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Steinberg SN, King TZ. Within-Individual BOLD Signal Variability and its Implications for Task-Based Cognition: A Systematic Review. Neuropsychol Rev 2023:10.1007/s11065-023-09619-x. [PMID: 37889371 DOI: 10.1007/s11065-023-09619-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 09/08/2023] [Indexed: 10/28/2023]
Abstract
Within-individual blood oxygen level-dependent (BOLD) signal variability, intrinsic moment-to-moment signal fluctuations within a single individual in specific voxels across a given time course, is a relatively new metric recognized in the neuroimaging literature. Within-individual BOLD signal variability has been postulated to provide information beyond that provided by mean-based analysis. Synthesis of the literature using within-individual BOLD signal variability methodology to examine various cognitive domains is needed to understand how intrinsic signal fluctuations contribute to optimal performance. This systematic review summarizes and integrates this literature to assess task-based cognitive performance in healthy groups and few clinical groups. Included papers were published through October 17, 2022. Searches were conducted on PubMed and APA PsycInfo. Studies eligible for inclusion used within-individual BOLD signal variability methodology to examine BOLD signal fluctuations during task-based functional magnetic resonance imaging (fMRI) and/or examined relationships between task-based BOLD signal variability and out-of-scanner behavioral measure performance, were in English, and were empirical research studies. Data from each of the included 19 studies were extracted and study quality was systematically assessed. Results suggest that variability patterns for different cognitive domains across the lifespan (ages 7-85) may depend on task demands, measures, variability quantification method used, and age. As neuroimaging methods explore individual-level contributions to cognition, within-individual BOLD signal variability may be a meaningful metric that can inform understanding of neurocognitive performance. Further research in understudied domains/populations, and with consistent quantification methods/cognitive measures, will help conceptualize how intrinsic BOLD variability impacts cognitive abilities in healthy and clinical groups.
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Affiliation(s)
- Stephanie N Steinberg
- Department of Psychology, Georgia State University, Urban Life Building, 11th Floor, 140 Decatur St, Atlanta, GA, 30303, USA
| | - Tricia Z King
- Department of Psychology, Georgia State University, Urban Life Building, 11th Floor, 140 Decatur St, Atlanta, GA, 30303, USA.
- Neuroscience Institute, Georgia State University, Atlanta, GA, 30302, USA.
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Millar PR, Gordon BA, Luckett PH, Benzinger TLS, Cruchaga C, Fagan AM, Hassenstab JJ, Perrin RJ, Schindler SE, Allegri RF, Day GS, Farlow MR, Mori H, Nübling G, Bateman RJ, Morris JC, Ances BM. Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study. eLife 2023; 12:e81869. [PMID: 36607335 PMCID: PMC9988262 DOI: 10.7554/elife.81869] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 12/30/2022] [Indexed: 01/07/2023] Open
Abstract
Background Estimates of 'brain-predicted age' quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD) but has not been well explored in presymptomatic AD. Prior studies have typically modeled BAG with structural MRI, but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored. Methods We trained three models to predict age from FC, structural (S), or multimodal MRI (S+FC) in 390 amyloid-negative cognitively normal (CN/A-) participants (18-89 years old). In independent samples of 144 CN/A-, 154 CN/A+, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid and tau, as well as a global cognitive composite. Results All models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG was significantly reduced in CN/A+ participants compared to CN/A-. In CI participants only, elevated S-BAG and S+FC BAG were associated with more advanced AD pathology and lower cognitive performance. Conclusions Both FC-BAG and S-BAG are elevated in CI participants. However, FC and structural MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to presymptomatic AD pathology, while S-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model improves sensitivity to healthy age differences. Funding This work was supported by the National Institutes of Health (P01-AG026276, P01- AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, and U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimer's Association (SG-20-690363-DIAN).
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Affiliation(s)
- Peter R Millar
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Brian A Gordon
- Department of Radiology, Washington University in St. LouisSt LouisUnited States
| | - Patrick H Luckett
- Department of Neurosurgery, Washington University in St. LouisSt LouisUnited States
| | - Tammie LS Benzinger
- Department of Radiology, Washington University in St. LouisSt LouisUnited States
- Department of Neurosurgery, Washington University in St. LouisSt LouisUnited States
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St. LouisSt LouisUnited States
| | - Anne M Fagan
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Jason J Hassenstab
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Richard J Perrin
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
- Department of Pathology and Immunology, Washington University in St. LouisSt LouisUnited States
| | - Suzanne E Schindler
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Ricardo F Allegri
- Department of Cognitive Neurology, Institute for Neurological Research (FLENI)Buenos AiresArgentina
| | - Gregory S Day
- Department of Neurology, Mayo Clinic FloridaJacksonvilleUnited States
| | - Martin R Farlow
- Department of Neurology, Indiana University School of MedicineIndianapolisUnited States
| | - Hiroshi Mori
- Department of Clinical Neuroscience, Osaka Metropolitan University Medical School, Nagaoka Sutoku UniversityOsakaJapan
| | - Georg Nübling
- Department of Neurology, Ludwig-Maximilians UniversityMunichGermany
- German Center for Neurodegenerative DiseasesMunichGermany
| | - Randall J Bateman
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - John C Morris
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Beau M Ances
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
- Department of Radiology, Washington University in St. LouisSt LouisUnited States
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Liu X, Tyler LK, Rowe JB, Tsvetanov KA. Multimodal fusion analysis of functional, cerebrovascular and structural neuroimaging in healthy aging subjects. Hum Brain Mapp 2022; 43:5490-5508. [PMID: 35855641 PMCID: PMC9704789 DOI: 10.1002/hbm.26025] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/24/2022] [Accepted: 07/07/2022] [Indexed: 01/15/2023] Open
Abstract
Brain aging is a complex process that requires a multimodal approach. Neuroimaging can provide insights into brain morphology, functional organization, and vascular dynamics. However, most neuroimaging studies of aging have focused on each imaging modality separately, limiting the understanding of interrelations between processes identified by different modalities and their relevance to cognitive decline in aging. Here, we used a data-driven multimodal approach, linked independent component analysis (ICA), to jointly analyze magnetic resonance imaging (MRI) of grey matter volume, cerebrovascular, and functional network topographies in relation to measures of fluid intelligence. Neuroimaging and cognitive data from the Cambridge Centre for Ageing and Neuroscience study were used, with healthy participants aged 18-88 years (main dataset n = 215 and secondary dataset n = 433). Using linked ICA, functional network activities were characterized in independent components but not captured in the same component as structural and cerebrovascular patterns. Split-sample (n = 108/107) and out-of-sample (n = 433) validation analyses using linked ICA were also performed. Global grey matter volume with regional cerebrovascular changes and the right frontoparietal network activity were correlated with age-related and individual differences in fluid intelligence. This study presents the insights from linked ICA to bring together measurements from multiple imaging modalities, with independent and additive information. We propose that integrating multiple neuroimaging modalities allows better characterization of brain pattern variability and changes associated with healthy aging.
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Affiliation(s)
- Xulin Liu
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
| | - Lorraine K. Tyler
- The Centre for Speech, Language and the Brain, Department of PsychologyUniversity of CambridgeCambridgeUK
| | - Cam‐CAN
- Cambridge Centre for Ageing and Neuroscience (Cam‐CAN), MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
| | - James B. Rowe
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
- MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
| | - Kamen A. Tsvetanov
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
- The Centre for Speech, Language and the Brain, Department of PsychologyUniversity of CambridgeCambridgeUK
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4
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Zhang C, Beste C, Prochazkova L, Wang K, Speer SPH, Smidts A, Boksem MAS, Hommel B. Resting-state BOLD signal variability is associated with individual differences in metacontrol. Sci Rep 2022; 12:18425. [PMID: 36319653 PMCID: PMC9626555 DOI: 10.1038/s41598-022-21703-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 09/30/2022] [Indexed: 11/07/2022] Open
Abstract
Numerous studies demonstrate that moment-to-moment neural variability is behaviorally relevant and beneficial for tasks and behaviors requiring cognitive flexibility. However, it remains unclear whether the positive effect of neural variability also holds for cognitive persistence. Moreover, different brain variability measures have been used in previous studies, yet comparisons between them are lacking. In the current study, we examined the association between resting-state BOLD signal variability and two metacontrol policies (i.e., persistence vs. flexibility). Brain variability was estimated from resting-state fMRI (rsfMRI) data using two different approaches (i.e., Standard Deviation (SD), and Mean Square Successive Difference (MSSD)) and metacontrol biases were assessed by three metacontrol-sensitive tasks. Results showed that brain variability measured by SD and MSSD was highly positively related. Critically, higher variability measured by MSSD in the attention network, parietal and frontal network, frontal and ACC network, parietal and motor network, and higher variability measured by SD in the parietal and motor network, parietal and frontal network were associated with reduced persistence (or greater flexibility) of metacontrol (i.e., larger Stroop effect or worse RAT performance). These results show that the beneficial effect of brain signal variability on cognitive control depends on the metacontrol states involved. Our study highlights the importance of temporal variability of rsfMRI activity in understanding the neural underpinnings of cognitive control.
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Affiliation(s)
- Chenyan Zhang
- grid.5132.50000 0001 2312 1970Cognitive Psychology Unit, Leiden Institute for Brain and Cognition, Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Christian Beste
- grid.4488.00000 0001 2111 7257Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany ,grid.4488.00000 0001 2111 7257University Neuropsychology Center, Faculty of Medicine, TU Dresden, Dresden, Germany ,grid.410585.d0000 0001 0495 1805School of Psychology, Shandong Normal University, Jinan, China
| | - Luisa Prochazkova
- grid.5132.50000 0001 2312 1970Cognitive Psychology Unit, Leiden Institute for Brain and Cognition, Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Kangcheng Wang
- grid.410585.d0000 0001 0495 1805School of Psychology, Shandong Normal University, Jinan, China
| | - Sebastian P. H. Speer
- grid.419918.c0000 0001 2171 8263Social Brain Lab, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands ,grid.16750.350000 0001 2097 5006Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Ale Smidts
- grid.6906.90000000092621349Rotterdam School of Management, Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - Maarten A. S. Boksem
- grid.6906.90000000092621349Rotterdam School of Management, Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - Bernhard Hommel
- grid.5132.50000 0001 2312 1970Cognitive Psychology Unit, Leiden Institute for Brain and Cognition, Institute of Psychology, Leiden University, Leiden, The Netherlands ,grid.4488.00000 0001 2111 7257Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany ,grid.410585.d0000 0001 0495 1805School of Psychology, Shandong Normal University, Jinan, China
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Xiang J, Fan C, Wei J, Li Y, Wang B, Niu Y, Yang L, Lv J, Cui X. The Task Pre-Configuration Is Associated With Cognitive Performance Evidence From the Brain Synchrony. Front Comput Neurosci 2022; 16:883660. [PMID: 35603133 PMCID: PMC9120823 DOI: 10.3389/fncom.2022.883660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 04/05/2022] [Indexed: 11/13/2022] Open
Abstract
Although many resting state and task state characteristics have been studied, it is still unclear how the brain network switches from the resting state during tasks. The current theory shows that the brain is a complex dynamic system and synchrony is defined to measure brain activity. The study compared the changes of synchrony between the resting state and different task states in healthy young participants (N = 954). It also examined the ability to switch from the resting state to the task-general architecture of synchrony. We found that the synchrony increased significantly during the tasks. And the results showed that the brain has a task-general architecture of synchrony during different tasks. The main feature of task-based reasoning is that the increase in synchrony of high-order cognitive networks is significant, while the increase in synchrony of sensorimotor networks is relatively low. In addition, the high synchrony of high-order cognitive networks in the resting state can promote task switching effectively and the pre-configured participants have better cognitive performance, which shows that spontaneous brain activity and cognitive ability are closely related. These results revealed changes in the brain network configuration for switching between the resting state and task state, highlighting the consistent changes in the brain network between different tasks. Also, there was an important relationship between the switching ability and the cognitive performance.
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Rieck JR, DeSouza B, Baracchini G, Grady CL. Reduced modulation of BOLD variability as a function of cognitive load in healthy aging. Neurobiol Aging 2022; 112:215-230. [DOI: 10.1016/j.neurobiolaging.2022.01.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 01/29/2022] [Accepted: 01/31/2022] [Indexed: 12/15/2022]
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7
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Tsvetanov KA, Spindler LRB, Stamatakis EA, Newcombe VFJ, Lupson VC, Chatfield DA, Manktelow AE, Outtrim JG, Elmer A, Kingston N, Bradley JR, Bullmore ET, Rowe JB, Menon DK. Hospitalisation for COVID-19 predicts long lasting cerebrovascular impairment: A prospective observational cohort study. Neuroimage Clin 2022; 36:103253. [PMID: 36451358 PMCID: PMC9639388 DOI: 10.1016/j.nicl.2022.103253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 10/06/2022] [Accepted: 10/31/2022] [Indexed: 11/09/2022]
Abstract
Human coronavirus disease 2019 (COVID-19) due to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has multiple neurological consequences, but its long-term effect on brain health is still uncertain. The cerebrovascular consequences of COVID-19 may also affect brain health. We studied the chronic effect of COVID-19 on cerebrovascular health, in relation to acute severity, adverse clinical outcomes and in contrast to control group data. Here we assess cerebrovascular health in 45 patients six months after hospitalisation for acute COVID-19 using the resting state fluctuation amplitudes (RSFA) from functional magnetic resonance imaging, in relation to disease severity and in contrast with 42 controls. Acute COVID-19 severity was indexed by COVID-19 WHO Progression Scale, inflammatory and coagulatory biomarkers. Chronic widespread changes in frontoparietal RSFA were related to the severity of the acute COVID-19 episode. This relationship was not explained by chronic cardiorespiratory dysfunction, age, or sex. The level of cerebrovascular dysfunction was associated with cognitive, mental, and physical health at follow-up. The principal findings were consistent across univariate and multivariate approaches. The results indicate chronic cerebrovascular impairment following severe acute COVID-19, with the potential for long-term consequences on cognitive function and mental wellbeing.
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Affiliation(s)
- Kamen A Tsvetanov
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom; Department of Psychology, University of Cambridge, Cambridge, United Kingdom.
| | - Lennart R B Spindler
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom; Division of Anaesthesia, Department of Medicine, University Cambridge, Cambridge, United Kingdom
| | - Emmanuel A Stamatakis
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom; Division of Anaesthesia, Department of Medicine, University Cambridge, Cambridge, United Kingdom
| | - Virginia F J Newcombe
- Division of Anaesthesia, Department of Medicine, University Cambridge, Cambridge, United Kingdom; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, United Kingdom
| | - Victoria C Lupson
- Division of Anaesthesia, Department of Medicine, University Cambridge, Cambridge, United Kingdom; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, United Kingdom
| | - Doris A Chatfield
- Division of Anaesthesia, Department of Medicine, University Cambridge, Cambridge, United Kingdom
| | - Anne E Manktelow
- Division of Anaesthesia, Department of Medicine, University Cambridge, Cambridge, United Kingdom
| | - Joanne G Outtrim
- Division of Anaesthesia, Department of Medicine, University Cambridge, Cambridge, United Kingdom
| | - Anne Elmer
- Cambridge Clinical Research Centre, NIHR Clinical Research Facility, Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Nathalie Kingston
- NIHR BioResource, Cambridge University Hospitals NHS Foundation, Cambridge Biomedical Campus, Cambridge, United Kingdom; Department of Haematology, School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - John R Bradley
- NIHR BioResource, Cambridge University Hospitals NHS Foundation, Cambridge Biomedical Campus, Cambridge, United Kingdom; Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Edward T Bullmore
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, United Kingdom; Department of Psychiatry, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom; Medical Research Council Cognition and Brain Sciences Unit, Department of Psychiatry, Cambridge, United Kingdom
| | - David K Menon
- Division of Anaesthesia, Department of Medicine, University Cambridge, Cambridge, United Kingdom; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, United Kingdom; Cambridge Clinical Research Centre, NIHR Clinical Research Facility, Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, United Kingdom; Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom
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