1
|
Ramduny J, Kelly C. Connectome-based fingerprinting: reproducibility, precision, and behavioral prediction. Neuropsychopharmacology 2024:10.1038/s41386-024-01962-8. [PMID: 39147868 DOI: 10.1038/s41386-024-01962-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 08/02/2024] [Accepted: 08/05/2024] [Indexed: 08/17/2024]
Abstract
Functional magnetic resonance imaging-based functional connectivity enables the non-invasive mapping of individual differences in brain functional organization to individual differences in a vast array of behavioral phenotypes. This flexibility has renewed the search for neuroimaging-based biomarkers that exhibit reproducibility, prediction, and precision. Functional connectivity-based measures that share these three characteristics are key to achieving this goal. Here, we review the functional connectome fingerprinting approach and discuss its value, not only as a simple and intuitive conceptualization of the "functional connectome" that provides new insights into how the connectome is altered in association with psychiatric symptoms, but also as a straightforward and interpretable method for indexing the reproducibility of functional connectivity-based measures. We discuss how these advantages provide new avenues for strengthening reproducibility, precision, and behavioral prediction for functional connectomics and we consider new directions toward discovering better biomarkers for neuropsychiatric conditions.
Collapse
Affiliation(s)
- Jivesh Ramduny
- Department of Psychology, Yale University, New Haven, CT, USA.
- Kavli Institute for Neuroscience, Yale University, New Haven, CT, USA.
| | - Clare Kelly
- School of Psychology, Trinity College Dublin, Dublin, Ireland.
- Department of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland.
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland.
| |
Collapse
|
2
|
Trevino G, Lee JJ, Shimony JS, Luckett PH, Leuthardt EC. Complexity organization of resting-state functional-MRI networks. Hum Brain Mapp 2024; 45:e26809. [PMID: 39185729 PMCID: PMC11345701 DOI: 10.1002/hbm.26809] [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: 01/12/2024] [Revised: 05/28/2024] [Accepted: 07/20/2024] [Indexed: 08/27/2024] Open
Abstract
Entropy measures are increasingly being used to analyze the structure of neural activity observed by functional magnetic resonance imaging (fMRI), with resting-state networks (RSNs) being of interest for their reproducible descriptions of the brain's functional architecture. Temporal correlations have shown a dichotomy among these networks: those that engage with the environment, known as extrinsic, which include the visual and sensorimotor networks; and those associated with executive control and self-referencing, known as intrinsic, which include the default mode network and the frontoparietal control network. While these inter-voxel temporal correlations enable the assessment of synchrony among the components of individual networks, entropic measures introduce an intra-voxel assessment that quantifies signal features encoded within each blood oxygen level-dependent (BOLD) time series. As a result, this framework offers insights into comprehending the representation and processing of information within fMRI signals. Multiscale entropy (MSE) has been proposed as a useful measure for characterizing the entropy of neural activity across different temporal scales. This measure of temporal entropy in BOLD data is dependent on the length of the time series; thus, high-quality data with fine-grained temporal resolution and a sufficient number of time frames is needed to improve entropy precision. We apply MSE to the Midnight Scan Club, a highly sampled and well-characterized publicly available dataset, to analyze the entropy distribution of RSNs and evaluate its ability to distinguish between different functional networks. Entropy profiles are compared across temporal scales and RSNs. Our results have shown that the spatial distribution of entropy at infra-slow frequencies (0.005-0.1 Hz) reproduces known parcellations of RSNs. We found a complexity hierarchy between intrinsic and extrinsic RSNs, with intrinsic networks robustly exhibiting higher entropy than extrinsic networks. Finally, we found new evidence that the topography of entropy in the posterior cerebellum exhibits high levels of entropy comparable to that of intrinsic RSNs.
Collapse
Affiliation(s)
- Gabriel Trevino
- Department of Neurological SurgeryWashington University School of MedicineSt. LouisMissouriUSA
| | - John J. Lee
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Joshua S. Shimony
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Patrick H. Luckett
- Center for Innovation in Neuroscience and TechnologyWashington University School of MedicineSt. LouisMissouriUSA
- Division of NeurotechnologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Eric C. Leuthardt
- Department of Neurological SurgeryWashington University School of MedicineSt. LouisMissouriUSA
- Center for Innovation in Neuroscience and TechnologyWashington University School of MedicineSt. LouisMissouriUSA
- Division of NeurotechnologyWashington University School of MedicineSt. LouisMissouriUSA
| |
Collapse
|
3
|
Demeter DV, Greene DJ. The promise of precision functional mapping for neuroimaging in psychiatry. Neuropsychopharmacology 2024:10.1038/s41386-024-01941-z. [PMID: 39085426 DOI: 10.1038/s41386-024-01941-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 06/14/2024] [Accepted: 07/17/2024] [Indexed: 08/02/2024]
Abstract
Precision functional mapping (PFM) is a neuroimaging approach to reliably estimate metrics of brain function from individual people via the collection of large amounts of fMRI data (hours per person). This method has revealed much about the inter-individual variation of functional brain networks. While standard group-level studies, in which we average brain measures across groups of people, are important in understanding the generalizable neural underpinnings of neuropsychiatric disorders, many disorders are heterogeneous in nature. This heterogeneity often complicates clinical care, leading to patient uncertainty when considering prognosis or treatment options. We posit that PFM methods may help streamline clinical care in the future, fast-tracking the choice of personalized treatment that is most compatible with the individual. In this review, we provide a history of PFM studies, foundational results highlighting the benefits of PFM methods in the pursuit of an advanced understanding of individual differences in functional network organization, and possible avenues where PFM can contribute to clinical translation of neuroimaging research results in the way of personalized treatment in psychiatry.
Collapse
Affiliation(s)
- Damion V Demeter
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA.
| | - Deanna J Greene
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA.
| |
Collapse
|
4
|
Makowski C, Nichols TE, Dale AM. Quality over quantity: powering neuroimaging samples in psychiatry. Neuropsychopharmacology 2024:10.1038/s41386-024-01893-4. [PMID: 38902353 DOI: 10.1038/s41386-024-01893-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 05/06/2024] [Accepted: 05/24/2024] [Indexed: 06/22/2024]
Abstract
Neuroimaging has been widely adopted in psychiatric research, with hopes that these non-invasive methods will provide important clues to the underpinnings and prediction of various mental health symptoms and outcomes. However, the translational impact of neuroimaging has not yet reached its promise, despite the plethora of computational methods, tools, and datasets at our disposal. Some have lamented that too many psychiatric neuroimaging studies have been underpowered with respect to sample size. In this review, we encourage this discourse to shift from a focus on sheer increases in sample size to more thoughtful choices surrounding experimental study designs. We propose considerations at multiple decision points throughout the study design, data modeling and analysis process that may help researchers working in psychiatric neuroimaging boost power for their research questions of interest without necessarily increasing sample size. We also provide suggestions for leveraging multiple datasets to inform each other and strengthen our confidence in the generalization of findings to both population-level and clinical samples. Through a greater emphasis on improving the quality of brain-based and clinical measures rather than merely quantity, meaningful and potentially translational clinical associations with neuroimaging measures can be achieved with more modest sample sizes in psychiatry.
Collapse
Affiliation(s)
- Carolina Makowski
- Department of Radiology, University of California San Diego, San Diego, CA, USA.
| | - Thomas E Nichols
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Anders M Dale
- Departments of Radiology and Neurosciences, University of California San Diego, San Diego, CA, USA
| |
Collapse
|
5
|
Saadon-Grosman N, Du J, Kosakowski HL, Angeli PA, DiNicola LM, Eldaief MC, Buckner RL. Within-Individual Organization of the Human Cognitive Cerebellum: Evidence for Closely Juxtaposed, Functionally Specialized Regions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.18.572062. [PMID: 38187706 PMCID: PMC10769291 DOI: 10.1101/2023.12.18.572062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
The human cerebellum possesses multiple regions linked to cerebral association cortex. Here we mapped the cerebellum using precision functional MRI within individual participants (N=15), first estimating regions using connectivity and then prospectively testing functional properties using independent task data. Network estimates in all participants revealed a Crus I / II cerebellar megacluster of five higher-order association networks often with multiple, discontinuous regions for the same network. Seed regions placed within the megaclusters, including the disjointed regions, yielded spatially selective networks in the cerebral cortex. Compelling evidence for functional specialization within the cerebellar megaclusters emerged from the task responses. Reflecting functional distinctions found in the cerebrum, domain-flexible cerebellar regions involved in cognitive control dissociated from distinct domain-specialized regions with differential responses to language, social, and spatial / episodic task demands. These findings provide a clear demonstration that the cerebellum encompasses multiple zones dedicated to cognition, featuring juxtaposed regions specialized for distinct processing domains.
Collapse
Affiliation(s)
- Noam Saadon-Grosman
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Jingnan Du
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Heather L Kosakowski
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Peter A Angeli
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Lauren M DiNicola
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Mark C Eldaief
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Randy L Buckner
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
| |
Collapse
|
6
|
Groot JM, Miletic S, Isherwood SJS, Tse DHY, Habli S, Håberg AK, Forstmann BU, Bazin PL, Mittner M. Echoes from Intrinsic Connectivity Networks in the Subcortex. J Neurosci 2023; 43:6609-6618. [PMID: 37562962 PMCID: PMC10538587 DOI: 10.1523/jneurosci.1020-23.2023] [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: 06/01/2023] [Revised: 07/11/2023] [Accepted: 07/28/2023] [Indexed: 08/12/2023] Open
Abstract
Decades of research have greatly improved our understanding of intrinsic human brain organization in terms of functional networks and the transmodal hubs within the cortex at which they converge. However, substrates of multinetwork integration in the human subcortex are relatively uncharted. Here, we leveraged recent advances in subcortical atlasing and ultra-high field (7 T) imaging optimized for the subcortex to investigate the functional architecture of 14 individual structures in healthy adult males and females with a fully data-driven approach. We revealed that spontaneous neural activity in subcortical regions can be decomposed into multiple independent subsignals that correlate with, or "echo," the activity in functional networks across the cortex. Distinct subregions of the thalamus, striatum, claustrum, and hippocampus showed a varied pattern of echoes from attention, control, visual, somatomotor, and default mode networks, demonstrating evidence for a heterogeneous organization supportive of functional integration. Multiple network activity furthermore converged within the globus pallidus externa, substantia nigra, and ventral tegmental area but was specific to one subregion, while the amygdala and pedunculopontine nucleus preferentially affiliated with a single network, showing a more homogeneous topography. Subregional connectivity of the globus pallidus interna, subthalamic nucleus, red nucleus, periaqueductal gray, and locus coeruleus did not resemble patterns of cortical network activity. Together, these finding describe potential mechanisms through which the subcortex participates in integrated and segregated information processing and shapes the spontaneous cognitive dynamics during rest.SIGNIFICANCE STATEMENT Despite the impact of subcortical dysfunction on brain health and cognition, large-scale functional mapping of subcortical structures severely lags behind that of the cortex. Recent developments in subcortical atlasing and imaging at ultra-high field provide new avenues for studying the intricate functional architecture of the human subcortex. With a fully data-driven analysis, we reveal subregional connectivity profiles of a large set of noncortical structures, including those rarely studied in fMRI research. The results have implications for understanding how the functional organization of the subcortex facilitates integrative processing through cross-network information convergence, paving the way for future work aimed at improving our knowledge of subcortical contributions to intrinsic brain dynamics and spontaneous cognition.
Collapse
Affiliation(s)
- Josephine M Groot
- Department of Psychology, UiT-Arctic University of Norway, Tromsø, 9037, Norway
- Integrative Model-based Cognitive Neuroscience research unit, University of Amsterdam, Amsterdam, 1001 NK, The Netherlands
| | - Steven Miletic
- Integrative Model-based Cognitive Neuroscience research unit, University of Amsterdam, Amsterdam, 1001 NK, The Netherlands
| | - Scott J S Isherwood
- Integrative Model-based Cognitive Neuroscience research unit, University of Amsterdam, Amsterdam, 1001 NK, The Netherlands
| | - Desmond H Y Tse
- Department of Neuropsychology and Psychopharmacology, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Sarah Habli
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, 8900, Norway
| | - Asta K Håberg
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, 8900, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim, 7006, Norway
| | - Birte U Forstmann
- Integrative Model-based Cognitive Neuroscience research unit, University of Amsterdam, Amsterdam, 1001 NK, The Netherlands
| | - Pierre-Louis Bazin
- Department of Psychology, UiT-Arctic University of Norway, Tromsø, 9037, Norway
- Departments of Neurophysics and Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, 04303, Germany
| | - Matthias Mittner
- Department of Psychology, UiT-Arctic University of Norway, Tromsø, 9037, Norway
| |
Collapse
|
7
|
Wolfs EML, van Lutterveld R, Varkevisser T, Klaus J, Geuze E, Schutter DJLG. Lower cerebello-cortical functional connectivity in veterans with reactive aggression symptoms: A pilot study. J Psychiatr Res 2023; 159:42-49. [PMID: 36657313 DOI: 10.1016/j.jpsychires.2023.01.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 12/05/2022] [Accepted: 01/10/2023] [Indexed: 01/13/2023]
Abstract
A significant number of veterans experience irritability and aggression symptoms as a result of being exposed to extremely stressful and life-threatening situations. In addition to the well-established involvement of the brain's cortico-subcortical circuit in aggression-related behaviours, a role of the deep cerebellar nuclei (DCN) in reactive aggression has been suggested. In the present study, seed-based resting-state functional connectivity between the DCN and cortico-subcortical areas was explored in veterans with and without reactive aggression symptoms. Nineteen male veterans with reactive aggression symptoms and twenty-two control veterans without reactive aggression symptoms underwent 3T resting-state functional MRI scans. Region-of-interest (ROI) analyses that included the amygdala, hypothalamus and periaqueductal grey as ROIs did not yield significant group-related differences in resting-state functional connectivity with the DCN. However, exploratory whole-brain analysis showed that veterans with reactive aggression symptoms exhibited lower functional connectivity between the DCN and the orbitofrontal cortex compared to control veterans. Our findings provide preliminary evidence for the possible involvement of a cerebello-prefrontal pathway in reactive aggression in male veterans.
Collapse
Affiliation(s)
- E M L Wolfs
- Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Heidelberglaan 1, 3584 CS, Utrecht, the Netherlands.
| | - R van Lutterveld
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands; Brain Research & Innovation Centre, Ministry of Defence, Lundlaan 1, 3584 EZ, Utrecht, the Netherlands
| | - T Varkevisser
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands; Brain Research & Innovation Centre, Ministry of Defence, Lundlaan 1, 3584 EZ, Utrecht, the Netherlands; Research and Documentation Centre, Ministry of Justice and Security, Koningskade 4, 2596 AA, The Hague, the Netherlands
| | - J Klaus
- Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Heidelberglaan 1, 3584 CS, Utrecht, the Netherlands
| | - E Geuze
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands; Brain Research & Innovation Centre, Ministry of Defence, Lundlaan 1, 3584 EZ, Utrecht, the Netherlands
| | - D J L G Schutter
- Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Heidelberglaan 1, 3584 CS, Utrecht, the Netherlands
| |
Collapse
|
8
|
Fang XT, Volpi T, Holmes SE, Esterlis I, Carson RE, Worhunsky PD. Linking resting-state network fluctuations with systems of coherent synaptic density: A multimodal fMRI and 11C-UCB-J PET study. Front Hum Neurosci 2023; 17:1124254. [PMID: 36908710 PMCID: PMC9995441 DOI: 10.3389/fnhum.2023.1124254] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 01/31/2023] [Indexed: 02/25/2023] Open
Abstract
Introduction: Resting-state network (RSN) connectivity is a widely used measure of the brain's functional organization in health and disease; however, little is known regarding the underlying neurophysiology of RSNs. The aim of the current study was to investigate associations between RSN connectivity and synaptic density assessed using the synaptic vesicle glycoprotein 2A radioligand 11C-UCB-J PET. Methods: Independent component analyses (ICA) were performed on resting-state fMRI and PET data from 34 healthy adult participants (16F, mean age: 46 ± 15 years) to identify a priori RSNs of interest (default-mode, right frontoparietal executive-control, salience, and sensorimotor networks) and select sources of 11C-UCB-J variability (medial prefrontal, striatal, and medial parietal). Pairwise correlations were performed to examine potential intermodal associations between the fractional amplitude of low-frequency fluctuations (fALFF) of RSNs and subject loadings of 11C-UCB-J source networks both locally and along known anatomical and functional pathways. Results: Greater medial prefrontal synaptic density was associated with greater fALFF of the anterior default-mode, posterior default-mode, and executive-control networks. Greater striatal synaptic density was associated with greater fALFF of the anterior default-mode and salience networks. Post-hoc mediation analyses exploring relationships between aging, synaptic density, and RSN activity revealed a significant indirect effect of greater age on fALFF of the anterior default-mode network mediated by the medial prefrontal 11C-UCB-J source. Discussion: RSN functional connectivity may be linked to synaptic architecture through multiple local and circuit-based associations. Findings regarding healthy aging, lower prefrontal synaptic density, and lower default-mode activity provide initial evidence of a neurophysiological link between RSN activity and local synaptic density, which may have relevance in neurodegenerative and psychiatric disorders.
Collapse
Affiliation(s)
- Xiaotian T. Fang
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - Tommaso Volpi
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - Sophie E. Holmes
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States
| | - Irina Esterlis
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States
- Department of Psychology, Yale University, New Haven, CT, United States
| | - Richard E. Carson
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | | |
Collapse
|
9
|
Wang MY, Korbmacher M, Eikeland R, Specht K. Deep brain imaging of three participants across 1 year: The Bergen breakfast scanning club project. Front Hum Neurosci 2022; 16:1021503. [PMID: 36325431 PMCID: PMC9620718 DOI: 10.3389/fnhum.2022.1021503] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 10/03/2022] [Indexed: 11/22/2023] Open
Abstract
Our understanding of the cognitive functions of the human brain has tremendously benefited from the population functional Magnetic Resonance Imaging (fMRI) studies in the last three decades. The reliability and replicability of the fMRI results, however, have been recently questioned, which has been named the replication crisis. Sufficient statistical power is fundamental to alleviate the crisis, by either "going big," leveraging big datasets, or by "going small," densely scanning several participants. Here we reported a "going small" project implemented in our department, the Bergen breakfast scanning club (BBSC) project, in which three participants were intensively scanned across a year. It is expected this kind of new data collection method can provide novel insights into the variability of brain networks, facilitate research designs and inference, and ultimately lead to the improvement of the reliability of the fMRI results.
Collapse
Affiliation(s)
- Meng-Yun Wang
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Haukeland University Hospital, Bergen, Norway
| | - Max Korbmacher
- Mohn Medical Imaging and Visualization Centre, Haukeland University Hospital, Bergen, Norway
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
| | - Rune Eikeland
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Haukeland University Hospital, Bergen, Norway
| | - Karsten Specht
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Haukeland University Hospital, Bergen, Norway
- Department of Education, UiT The Arctic University of Norway, Tromsø, Norway
| |
Collapse
|
10
|
Lee PL, Kuo CY, Wang PN, Chen LK, Lin CP, Chou KH, Chung CP. Regional rather than global brain age mediates cognitive function in cerebral small vessel disease. Brain Commun 2022; 4:fcac233. [PMID: 36196084 PMCID: PMC9525017 DOI: 10.1093/braincomms/fcac233] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 07/24/2022] [Accepted: 09/14/2022] [Indexed: 11/15/2022] Open
Abstract
The factors and mechanisms underlying the heterogeneous cognitive outcomes of cerebral small vessel disease are largely unknown. Brain biological age can be estimated by machine learning algorithms that use large brain MRI data sets to integrate and compute neuroimaging-derived age-related features. Predicted and chronological ages difference (brain-age gap) reflects advanced or delayed brain aging in an individual. The present study firstly reports the brain aging status of cerebral small vessel disease. In addition, we investigated whether global or certain regional brain age could mediate the cognitive functions in cerebral small vessel disease. Global and regional (400 cortical, 14 subcortical and 28 cerebellum regions of interest) brain-age prediction models were constructed using grey matter features from MRI of 1482 healthy individuals (age: 18–92 years). Predicted and chronological ages differences were obtained and then applied to non-stroke, non-demented individuals, aged ≥50 years, from another community-dwelling population (I-Lan Longitudinal Aging Study cohort). Among the 734 participants from the I-Lan Longitudinal Aging Study cohort, 124 were classified into the cerebral small vessel disease group. The cerebral small vessel disease group demonstrated significantly poorer performances in global cognitive, verbal memory and executive functions than that of non-cerebral small vessel disease group. Global brain-age gap was significantly higher in the cerebral small vessel disease (3.71 ± 7.60 years) than that in non-cerebral small vessel disease (−0.43 ± 9.47 years) group (P = 0.003, η2 = 0.012). There were 82 cerebral cortical, 3 subcortical and 4 cerebellar regions showing significantly different brain-age gap between the cerebral small vessel disease and non-cerebral small vessel disease groups. Global brain-age gap failed to mediate the relationship between cerebral small vessel disease and any of the cognitive domains. In 89 regions with increased brain-age gap in the cerebral small vessel disease group, seven regional brain-age gaps were able to show significant mediation effects in cerebral small vessel disease-related cognitive impairment (we set the statistical significance P < 0.05 uncorrected in 89 mediation models). Of these, the left thalamus and left hippocampus brain-age gap explained poorer global cognitive performance in cerebral small vessel disease. We demonstrated the interconnections between cerebral small vessel disease and brain age. Strategic brain aging, i.e. advanced brain aging in critical regions, may be involved in the pathophysiology of cerebral small vessel disease-related cognitive impairment. Regional rather than global brain-age gap could potentially serve as a biomarker for predicting heterogeneous cognitive outcomes in patients with cerebral small vessel disease.
Collapse
Affiliation(s)
- Pei-Lin Lee
- Institute of Neuroscience, National Yang Ming Chiao Tung University , Taipei , Taiwan
| | - Chen-Yuan Kuo
- Aging and Health Research Center, National Yang Ming Chiao Tung University , Taipei , Taiwan
| | - Pei-Ning Wang
- Aging and Health Research Center, National Yang Ming Chiao Tung University , Taipei , Taiwan
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital , Taipei , Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University , Taipei , Taiwan
- Center for Geriatric and Gerontology, Taipei Veterans General Hospital , Taipei , Taiwan
| | - Liang-Kung Chen
- Aging and Health Research Center, National Yang Ming Chiao Tung University , Taipei , Taiwan
- Center for Geriatric and Gerontology, Taipei Veterans General Hospital , Taipei , Taiwan
- Taipei Municipal Gan-Dau Hospital (managed by Taipei Veterans General Hospital) , Taipei , Taiwan
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University , Taipei , Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University , Taipei , Taiwan
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University , Taipei , Taiwan
| | - Kun-Hsien Chou
- Institute of Neuroscience, National Yang Ming Chiao Tung University , Taipei , Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University , Taipei , Taiwan
| | - Chih-Ping Chung
- Aging and Health Research Center, National Yang Ming Chiao Tung University , Taipei , Taiwan
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital , Taipei , Taiwan
| |
Collapse
|
11
|
Abstract
The federated learning technique (FL) supports the collaborative training of machine learning and deep learning models for edge network optimization. Although a complex edge network with heterogeneous devices having different constraints can affect its performance, this leads to a problem in this area. Therefore, some research can be seen to design new frameworks and approaches to improve federated learning processes. The purpose of this study is to provide an overview of the FL technique and its applicability in different domains. The key focus of the paper is to produce a systematic literature review of recent research studies that clearly describes the adoption of FL in edge networks. The search procedure was performed from April 2020 to May 2021 with a total initial number of papers being 7546 published in the duration of 2016 to 2020. The systematic literature synthesizes and compares the algorithms, models, and frameworks of federated learning. Additionally, we have presented the scope of FL applications in different industries and domains. It has been revealed after careful investigation of studies that 25% of the studies used FL in IoT and edge-based applications and 30% of studies implement the FL concept in the health industry, 10% for NLP, 10% for autonomous vehicles, 10% for mobile services, 10% for recommender systems, and 5% for FinTech. A taxonomy is also proposed on implementing FL for edge networks in different domains. Moreover, another novelty of this paper is that datasets used for the implementation of FL are discussed in detail to provide the researchers an overview of the distributed datasets, which can be used for employing FL techniques. Lastly, this study discusses the current challenges of implementing the FL technique. We have found that the areas of medical AI, IoT, edge systems, and the autonomous industry can adapt the FL in many of its sub-domains; however, the challenges these domains can encounter are statistical heterogeneity, system heterogeneity, data imbalance, resource allocation, and privacy.
Collapse
|
12
|
Bernard JA. Understanding cerebellar function through network perspectives: A review of resting-state connectivity of the cerebellum. PSYCHOLOGY OF LEARNING AND MOTIVATION 2022. [DOI: 10.1016/bs.plm.2022.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
13
|
Gratton C, Braga RM. Editorial overview: Deep imaging of the individual brain: past, practice, and promise. Curr Opin Behav Sci 2021. [DOI: 10.1016/j.cobeha.2021.06.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|