Alpes, Inria, CNRS, LJK, Grenoble, France; Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, GIN, Grenoble, France
| | - Martial Mermillod
- Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LPNC, Grenoble, France
| | - Sophie Achard
- Univ. Grenoble Alpes, Inria, CNRS, LJK, Grenoble, France
| | - Michel Dojat
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, GIN, Grenoble, France.
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Zarate-Lopez D, Torres-Chávez AL, Gálvez-Contreras AY, Gonzalez-Perez O. Three Decades of Valproate: A Current Model for Studying Autism Spectrum Disorder. Curr Neuropharmacol 2024; 22:260-289. [PMID: 37873949 PMCID: PMC10788883 DOI: 10.2174/1570159x22666231003121513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 08/30/2023] [Accepted: 08/30/2023] [Indexed: 10/25/2023] Open
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with increased prevalence and incidence in recent decades. Its etiology remains largely unclear, but it seems to involve a strong genetic component and environmental factors that, in turn, induce epigenetic changes during embryonic and postnatal brain development. In recent decades, clinical studies have shown that inutero exposure to valproic acid (VPA), a commonly prescribed antiepileptic drug, is an environmental factor associated with an increased risk of ASD. Subsequently, prenatal VPA exposure in rodents has been established as a reliable translational model to study the pathophysiology of ASD, which has helped demonstrate neurobiological changes in rodents, non-human primates, and brain organoids from human pluripotent stem cells. This evidence supports the notion that prenatal VPA exposure is a valid and current model to replicate an idiopathic ASD-like disorder in experimental animals. This review summarizes and describes the current features reported with this animal model of autism and the main neurobiological findings and correlates that help elucidate the pathophysiology of ASD. Finally, we discuss the general framework of the VPA model in comparison to other environmental and genetic ASD models.
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Affiliation(s)
- David Zarate-Lopez
- Laboratory of Neuroscience, School of Psychology, University of Colima, Colima 28040, México
- Physiological Science Ph.D. Program, School of Medicine, University of Colima, Colima 28040, Mexico
| | - Ana Laura Torres-Chávez
- Laboratory of Neuroscience, School of Psychology, University of Colima, Colima 28040, México
- Physiological Science Ph.D. Program, School of Medicine, University of Colima, Colima 28040, Mexico
| | - Alma Yadira Gálvez-Contreras
- Department of Neuroscience, Centro Universitario de Ciencias de la Salud, University of Guadalajara, Guadalajara 44340, México
| | - Oscar Gonzalez-Perez
- Laboratory of Neuroscience, School of Psychology, University of Colima, Colima 28040, México
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Chino B, López-Sanz D, Doval S, Torres-Simón L, de Frutos Lucas J, Giménez-Llort L, Zegarra-Valdivia J, Maestú F. Resting State Electrophysiological Profiles and Their Relationship with Cognitive Performance in Cognitively Unimpaired Older Adults: A Systematic Review. J Alzheimers Dis 2024; 100:453-468. [PMID: 38875030 DOI: 10.3233/jad-231009] [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: 06/16/2024]
Abstract
Background Aging is a complex and natural process. The physiological decline related to aging is accompanied by a slowdown in cognitive processes, which begins shortly after individuals reach maturity. These changes have been sometimes interpreted as a compensatory sign and others as a fingerprint of deterioration. Objective In this context, our aim is to uncover the mechanisms that underlie and support normal cognitive functioning in the brain during the later stages of life. Methods With this purpose, a systematic literature search was conducted using PubMed, Scopus, and Web of Science databases, which identified 781 potential articles. After applying inclusion and exclusion criteria, we selected 12 studies that examined the brain oscillations patterns in resting-state conditions associated with cognitive performance in cognitively unimpaired older adults. Results Although cognitive healthy aging was characterized differently across studies, and various approaches to analyzing brain activity were employed, our review indicates a relationship between alpha peak frequency (APF) and improved performance in neuropsychological scores among cognitively unimpaired older adults. Conclusions A higher APF is linked with a higher score in intelligence, executive function, and general cognitive performance, and could be considered an optimal, and easy-to-assess, electrophysiological marker of cognitive health in older adults.
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Affiliation(s)
- Brenda Chino
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Institute of Neuroscience, Autonomous University of Barcelona (UAB), Barcelona, Spain
- Achucarro Basque Center for Neuroscience, Leioa, Spain
| | - David López-Sanz
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
| | - Sandra Doval
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
| | - Lucía Torres-Simón
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
| | - Jaisalmer de Frutos Lucas
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
- Centre for Precision Health, Edith Cowan University, Joondalup, WA, Australia
| | - Lydia Giménez-Llort
- Institute of Neuroscience, Autonomous University of Barcelona (UAB), Barcelona, Spain
- Department of Psychiatry and Forensic Medicine, Autonomous University of Barcelona (UAB), Barcelona, Spain
| | | | - Fernando Maestú
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
- Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain
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El-Yaagoubi AB, Chung MK, Ombao H. Statistical inference for dependence networks in topological data analysis. Front Artif Intell 2023; 6:1293504. [PMID: 38156039 PMCID: PMC10752923 DOI: 10.3389/frai.2023.1293504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 11/22/2023] [Indexed: 12/30/2023] Open
Abstract
Topological data analysis (TDA) provide tools that are becoming increasingly popular for analyzing multivariate time series data. One key aspect in analyzing multivariate time series is dependence between components. One application is on brain signal analysis. In particular, various dependence patterns in brain networks may be linked to specific tasks and cognitive processes. These dependence patterns may be altered by various neurological and cognitive impairments such as Alzheimer's and Parkinson's diseases, as well as attention deficit hyperactivity disorder (ADHD). Because there is no ground-truth with known dependence patterns in real brain signals, testing new TDA methods on multivariate time series is still a challenge. Our goal here is to develop novel statistical inference procedures via simulations. Simulations are useful for generating some null distributions of a test statistic (for hypothesis testing), forming confidence regions, and for evaluating the performance of proposed TDA methods. To the best of our knowledge, there are no methods that simulate multivariate time series data with potentially complex user-specified connectivity patterns. In this paper we present a novel approach to simulate multivariate time series with specific number of cycles/holes in its dependence network. Furthermore, we also provide a procedure for generating higher dimensional topological features.
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Affiliation(s)
- Anass B. El-Yaagoubi
- Statistics Program, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Moo K. Chung
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, United States
| | - Hernando Ombao
- Statistics Program, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
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Filippi M, Cividini C, Basaia S, Spinelli EG, Castelnovo V, Leocadi M, Canu E, Agosta F. Age-related vulnerability of the human brain connectome. Mol Psychiatry 2023; 28:5350-5358. [PMID: 37414925 DOI: 10.1038/s41380-023-02157-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 06/05/2023] [Accepted: 06/22/2023] [Indexed: 07/08/2023]
Abstract
Multifactorial models integrating brain variables at multiple scales are warranted to investigate aging and its relationship with neurodegeneration. Our aim was to evaluate how aging affects functional connectivity of pivotal regions of the human brain connectome (i.e., hubs), which represent potential vulnerability 'stations' to aging, and whether such effects influence the functional and structural changes of the whole brain. We combined the information of the functional connectome vulnerability, studied through an innovative graph-analysis approach (stepwise functional connectivity), with brain cortical thinning in aging. Using data from 128 cognitively normal participants (aged 20-85 years), we firstly investigated the topological functional network organization in the optimal healthy condition (i.e., young adults) and observed that fronto-temporo-parietal hubs showed a highly direct functional connectivity with themselves and among each other, while occipital hubs showed a direct functional connectivity within occipital regions and sensorimotor areas. Subsequently, we modeled cortical thickness changes over lifespan, revealing that fronto-temporo-parietal hubs were among the brain regions that changed the most, whereas occipital hubs showed a quite spared cortical thickness across ages. Finally, we found that cortical regions highly functionally linked to the fronto-temporo-parietal hubs in healthy adults were characterized by the greatest cortical thinning along the lifespan, demonstrating that the topology and geometry of hub functional connectome govern the region-specific structural alterations of the brain regions.
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Affiliation(s)
- Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Vita-Salute San Raffaele University, Milan, Italy.
| | - Camilla Cividini
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Silvia Basaia
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Edoardo G Spinelli
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Veronica Castelnovo
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Michela Leocadi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Elisa Canu
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Federica Agosta
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
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George AB, Beniwal RP, Singh S, Bhatia T, Khushu S, Deshpande SN. Association between thyroid functions, cognition, and functional connectivity of the brain in early-course schizophrenia: A preliminary study. Ind Psychiatry J 2023; 32:S76-S82. [PMID: 38370920 PMCID: PMC10871410 DOI: 10.4103/ipj.ipj_198_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/28/2023] [Accepted: 07/16/2023] [Indexed: 02/20/2024] Open
Abstract
Background The functional outcome of the debilitating mental illness schizophrenia (SZ) has an integral role in cognition. The thyroid hormone has a vital role in the developmental stages and functioning of the human brain. Aim This study aimed to evaluate the relationship between thyroid functions, cognition, and functional imaging of the brain in persons with SZ. Materials and Methods Sixty SZ (Diagnostic and Statistical Manual (DSM-5)) persons, aged 18-50 years of both genders, were recruited in this cross-sectional observational study. Positive and Negative Syndrome Scale (PANSS) and Trail Making Tests (TMTs) A and B were administered to all patients. To assess the level of thyroid hormone, a test was conducted. Functional connectivity of the brain was assessed using resting-state functional magnetic resonance imaging (rs-fMRI). Data analysis was performed by descriptive and analytical statistical methods. FSL version 5.9 (FMRIB's) software was used for analyses of fMRI neuroimages. Results There were no significant differences between the two populations on sociodemographic factors. The average value for thyroid-stimulating hormone (TSH) in the hypothyroid group (n = 12) and the euthyroid group (n = 47) was 8.38 mIU/l and 2.44 mIU/l, respectively. The average time in seconds for TMT-A and TMT-B was 87.27 and 218.27 in the hypothyroid group and 97.07 and 293.27 in the euthyroid group, respectively. Similarly, in the sample matched on age, gender, and age at onset of illness, there were no significant differences in demographic and clinical factors and resting-state network (RSN) between the hypothyroid (N = 10) and euthyroid (N = 10) groups. Conclusion No differences were found in the functional brain network between the hypothyroid and euthyroid groups as the study sample did not include clinically hypothyroid persons with SZ.
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Affiliation(s)
- Aishwariya B George
- Department of Psychiatry, Malabar Medical College Hospital and Research Centre, Kozhikode, Kerala, India
| | - Ram P Beniwal
- Department of Psychiatry, Centre of Excellence in Mental Health, Atal Bihari Vajpayee Institute of Medical Sciences and Dr. Ram Manohar Lohia Hospital, New Delhi, India
| | - Sadhana Singh
- Centre for Brain Research, Indian Institute of Science, Bengaluru, Karnataka, India
| | - Triptish Bhatia
- Department of Psychiatry, Centre of Excellence in Mental Health, Atal Bihari Vajpayee Institute of Medical Sciences and Dr. Ram Manohar Lohia Hospital, New Delhi, India
| | - Subhash Khushu
- Division of Radiological Imaging, and Bio-Medical Imaging, The University of Trans-Disciplinary Health Sciences and Technology, Bengaluru, Karnataka, India
| | - Smita N Deshpande
- Department of Psychiatry, St John's Medical College Hospital, Bengaluru, Karnataka, India
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de Andrade DC, García-Larrea L. Beyond trial-and-error: Individualizing therapeutic transcranial neuromodulation for chronic pain. Eur J Pain 2023; 27:1065-1083. [PMID: 37596980 PMCID: PMC7616049 DOI: 10.1002/ejp.2164] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/18/2023] [Accepted: 07/19/2023] [Indexed: 08/21/2023]
Abstract
BACKGROUND AND OBJECTIVE Repetitive transcranial magnetic stimulation (rTMS) applied to the motor cortex provides supplementary relief for some individuals with chronic pain who are refractory to pharmacological treatment. As rTMS slowly enters treatment guidelines for pain relief, its starts to be confronted with challenges long known to pharmacological approaches: efficacy at the group-level does not grant pain relief for a particular patient. In this review, we present and discuss a series of ongoing attempts to overcome this therapeutic challenge in a personalized medicine framework. DATABASES AND DATA TREATMENT Relevant scientific publications published in main databases such as PubMed and EMBASE from inception until March 2023 were systematically assessed, as well as a wide number of studies dedicated to the exploration of the mechanistic grounds of rTMS analgesic effects in humans, primates and rodents. RESULTS The main strategies reported to personalize cortical neuromodulation are: (i) the use of rTMS to predict individual response to implanted motor cortex stimulation; (ii) modifications of motor cortex stimulation patterns; (iii) stimulation of extra-motor targets; (iv) assessment of individual cortical networks and rhythms to personalize treatment; (v) deep sensory phenotyping; (vi) personalization of location, precision and intensity of motor rTMS. All approaches except (i) have so far low or moderate levels of evidence. CONCLUSIONS Although current evidence for most strategies under study remains at best moderate, the multiple mechanisms set up by cortical stimulation are an advantage over single-target 'clean' drugs, as they can influence multiple pathophysiologic paths and offer multiple possibilities of individualization. SIGNIFICANCE Non-invasive neuromodulation is on the verge of personalised medicine. Strategies ranging from integration of detailed clinical phenotyping into treatment design to advanced patient neurophysiological characterisation are being actively explored and creating a framework for actual individualisation of care.
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Affiliation(s)
- Daniel Ciampi de Andrade
- Department of Health Science and Technology, Faculty of Medicine, Center for Neuroplasticity and Pain (CNAP), Aalborg University, Aalborg, Denmark
| | - Luís García-Larrea
- University Hospital Pain Center (CETD), Neurological Hospital P. Wertheimer, Hospices Civils de Lyon, Lyon, France
- NeuroPain Lab, INSERM U1028, UMR5292, Lyon Neuroscience Research Center, CNRS, University Claude Bernard Lyon 1, Lyon, France
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Brofiga M, Losacco S, Poggio F, Zerbo RA, Milanese M, Massobrio P, Burlando B. Multiple neuron clusters on Micro-Electrode Arrays as an in vitro model of brain network. Sci Rep 2023; 13:15604. [PMID: 37730890 PMCID: PMC10511538 DOI: 10.1038/s41598-023-42168-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 09/06/2023] [Indexed: 09/22/2023] Open
Abstract
Understanding the brain functioning is essential for governing brain processes with the aim of managing pathological network dysfunctions. Due to the morphological and biochemical complexity of the central nervous system, the development of general models with predictive power must start from in vitro brain network engineering. In the present work, we realized a micro-electrode array (MEA)-based in vitro brain network and studied its emerging dynamical properties. We obtained four-neuron-clusters (4N) assemblies by plating rat embryo cortical neurons on 60-electrode MEA with cross-shaped polymeric masks and compared the emerging dynamics with those of sister single networks (1N). Both 1N and 4N assemblies exhibited spontaneous electrical activity characterized by spiking and bursting signals up to global activation by means of network bursts. Data revealed distinct patterns of network activity with differences between 1 and 4N. Rhythmic network bursts and dominant initiator clusters suggested pacemaker activities in both assembly types, but the propagation of activation sequences was statistically influenced by the assembly topology. We proved that this rhythmic activity was ivabradine sensitive, suggesting the involvement of hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, and propagated across the real clusters of 4N, or corresponding virtual clusters of 1N, with dominant initiator clusters, and nonrandom cluster activation sequences. The occurrence of nonrandom series of identical activation sequences in 4N revealed processes possibly ascribable to neuroplasticity. Hence, our multi-network dissociated cortical assemblies suggest the relevance of pacemaker neurons as essential elements for generating brain network electrophysiological patterns; indeed, such evidence should be considered in the development of computational models for envisaging network behavior both in physiological and pathological conditions.
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Affiliation(s)
- Martina Brofiga
- Department of Informatics, Bioengineering, Robotics, Systems Engineering (DIBRIS), University of Genova, Genova, Italy
- ScreenNeuroPharm, Sanremo, Italy
| | - Serena Losacco
- Department of Pharmacy (DIFAR), University of Genova, Genova, Italy
| | - Fabio Poggio
- Department of Informatics, Bioengineering, Robotics, Systems Engineering (DIBRIS), University of Genova, Genova, Italy
| | - Roberta Arianna Zerbo
- Department of Pharmacy (DIFAR), Pharmacology and Toxicology Unit, University of Genova, Genova, Italy
| | - Marco Milanese
- Department of Pharmacy (DIFAR), Pharmacology and Toxicology Unit, University of Genova, Genova, Italy
- IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132, Genova, Italy
| | - Paolo Massobrio
- Department of Informatics, Bioengineering, Robotics, Systems Engineering (DIBRIS), University of Genova, Genova, Italy.
- National Institute for Nuclear Physics (INFN), Genova, Italy.
| | - Bruno Burlando
- Department of Pharmacy (DIFAR), University of Genova, Genova, Italy
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Wang Y, Long H, Zhou Q, Bo T, Zheng J. PLSNet: Position-aware GCN-based autism spectrum disorder diagnosis via FC learning and ROIs sifting. Comput Biol Med 2023; 163:107184. [PMID: 37356292 DOI: 10.1016/j.compbiomed.2023.107184] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 05/25/2023] [Accepted: 06/13/2023] [Indexed: 06/27/2023]
Abstract
Brain function connectivity, derived from functional magnetic resonance imaging (fMRI), has enjoyed high popularity in the studies of Autism Spectrum Disorder (ASD) diagnosis. Albeit rapid progress has been made, most studies still suffer from several knotty issues: (1) the hardship of modeling the sophisticated brain neuronal connectivity; (2) the mismatch of identically graph node setup to the variations of different brain regions; (3) the dimensionality explosion resulted from excessive voxels in each fMRI sample; (4) the poor interpretability giving rise to unpersuasive diagnosis. To ameliorate these issues, we propose a position-aware graph-convolution-network-based model, namely PLSNet, with superior accuracy and compelling built-in interpretability for ASD diagnosis. Specifically, a time-series encoder is designed for context-rich feature extraction, followed by a function connectivity generator to model the correlation with long range dependencies. In addition, to discriminate the brain nodes with different locations, the position embedding technique is adopted, giving a unique identity to each graph region. We then embed a rarefying method to sift the salient nodes during message diffusion, which would also benefit the reduction of the dimensionality complexity. Extensive experiments conducted on Autism Brain Imaging Data Exchange demonstrate that our PLSNet achieves state-of-the-art performance. Notably, on CC200 atlas, PLSNet reaches an accuracy of 76.4% and a specificity of 78.6%, overwhelming the previous state-of-the-art with 2.5% and 6.5% under five-fold cross-validation policy. Moreover, the most salient brain regions predicted by PLSNet are closely consistent with the theoretical knowledge in the medical domain, providing potential biomarkers for ASD clinical diagnosis. Our code is available at https://github.com/CodeGoat24/PLSNet.
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Affiliation(s)
- Yibin Wang
- College of Computer Science and Engineering, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China
| | - Haixia Long
- College of Computer Science and Engineering, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China
| | - Qianwei Zhou
- College of Computer Science and Engineering, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China
| | - Tao Bo
- Scientific Center, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan 250021, Shandong, China
| | - Jianwei Zheng
- College of Computer Science and Engineering, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China.
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Matarrese MAG, Loppini A, Fabbri L, Tamilia E, Perry MS, Madsen JR, Bolton J, Stone SSD, Pearl PL, Filippi S, Papadelis C. Spike propagation mapping reveals effective connectivity and predicts surgical outcome in epilepsy. Brain 2023; 146:3898-3912. [PMID: 37018068 PMCID: PMC10473571 DOI: 10.1093/brain/awad118] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/14/2023] [Accepted: 03/23/2023] [Indexed: 04/06/2023] Open
Abstract
Neurosurgical intervention is the best available treatment for selected patients with drug resistant epilepsy. For these patients, surgical planning requires biomarkers that delineate the epileptogenic zone, the brain area that is indispensable for the generation of seizures. Interictal spikes recorded with electrophysiological techniques are considered key biomarkers of epilepsy. Yet, they lack specificity, mostly because they propagate across brain areas forming networks. Understanding the relationship between interictal spike propagation and functional connections among the involved brain areas may help develop novel biomarkers that can delineate the epileptogenic zone with high precision. Here, we reveal the relationship between spike propagation and effective connectivity among onset and areas of spread and assess the prognostic value of resecting these areas. We analysed intracranial EEG data from 43 children with drug resistant epilepsy who underwent invasive monitoring for neurosurgical planning. Using electric source imaging, we mapped spike propagation in the source domain and identified three zones: onset, early-spread and late-spread. For each zone, we calculated the overlap and distance from surgical resection. We then estimated a virtual sensor for each zone and the direction of information flow among them via Granger causality. Finally, we compared the prognostic value of resecting these zones, the clinically-defined seizure onset zone and the spike onset on intracranial EEG channels by estimating their overlap with resection. We observed a spike propagation in source space for 37 patients with a median duration of 95 ms (interquartile range: 34-206), a spatial displacement of 14 cm (7.5-22 cm) and a velocity of 0.5 m/s (0.3-0.8 m/s). In patients with good surgical outcome (25 patients, Engel I), the onset had higher overlap with resection [96% (40-100%)] than early-spread [86% (34-100%), P = 0.01] and late-spread [59% (12-100%), P = 0.002], and it was also closer to resection than late-spread [5 mm versus 9 mm, P = 0.007]. We found an information flow from onset to early-spread in 66% of patients with good outcomes, and from early-spread to onset in 50% of patients with poor outcome. Finally, resection of spike onset, but not area of spike spread or the seizure onset zone, predicted outcome with positive predictive value of 79% and negative predictive value of 56% (P = 0.04). Spatiotemporal mapping of spike propagation reveals information flow from onset to areas of spread in epilepsy brain. Surgical resection of the spike onset disrupts the epileptogenic network and may render patients with drug resistant epilepsy seizure-free without having to wait for a seizure to occur during intracranial monitoring.
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Affiliation(s)
- Margherita A G Matarrese
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA
- Laboratory of Nonlinear Physics and Mathematical Modeling, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, USA
| | - Alessandro Loppini
- Laboratory of Nonlinear Physics and Mathematical Modeling, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Lorenzo Fabbri
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, USA
| | - Eleonora Tamilia
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - M Scott Perry
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA
| | - Joseph R Madsen
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Jeffrey Bolton
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Scellig S D Stone
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Phillip L Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Simonetta Filippi
- Laboratory of Nonlinear Physics and Mathematical Modeling, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Christos Papadelis
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, USA
- School of Medicine, Texas Christian University, Fort Worth, TX, USA
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Li LM, Carson A, Dams-O'Connor K. Psychiatric sequelae of traumatic brain injury - future directions in research. Nat Rev Neurol 2023; 19:556-571. [PMID: 37591931 DOI: 10.1038/s41582-023-00853-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/06/2023] [Indexed: 08/19/2023]
Abstract
Despite growing appreciation that traumatic brain injury (TBI) is an important public health burden, our understanding of the psychiatric and behavioural consequences of TBI remains limited. These changes are particularly detrimental to a person's sense of self, their relationships and their participation in the wider community, and they continue to have devastating individual and cumulative effects long after TBI. This Review relates specifically to TBIs that confer objective clinical or biomarker evidence of structural brain injury; symptomatic head injuries without such evidence are outside the scope of this article. Common psychiatric, affective and behavioural sequelae of TBI and their proposed underlying mechanisms are outlined, along with a brief overview of current treatments. Suggestions for how scientists and clinicians can work together in the future to address the chasms in clinical care and knowledge are discussed in depth.
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Affiliation(s)
- Lucia M Li
- Department of Brain Sciences, Imperial College London, London, UK.
| | - Alan Carson
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Kristen Dams-O'Connor
- Brain Injury Research Center, Department of Rehabilitation and Human Performance, Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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47
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Dadario NB, Sughrue ME. The functional role of the precuneus. Brain 2023; 146:3598-3607. [PMID: 37254740 DOI: 10.1093/brain/awad181] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 05/08/2023] [Accepted: 05/10/2023] [Indexed: 06/01/2023] Open
Abstract
Recent advancements in computational approaches and neuroimaging techniques have refined our understanding of the precuneus. While previously believed to be largely a visual processing region, the importance of the precuneus in complex cognitive functions has been previously less familiar due to a lack of focal lesions in this deeply seated region, but also a poor understanding of its true underlying anatomy. Fortunately, recent studies have revealed significant information on the structural and functional connectivity of this region, and this data has provided a more detailed mechanistic understanding of the importance of the precuneus in healthy and pathologic states. Through improved resting-state functional MRI analyses, it has become clear that the function of the precuneus can be better understood based on its functional association with large scale brain networks. Dual default mode network systems have been well explained in recent years in supporting episodic memory and theory of mind; however, a novel 'para-cingulate' network, which is a subnetwork of the larger central executive network, with likely significant roles in self-referential processes and related psychiatric symptoms is introduced here and requires further clarification. Importantly, detailed anatomic studies on the precuneus structural connectivity inside and beyond the cingulate cortex has demonstrated the presence of large structural white matter connections, which provide an additional layer of meaning to the structural-functional significance of this region and its association with large scale brain networks. Together, the structural-functional connectivity of the precuneus has provided central elements which can model various neurodegenerative diseases and psychiatric disorders, such as Alzheimer's disease and depression.
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Affiliation(s)
- Nicholas B Dadario
- Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ 07102, USA
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Argiris G, Stern Y, Lee S, Ryu H, Habeck C. Simple topological task-based functional connectivity features predict longitudinal behavioral change of fluid reasoning in the RANN cohort. Neuroimage 2023; 277:120237. [PMID: 37343735 PMCID: PMC10999229 DOI: 10.1016/j.neuroimage.2023.120237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 06/18/2023] [Indexed: 06/23/2023] Open
Abstract
Recent attention has been given to topological data analysis (TDA), and more specifically persistent homology (PH), to identify the underlying shape of brain network connectivity beyond simple edge pairings by computing connective components across different connectivity thresholds (see Sizemore et al., 2019). In the present study, we applied PH to task-based functional connectivity, computing 0-dimension Betti (B0) curves and calculating the area under these curves (AUC); AUC indicates how quickly a single connected component is formed across correlation filtration thresholds, with lower values interpreted as potentially analogous to lower whole-brain system segregation (e.g., Gracia-Tabuenca et al., 2020). One hundred sixty-three participants from the Reference Ability Neural Network (RANN) longitudinal lifespan cohort (age 20-80 years) were tested in-scanner at baseline and five-year follow-up on a battery of tests comprising four domains of cognition (i.e., Stern et al., 2014). We tested for 1.) age-related change in the AUC of the B0 curve over time, 2.) the predictive utility of AUC in accounting for longitudinal change in behavioral performance and 3.) compared system segregation to the PH approach. Results demonstrated longitudinal age-related decreases in AUC for Fluid Reasoning, with these decreases predicting longitudinal declines in cognition, even after controlling for demographic and brain integrity factors; moreover, change in AUC partially mediated the effect of age on change in cognitive performance. System segregation also significantly decreased with age in three of the four cognitive domains but did not predict change in cognition. These results argue for greater application of TDA to the study of aging.
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Affiliation(s)
- Georgette Argiris
- Cognitive Neuroscience Division, Department of Neurology, Columbia University Irving Medical Center, 710 West 168th Street, 3rd floor, New York, NY 10032, United States
| | - Yaakov Stern
- Cognitive Neuroscience Division, Department of Neurology, Columbia University Irving Medical Center, 710 West 168th Street, 3rd floor, New York, NY 10032, United States
| | - Seonjoo Lee
- Mental Health Data Science, New York State Psychiatric Institute, New York, NY, United States; Department of Biostatistics, Mailman School of Public Health, New York, NY, United States; Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States
| | - Hyunnam Ryu
- Cognitive Neuroscience Division, Department of Neurology, Columbia University Irving Medical Center, 710 West 168th Street, 3rd floor, New York, NY 10032, United States; Taub Institute, Columbia University, New York, NY, United States; Mental Health Data Science, New York State Psychiatric Institute, New York, NY, United States
| | - Christian Habeck
- Cognitive Neuroscience Division, Department of Neurology, Columbia University Irving Medical Center, 710 West 168th Street, 3rd floor, New York, NY 10032, United States; Taub Institute, Columbia University, New York, NY, United States.
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Kim SH, Kim H, Kim JB. Differences in functional network between focal onset nonconvulsive status epilepticus and toxic metabolic encephalopathy: application to machine learning models for differential diagnosis. Cogn Neurodyn 2023; 17:845-853. [PMID: 37522045 PMCID: PMC10374505 DOI: 10.1007/s11571-022-09877-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 05/19/2022] [Accepted: 08/19/2022] [Indexed: 11/29/2022] Open
Abstract
We aimed to compare network properties between focal-onset nonconvulsive status epilepticus (NCSE) and toxic/metabolic encephalopathy (TME) during periods of periodic discharge using graph theoretical analysis, and to evaluate the applicability of graph measures as markers for the differential diagnosis between focal-onset NCSE and TME, using machine learning algorithms. Electroencephalography (EEG) data from 50 focal-onset NCSE and 44 TMEs were analyzed. Epochs with nonictal periodic discharges were selected, and the coherence in each frequency band was analyzed. Graph theoretical analysis was performed to compare brain network properties between the groups. Eight different traditional machine learning methods were implemented to evaluate the utility of graph theoretical measures as input features to discriminate between the two conditions. The average degree (in delta, alpha, beta, and gamma bands), strength (in delta band), global efficiency (in delta and alpha bands), local efficiency (in delta band), clustering coefficient (in delta band), and transitivity (in delta band) were higher in TME than in NCSE. TME showed lower modularity (in delta band) and assortativity (in alpha, beta, and gamma bands) than NCSE. Machine learning algorithms based on EEG global graph measures classified NCSE and TME with high accuracy, and gradient boosting was the most accurate classification model with an area under the receiver operating characteristics curve of 0.904. Our findings on differences in network properties may provide novel insights that graph measures reflecting the network properties could be quantitative markers for the differential diagnosis between focal-onset NCSE and TME.
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Affiliation(s)
- Seong Hwan Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Hayom Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Jung Bin Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
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Sharma R, Dillon K, Williams SEE, McIntosh R. Does emotion regulation network mediate the effect of social network on psychological distress among older adults? Soc Neurosci 2023; 18:142-154. [PMID: 37267049 DOI: 10.1080/17470919.2023.2218619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 05/12/2023] [Indexed: 06/04/2023]
Abstract
Socio-emotional interactions are integral for regulating emotions and buffering psychological distress. Social neuroscience perspectives on aging suggest that empathetic interpersonal interactions are supported by the activation of brain regions involved in regulating negative affect. The current study tested whether resting state functional connectivity of a network of brain regions activated during cognitive emotion regulation, i.e., emotion regulation network (ERN), statistically mediates the frequency of social contact with friends or family on psychological distress. Here, a 10-min resting-state functional MRI scan was collected along with self-reported anxiety/depressive, somatic, and thought problems and social networking from 90 community-dwelling older adults (aged 65-85 years). The frequency of social interactions with family, but not friends and neighbors, was associated with lower psychological distress. The magnitude of this effect was reduced by 33.34% to non-significant upon adding resting state ERN connectivity as a mediator. Follow-up whole-brain graph network analyses revealed that efficiency and centrality of the left inferior frontal gyrus and the right middle temporal gyrus relate to greater family interactions and lower distress. These hubs may help to buffer psychological problems in older adults through interactions involving empathetic and cognitive emotion regulation with close family.
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Affiliation(s)
| | - Kaitlyn Dillon
- Department of Psychology, University of Miami, Miami, Florida, USA
| | | | - Roger McIntosh
- Department of Psychology, University of Miami, Miami, Florida, USA
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