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Teles M, Maximo JO, Lahti AC, Kraguljac NV. Topological Perturbations in the Functional Connectome Support the Deficit/Non-deficit Distinction in Antipsychotic Medication-Naïve First Episode Psychosis Patients. Schizophr Bull 2024:sbae054. [PMID: 38666705 DOI: 10.1093/schbul/sbae054] [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: 06/11/2024]
Abstract
BACKGROUND Heterogeneity in the etiology, pathophysiology, and clinical features of schizophrenia challenges clinicians and researchers. A helpful approach could be stratifying patients according to the presence or absence of clinical features of the deficit syndrome (DS). DS is characterized by enduring and primary negative symptoms, a clinically less heterogeneous subtype of the illness, and patients with features of DS are thought to present abnormal brain network characteristics, however, this idea has received limited attention. We investigated functional brain network topology in patients displaying deficit features and those who do not. DESIGN We applied graph theory analytics to resting-state functional magnetic resonance imaging data of 61 antipsychotic medication-naïve first episode psychosis patients, 18 DS and 43 non-deficit schizophrenia (NDS), and 72 healthy controls (HC). We quantified small-worldness, global and nodal efficiency measures, shortest path length, nodal local efficiency, and synchronization and contrasted them among the 3 groups. RESULTS DS presented decreased network integration and segregation compared to HC and NDS. DS showed lower global efficiency, longer global path lengths, and lower global local efficiency. Nodal efficiency was lower and the shortest path length was longer in DS in default mode, ventral attention, dorsal attention, frontoparietal, limbic, somatomotor, and visual networks compared to HC. Compared to NDS, DS showed lower efficiency and longer shortest path length in default mode, limbic, somatomotor, and visual networks. CONCLUSIONS Our data supports increasing evidence, based on topological perturbations of the functional connectome, that deficit syndrome may be a distinct form of the illness.
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Affiliation(s)
- Matheus Teles
- Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jose Omar Maximo
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Adrienne Carol Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Nina Vanessa Kraguljac
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, USA
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2
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Morgado F, Vandewouw MM, Hammill C, Kelley E, Crosbie J, Schachar R, Ayub M, Nicolson R, Georgiades S, Arnold P, Iaboni A, Kushki A, Taylor MJ, Anagnostou E, Lerch JP. Behaviour-correlated profiles of cerebellar-cerebral functional connectivity observed in independent neurodevelopmental disorder cohorts. Transl Psychiatry 2024; 14:173. [PMID: 38570480 PMCID: PMC10991387 DOI: 10.1038/s41398-024-02857-4] [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: 11/08/2022] [Revised: 02/27/2024] [Accepted: 02/29/2024] [Indexed: 04/05/2024] Open
Abstract
The cerebellum, through its connectivity with the cerebral cortex, plays an integral role in regulating cognitive and affective processes, and its dysregulation can result in neurodevelopmental disorder (NDD)-related behavioural deficits. Identifying cerebellar-cerebral functional connectivity (FC) profiles in children with NDDs can provide insight into common connectivity profiles and their correlation to NDD-related behaviours. 479 participants from the Province of Ontario Neurodevelopmental Disorders (POND) network (typically developing = 93, Autism Spectrum Disorder = 172, Attention Deficit/Hyperactivity Disorder = 161, Obsessive-Compulsive Disorder = 53, mean age = 12.2) underwent resting-state functional magnetic resonance imaging and behaviour testing (Social Communication Questionnaire, Toronto Obsessive-Compulsive Scale, and Child Behaviour Checklist - Attentional Problems Subscale). FC components maximally correlated to behaviour were identified using canonical correlation analysis. Results were then validated by repeating the investigation in 556 participants from an independent NDD cohort provided from a separate consortium (Healthy Brain Network (HBN)). Replication of canonical components was quantified by correlating the feature vectors between the two cohorts. The two cerebellar-cerebral FC components that replicated to the greatest extent were correlated to, respectively, obsessive-compulsive behaviour (behaviour feature vectors, rPOND-HBN = -0.97; FC feature vectors, rPOND-HBN = -0.68) and social communication deficit contrasted against attention deficit behaviour (behaviour feature vectors, rPOND-HBN = -0.99; FC feature vectors, rPOND-HBN = -0.78). The statistically stable (|z| > 1.96) features of the FC feature vectors, measured via bootstrap re-sampling, predominantly comprised of correlations between cerebellar attentional and control network regions and cerebral attentional, default mode, and control network regions. In both cohorts, spectral clustering on FC loading values resulted in subject clusters mixed across diagnostic categories, but no cluster was significantly enriched for any given diagnosis as measured via chi-squared test (p > 0.05). Overall, two behaviour-correlated components of cerebellar-cerebral functional connectivity were observed in two independent cohorts. This suggests the existence of generalizable cerebellar network differences that span across NDD diagnostic boundaries.
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Affiliation(s)
- Felipe Morgado
- Dept. Medical Biophysics, University of Toronto, Toronto, Canada.
- Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Canada.
| | - Marlee M Vandewouw
- Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
- Autism Research Centre, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Canada
| | - Christopher Hammill
- Data Science & Advanced Analytics, St. Michael's Hospital, University of Toronto, Toronto, Canada
| | | | - Jennifer Crosbie
- Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Russell Schachar
- Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Muhammad Ayub
- Department of Psychiatry, University College London, London, UK
| | - Robert Nicolson
- Department of Psychiatry, University of Western Ontario, London, Canada
- Lawson Research Institute, London, Canada
| | - Stelios Georgiades
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Canada
- Offord Centre for Child Studies, McMaster University, Hamilton, Canada
| | - Paul Arnold
- The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
- Department of Psychiatry, University of Calgary, Calgary, Canada
| | - Alana Iaboni
- Autism Research Centre, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Canada
| | - Azadeh Kushki
- Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
- Autism Research Centre, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Canada
| | - Margot J Taylor
- Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Canada
- Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada
- Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Evdokia Anagnostou
- Autism Research Centre, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Canada
- Institute of Medical Science, University of Toronto, Toronto, Canada
| | - Jason P Lerch
- Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, UK
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3
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Guichet C, Banjac S, Achard S, Mermillod M, Baciu M. Modeling the neurocognitive dynamics of language across the lifespan. Hum Brain Mapp 2024; 45:e26650. [PMID: 38553863 PMCID: PMC10980845 DOI: 10.1002/hbm.26650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 02/08/2024] [Accepted: 02/26/2024] [Indexed: 04/02/2024] Open
Abstract
Healthy aging is associated with a heterogeneous decline across cognitive functions, typically observed between language comprehension and language production (LP). Examining resting-state fMRI and neuropsychological data from 628 healthy adults (age 18-88) from the CamCAN cohort, we performed state-of-the-art graph theoretical analysis to uncover the neural mechanisms underlying this variability. At the cognitive level, our findings suggest that LP is not an isolated function but is modulated throughout the lifespan by the extent of inter-cognitive synergy between semantic and domain-general processes. At the cerebral level, we show that default mode network (DMN) suppression coupled with fronto-parietal network (FPN) integration is the way for the brain to compensate for the effects of dedifferentiation at a minimal cost, efficiently mitigating the age-related decline in LP. Relatedly, reduced DMN suppression in midlife could compromise the ability to manage the cost of FPN integration. This may prompt older adults to adopt a more cost-efficient compensatory strategy that maintains global homeostasis at the expense of LP performances. Taken together, we propose that midlife represents a critical neurocognitive juncture that signifies the onset of LP decline, as older adults gradually lose control over semantic representations. We summarize our findings in a novel synergistic, economical, nonlinear, emergent, cognitive aging model, integrating connectomic and cognitive dimensions within a complex system perspective.
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Affiliation(s)
| | - Sonja Banjac
- Université Grenoble Alpes, CNRS LPNC UMR 5105GrenobleFrance
| | - Sophie Achard
- LJK, UMR CNRS 5224, Université Grenoble AlpesGrenobleFrance
| | | | - Monica Baciu
- Université Grenoble Alpes, CNRS LPNC UMR 5105GrenobleFrance
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4
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Orwig W, Setton R, Diez I, Bueichekú E, Meyer ML, Tamir DI, Sepulcre J, Schacter DL. Creativity at rest: Exploring functional network connectivity of creative experts. Netw Neurosci 2023; 7:1022-1033. [PMID: 37781148 PMCID: PMC10473280 DOI: 10.1162/netn_a_00317] [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: 10/21/2022] [Accepted: 03/31/2023] [Indexed: 10/03/2023] Open
Abstract
The neuroscience of creativity seeks to disentangle the complex brain processes that underpin the generation of novel ideas. Neuroimaging studies of functional connectivity, particularly functional magnetic resonance imaging (fMRI), have revealed individual differences in brain network organization associated with creative ability; however, much of the extant research is limited to laboratory-based divergent thinking measures. To overcome these limitations, we compare functional brain connectivity in a cohort of creative experts (n = 27) and controls (n = 26) and examine links with creative behavior. First, we replicate prior findings showing reduced connectivity in visual cortex related to higher creative performance. Second, we examine whether this result is driven by integrated or segregated connectivity. Third, we examine associations between functional connectivity and vivid distal simulation separately in creative experts and controls. In accordance with past work, our results show reduced connectivity to the primary visual cortex in creative experts at rest. Additionally, we observe a negative association between distal simulation vividness and connectivity to the lateral visual cortex in creative experts. Taken together, these results highlight connectivity profiles of highly creative people and suggest that creative thinking may be related to, though not fully redundant with, the ability to vividly imagine the future.
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Affiliation(s)
- William Orwig
- Department of Psychology, Harvard University, Cambridge, MA, USA
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Roni Setton
- Department of Psychology, Harvard University, Cambridge, MA, USA
| | - Ibai Diez
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Elisenda Bueichekú
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Meghan L. Meyer
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Diana I. Tamir
- Department of Psychology, Princeton University, Princeton, NJ, USA
| | - Jorge Sepulcre
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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Longitudinal Infant Functional Connectivity Prediction via Conditional Intensive Triplet Network. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2022; 13438:255-264. [PMID: 36563062 PMCID: PMC9769983 DOI: 10.1007/978-3-031-16452-1_25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Longitudinal infant brain functional connectivity (FC) constructed from resting-state functional MRI (rs-fMRI) has increasingly become a pivotal tool in studying the dynamics of early brain development. However, due to various reasons including high acquisition cost, strong motion artifact, and subject dropout, there has been an extreme shortage of usable longitudinal infant rs-fMRI scans to construct longitudinal FCs, which hinders comprehensive understanding and modeling of brain functional development at early ages. To address this issue, in this paper, we propose a novel conditional intensive triplet network (CITN) for longitudinal prediction of the dynamic development of infant FC, which can traverse FCs within a long duration and predict the target FC at any specific age during infancy. Targeting at accurately modeling of the progression pattern of FC, while maintaining the individual functional uniqueness, our model effectively disentangles the intrinsically mixed age-related and identity-related information from the source FC and predicts the target FC by fusing well-disentangled identity-related information with the specific age-related information. Specifically, we introduce an intensive triplet auto-encoder for effective disentanglement of age-related and identity-related information and an identity conditional module to mix identity-related information with designated age-related information. We train the proposed model in a self-supervised way and design downstream tasks to help robustly disentangle age-related and identity-related features. Experiments on 464 longitudinal infant fMRI scans show the superior performance of the proposed method in longitudinal FC prediction in comparison with state-of-the-art approaches.
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6
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Shah D, Gsell W, Wahis J, Luckett ES, Jamoulle T, Vermaercke B, Preman P, Moechars D, Hendrickx V, Jaspers T, Craessaerts K, Horré K, Wolfs L, Fiers M, Holt M, Thal DR, Callaerts-Vegh Z, D'Hooge R, Vandenberghe R, Himmelreich U, Bonin V, De Strooper B. Astrocyte calcium dysfunction causes early network hyperactivity in Alzheimer's disease. Cell Rep 2022; 40:111280. [PMID: 36001964 PMCID: PMC9433881 DOI: 10.1016/j.celrep.2022.111280] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 06/30/2022] [Accepted: 08/05/2022] [Indexed: 12/15/2022] Open
Abstract
Dysfunctions of network activity and functional connectivity (FC) represent early events in Alzheimer’s disease (AD), but the underlying mechanisms remain unclear. Astrocytes regulate local neuronal activity in the healthy brain, but their involvement in early network hyperactivity in AD is unknown. We show increased FC in the human cingulate cortex several years before amyloid deposition. We find the same early cingulate FC disruption and neuronal hyperactivity in AppNL-F mice. Crucially, these network disruptions are accompanied by decreased astrocyte calcium signaling. Recovery of astrocytic calcium activity normalizes neuronal hyperactivity and FC, as well as seizure susceptibility and day/night behavioral disruptions. In conclusion, we show that astrocytes mediate initial features of AD and drive clinically relevant phenotypes. The cingulate cortex of humans and mice shows early functional deficits in AD Astrocyte calcium signaling is decreased before the presence of amyloid plaques Recovery of astrocyte calcium signals mitigates neuronal hyperactivity Recovery of astrocytes normalizes cingulate connectivity and behavior disruptions
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Affiliation(s)
- Disha Shah
- Laboratory for the Research of Neurodegenerative Diseases, VIB Center for Brain and Disease Research, KU Leuven, 3000 Leuven, Belgium.
| | - Willy Gsell
- Biomedical MRI, Department of Imaging and Pathology, KU Leuven, 3000 Leuven, Belgium
| | - Jérôme Wahis
- Laboratory of Glia Biology, VIB Center for Brain and Disease Research, KU Leuven, 3000 Leuven, Belgium
| | - Emma S Luckett
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | - Tarik Jamoulle
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | - Ben Vermaercke
- Neuro-electronics Research Flanders, 3000 Leuven, Belgium
| | - Pranav Preman
- Laboratory for the Research of Neurodegenerative Diseases, VIB Center for Brain and Disease Research, KU Leuven, 3000 Leuven, Belgium
| | - Daan Moechars
- Laboratory for the Research of Neurodegenerative Diseases, VIB Center for Brain and Disease Research, KU Leuven, 3000 Leuven, Belgium
| | - Véronique Hendrickx
- Laboratory for the Research of Neurodegenerative Diseases, VIB Center for Brain and Disease Research, KU Leuven, 3000 Leuven, Belgium
| | - Tom Jaspers
- Laboratory for the Research of Neurodegenerative Diseases, VIB Center for Brain and Disease Research, KU Leuven, 3000 Leuven, Belgium
| | - Katleen Craessaerts
- Laboratory for the Research of Neurodegenerative Diseases, VIB Center for Brain and Disease Research, KU Leuven, 3000 Leuven, Belgium
| | - Katrien Horré
- Laboratory for the Research of Neurodegenerative Diseases, VIB Center for Brain and Disease Research, KU Leuven, 3000 Leuven, Belgium
| | - Leen Wolfs
- Laboratory for the Research of Neurodegenerative Diseases, VIB Center for Brain and Disease Research, KU Leuven, 3000 Leuven, Belgium
| | - Mark Fiers
- Laboratory for the Research of Neurodegenerative Diseases, VIB Center for Brain and Disease Research, KU Leuven, 3000 Leuven, Belgium
| | - Matthew Holt
- Laboratory of Glia Biology, VIB Center for Brain and Disease Research, KU Leuven, 3000 Leuven, Belgium
| | - Dietmar Rudolf Thal
- Laboratory for Neuropathology, Department of Imaging and Pathology, LBI, KU Leuven, 3000 Leuven, Belgium
| | | | - Rudi D'Hooge
- Laboratory of Biological Psychology, KU-Leuven, 3000 Leuven, Belgium
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | - Uwe Himmelreich
- Biomedical MRI, Department of Imaging and Pathology, KU Leuven, 3000 Leuven, Belgium
| | - Vincent Bonin
- Neuro-electronics Research Flanders, 3000 Leuven, Belgium
| | - Bart De Strooper
- Laboratory for the Research of Neurodegenerative Diseases, VIB Center for Brain and Disease Research, KU Leuven, 3000 Leuven, Belgium; UK Dementia Research Institute at University College London, WC1E 6BT London, UK.
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7
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Abstract
PURPOSE OF REVIEW Machine learning solutions are being increasingly used in the analysis of neuroimaging (NI) data, and as a result, there is an increase in the emphasis of the reproducibility and replicability of these data-driven solutions. Although this is a very positive trend, related terminology is often not properly defined, and more importantly, (computational) reproducibility that refers to obtaining consistent results using the same data and the same code is often disregarded. RECENT FINDINGS We review the findings of a recent paper on the topic along with other relevant literature, and present two examples that demonstrate the importance of accounting for reproducibility in widely used software for NI data. SUMMARY We note that reproducibility should be a first step in all NI data analyses including those focusing on replicability, and introduce available solutions for assessing reproducibility. We add the cautionary remark that when not taken into account, lack of reproducibility can significantly bias all subsequent analysis stages.
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Affiliation(s)
- Tü Lay Adali
- Department of CSEE, University of Maryland, Baltimore County, Baltimore, Maryland
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
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8
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Wilsenach JB, Warnaby CE, Deane CM, Reinert GD. Ranking of communities in multiplex spatiotemporal models of brain dynamics. APPLIED NETWORK SCIENCE 2022; 7:15. [PMID: 35308059 PMCID: PMC8921068 DOI: 10.1007/s41109-022-00454-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 03/01/2022] [Indexed: 06/14/2023]
Abstract
UNLABELLED As a relatively new field, network neuroscience has tended to focus on aggregate behaviours of the brain averaged over many successive experiments or over long recordings in order to construct robust brain models. These models are limited in their ability to explain dynamic state changes in the brain which occurs spontaneously as a result of normal brain function. Hidden Markov Models (HMMs) trained on neuroimaging time series data have since arisen as a method to produce dynamical models that are easy to train but can be difficult to fully parametrise or analyse. We propose an interpretation of these neural HMMs as multiplex brain state graph models we term Hidden Markov Graph Models. This interpretation allows for dynamic brain activity to be analysed using the full repertoire of network analysis techniques. Furthermore, we propose a general method for selecting HMM hyperparameters in the absence of external data, based on the principle of maximum entropy, and use this to select the number of layers in the multiplex model. We produce a new tool for determining important communities of brain regions using a spatiotemporal random walk-based procedure that takes advantage of the underlying Markov structure of the model. Our analysis of real multi-subject fMRI data provides new results that corroborate the modular processing hypothesis of the brain at rest as well as contributing new evidence of functional overlap between and within dynamic brain state communities. Our analysis pipeline provides a way to characterise dynamic network activity of the brain under novel behaviours or conditions. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s41109-022-00454-2.
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Affiliation(s)
- James B. Wilsenach
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Catherine E. Warnaby
- Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, FMRIB Centre, University of Oxford, Oxford, UK
| | | | - Gesine D. Reinert
- Department of Statistics, University of Oxford, Oxford, UK
- The Alan Turing Institute, London, UK
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9
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Bottino F, Lucignani M, Pasquini L, Mastrogiovanni M, Gazzellini S, Ritrovato M, Longo D, Figà-Talamanca L, Rossi Espagnet MC, Napolitano A. Spatial Stability of Functional Networks: A Measure to Assess the Robustness of Graph-Theoretical Metrics to Spatial Errors Related to Brain Parcellation. Front Neurosci 2022; 15:736524. [PMID: 35250432 PMCID: PMC8894326 DOI: 10.3389/fnins.2021.736524] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 12/28/2021] [Indexed: 12/12/2022] Open
Abstract
There is growing interest in studying human brain connectivity and in modelling the brain functional structure as a network. Brain network creation requires parcellation of the cerebral cortex to define nodes. Parcellation might be affected by possible errors due to inter- and intra-subject variability as a consequence of brain structural and physiological characteristics and shape variations related to ageing and diseases, acquisition noise, and misregistration. These errors could induce a knock-on effect on network measure variability. The aim of this study was to investigate spatial stability, a measure of functional connectivity variations induced by parcellation errors. We simulated parcellation variability with random small spatial changes and evaluated its effects on twenty-seven graph-theoretical measures. The study included subjects from three public online datasets. Two brain parcellations were performed using FreeSurfer with geometric atlases. Starting from these, 100 new parcellations were created by increasing the area of 30% of parcels, reducing the area of neighbour parcels, with a rearrangement of vertices. fMRI data were filtered with linear regression, CompCor, and motion correction. Adjacency matrices were constructed with 0.1, 0.2, 0.3, and 0.4 thresholds. Differences in spatial stability between datasets, atlases, and threshold were evaluated. The higher spatial stability resulted for Characteristic-path-length, Density, Transitivity, and Closeness-centrality, and the lower spatial stability resulted for Bonacich and Katz. Multivariate analysis showed a significant effect of atlas, datasets, and thresholds. Katz and Bonacich centrality, which was subject to larger variations, can be considered an unconventional graph measure, poorly implemented in the clinical field and not yet investigated for reliability assessment. Spatial stability (SS) is affected by threshold, and it decreases with increasing threshold for several measures. Moreover, SS seems to depend on atlas choice and scanning parameters. Our study highlights the importance of paying close attention to possible parcellation-related spatial errors, which may affect the reliability of functional connectivity measures.
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Affiliation(s)
- Francesca Bottino
- Medical Physics Department, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
| | - Martina Lucignani
- Medical Physics Department, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
| | - Luca Pasquini
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
- Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | | | - Simone Gazzellini
- Neuroscience and Neurorehabilitation Department, Bambino Gesù Children’s Hospital – IRCCS, Rome, Italy
| | - Matteo Ritrovato
- Health Technology and Safety Research Unit, Bambino Gesù Children’s Hospital – IRCCS, Rome, Italy
| | - Daniela Longo
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Lorenzo Figà-Talamanca
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Maria Camilla Rossi Espagnet
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
- NESMOS, Neuroradiology Department, S. Andrea Hospital Sapienza Rome University, Rome, Italy
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
- *Correspondence: Antonio Napolitano,
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10
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Mertens N, Sunaert S, Van Laere K, Koole M. The Effect of Aging on Brain Glucose Metabolic Connectivity Revealed by [18F]FDG PET-MR and Individual Brain Networks. Front Aging Neurosci 2022; 13:798410. [PMID: 35221983 PMCID: PMC8865456 DOI: 10.3389/fnagi.2021.798410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 12/27/2021] [Indexed: 11/13/2022] Open
Abstract
Contrary to group-based brain connectivity analyses, the aim of this study was to construct individual brain metabolic networks to determine age-related effects on brain metabolic connectivity. Static 40–60 min [18F]FDG positron emission tomography (PET) images of 67 healthy subjects between 20 and 82 years were acquired with an integrated PET-MR system. Network nodes were defined by brain parcellation using the Schaefer atlas, while connectivity strength between two nodes was determined by comparing the distribution of PET uptake values within each node using a Kullback–Leibler divergence similarity estimation (KLSE). After constructing individual brain networks, a linear and quadratic regression analysis of metabolic connectivity strengths within- and between-networks was performed to model age-dependency. In addition, the age dependency of metrics for network integration (characteristic path length), segregation (clustering coefficient and local efficiency), and centrality (number of hubs) was assessed within the whole brain and within predefined functional subnetworks. Overall, a decrease of metabolic connectivity strength with healthy aging was found within the whole-brain network and several subnetworks except within the somatomotor, limbic, and visual network. The same decrease of metabolic connectivity was found between several networks across the whole-brain network and the functional subnetworks. In terms of network topology, a less integrated and less segregated network was observed with aging, while the distribution and the number of hubs did not change with aging, suggesting that brain metabolic networks are not reorganized during the adult lifespan. In conclusion, using an individual brain metabolic network approach, a decrease in metabolic connectivity strength was observed with healthy aging, both within the whole brain and within several predefined networks. These findings can be used in a diagnostic setting to differentiate between age-related changes in brain metabolic connectivity strength and changes caused by early development of neurodegeneration.
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Affiliation(s)
- Nathalie Mertens
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- *Correspondence: Nathalie Mertens,
| | - Stefan Sunaert
- Translational MRI, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- Department of Radiology, University Hospitals Leuven, Leuven, Belgium
| | - Koen Van Laere
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- Division of Nuclear Medicine, University Hospitals Leuven, Leuven, Belgium
| | - Michel Koole
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
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Labus JS, Mayer EA, Tillisch K, Aagaard KM, Stains J, Broniowska K, Van Remortel C, Tun G, Rapkin A. Dysregulation in Sphingolipid Signaling Pathways is Associated With Symptoms and Functional Connectivity of Pain Processing Brain Regions in Provoked Vestibulodynia. THE JOURNAL OF PAIN 2021; 22:1586-1605. [PMID: 34029688 PMCID: PMC10460622 DOI: 10.1016/j.jpain.2021.04.017] [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: 12/22/2020] [Revised: 03/27/2021] [Accepted: 04/29/2021] [Indexed: 10/21/2022]
Abstract
Provoked vestibulodynia (PVD) is a chronic pain disorder characterized by local hypersensitivity and severe pain with pressure localized to the vulvar vestibule. Despite decades of study, the lack of identified biomarkers has slowed the development of effective therapies. The primary aim of this study was to use metabolomics to identify novel biochemical mechanisms in vagina and blood underlying brain biomarkers and symptoms in PVD, thereby closing this knowledge gap. Using a cross-sectional case-control observational study design, untargeted and unbiased metabolomic profiling of vaginal fluid and plasma was performed in women with PVD compared to healthy controls. In women with PVD, we also obtained assessments of vulvar pain, vestibular and vaginal muscle tenderness, and 24-hour symptom intensity alongside resting-state brain functional connectivity of brain regions involved in pain processing and modulation. Compared to healthy controls, women with PVD demonstrated differences primarily in vaginal (but not plasma) concentrations of metabolites of the sphingolipid signaling pathways, suggesting localized effects in vagina and vulvar vestibule rather than systemic effects. Our findings reveal that dysregulation of sphingolipid metabolism in PVD is associated with increased vulvar pain and muscle tenderness, sexual dysfunction, and decreased functional connectivity strength in pain processing/modulatory brain regions. This data collectively suggests that alterations in sphingolipid signaling pathways are likely an important molecular biomarker in PVD that could lead to new targets for therapeutic intervention. PERSPECTIVE: This manuscript presents the results of a robust, unbiased molecular assessment of plasma and vaginal fluid samples in women with provoked vestibulodynia compared to healthy controls. The findings suggest that alterations in sphingolipid signaling pathways are associated with symptoms and brain biomarkers and may be an important molecular marker that could provide new targets for therapeutic intervention.
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Affiliation(s)
- Jennifer S Labus
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine at the University of California, Los Angeles, California; Brain Research Institute UCLA, Gonda (Goldschmied) Neuroscience and Genetics Research Center, Los Angeles, California.
| | - Emeran A Mayer
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine at the University of California, Los Angeles, California
| | - Kirsten Tillisch
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine at the University of California, Los Angeles, California
| | - Kjersti M Aagaard
- Division of Maternal-Fetal Medicine, Departments of Obstetrics and Gynecology, Baylor College of Medicine and Texas Children's Hospital, Houston, Texas; Department of Molecular and Human Genetics, Bioinformatics Research Laboratory, Baylor College of Medicine, Houston, Texas; Department of Molecular and Cell Biology, Baylor College of Medicine, Houston, Texas
| | - Jean Stains
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine at the University of California, Los Angeles, California
| | | | - Charlotte Van Remortel
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine at the University of California, Los Angeles, California
| | - Guistinna Tun
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine at the University of California, Los Angeles, California
| | - Andrea Rapkin
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine at the University of California, Los Angeles, California; Department of Obstetrics and Gynecology, David Geffen School of Medicine at the University of California, Los Angeles, California
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Ran Q, Jamoulle T, Schaeverbeke J, Meersmans K, Vandenberghe R, Dupont P. Reproducibility of graph measures at the subject level using resting-state fMRI. Brain Behav 2020; 10:2336-2351. [PMID: 32614515 PMCID: PMC7428495 DOI: 10.1002/brb3.1705] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 05/07/2020] [Accepted: 05/17/2020] [Indexed: 01/08/2023] Open
Abstract
INTRODUCTION Graph metrics have been proposed as potential biomarkers for diagnosis in clinical work. However, before it can be applied in a clinical setting, their reproducibility should be evaluated. METHODS This study systematically investigated the effect of two denoising pipelines and different whole-brain network constructions on reproducibility of subject-specific graph measures. We used the multi-session fMRI dataset from the Brain Genomics Superstruct Project consisting of 69 healthy young adults. RESULTS In binary networks, the test-retest variability for global measures was large at low density irrespective of the denoising strategy or the type of correlation. Weighted networks showed very low test-retest values (and thus a good reproducibility) for global graph measures irrespective of the strategy used. Comparing the test-retest values for different strategies, there were significant main effects of the type of correlation (Pearson correlation vs. partial correlation), the (partial) correlation value (absolute vs. positive vs. negative), and weight calculation (based on the raw (partial) correlation values vs. based on transformed Z-values). There was also a significant interaction effect between type of correlation and weight calculation. Similarly as for the binary networks, there was no main effect of the denoising pipeline. CONCLUSION Our results demonstrated that normalized global graph measures based on a weighted network using the absolute (partial) correlation as weight were reproducible. The denoising pipeline and the granularity of the whole-brain parcellation used to define the nodes were not critical for the reproducibility of normalized graph measures.
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Affiliation(s)
- Qian Ran
- Laboratory for Cognitive NeurologyDepartment of Neurosciences, KU LeuvenLeuvenBelgium
- Department of RadiologyXinqiao HospitalChongqingChina
| | - Tarik Jamoulle
- Laboratory for Cognitive NeurologyDepartment of Neurosciences, KU LeuvenLeuvenBelgium
| | - Jolien Schaeverbeke
- Laboratory for Cognitive NeurologyDepartment of Neurosciences, KU LeuvenLeuvenBelgium
- Alzheimer Research Centre KU LeuvenLeuven Brain Instititute, KU LeuvenLeuvenBelgium
| | - Karen Meersmans
- Laboratory for Cognitive NeurologyDepartment of Neurosciences, KU LeuvenLeuvenBelgium
| | - Rik Vandenberghe
- Laboratory for Cognitive NeurologyDepartment of Neurosciences, KU LeuvenLeuvenBelgium
- Alzheimer Research Centre KU LeuvenLeuven Brain Instititute, KU LeuvenLeuvenBelgium
- Neurology DepartmentUniversity Hospitals Leuven (UZ Leuven)LeuvenBelgium
| | - Patrick Dupont
- Laboratory for Cognitive NeurologyDepartment of Neurosciences, KU LeuvenLeuvenBelgium
- Alzheimer Research Centre KU LeuvenLeuven Brain Instititute, KU LeuvenLeuvenBelgium
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