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Roberts R, Wiebels K, Moreau D, Addis DR. Reassessing the Functional Significance of Blood Oxygen Level Dependent Signal Variability. J Cogn Neurosci 2024; 36:2281-2297. [PMID: 38940728 DOI: 10.1162/jocn_a_02202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024]
Abstract
BOLD signal variability (SDBOLD) has emerged as a unique measure of the adaptive properties of neural systems that facilitate fast, stable responding, based on claims that SDBOLD is independent of mean BOLD signal (meanBOLD) and is a powerful predictor of behavioral performance. We challenge these two claims. First, the apparent independence of SDBOLD and meanBOLD may reflect the presence of deactivations; we hypothesize that although SDBOLD may not be related to raw meanBOLD, it will be linearly related to "absolute" meanBOLD. Second, the observed relationship between SDBOLD and performance may be an artifact of using fixed-length trials longer than RTs. Such designs provide opportunities to toggle between on- and off-task states, and fast responders likely engage in more frequent state-switching, thereby artificially elevating SDBOLD. We hypothesize that SDBOLD will be higher and more strongly related to performance when using such fixed-length trials relative to self-paced trials that terminate upon a response. We test these two hypotheses in an fMRI study using blocks of fixed-length or self-paced trials. Results confirmed both hypotheses: (1) SDBOLD was robustly related with absolute meanBOLD, and (2) toggling between on- and off-task states during fixed-length trials reliably contributed to SDBOLD. Together, these findings suggest that a reappraisal of the functional significance of SDBOLD as a unique marker of cognitive performance is warranted.
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Affiliation(s)
| | | | | | - Donna Rose Addis
- The University of Auckland
- Rotman Research Institute, Baycrest
- University of Toronto
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2
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Tuovinen T, Häkli J, Rytty R, Krüger J, Korhonen V, Järvelä M, Helakari H, Kananen J, Nikkinen J, Veijola J, Remes AM, Kiviniemi V. The relative brain signal variability increases in the behavioral variant of frontotemporal dementia and Alzheimer's disease but not in schizophrenia. J Cereb Blood Flow Metab 2024:271678X241262583. [PMID: 38897598 DOI: 10.1177/0271678x241262583] [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] [Indexed: 06/21/2024]
Abstract
Overlapping symptoms between Alzheimer's disease (AD), behavioral variant of frontotemporal dementia (bvFTD), and schizophrenia (SZ) can lead to misdiagnosis and delays in appropriate treatment, especially in cases of early-onset dementia. To determine the potential of brain signal variability as a diagnostic tool, we assessed the coefficient of variation of the BOLD signal (CVBOLD) in 234 participants spanning bvFTD (n = 53), AD (n = 17), SZ (n = 23), and controls (n = 141). All underwent functional and structural MRI scans. Data unveiled a notable increase in CVBOLD in bvFTD patients across both datasets (local and international, p < 0.05), revealing an association with clinical scores (CDR and MMSE, r = 0.46 and r = -0.48, p < 0.0001). While SZ and control group demonstrated no significant differences, a comparative analysis between AD and bvFTD patients spotlighted elevated CVBOLD in the frontopolar cortices for the latter (p < 0.05). Furthermore, CVBOLD not only presented excellent diagnostic accuracy for bvFTD (AUC 0.78-0.95) but also showcased longitudinal repeatability. During a one-year follow-up, the CVBOLD levels increased by an average of 35% in the bvFTD group, compared to a 2% increase in the control group (p < 0.05). Our findings suggest that CVBOLD holds promise as a biomarker for bvFTD, offering potential for monitoring disease progression and differentiating bvFTD from AD and SZ.
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Affiliation(s)
- Timo Tuovinen
- Oulu Functional NeuroImaging, Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland
- Medical Research Center, Oulu University Hospital, The Wellbeing Services County of North Ostrobothnia, Oulu, Finland
| | - Jani Häkli
- Oulu Functional NeuroImaging, Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland
- Medical Research Center, Oulu University Hospital, The Wellbeing Services County of North Ostrobothnia, Oulu, Finland
| | - Riikka Rytty
- Oulu Functional NeuroImaging, Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland
- Neurology, Hyvinkää Hospital, The Wellbeing Services County of Central Uusimaa, Hyvinkää, Finland
| | - Johanna Krüger
- Medical Research Center, Oulu University Hospital, The Wellbeing Services County of North Ostrobothnia, Oulu, Finland
- Research Unit of Clinical Medicine, Neurology, University of Oulu, Oulu, Finland
- Neurology, Neurocenter, Oulu University Hospital, The Wellbeing Services County of North Ostrobothnia, Oulu, Finland
| | - Vesa Korhonen
- Oulu Functional NeuroImaging, Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland
- Medical Research Center, Oulu University Hospital, The Wellbeing Services County of North Ostrobothnia, Oulu, Finland
| | - Matti Järvelä
- Oulu Functional NeuroImaging, Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland
- Medical Research Center, Oulu University Hospital, The Wellbeing Services County of North Ostrobothnia, Oulu, Finland
| | - Heta Helakari
- Oulu Functional NeuroImaging, Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland
- Medical Research Center, Oulu University Hospital, The Wellbeing Services County of North Ostrobothnia, Oulu, Finland
| | - Janne Kananen
- Oulu Functional NeuroImaging, Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland
- Medical Research Center, Oulu University Hospital, The Wellbeing Services County of North Ostrobothnia, Oulu, Finland
- Clinical Neurophysiology, Oulu University Hospital, The Wellbeing Services County of North Ostrobothnia, Oulu, Finland
| | - Juha Nikkinen
- Medical Research Center, Oulu University Hospital, The Wellbeing Services County of North Ostrobothnia, Oulu, Finland
- Department of Oncology and Radiotherapy, Oulu University Hospital, The Wellbeing Services County of North Ostrobothnia, Oulu, Finland
| | - Juha Veijola
- Medical Research Center, Oulu University Hospital, The Wellbeing Services County of North Ostrobothnia, Oulu, Finland
- Research Unit of Clinical Medicine, Department of Psychiatry, University of Oulu, Oulu, Finland
- Department of Psychiatry, Oulu University Hospital, The Wellbeing Services County of North Ostrobothnia, Oulu, Finland
| | - Anne M Remes
- Research Unit of Clinical Medicine, Neurology, University of Oulu, Oulu, Finland
- Clinical Neurosciences, University of Helsinki, Helsinki, Finland
| | - Vesa Kiviniemi
- Oulu Functional NeuroImaging, Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland
- Medical Research Center, Oulu University Hospital, The Wellbeing Services County of North Ostrobothnia, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
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Schneider A, Weber S, Wyss A, Loukas S, Aybek S. BOLD signal variability as potential new biomarker of functional neurological disorders. Neuroimage Clin 2024; 43:103625. [PMID: 38833899 PMCID: PMC11179625 DOI: 10.1016/j.nicl.2024.103625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Revised: 05/16/2024] [Accepted: 05/29/2024] [Indexed: 06/06/2024]
Abstract
BACKGROUND Functional neurological disorder (FND) is a common neuropsychiatric condition with established diagnostic criteria and effective treatments but for which the underlying neuropathophysiological mechanisms remain incompletely understood. Recent neuroimaging studies have revealed FND as a multi-network brain disorder, unveiling alterations across limbic, self-agency, attentional/salience, and sensorimotor networks. However, the relationship between identified brain alterations and disease progression or improvement is less explored. METHODS This study included resting-state functional magnetic resonance imaging (fMRI) data from 79 patients with FND and 74 age and sex-matched healthy controls (HC). First, voxel-wise BOLD signal variability was computed for each participant and the group-wise difference was calculated. Second, we investigated the potential of BOLD signal variability to serve as a prognostic biomarker for clinical outcome in 47 patients who attended a follow-up measurement after eight months. RESULTS The results demonstrated higher BOLD signal variability in key networks, including the somatomotor, salience, limbic, and dorsal attention networks, in patients compared to controls. Longitudinal analysis revealed an increase in BOLD signal variability in the supplementary motor area (SMA) in FND patients who had an improved clinical outcome, suggesting SMA variability as a potential state biomarker. Additionally, higher BOLD signal variability in the left insula at baseline predicted a worse clinical outcome. CONCLUSION This study contributes to the understanding of FND pathophysiology, emphasizing the dynamic nature of neural activity and highlighting the potential of BOLD signal variability as a valuable research tool. The insula and SMA emerge as promising regions for further investigation as prognostic and state markers.
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Affiliation(s)
- Ayla Schneider
- Department of Neurology, Psychosomatic Medicine Unit, Inselspital Bern University Hospital, University of Bern, 3012 Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, 3010 Bern, Switzerland
| | - Samantha Weber
- Department of Neurology, Psychosomatic Medicine Unit, Inselspital Bern University Hospital, University of Bern, 3012 Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, 3010 Bern, Switzerland; University of Zurich, Psychiatric University Hospital Zurich, Department of Psychiatry, Psychotherapy and Psychosomatics, 8032 Zurich, Switzerland
| | - Anna Wyss
- Department of Neurology, Psychosomatic Medicine Unit, Inselspital Bern University Hospital, University of Bern, 3012 Bern, Switzerland; Graduate School for Health Sciences (GHS), University of Bern, 3006 Bern, Switzerland
| | - Serafeim Loukas
- Department of Neurology, Psychosomatic Medicine Unit, Inselspital Bern University Hospital, University of Bern, 3012 Bern, Switzerland; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Selma Aybek
- Department of Neurology, Psychosomatic Medicine Unit, Inselspital Bern University Hospital, University of Bern, 3012 Bern, Switzerland; Faculty of Science and Medicine, University of Fribourg, 1700 Fribourg, Switzerland.
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Choi M, Kim HC, Youn I, Lee SJ, Lee JH. Use of functional magnetic resonance imaging to identify cortical loci for lower limb movements and their efficacy for individuals after stroke. J Neuroeng Rehabil 2024; 21:58. [PMID: 38627779 PMCID: PMC11020805 DOI: 10.1186/s12984-024-01319-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 01/29/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Identification of cortical loci for lower limb movements for stroke rehabilitation is crucial for better rehabilitation outcomes via noninvasive brain stimulation by targeting the fine-grained cortical loci of the movements. However, identification of the cortical loci for lower limb movements using functional MRI (fMRI) is challenging due to head motion and difficulty in isolating different types of movement. Therefore, we developed a custom-made MR-compatible footplate and leg cushion to identify the cortical loci for lower limb movements and conducted multivariate analysis on the fMRI data. We evaluated the validity of the identified loci using both fMRI and behavioral data, obtained from healthy participants as well as individuals after stroke. METHODS We recruited 33 healthy participants who performed four different lower limb movements (ankle dorsiflexion, ankle rotation, knee extension, and toe flexion) using our custom-built equipment while fMRI data were acquired. A subgroup of these participants (Dataset 1; n = 21) was used to identify the cortical loci associated with each lower limb movement in the paracentral lobule (PCL) using multivoxel pattern analysis and representational similarity analysis. The identified cortical loci were then evaluated using the remaining healthy participants (Dataset 2; n = 11), for whom the laterality index (LI) was calculated for each lower limb movement using the cortical loci identified for the left and right lower limbs. In addition, we acquired a dataset from 15 individuals with chronic stroke for regression analysis using the LI and the Fugl-Meyer Assessment (FMA) scale. RESULTS The cortical loci associated with the lower limb movements were hierarchically organized in the medial wall of the PCL following the cortical homunculus. The LI was clearer using the identified cortical loci than using the PCL. The healthy participants (mean ± standard deviation: 0.12 ± 0.30; range: - 0.63 to 0.91) exhibited a higher contralateral LI than the individuals after stroke (0.07 ± 0.47; - 0.83 to 0.97). The corresponding LI scores for individuals after stroke showed a significant positive correlation with the FMA scale for paretic side movement in ankle dorsiflexion (R2 = 0.33, p = 0.025) and toe flexion (R2 = 0.37, p = 0.016). CONCLUSIONS The cortical loci associated with lower limb movements in the PCL identified in healthy participants were validated using independent groups of healthy participants and individuals after stroke. Our findings suggest that these cortical loci may be beneficial for the neurorehabilitation of lower limb movement in individuals after stroke, such as in developing effective rehabilitation interventions guided by the LI scores obtained for neuronal activations calculated from the identified cortical loci across the paretic and non-paretic sides of the brain.
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Affiliation(s)
- Minseok Choi
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Hyun-Chul Kim
- Department of Artificial Intelligence, Kyungpook National University, Daegu, South Korea
| | - Inchan Youn
- Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology, Seoul, South Korea
| | - Song Joo Lee
- Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology, Seoul, South Korea.
| | - Jong-Hwan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea.
- Interdisciplinary Program in Precision Public Health, Korea University, Seoul, South Korea.
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Boston, Massachusetts, USA.
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Golestani AM, Chen JJ. Comparing data-driven physiological denoising approaches for resting-state fMRI: implications for the study of aging. Front Neurosci 2024; 18:1223230. [PMID: 38379761 PMCID: PMC10876882 DOI: 10.3389/fnins.2024.1223230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 01/17/2024] [Indexed: 02/22/2024] Open
Abstract
Introduction Physiological nuisance contributions by cardiac and respiratory signals have a significant impact on resting-state fMRI data quality. As these physiological signals are often not recorded, data-driven denoising methods are commonly used to estimate and remove physiological noise from fMRI data. To investigate the efficacy of these denoising methods, one of the first steps is to accurately capture the cardiac and respiratory signals, which requires acquiring fMRI data with high temporal resolution. Methods In this study, we used such high-temporal resolution fMRI data to evaluate the effectiveness of several data-driven denoising methods, including global-signal regression (GSR), white matter and cerebrospinal fluid regression (WM-CSF), anatomical (aCompCor) and temporal CompCor (tCompCor), ICA-AROMA. Our analysis focused on the consequence of changes in low-frequency, cardiac and respiratory signal power, as well as age-related differences in terms of functional connectivity (fcMRI). Results Our results confirm that the ICA-AROMA and GSR removed the most physiological noise but also more low-frequency signals. These methods are also associated with substantially lower age-related fcMRI differences. On the other hand, aCompCor and tCompCor appear to be better at removing high-frequency physiological signals but not low-frequency signal power. These methods are also associated with relatively higher age-related fcMRI differences, whether driven by neuronal signal or residual artifact. These results were reproduced in data downsampled to represent conventional fMRI sampling frequency. Lastly, methods differ in performance depending on the age group. Discussion While this study cautions direct comparisons of fcMRI results based on different denoising methods in the study of aging, it also enhances the understanding of different denoising methods in broader fcMRI applications.
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Affiliation(s)
- Ali M. Golestani
- Department of Physics and Astronomy, University of Calgary, Calgary, AB, Canada
- Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - J. Jean Chen
- Rotman Research Institute at Baycrest, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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Eby AL, Remedios LW, Kim ME, Li M, Gao Y, Gore JC, Schilling KG, Landman BA. Identification of functional white matter networks in BOLD fMRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.08.556881. [PMID: 38328148 PMCID: PMC10849525 DOI: 10.1101/2023.09.08.556881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
White matter signals in resting state blood oxygen level dependent functional magnetic resonance (BOLD-fMRI) have been largely discounted, yet there is growing evidence that these signals are indicative of brain activity. Understanding how these white matter signals capture function can provide insight into brain physiology. Moreover, functional signals could potentially be used as early markers for neurological changes, such as in Alzheimer's Disease. To investigate white matter brain networks, we leveraged the OASIS-3 dataset to extract white matter signals from resting state BOLD-FMRI data on 711 subjects. The imaging was longitudinal with a total of 2,026 images. Hierarchical clustering was performed to investigate clusters of voxel-level correlations on the timeseries data. The stability of clusters was measured with the average Dice coefficients on two different cross fold validations. The first validated the stability between scans, and the second validated the stability between subject populations. Functional clusters at hierarchical levels 4, 9, 13, 18, and 24 had local maximum stability, suggesting better clustered white matter. In comparison with JHU-DTI-SS Type-I Atlas defined regions, clusters at lower hierarchical levels identified well defined anatomical lobes. At higher hierarchical levels, functional clusters mapped motor and memory functional regions, identifying 50.00%, 20.00%, 27.27%, and 35.14% of the frontal, occipital, parietal, and temporal lobe regions respectively.
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Ao Y, Yang C, Drewes J, Jiang M, Huang L, Jing X, Northoff G, Wang Y. Spatiotemporal dedifferentiation of the global brain signal topography along the adult lifespan. Hum Brain Mapp 2023; 44:5906-5918. [PMID: 37800366 PMCID: PMC10619384 DOI: 10.1002/hbm.26484] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 08/17/2023] [Accepted: 08/28/2023] [Indexed: 10/07/2023] Open
Abstract
Age-related variations in many regions and/or networks of the human brain have been uncovered using resting-state functional magnetic resonance imaging. However, these findings did not account for the dynamical effect the brain's global activity (global signal [GS]) causes on local characteristics, which is measured by GS topography. To address this gap, we tested GS topography including its correlation with age using a large-scale cross-sectional adult lifespan dataset (n = 492). Both GS topography and its variation with age showed frequency-specific patterns, reflecting the spatiotemporal characteristics of the dynamic change of GS topography with age. A general trend toward dedifferentiation of GS topography with age was observed in both spatial (i.e., less differences of GS between different regions) and temporal (i.e., less differences of GS between different frequencies) dimensions. Further, methodological control analyses suggested that although most age-related dedifferentiation effects remained across different preprocessing strategies, some were triggered by neuro-vascular coupling and physiological noises. Together, these results provide the first evidence for age-related effects on global brain activity and its topographic-dynamic representation in terms of spatiotemporal dedifferentiation.
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Affiliation(s)
- Yujia Ao
- Institute of Brain and Psychological SciencesSichuan Normal UniversityChengduChina
- Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health Research, Faculty of MedicineUniversity of OttawaOttawaOntarioCanada
| | - Chengxiao Yang
- Institute of Brain and Psychological SciencesSichuan Normal UniversityChengduChina
| | - Jan Drewes
- Institute of Brain and Psychological SciencesSichuan Normal UniversityChengduChina
| | - Muliang Jiang
- First Affiliated HospitalGuangxi Medical UniversityNanningChina
| | - Lihui Huang
- Institute of Brain and Psychological SciencesSichuan Normal UniversityChengduChina
| | - Xiujuan Jing
- Institute of Brain and Psychological SciencesSichuan Normal UniversityChengduChina
| | - Georg Northoff
- Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health Research, Faculty of MedicineUniversity of OttawaOttawaOntarioCanada
| | - Yifeng Wang
- Institute of Brain and Psychological SciencesSichuan Normal UniversityChengduChina
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Lavanga M, Stumme J, Yalcinkaya BH, Fousek J, Jockwitz C, Sheheitli H, Bittner N, Hashemi M, Petkoski S, Caspers S, Jirsa V. The virtual aging brain: Causal inference supports interhemispheric dedifferentiation in healthy aging. Neuroimage 2023; 283:120403. [PMID: 37865260 DOI: 10.1016/j.neuroimage.2023.120403] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 09/20/2023] [Accepted: 10/05/2023] [Indexed: 10/23/2023] Open
Abstract
The mechanisms of cognitive decline and its variability during healthy aging are not fully understood, but have been associated with reorganization of white matter tracts and functional brain networks. Here, we built a brain network modeling framework to infer the causal link between structural connectivity and functional architecture and the consequent cognitive decline in aging. By applying in-silico interhemispheric degradation of structural connectivity, we reproduced the process of functional dedifferentiation during aging. Thereby, we found the global modulation of brain dynamics by structural connectivity to increase with age, which was steeper in older adults with poor cognitive performance. We validated our causal hypothesis via a deep-learning Bayesian approach. Our results might be the first mechanistic demonstration of dedifferentiation during aging leading to cognitive decline.
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Affiliation(s)
- Mario Lavanga
- Institut de Neurosciences des Systèmes (INS), Inserm, Aix-Marseille University, Marseille 13005, France
| | - Johanna Stumme
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Bahar Hazal Yalcinkaya
- Institut de Neurosciences des Systèmes (INS), Inserm, Aix-Marseille University, Marseille 13005, France
| | - Jan Fousek
- Institut de Neurosciences des Systèmes (INS), Inserm, Aix-Marseille University, Marseille 13005, France
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Hiba Sheheitli
- Institut de Neurosciences des Systèmes (INS), Inserm, Aix-Marseille University, Marseille 13005, France
| | - Nora Bittner
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Meysam Hashemi
- Institut de Neurosciences des Systèmes (INS), Inserm, Aix-Marseille University, Marseille 13005, France
| | - Spase Petkoski
- Institut de Neurosciences des Systèmes (INS), Inserm, Aix-Marseille University, Marseille 13005, France
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes (INS), Inserm, Aix-Marseille University, Marseille 13005, France.
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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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10
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Liu X, Tyler LK, Cam-Can, Davis SW, Rowe JB, Tsvetanov KA. Cognition's dependence on functional network integrity with age is conditional on structural network integrity. Neurobiol Aging 2023; 129:195-208. [PMID: 37392579 DOI: 10.1016/j.neurobiolaging.2023.06.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 05/29/2023] [Accepted: 06/02/2023] [Indexed: 07/03/2023]
Abstract
Maintaining good cognitive function is crucial for well-being across the lifespan. We proposed that the degree of cognitive maintenance is determined by the functional interactions within and between large-scale brain networks. Such connectivity can be represented by the white matter architecture of structural brain networks that shape intrinsic neuronal activity into integrated and distributed functional networks. We explored how the function-structure connectivity convergence, and the divergence of functional connectivity from structural connectivity, contribute to the maintenance of cognitive function across the adult lifespan. Multivariate analyses were used to investigate the relationship between function-structure connectivity convergence and divergence with multivariate cognitive profiles, respectively. Cognitive function was increasingly dependent on function-structure connectivity convergence as age increased. The dependency of cognitive function on connectivity was particularly strong for high-order cortical networks and subcortical networks. The results suggest that brain functional network integrity sustains cognitive functions in old age, as a function of the integrity of the brain's structural connectivity.
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Affiliation(s)
- Xulin Liu
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
| | - Lorraine K Tyler
- The Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge, UK
| | - Cam-Can
- Cambridge Centre for Ageing and Neuroscience (Cam-CAN), MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Simon W Davis
- Department of Neurology, Duke University, School of Medicine, Durham, NC, USA
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Kamen A Tsvetanov
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; The Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge, UK
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Williams RJ, Specht JL, Mazerolle EL, Lebel RM, MacDonald ME, Pike GB. Correspondence between BOLD fMRI task response and cerebrovascular reactivity across the cerebral cortex. Front Physiol 2023; 14:1167148. [PMID: 37228813 PMCID: PMC10203231 DOI: 10.3389/fphys.2023.1167148] [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: 02/16/2023] [Accepted: 04/24/2023] [Indexed: 05/27/2023] Open
Abstract
BOLD sensitivity to baseline perfusion and blood volume is a well-acknowledged fMRI confound. Vascular correction techniques based on cerebrovascular reactivity (CVR) might reduce variance due to baseline cerebral blood volume, however this is predicated on an invariant linear relationship between CVR and BOLD signal magnitude. Cognitive paradigms have relatively low signal, high variance and involve spatially heterogenous cortical regions; it is therefore unclear whether the BOLD response magnitude to complex paradigms can be predicted by CVR. The feasibility of predicting BOLD signal magnitude from CVR was explored in the present work across two experiments using different CVR approaches. The first utilized a large database containing breath-hold BOLD responses and 3 different cognitive tasks. The second experiment, in an independent sample, calculated CVR using the delivery of a fixed concentration of carbon dioxide and a different cognitive task. An atlas-based regression approach was implemented for both experiments to evaluate the shared variance between task-invoked BOLD responses and CVR across the cerebral cortex. Both experiments found significant relationships between CVR and task-based BOLD magnitude, with activation in the right cuneus (R 2 = 0.64) and paracentral gyrus (R 2 = 0.71), and the left pars opercularis (R 2 = 0.67), superior frontal gyrus (R 2 = 0.62) and inferior parietal cortex (R 2 = 0.63) strongly predicted by CVR. The parietal regions bilaterally were highly consistent, with linear regressions significant in these regions for all four tasks. Group analyses showed that CVR correction increased BOLD sensitivity. Overall, this work suggests that BOLD signal response magnitudes to cognitive tasks are predicted by CVR across different regions of the cerebral cortex, providing support for the use of correction based on baseline vascular physiology.
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Affiliation(s)
- Rebecca J. Williams
- Faculty of Health, School of Human Services, Charles Darwin University, Darwin, NT, Australia
| | - Jacinta L. Specht
- Department of Clinical Neuroscience, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Erin L. Mazerolle
- Departments of Psychology and Computer Science, St. Francis Xavier University, Antigonish, NS, Canada
| | - R. Marc Lebel
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- GE HealthCare, Calgary, AB, Canada
| | - M. Ethan MacDonald
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
- Department of Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
| | - G. Bruce Pike
- Department of Clinical Neuroscience, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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12
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King DLO, Henson RN, Kievit R, Wolpe N, Brayne C, Tyler LK, Rowe JB, Tsvetanov KA. Distinct components of cardiovascular health are linked with age-related differences in cognitive abilities. Sci Rep 2023; 13:978. [PMID: 36653428 PMCID: PMC9849401 DOI: 10.1038/s41598-022-27252-1] [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: 04/22/2022] [Accepted: 12/28/2022] [Indexed: 01/19/2023] Open
Abstract
Cardiovascular ageing contributes to cognitive impairment. However, the unique and synergistic contributions of multiple cardiovascular factors to cognitive function remain unclear because they are often condensed into a single composite score or examined in isolation. We hypothesized that vascular risk factors, electrocardiographic features and blood pressure indices reveal multiple latent vascular factors, with independent contributions to cognition. In a population-based deep-phenotyping study (n = 708, age 18-88), path analysis revealed three latent vascular factors dissociating the autonomic nervous system response from two components of blood pressure. These three factors made unique and additive contributions to the variability in crystallized and fluid intelligence. The discrepancy in fluid relative to crystallized intelligence, indicative of cognitive decline, was associated with a latent vascular factor predominantly expressing pulse pressure. This suggests that higher pulse pressure is associated with cognitive decline from expected performance. The effect was stronger in older adults. Controlling pulse pressure may help to preserve cognition, particularly in older adults. Our findings highlight the need to better understand the multifactorial nature of vascular aging.
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Affiliation(s)
- Deborah L O King
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0SP, UK.
- Department of Psychology, Centre for Speech, Language and the Brain, University of Cambridge, Cambridge, CB23 6HT, UK.
| | - Richard N Henson
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 2QQ, UK
- Medical Research Council Cognition and Brain Sciences Unit, Cambridge, CB2 7EF, UK
- Cambridge Centre for Ageing and Neuroscience (Cam-CAN), University of Cambridge and MRC Cognition and Brain Sciences Unit, Cambridge, CB2 7EF, UK
| | - Rogier Kievit
- Donders Research Institute for Brain, Cognition and Behaviour, Radboud University, 6525 AJ, Nijmegen, The Netherlands
| | - Noham Wolpe
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 2QQ, UK
- Department of Physical Therapy, The Stanley Steer School of Health Professions, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Carol Brayne
- Cambridge Public Health, Cambridge Public Health, University of Cambridge, Cambridge, CB2 0SR, UK
| | - Lorraine K Tyler
- Department of Psychology, Centre for Speech, Language and the Brain, University of Cambridge, Cambridge, CB23 6HT, UK
- Cambridge Centre for Ageing and Neuroscience (Cam-CAN), University of Cambridge and MRC Cognition and Brain Sciences Unit, Cambridge, CB2 7EF, UK
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0SP, UK
- Medical Research Council Cognition and Brain Sciences Unit, Cambridge, CB2 7EF, UK
- Cambridge Centre for Ageing and Neuroscience (Cam-CAN), University of Cambridge and MRC Cognition and Brain Sciences Unit, Cambridge, CB2 7EF, UK
| | - Kamen A Tsvetanov
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0SP, UK
- Department of Psychology, Centre for Speech, Language and the Brain, University of Cambridge, Cambridge, CB23 6HT, UK
- Cambridge Centre for Ageing and Neuroscience (Cam-CAN), University of Cambridge and MRC Cognition and Brain Sciences Unit, Cambridge, CB2 7EF, UK
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13
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Deery HA, Di Paolo R, Moran C, Egan GF, Jamadar SD. The older adult brain is less modular, more integrated, and less efficient at rest: A systematic review of large-scale resting-state functional brain networks in aging. Psychophysiology 2023; 60:e14159. [PMID: 36106762 PMCID: PMC10909558 DOI: 10.1111/psyp.14159] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 07/22/2022] [Accepted: 07/25/2022] [Indexed: 12/23/2022]
Abstract
The literature on large-scale resting-state functional brain networks across the adult lifespan was systematically reviewed. Studies published between 1986 and July 2021 were retrieved from PubMed. After reviewing 2938 records, 144 studies were included. Results on 11 network measures were summarized and assessed for certainty of the evidence using a modified GRADE method. The evidence provides high certainty that older adults display reduced within-network and increased between-network functional connectivity. Older adults also show lower segregation, modularity, efficiency and hub function, and decreased lateralization and a posterior to anterior shift at rest. Higher-order functional networks reliably showed age differences, whereas primary sensory and motor networks showed more variable results. The inflection point for network changes is often the third or fourth decade of life. Age effects were found with moderate certainty for within- and between-network altered patterns and speed of dynamic connectivity. Research on within-subject bold variability and connectivity using glucose uptake provides low certainty of age differences but warrants further study. Taken together, these age-related changes may contribute to the cognitive decline often seen in older adults.
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Affiliation(s)
- Hamish A. Deery
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Robert Di Paolo
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Chris Moran
- Peninsula Clinical School, Central Clinical SchoolMonash UniversityFrankstonVictoriaAustralia
- Department of Geriatric MedicinePeninsula HealthFrankstonVictoriaAustralia
| | - Gary F. Egan
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
- Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneVictoriaAustralia
| | - Sharna D. Jamadar
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
- Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneVictoriaAustralia
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14
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Liu ES, Hou M, Koen JD, Rugg MD. Effects of age on the neural correlates of encoding source and item information: An fMRI study. Neuropsychologia 2022; 177:108415. [PMID: 36343706 PMCID: PMC9729408 DOI: 10.1016/j.neuropsychologia.2022.108415] [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: 06/11/2022] [Revised: 09/27/2022] [Accepted: 11/01/2022] [Indexed: 11/06/2022]
Abstract
The effects of age on encoding-related neural activity predictive of accurate item and source memory judgments were examined with fMRI, with an a priori focus of the inferior frontal gyrus (IFG) and hippocampus. During a scanned study phase, young and older adults viewed a series of pictures of objects and made one of two judgments on each object. At test, which occurred outside of the scanner, an 'old/new' judgment on each test item was followed, for those items endorsed old, by a source judgment querying the study task. Neural activity predictive of accurate subsequent item and source memory judgments was identified in bilateral IFG, several other cortical regions and bilateral hippocampus. Cortical effects were graded in the young group (source > item > miss) but predicted item memory only in the older group. Hippocampal effects exclusively predicted source memory, and the magnitude of these effects did not reliably differ between the age groups. In the older group only, IFG and hippocampal encoding effects were positively correlated across participants with memory performance. Similar findings were evident in the extra-IFG regions demonstrating encoding effects. With the exception of the age-dependent relationship identified for hippocampal encoding effects, the present findings are broadly consistent with those from prior aging studies that employed verbal memoranda and tests of associative recognition. Thus, they extend these prior findings to include non-verbal materials and a different operationalization of episodic recollection. Additionally, the present findings suggest that the sensitivity in older adults of IFG encoding effects to subsequent memory performance reflects a more general tendency for cortical encoding effects to predict memory performance in this age group.
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Affiliation(s)
- E Song Liu
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, USA
| | - Mingzhu Hou
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, USA.
| | - Joshua D Koen
- Department of Psychology, University of Notre Dame, USA
| | - Michael D Rugg
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, USA; School of Psychology, University of East Anglia, UK
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15
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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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16
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Li H, Jiang S, Dong D, Hu J, He C, Hou C, He H, Huang H, Shen D, Pei H, Zhao G, Dong L, Yao D, Luo C. Vascular feature as a modulator of the aging brain. Cereb Cortex 2022; 32:5609-5621. [PMID: 35174854 DOI: 10.1093/cercor/bhac039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/17/2022] [Accepted: 01/18/2022] [Indexed: 01/25/2023] Open
Abstract
The cerebral functional reorganization and declined cognitive function of aging might associate with altered vascular features. Here, we explored the altered cerebral hierarchical functional network of 2 conditions (task-free and naturalistic stimuli) in older adults and its relationship with vascular features (systemic microvascular and perfusion features, measured by magnetic resonance imaging) and behavior. Using cerebral gradient analysis, we found that compressive gradient of resting-state mainly located on the primary sensory-motor system and transmodal regions in aging, and further compress in these regions under the continuous naturalistic stimuli. Combining cerebral functional gradient, vascular features, and cognitive performance, the more compressive gradient in the resting-state, the worse vascular state, the lower cognitive function in older adults. Further modulation analysis demonstrated that both vascular features can regulate the relationship between gradient scores in the insula and behavior. Interestingly, systemic microvascular oxygenation also can modulate the relationship between cerebral gradient and cerebral perfusion. Furthermore, the less alteration of the compressive gradient with naturalistic stimuli came with lower cognitive function. Our findings demonstrated that the altered cerebral hierarchical functional structure in aging was linked with changed vascular features and behavior, offering a new framework for studying the physiological mechanism of functional connectivity in aging.
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Affiliation(s)
- Hechun Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
| | - Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- Radiology Department, Chengdu Mental Health Center, Chengdu 610036, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, P. R. China
| | - Debo Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Jian Hu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
| | - Chuan He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
| | - Changyue Hou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- Radiology Department, Chengdu Mental Health Center, Chengdu 610036, P. R. China
| | - Huan Huang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
| | - Dai Shen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
| | - Haonan Pei
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
| | - Guocheng Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- Radiology Department, Chengdu Mental Health Center, Chengdu 610036, P. R. China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, P. R. China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, P. R. China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Cancer Hospital affiliate to School of Medicine, University of Electronic Science and Technology of China, Chengdu 610042, P. R. China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, P. R. China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Cancer Hospital affiliate to School of Medicine, University of Electronic Science and Technology of China, Chengdu 610042, P. R. China
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17
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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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18
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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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Scheel N, Keller JN, Binder EF, Vidoni ED, Burns JM, Thomas BP, Stowe AM, Hynan LS, Kerwin DR, Vongpatanasin W, Rossetti H, Cullum CM, Zhang R, Zhu DC. Evaluation of noise regression techniques in resting-state fMRI studies using data of 434 older adults. Front Neurosci 2022; 16:1006056. [PMID: 36340768 PMCID: PMC9626831 DOI: 10.3389/fnins.2022.1006056] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 09/27/2022] [Indexed: 01/24/2023] Open
Abstract
Subject motion is a well-known confound in resting-state functional MRI (rs-fMRI) and the analysis of functional connectivity. Consequently, several clean-up strategies have been established to minimize the impact of subject motion. Physiological signals in response to cardiac activity and respiration are also known to alter the apparent rs-fMRI connectivity. Comprehensive comparisons of common noise regression techniques showed that the "Independent Component Analysis based strategy for Automatic Removal of Motion Artifacts" (ICA-AROMA) was a preferred pre-processing technique for teenagers and adults. However, motion and physiological noise characteristics may differ substantially for older adults. Here, we present a comprehensive comparison of noise-regression techniques for older adults from a large multi-site clinical trial of exercise and intensive pharmacological vascular risk factor reduction. The Risk Reduction for Alzheimer's Disease (rrAD) trial included hypertensive older adults (60-84 years old) at elevated risk of developing Alzheimer's Disease (AD). We compared the performance of censoring, censoring combined with global signal regression, non-aggressive and aggressive ICA-AROMA, as well as the Spatially Organized Component Klassifikator (SOCK) on the rs-fMRI baseline scans from 434 rrAD subjects. All techniques were rated based on network reproducibility, network identifiability, edge activity, spatial smoothness, and loss of temporal degrees of freedom (tDOF). We found that non-aggressive ICA-AROMA did not perform as well as the other four techniques, which performed table with marginal differences, demonstrating the validity of these techniques. Considering reproducibility as the most important factor for longitudinal studies, given low false-positive rates and a better preserved, more cohesive temporal structure, currently aggressive ICA-AROMA is likely the most suitable noise regression technique for rs-fMRI studies of older adults.
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Affiliation(s)
- Norman Scheel
- Department of Radiology, Michigan State University, East Lansing, MI, United States
| | - Jeffrey N. Keller
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | - Ellen F. Binder
- Division of Geriatrics and Nutritional Science, Washington University School of Medicine, St. Louis, MO, United States
| | - Eric D. Vidoni
- Alzheimer’s Disease Center, University of Kansas, Fairway, KS, United States
| | - Jeffrey M. Burns
- Alzheimer’s Disease Center, University of Kansas, Fairway, KS, United States
| | - Binu P. Thomas
- UT Southwestern Medical Center, Dallas, TX, United States
| | - Ann M. Stowe
- Department of Neurology, University of Kentucky, Lexington, KY, United States
| | - Linda S. Hynan
- UT Southwestern Medical Center, Dallas, TX, United States
| | - Diana R. Kerwin
- Texas Health Presbyterian Hospital, Dallas, TX, United States
| | | | - Heidi Rossetti
- UT Southwestern Medical Center, Dallas, TX, United States
| | | | - Rong Zhang
- UT Southwestern Medical Center, Dallas, TX, United States,Texas Health Presbyterian Hospital, Dallas, TX, United States
| | - David C. Zhu
- Department of Radiology, Michigan State University, East Lansing, MI, United States,*Correspondence: David C. Zhu,
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20
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Mills-Finnerty C, Frangos E, Allen K, Komisaruk B, Wise N. Functional Magnetic Resonance Imaging Studies in Sexual Medicine: A Primer. J Sex Med 2022; 19:1073-1089. [DOI: 10.1016/j.jsxm.2022.03.217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 01/27/2022] [Accepted: 03/04/2022] [Indexed: 11/17/2022]
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21
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Liu ZQ, Vázquez-Rodríguez B, Spreng RN, Bernhardt BC, Betzel RF, Misic B. Time-resolved structure-function coupling in brain networks. Commun Biol 2022; 5:532. [PMID: 35654886 PMCID: PMC9163085 DOI: 10.1038/s42003-022-03466-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 05/09/2022] [Indexed: 12/23/2022] Open
Abstract
The relationship between structural and functional connectivity in the brain is a key question in systems neuroscience. Modern accounts assume a single global structure-function relationship that persists over time. Here we study structure-function coupling from a dynamic perspective, and show that it is regionally heterogeneous. We use a temporal unwrapping procedure to identify moment-to-moment co-fluctuations in neural activity, and reconstruct time-resolved structure-function coupling patterns. We find that patterns of dynamic structure-function coupling are region-specific. We observe stable coupling in unimodal and transmodal cortex, and dynamic coupling in intermediate regions, particularly in insular cortex (salience network) and frontal eye fields (dorsal attention network). Finally, we show that the variability of a region’s structure-function coupling is related to the distribution of its connection lengths. Collectively, our findings provide a way to study structure-function relationships from a dynamic perspective. Temporal unwrapping analysis of diffusion weighted MRI connectivity and functional MRI scans reveals that the coupling between structure and function in the human brain is regionally heterogeneous and provides a framework to evaluate these relationships from a dynamic perspective.
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22
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Ribeiro M, Castelo-Branco M. Slow fluctuations in ongoing brain activity decrease in amplitude with ageing yet their impact on task-related evoked responses is dissociable from behavior. eLife 2022; 11:e75722. [PMID: 35608164 PMCID: PMC9129875 DOI: 10.7554/elife.75722] [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: 11/20/2021] [Accepted: 05/12/2022] [Indexed: 11/21/2022] Open
Abstract
In humans, ageing is characterized by decreased brain signal variability and increased behavioral variability. To understand how reduced brain variability segregates with increased behavioral variability, we investigated the association between reaction time variability, evoked brain responses and ongoing brain signal dynamics, in young (N=36) and older adults (N=39). We studied the electroencephalogram (EEG) and pupil size fluctuations to characterize the cortical and arousal responses elicited by a cued go/no-go task. Evoked responses were strongly modulated by slow (<2 Hz) fluctuations of the ongoing signals, which presented reduced power in the older participants. Although variability of the evoked responses was lower in the older participants, once we adjusted for the effect of the ongoing signal fluctuations, evoked responses were equally variable in both groups. Moreover, the modulation of the evoked responses caused by the ongoing signal fluctuations had no impact on reaction time, thereby explaining why although ongoing brain signal variability is decreased in older individuals, behavioral variability is not. Finally, we showed that adjusting for the effect of the ongoing signal was critical to unmask the link between neural responses and behavior as well as the link between task-related evoked EEG and pupil responses.
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Affiliation(s)
- Maria Ribeiro
- CIBIT-ICNAS, University of CoimbraCoimbraPortugal
- Faculty of Medicine, University of CoimbraCoimbraPortugal
| | - Miguel Castelo-Branco
- CIBIT-ICNAS, University of CoimbraCoimbraPortugal
- Faculty of Medicine, University of CoimbraCoimbraPortugal
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23
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Treder MS, Codrai R, Tsvetanov KA. Quality assessment of anatomical MRI images from generative adversarial networks: Human assessment and image quality metrics. J Neurosci Methods 2022; 374:109579. [PMID: 35364110 DOI: 10.1016/j.jneumeth.2022.109579] [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: 10/30/2021] [Revised: 03/01/2022] [Accepted: 03/20/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND Generative Adversarial Networks (GANs) can synthesize brain images from image or noise input. So far, the gold standard for assessing the quality of the generated images has been human expert ratings. However, due to limitations of human assessment in terms of cost, scalability, and the limited sensitivity of the human eye to more subtle statistical relationships, a more automated approach towards evaluating GANs is required. NEW METHOD We investigated to what extent visual quality can be assessed using image quality metrics and we used group analysis and spatial independent components analysis to verify that the GAN reproduces multivariate statistical relationships found in real data. Reference human data was obtained by recruiting neuroimaging experts to assess real Magnetic Resonance (MR) images and images generated by a GAN. Image quality was manipulated by exporting images at different stages of GAN training. RESULTS Experts were sensitive to changes in image quality as evidenced by ratings and reaction times, and the generated images reproduced group effects (age, gender) and spatial correlations moderately well. We also surveyed a number of image quality metrics. Overall, Fréchet Inception Distance (FID), Maximum Mean Discrepancy (MMD) and Naturalness Image Quality Evaluator (NIQE) showed sensitivity to image quality and good correspondence with the human data, especially for lower-quality images (i.e., images from early stages of GAN training). However, only a Deep Quality Assessment (QA) model trained on human ratings was able to reproduce the subtle differences between higher-quality images. CONCLUSIONS We recommend a combination of group analyses, spatial correlation analyses, and both distortion metrics (FID, MMD, NIQE) and perceptual models (Deep QA) for a comprehensive evaluation and comparison of brain images produced by GANs.
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Affiliation(s)
- Matthias S Treder
- School of Computer Science and Informatics, Cardiff University, Cardiff CF24 3AA, UK.
| | - Ryan Codrai
- School of Computer Science and Informatics, Cardiff University, Cardiff CF24 3AA, UK
| | - Kamen A Tsvetanov
- Department of Clinical Neurosciences, University of Cambridge, CB2 0SZ, UK; Department of Psychology, University of Cambridge, Cambridge CB2 3EB, UK
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24
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The temporal dedifferentiation of global brain signal fluctuations during human brain ageing. Sci Rep 2022; 12:3616. [PMID: 35256664 PMCID: PMC8901682 DOI: 10.1038/s41598-022-07578-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 02/22/2022] [Indexed: 01/18/2023] Open
Abstract
The variation of brain functions as healthy ageing has been discussed widely using resting-state brain imaging. Previous conclusions may be misinterpreted without considering the effects of global signal (GS) on local brain activities. Up to now, the variation of GS with ageing has not been estimated. To fill this gap, we defined the GS as the mean signal of all voxels in the gray matter and systematically investigated correlations between age and indices of GS fluctuations. What's more, these tests were replicated with data after hemodynamic response function (HRF) de-convolution and data without noise regression as well as head motion data to verify effects of non-neural information on age. The results indicated that GS fluctuations varied as ageing in three ways. First, GS fluctuations were reduced with age. Second, the GS power transferred from lower frequencies to higher frequencies with age. Third, the GS power was more evenly distributed across frequencies in ageing brain. These trends were partly influenced by HRF and physiological noise, indicating that the age effects of GS fluctuations are associated with a variety of physiological activities. These results may indicate the temporal dedifferentiation hypothesis of brain ageing from the global perspective.
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25
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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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26
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Calvert GHM, Carson RG. Neural mechanisms mediating cross education: With additional considerations for the ageing brain. Neurosci Biobehav Rev 2021; 132:260-288. [PMID: 34801578 DOI: 10.1016/j.neubiorev.2021.11.025] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 11/03/2021] [Accepted: 11/16/2021] [Indexed: 12/14/2022]
Abstract
CALVERT, G.H.M., and CARSON, R.G. Neural mechanisms mediating cross education: With additional considerations for the ageing brain. NEUROSCI BIOBEHAV REV 21(1) XXX-XXX, 2021. - Cross education (CE) is the process whereby a regimen of unilateral limb training engenders bilateral improvements in motor function. The contralateral gains thus derived may impart therapeutic benefits for patients with unilateral deficits arising from orthopaedic injury or stroke. Despite this prospective therapeutic utility, there is little consensus concerning its mechanistic basis. The precise means through which the neuroanatomical structures and cellular processes that mediate CE may be influenced by age-related neurodegeneration are also almost entirely unknown. Notwithstanding the increased incidence of unilateral impairment in later life, age-related variations in the expression of CE have been examined only infrequently. In this narrative review, we consider several mechanisms which may mediate the expression of CE with specific reference to the ageing CNS. We focus on the adaptive potential of cellular processes that are subserved by a specific set of neuroanatomical pathways including: the corticospinal tract, corticoreticulospinal projections, transcallosal fibres, and thalamocortical radiations. This analysis may inform the development of interventions that exploit the therapeutic utility of CE training in older persons.
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Affiliation(s)
- Glenn H M Calvert
- Trinity College Institute of Neuroscience and School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Richard G Carson
- Trinity College Institute of Neuroscience and School of Psychology, Trinity College Dublin, Dublin, Ireland; School of Psychology, Queen's University Belfast, Belfast, Northern Ireland, UK; School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, Australia.
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27
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Garrett DD, Skowron A, Wiegert S, Adolf J, Dahle CL, Lindenberger U, Raz N. Lost Dynamics and the Dynamics of Loss: Longitudinal Compression of Brain Signal Variability is Coupled with Declines in Functional Integration and Cognitive Performance. Cereb Cortex 2021; 31:5239-5252. [PMID: 34297815 PMCID: PMC8491679 DOI: 10.1093/cercor/bhab154] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 04/08/2021] [Accepted: 05/06/2021] [Indexed: 11/24/2022] Open
Abstract
Reduced moment-to-moment blood oxygen level-dependent (BOLD) signal variability has been consistently linked to advanced age and poorer cognitive performance, showing potential as a functional marker of brain aging. To date, however, this promise has rested exclusively on cross-sectional comparisons. In a sample of 74 healthy adults, we provide the first longitudinal evidence linking individual differences in BOLD variability, age, and performance across multiple cognitive domains over an average period of 2.5 years. As expected, those expressing greater loss of BOLD variability also exhibited greater decline in cognition. The fronto-striato-thalamic system emerged as a core neural substrate for these change-change associations. Preservation of signal variability within regions of the fronto-striato-thalamic system also cohered with preservation of functional integration across regions of this system, suggesting that longitudinal maintenance of "local" dynamics may require across-region communication. We therefore propose this neural system as a primary target in future longitudinal studies on the neural substrates of cognitive aging. Given that longitudinal change-change associations between brain and cognition are notoriously difficult to detect, the presence of such an association within a relatively short follow-up period bolsters the promise of brain signal variability as a viable, experimentally sensitive probe for studying individual differences in human cognitive aging.
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Affiliation(s)
- Douglas D Garrett
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, Lentzeallee 94, Berlin 14195, Germany
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, Berlin 14195, Germany
| | - Alexander Skowron
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, Lentzeallee 94, Berlin 14195, Germany
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, Berlin 14195, Germany
| | - Steffen Wiegert
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, Lentzeallee 94, Berlin 14195, Germany
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, Berlin 14195, Germany
| | - Janne Adolf
- Research Group of Quantitative Psychology and Individual Differences, Faculty of Psychology and Educational Sciences, KU Leuven, Leuven 3000, Belgium
| | - Cheryl L Dahle
- Institute of Gerontology, Wayne State University, 87 East Ferry Street, Detroit, MI 48202, USA
| | - Ulman Lindenberger
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, Lentzeallee 94, Berlin 14195, Germany
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, Berlin 14195, Germany
| | - Naftali Raz
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, Berlin 14195, Germany
- Institute of Gerontology, Wayne State University, 87 East Ferry Street, Detroit, MI 48202, USA
- Department of Psychology, Wayne State University, 87 East Ferry Street, Detroit, MI 48202, USA
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28
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Fesharaki NJ, Mathew AB, Mathis JR, Huddleston WE, Reuss JL, Pillai JJ, DeYoe EA. Effects of Thresholding on Voxel-Wise Correspondence of Breath-Hold and Resting-State Maps of Cerebrovascular Reactivity. Front Neurosci 2021; 15:654957. [PMID: 34504411 PMCID: PMC8421787 DOI: 10.3389/fnins.2021.654957] [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: 01/18/2021] [Accepted: 07/22/2021] [Indexed: 11/13/2022] Open
Abstract
Functional magnetic resonance imaging for presurgical brain mapping enables neurosurgeons to identify viable tissue near a site of operable pathology which might be at risk of surgery-induced damage. However, focal brain pathology (e.g., tumors) may selectively disrupt neurovascular coupling while leaving the underlying neurons functionally intact. Such neurovascular uncoupling can result in false negatives on brain activation maps thereby compromising their use for surgical planning. One way to detect potential neurovascular uncoupling is to map cerebrovascular reactivity using either an active breath-hold challenge or a passive resting-state scan. The equivalence of these two methods has yet to be fully established, especially at a voxel level of resolution. To quantitatively compare breath-hold and resting-state maps of cerebrovascular reactivity, we first identified threshold settings that optimized coverage of gray matter while minimizing false responses in white matter. When so optimized, the resting-state metric had moderately better gray matter coverage and specificity. We then assessed the spatial correspondence between the two metrics within cortical gray matter, again, across a wide range of thresholds. Optimal spatial correspondence was strongly dependent on threshold settings which if improperly set tended to produce statistically biased maps. When optimized, the two CVR maps did have moderately good correspondence with each other (mean accuracy of 73.6%). Our results show that while the breath-hold and resting-state maps may appear qualitatively similar they are not quantitatively identical at a voxel level of resolution.
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Affiliation(s)
- Nooshin J Fesharaki
- College of Health Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI, United States.,Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Amy B Mathew
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Jedidiah R Mathis
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Wendy E Huddleston
- College of Health Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
| | - James L Reuss
- Prism Clinical Imaging, Inc., Milwaukee, WI, United States
| | - Jay J Pillai
- Neuroradiology Division, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States.,Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Edgar A DeYoe
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, United States
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29
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Kucikova L, Goerdten J, Dounavi ME, Mak E, Su L, Waldman AD, Danso S, Muniz-Terrera G, Ritchie CW. Resting-state brain connectivity in healthy young and middle-aged adults at risk of progressive Alzheimer's disease. Neurosci Biobehav Rev 2021; 129:142-153. [PMID: 34310975 DOI: 10.1016/j.neubiorev.2021.07.024] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 05/18/2021] [Accepted: 07/21/2021] [Indexed: 11/15/2022]
Abstract
Functional brain connectivity of the resting-state networks has gained recent attention as a possible biomarker of Alzheimer's Disease (AD). In this paper, we review the literature of functional connectivity differences in young adults and middle-aged cognitively intact individuals with non-modifiable risk factors of AD (n = 17). We focus on three main intrinsic resting-state networks: The Default Mode network, Executive network, and the Salience network. Overall, the evidence from the literature indicated early vulnerability of functional connectivity across different at-risk groups, particularly in the Default Mode Network. While there was little consensus on the interpretation on directionality, the topography of the findings showed frequent overlap across studies, especially in regions that are characteristic of AD (i.e., precuneus, posterior cingulate cortex, and medial prefrontal cortex areas). We conclude that while resting-state functional connectivity markers have great potential to identify at-risk individuals, implementing more data-driven approaches, further longitudinal and cross-validation studies, and the analysis of greater sample sizes are likely to be necessary to fully establish the effectivity and utility of resting-state network-based analyses.
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Affiliation(s)
- Ludmila Kucikova
- Edinburgh Dementia Prevention and Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom.
| | - Jantje Goerdten
- Department of Epidemiological Methods and Etiological Research, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | - Maria-Eleni Dounavi
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Elijah Mak
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Li Su
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Adam D Waldman
- Edinburgh Dementia Prevention and Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
| | - Samuel Danso
- Edinburgh Dementia Prevention and Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
| | - Graciela Muniz-Terrera
- Edinburgh Dementia Prevention and Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
| | - Craig W Ritchie
- Edinburgh Dementia Prevention and Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
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30
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Tsvetanov KA, Henson RNA, Jones PS, Mutsaerts H, Fuhrmann D, Tyler LK, Rowe JB. The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors. Psychophysiology 2021; 58:e13714. [PMID: 33210312 PMCID: PMC8244027 DOI: 10.1111/psyp.13714] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 07/27/2020] [Accepted: 09/28/2020] [Indexed: 12/18/2022]
Abstract
Accurate identification of brain function is necessary to understand neurocognitive aging, and thereby promote health and well-being. Many studies of neurocognitive aging have investigated brain function with the blood-oxygen level-dependent (BOLD) signal measured by functional magnetic resonance imaging. However, the BOLD signal is a composite of neural and vascular signals, which are differentially affected by aging. It is, therefore, essential to distinguish the age effects on vascular versus neural function. The BOLD signal variability at rest (known as resting state fluctuation amplitude, RSFA), is a safe, scalable, and robust means to calibrate vascular responsivity, as an alternative to breath-holding and hypercapnia. However, the use of RSFA for normalization of BOLD imaging assumes that age differences in RSFA reflecting only vascular factors, rather than age-related differences in neural function (activity) or neuronal loss (atrophy). Previous studies indicate that two vascular factors, cardiovascular health (CVH) and cerebrovascular function, are insufficient when used alone to fully explain age-related differences in RSFA. It remains possible that their joint consideration is required to fully capture age differences in RSFA. We tested the hypothesis that RSFA no longer varies with age after adjusting for a combination of cardiovascular and cerebrovascular measures. We also tested the hypothesis that RSFA variation with age is not associated with atrophy. We used data from the population-based, lifespan Cam-CAN cohort. After controlling for cardiovascular and cerebrovascular estimates alone, the residual variance in RSFA across individuals was significantly associated with age. However, when controlling for both cardiovascular and cerebrovascular estimates, the variance in RSFA was no longer associated with age. Grey matter volumes did not explain age differences in RSFA, after controlling for CVH. The results were consistent between voxel-level analysis and independent component analysis. Our findings indicate that cardiovascular and cerebrovascular signals are together sufficient predictors of age differences in RSFA. We suggest that RSFA can be used to separate vascular from neuronal factors, to characterize neurocognitive aging. We discuss the implications and make recommendations for the use of RSFA in the research of aging.
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Affiliation(s)
- Kamen A. Tsvetanov
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
- Department of PsychologyCentre for Speech, Language and the BrainUniversity of CambridgeCambridgeUK
| | - Richard N. A. Henson
- Medical Research Council Cognition and Brain Sciences UnitCambridgeUK
- Department of PsychiatryUniversity of CambridgeCambridgeUK
| | - P. Simon Jones
- Department of PsychologyCentre for Speech, Language and the BrainUniversity of CambridgeCambridgeUK
| | - Henk Mutsaerts
- Department of Radiology and Nuclear MedicineAmsterdam University Medical CenterAmsterdamthe Netherlands
| | - Delia Fuhrmann
- Medical Research Council Cognition and Brain Sciences UnitCambridgeUK
| | - Lorraine K. Tyler
- Department of PsychologyCentre for Speech, Language and the BrainUniversity of CambridgeCambridgeUK
| | - Cam‐CAN
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
- Department of PsychologyCentre for Speech, Language and the BrainUniversity of CambridgeCambridgeUK
| | - James B. Rowe
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
- Medical Research Council Cognition and Brain Sciences UnitCambridgeUK
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31
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Fabiani M, Rypma B, Gratton G. Aging and cerebrovascular health: Structural, functional, cognitive, and methodological implications. Psychophysiology 2021; 58:e13842. [PMID: 34021598 DOI: 10.1111/psyp.13842] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 04/28/2021] [Indexed: 12/27/2022]
Affiliation(s)
- Monica Fabiani
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.,Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Bart Rypma
- School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX, USA.,Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Gabriele Gratton
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.,Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
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32
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Chen JJ, Gauthier CJ. The Role of Cerebrovascular-Reactivity Mapping in Functional MRI: Calibrated fMRI and Resting-State fMRI. Front Physiol 2021; 12:657362. [PMID: 33841190 PMCID: PMC8027080 DOI: 10.3389/fphys.2021.657362] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 03/02/2021] [Indexed: 12/14/2022] Open
Abstract
Task and resting-state functional MRI (fMRI) is primarily based on the same blood-oxygenation level-dependent (BOLD) phenomenon that MRI-based cerebrovascular reactivity (CVR) mapping has most commonly relied upon. This technique is finding an ever-increasing role in neuroscience and clinical research as well as treatment planning. The estimation of CVR has unique applications in and associations with fMRI. In particular, CVR estimation is part of a family of techniques called calibrated BOLD fMRI, the purpose of which is to allow the mapping of cerebral oxidative metabolism (CMRO2) using a combination of BOLD and cerebral-blood flow (CBF) measurements. Moreover, CVR has recently been shown to be a major source of vascular bias in computing resting-state functional connectivity, in much the same way that it is used to neutralize the vascular contribution in calibrated fMRI. Furthermore, due to the obvious challenges in estimating CVR using gas challenges, a rapidly growing field of study is the estimation of CVR without any form of challenge, including the use of resting-state fMRI for that purpose. This review addresses all of these aspects in which CVR interacts with fMRI and the role of CVR in calibrated fMRI, provides an overview of the physiological biases and assumptions underlying hypercapnia-based CVR and calibrated fMRI, and provides a view into the future of non-invasive CVR measurement.
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Affiliation(s)
- J Jean Chen
- Baycrest Centre for Geriatric Care, Rotman Research Institute, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Claudine J Gauthier
- Department of Physics, Concordia University, Montreal, QC, Canada.,Montreal Heart Institute, Montreal, QC, Canada
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33
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Millar PR, Ances BM, Gordon BA, Benzinger TLS, Morris JC, Balota DA. Evaluating Cognitive Relationships with Resting-State and Task-driven Blood Oxygen Level-Dependent Variability. J Cogn Neurosci 2021; 33:279-302. [PMID: 33135966 PMCID: PMC7877897 DOI: 10.1162/jocn_a_01645] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Recent functional magnetic resonance imaging studies have reported that moment-to-moment variability in the blood oxygen level-dependent (BOLD) signal is positively associated with task performance and, thus, may reflect a behaviorally sensitive signal. However, it is not clear whether estimates of resting-state and task-driven BOLD variability are differentially related to cognition, as they may be driven by distinct sources of variance in the BOLD signal. Moreover, other studies have suggested that age differences in resting-state BOLD variability may be particularly sensitive to individual differences in cardiovascular, rather than neural, factors. In this study, we tested relationships between measures of behavioral task performance and BOLD variability during both resting-state and task-driven runs of a Stroop and an animacy judgment task in a large, well-characterized sample of cognitively normal middle-aged to older adults. Resting-state BOLD variability was related to composite measures of global cognition and attentional control, but these relationships were eliminated after correction for age or cardiovascular estimates. In contrast, task-driven BOLD variability was related to attentional control measured both inside and outside the scanner, and importantly, these relationships persisted after correction for age and cardiovascular measures. Overall, these results suggest that BOLD variability is a behaviorally sensitive signal. However, resting-state and task-driven estimates of BOLD variability may differ in the degree to which they are sensitive to age-related, cardiovascular, and neural mechanisms.
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Affiliation(s)
- Peter R Millar
- Department of Psychological & Brain Sciences, Washington University in St. Louis,Department of Neurology, Washington University in St. Louis,Peter R Millar, , 314-935-6524, One Brookings Drive, Campus Box 1125, St. Louis, MO 63130
| | - Beau M Ances
- Department of Neurology, Washington University in St. Louis,Department of Radiology, Washington University in St. Louis
| | - Brian A Gordon
- Department of Psychological & Brain Sciences, Washington University in St. Louis,Department of Radiology, Washington University in St. Louis
| | | | - John C Morris
- Department of Neurology, Washington University in St. Louis
| | - David A Balota
- Department of Psychological & Brain Sciences, Washington University in St. Louis,Department of Neurology, Washington University in St. Louis
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34
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Tsvetanov KA, Henson RNA, Rowe JB. Separating vascular and neuronal effects of age on fMRI BOLD signals. Philos Trans R Soc Lond B Biol Sci 2021; 376:20190631. [PMID: 33190597 PMCID: PMC7741031 DOI: 10.1098/rstb.2019.0631] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/19/2020] [Indexed: 12/14/2022] Open
Abstract
Accurate identification of brain function is necessary to understand the neurobiology of cognitive ageing, and thereby promote well-being across the lifespan. A common tool used to investigate neurocognitive ageing is functional magnetic resonance imaging (fMRI). However, although fMRI data are often interpreted in terms of neuronal activity, the blood oxygenation level-dependent (BOLD) signal measured by fMRI includes contributions of both vascular and neuronal factors, which change differentially with age. While some studies investigate vascular ageing factors, the results of these studies are not well known within the field of neurocognitive ageing and therefore vascular confounds in neurocognitive fMRI studies are common. Despite over 10 000 BOLD-fMRI papers on ageing, fewer than 20 have applied techniques to correct for vascular effects. However, neurovascular ageing is not only a confound in fMRI, but an important feature in its own right, to be assessed alongside measures of neuronal ageing. We review current approaches to dissociate neuronal and vascular components of BOLD-fMRI of regional activity and functional connectivity. We highlight emerging evidence that vascular mechanisms in the brain do not simply control blood flow to support the metabolic needs of neurons, but form complex neurovascular interactions that influence neuronal function in health and disease. This article is part of the theme issue 'Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity'.
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Affiliation(s)
- Kamen A. Tsvetanov
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0SZ, UK
- Department of Psychology, University of Cambridge, Cambridge CB2 3EB, UK
| | - Richard N. A. Henson
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SP, UK
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - James B. Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0SZ, UK
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
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35
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Treder MS, Shock JP, Stein DJ, du Plessis S, Seedat S, Tsvetanov KA. Correlation Constraints for Regression Models: Controlling Bias in Brain Age Prediction. Front Psychiatry 2021; 12:615754. [PMID: 33679476 PMCID: PMC7930839 DOI: 10.3389/fpsyt.2021.615754] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 01/04/2021] [Indexed: 11/13/2022] Open
Abstract
In neuroimaging, the difference between chronological age and predicted brain age, also known as brain age delta, has been proposed as a pathology marker linked to a range of phenotypes. Brain age delta is estimated using regression, which involves a frequently observed bias due to a negative correlation between chronological age and brain age delta. In brain age prediction models, this correlation can manifest as an overprediction of the age of young brains and an underprediction for elderly ones. We show that this bias can be controlled for by adding correlation constraints to the model training procedure. We develop an analytical solution to this constrained optimization problem for Linear, Ridge, and Kernel Ridge regression. The solution is optimal in the least-squares sense i.e., there is no other model that satisfies the correlation constraints and has a better fit. Analyses on the PAC2019 competition data demonstrate that this approach produces optimal unbiased predictive models with a number of advantages over existing approaches. Finally, we introduce regression toolboxes for Python and MATLAB that implement our algorithm.
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Affiliation(s)
- Matthias S Treder
- School of Computer Science & Informatics, Cardiff University, Cardiff, United Kingdom
| | - Jonathan P Shock
- Department of Mathematics and Applied Mathematics, University of Cape Town, Cape Town, South Africa.,National Institute for Theoretical Physics, Matieland, South Africa
| | - Dan J Stein
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Stéfan du Plessis
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Soraya Seedat
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Kamen A Tsvetanov
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom.,Department of Psychology, University of Cambridge, Cambridge, United Kingdom
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36
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Peterson KA, Jones PS, Patel N, Tsvetanov KA, Ingram R, Cappa SF, Lambon Ralph MA, Patterson K, Garrard P, Rowe JB. Language Disorder in Progressive Supranuclear Palsy and Corticobasal Syndrome: Neural Correlates and Detection by the MLSE Screening Tool. Front Aging Neurosci 2021; 13:675739. [PMID: 34381350 PMCID: PMC8351757 DOI: 10.3389/fnagi.2021.675739] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 06/28/2021] [Indexed: 11/25/2022] Open
Abstract
Background: Progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS) affect speech and language as well as motor functions. Clinical and neuropathological data indicate a close relationship between these two disorders and the non-fluent variant of primary progressive aphasia (nfvPPA). We use the recently developed Mini Linguistic State Examination tool (MLSE) to study speech and language disorders in patients with PSP, CBS, and nfvPPA, in combination with structural magnetic resonance imaging (MRI). Methods: Fifty-one patients (PSP N = 13, CBS N = 19, nfvPPA N = 19) and 30 age-matched controls completed the MLSE, the short form of the Boston Diagnostic Aphasia Examination (BDAE), and the Addenbrooke's Cognitive Examination III. Thirty-eight patients and all controls underwent structural MRI at 3 Tesla, with T1 and T2-weighted images processed by surface-based and subcortical segmentation within FreeSurfer 6.0.0 to extract cortical thickness and subcortical volumes. Morphometric differences were compared between groups and correlated with the severity of speech and language impairment. Results: CBS and PSP patients showed impaired MLSE performance, compared to controls, with a similar language profile to nfvPPA, albeit less severe. All patient groups showed reduced cortical thickness in bilateral frontal regions and striatal volume. PSP and nfvPPA patients also showed reduced superior temporal cortical thickness, with additional thalamic and amygdalo-hippocampal volume reductions in nfvPPA. Multivariate analysis of brain-wide cortical thickness and subcortical volumes with MLSE domain scores revealed associations between performance on multiple speech and language domains with atrophy of left-lateralised fronto-temporal cortex, amygdala, hippocampus, putamen, and caudate. Conclusions: The effect of PSP and CBS on speech and language overlaps with nfvPPA. These three disorders cause a common anatomical pattern of atrophy in the left frontotemporal language network and striatum. The MLSE is a short clinical screening tool that can identify the language disorder of PSP and CBS, facilitating clinical management and patient access to future clinical trials.
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Affiliation(s)
- Katie A. Peterson
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, United Kingdom
| | - P. Simon Jones
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, United Kingdom
| | - Nikil Patel
- Department of Neurosciences, St. George’s, University of London, London, United Kingdom
| | - Kamen A. Tsvetanov
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - Ruth Ingram
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, United Kingdom
| | - Stefano F. Cappa
- IUSS Cognitive Neuroscience Center (ICoN), University Institute for Advanced Studies IUSS, Pavia, Italy
- Dementia Research Center, IRCCS Mondino Foundation, Pavia, Italy
| | | | - Karalyn Patterson
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, United Kingdom
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Peter Garrard
- Department of Neurosciences, St. George’s, University of London, London, United Kingdom
| | - James B. Rowe
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, United Kingdom
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
- *Correspondence: James B. Rowe
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37
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Millar PR, Ances BM, Gordon BA, Benzinger TLS, Fagan AM, Morris JC, Balota DA. Evaluating resting-state BOLD variability in relation to biomarkers of preclinical Alzheimer's disease. Neurobiol Aging 2020; 96:233-245. [PMID: 33039901 DOI: 10.1016/j.neurobiolaging.2020.08.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 07/21/2020] [Accepted: 08/10/2020] [Indexed: 02/09/2023]
Abstract
Recent functional magnetic resonance imaging studies have demonstrated that moment-to-moment variability in the blood oxygen level-dependent (BOLD) signal is related to age differences, cognition, and symptomatic Alzheimer's disease (AD). However, no studies have examined BOLD variability in the context of preclinical AD. We tested relationships between resting-state BOLD variability and biomarkers of amyloidosis, tauopathy, and neurodegeneration in a large (N = 321), well-characterized sample of cognitively normal adults (age = 39-93), using multivariate machine learning techniques. Furthermore, we controlled for cardiovascular health factors, which may contaminate resting-state BOLD variability estimates. BOLD variability, particularly in the default mode network, was related to cerebrospinal fluid (CSF) amyloid-β42 but was not related to CSF phosphorylated tau-181. Furthermore, BOLD variability estimates were also related to markers of neurodegeneration, including CSF neurofilament light protein, hippocampal volume, and a cortical thickness composite. Notably, relationships with hippocampal volume and cortical thickness survived correction for cardiovascular health and also contributed to age-related differences in BOLD variability. Thus, BOLD variability may be sensitive to preclinical pathology, including amyloidosis and neurodegeneration in AD-sensitive areas.
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Affiliation(s)
- Peter R Millar
- Department of Psychological & Brain Sciences, St. Louis, MO, USA; Department of Neurology, St. Louis, MO, USA.
| | - Beau M Ances
- Department of Neurology, St. Louis, MO, USA; Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Brian A Gordon
- Department of Psychological & Brain Sciences, St. Louis, MO, USA; Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | | | | | | | - David A Balota
- Department of Psychological & Brain Sciences, St. Louis, MO, USA; Department of Neurology, St. Louis, MO, USA
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38
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Millar PR, Petersen SE, Ances BM, Gordon BA, Benzinger TLS, Morris JC, Balota DA. Evaluating the Sensitivity of Resting-State BOLD Variability to Age and Cognition after Controlling for Motion and Cardiovascular Influences: A Network-Based Approach. Cereb Cortex 2020; 30:5686-5701. [PMID: 32515824 DOI: 10.1093/cercor/bhaa138] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 05/01/2020] [Accepted: 05/01/2020] [Indexed: 12/14/2022] Open
Abstract
Recent functional magnetic resonance imaging (fMRI) studies report that moment-to-moment variability in the BOLD signal is related to differences in age and cognition and, thus, may be sensitive to age-dependent decline. However, head motion and/or cardiovascular health (CVH) may contaminate these relationships. We evaluated relationships between resting-state BOLD variability, age, and cognition, after characterizing and controlling for motion-related and cardiovascular influences, including pulse, blood pressure, BMI, and white matter hyperintensities (WMH), in a large (N = 422) resting-state fMRI sample of cognitively normal individuals (age 43-89). We found that resting-state BOLD variability was negatively related to age and positively related to cognition after maximally controlling for head motion. Age relationships also survived correction for CVH, but were greatly reduced when correcting for WMH alone. Our results suggest that network-based machine learning analyses of resting-state BOLD variability might yield reliable, sensitive measures to characterize age-related decline across a broad range of networks. Age-related differences in resting-state BOLD variability may be largely sensitive to processes related to WMH burden.
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Affiliation(s)
- Peter R Millar
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Steven E Petersen
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, USA.,Department of Neurology, Washington University in St. Louis, St. Louis, MO 63130, USA.,Department of Radiology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Beau M Ances
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63130, USA.,Department of Radiology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Brian A Gordon
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, USA.,Department of Radiology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Tammie L S Benzinger
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - John C Morris
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - David A Balota
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, USA.,Department of Neurology, Washington University in St. Louis, St. Louis, MO 63130, USA
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