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Lugtmeijer S, Geerligs L, Tsvetanov KA, Mitchell DJ, Cam-Can, Campbell KL. Lifespan differences in visual short-term memory load-modulated functional connectivity. Neuroimage 2023; 270:119982. [PMID: 36848967 DOI: 10.1016/j.neuroimage.2023.119982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 02/19/2023] [Accepted: 02/23/2023] [Indexed: 02/27/2023] Open
Abstract
Working memory is critical to higher-order executive processes and declines throughout the adult lifespan. However, our understanding of the neural mechanisms underlying this decline is limited. Recent work suggests that functional connectivity between frontal control and posterior visual regions may be critical, but examinations of age differences therein have been limited to a small set of brain regions and extreme group designs (i.e., comparing young and older adults). In this study, we build on previous research by using a lifespan cohort and a whole-brain approach to investigate working memory load-modulated functional connectivity in relation to age and performance. The article reports on analysis of the Cambridge center for Ageing and Neuroscience (Cam-CAN) data. Participants from a population-based lifespan cohort (N = 101, age 23-86) performed a visual short-term memory task during functional magnetic resonance imaging. Visual short-term memory was measured with a delayed recall task for visual motion with three different loads. Whole-brain load-modulated functional connectivity was estimated using psychophysiological interactions in a hundred regions of interest, sorted into seven networks (Schaefer et al., 2018, Yeo et al., 2011). Results showed that load-modulated functional connectivity was strongest within the dorsal attention and visual networks during encoding and maintenance. With increasing age, load-modulated functional connectivity strength decreased throughout the cortex. Whole-brain analyses for the relation between connectivity and behavior were non-significant. Our results give additional support to the sensory recruitment model of working memory. We also demonstrate the widespread negative impact of age on the modulation of functional connectivity by working memory load. Older adults might already be close to ceiling in terms of their neural resources at the lowest load and therefore less able to further increase connectivity with increasing task demands.
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Affiliation(s)
- Selma Lugtmeijer
- Department of Psychology, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, ON L2S 3A1, Canada.
| | - Linda Geerligs
- Radboud University, Thomas van Aquinostraat 4, 6525 GD, Nijmegen, the Netherlands.
| | - Kamen A Tsvetanov
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, United Kingdom; Department of Psychology, University of Cambridge, Cambridge CB2 3EB, United Kingdom.
| | - Daniel J Mitchell
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, United Kingdom.
| | - Cam-Can
- Cambridge Centre for Ageing and Neuroscience (Cam-CAN), University of Cambridge and MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, United Kingdom.
| | - Karen L Campbell
- Department of Psychology, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, ON L2S 3A1, Canada.
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Sanders AM, Richard G, Kolskår K, Ulrichsen KM, Kaufmann T, Alnæs D, Beck D, Dørum ES, de Lange AMG, Egil Nordvik J, Westlye LT. Linking objective measures of physical activity and capability with brain structure in healthy community dwelling older adults. Neuroimage Clin 2021; 31:102767. [PMID: 34330086 PMCID: PMC8329542 DOI: 10.1016/j.nicl.2021.102767] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 07/19/2021] [Accepted: 07/20/2021] [Indexed: 11/28/2022]
Abstract
Maintaining high levels of daily activity and physical capability have been proposed as important constituents to promote healthy brain and cognitive aging. Studies investigating the associations between brain health and physical activity in late life have, however, mainly been based on self-reported data or measures designed for clinical populations. In the current study, we examined cross-sectional associations between physical activity, recorded by an ankle-positioned accelerometer for seven days, physical capability (grip strength, postural control, and walking speed), and neuroimaging based surrogate markers of brain health in 122 healthy older adults aged 65-88 years. We used a multimodal brain imaging approach offering complementary structural MRI based indicators of brain health: global white matter fractional anisotropy (FA) and mean diffusivity (MD) based on diffusion tensor imaging, and subcortical and global brain age based on brain morphology inferred from T1-weighted MRI data. In addition, based on the results from the main analysis, follow-up regression analysis was performed to test for association between the volume of key subcortical regions of interest (hippocampus, caudate, thalamus and cerebellum) and daily steps, and a follow-up voxelwise analysis to test for associations between walking speed and FA across the white matter Tract-Based Spatial Statistics (TBSS) skeleton. The analyses revealed a significant association between global FA and walking speed, indicating higher white matter integrity in people with higher pace. Voxelwise analysis supported widespread significant associations. We also found a significant interaction between sex and subcortical brain age on number of daily steps, indicating younger-appearing brains in more physically active women, with no significant associations among men. These results provide insight into the intricate associations between different measures of brain and physical health in old age, and corroborate established public health advice promoting physical activity.
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Affiliation(s)
- Anne-Marthe Sanders
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; Sunnaas Rehabilitation Hospital HT, Nesodden, Norway.
| | - Geneviève Richard
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Knut Kolskår
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Kristine M Ulrichsen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Tobias Kaufmann
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatry and Psychotherapy, University of Tübingen, Germany
| | - Dag Alnæs
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Bjørknes College, Oslo, Norway
| | - Dani Beck
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Erlend S Dørum
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Ann-Marie G de Lange
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | | | - Lars T Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
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Lund MJ, Alnæs D, Schwab S, van der Meer D, Andreassen OA, Westlye LT, Kaufmann T. Differences in directed functional brain connectivity related to age, sex and mental health. Hum Brain Mapp 2020; 41:4173-4186. [PMID: 32613721 PMCID: PMC7502836 DOI: 10.1002/hbm.25116] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 06/07/2020] [Accepted: 06/16/2020] [Indexed: 01/09/2023] Open
Abstract
Functional interconnections between brain regions define the "connectome" which is of central interest for understanding human brain function. Resting-state functional magnetic resonance (rsfMRI) work has revealed changes in static connectivity related to age, sex, cognitive abilities and psychiatric symptoms, yet little is known how these factors may alter the information flow. The commonly used approach infers functional brain connectivity using stationary coefficients yielding static estimates of the undirected connection strength between brain regions. Dynamic graphical models (DGMs) are a multivariate model with dynamic coefficients reflecting directed temporal associations between nodes, and can yield novel insight into directed functional connectivity. Here, we leveraged this approach to test for associations between edge-wise estimates of direction flow across the functional connectome and age, sex, intellectual abilities and mental health. We applied DGM to investigate patterns of information flow in data from 984 individuals from the Human Connectome Project (HCP) and 10,249 individuals from the UK Biobank. Our analysis yielded patterns of directed connectivity in independent HCP and UK Biobank data similar to those previously reported, including that the cerebellum consistently receives information from other networks. We show robust associations between information flow and age and sex for several connections, with strongest effects of age observed in the sensorimotor network. Visual, auditory and sensorimotor nodes were also linked to mental health. Our findings support the use of DGM as a measure of directed connectivity in rsfMRI data and provide new insight into the shaping of the connectome during aging.
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Affiliation(s)
- Martina J. Lund
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital, and Institute of Clinical Medicine, University of OsloOsloNorway
| | - Dag Alnæs
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital, and Institute of Clinical Medicine, University of OsloOsloNorway
- Bjørknes University CollegeOsloNorway
| | - Simon Schwab
- Center for Reproducible Science (CRS) & Epidemiology, Biostatistics and Prevention Institute (EBPI)University of ZürichZurichSwitzerland
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital, and Institute of Clinical Medicine, University of OsloOsloNorway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life SciencesMaastricht UniversityMaastrichtThe Netherlands
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital, and Institute of Clinical Medicine, University of OsloOsloNorway
- KG Jebsen Centre for neurodevelopmental disorders, University of OsloOsloNorway
| | - Lars T. Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital, and Institute of Clinical Medicine, University of OsloOsloNorway
- KG Jebsen Centre for neurodevelopmental disorders, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital, and Institute of Clinical Medicine, University of OsloOsloNorway
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Dørum ES, Kaufmann T, Alnæs D, Richard G, Kolskår KK, Engvig A, Sanders AM, Ulrichsen K, Ihle-Hansen H, Nordvik JE, Westlye LT. Functional brain network modeling in sub-acute stroke patients and healthy controls during rest and continuous attentive tracking. Heliyon 2020; 6:e04854. [PMID: 32995596 PMCID: PMC7501434 DOI: 10.1016/j.heliyon.2020.e04854] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Revised: 04/25/2020] [Accepted: 09/02/2020] [Indexed: 01/21/2023] Open
Abstract
A cerebral stroke is characterized by compromised brain function due to an interruption in cerebrovascular blood supply. Although stroke incurs focal damage determined by the vascular territory affected, clinical symptoms commonly involve multiple functions and cognitive faculties that are insufficiently explained by the focal damage alone. Functional connectivity (FC) refers to the synchronous activity between spatially remote brain regions organized in a network of interconnected brain regions. Functional magnetic resonance imaging (fMRI) has advanced this system-level understanding of brain function, elucidating the complexity of stroke outcomes, as well as providing information useful for prognostic and rehabilitation purposes. We tested for differences in brain network connectivity between a group of patients with minor ischemic strokes in sub-acute phase (n = 44) and matched controls (n = 100). As neural network configuration is dependent on cognitive effort, we obtained fMRI data during rest and two load levels of a multiple object tracking (MOT) task. Network nodes and time-series were estimated using independent component analysis (ICA) and dual regression, with network edges defined as the partial temporal correlations between node pairs. The full set of edgewise FC went into a cross-validated regularized linear discriminant analysis (rLDA) to classify groups and cognitive load. MOT task performance and cognitive tests revealed no significant group differences. While multivariate machine learning revealed high sensitivity to experimental condition, with classification accuracies between rest and attentive tracking approaching 100%, group classification was at chance level, with negligible differences between conditions. Repeated measures ANOVA showed significantly stronger synchronization between a temporal node and a sensorimotor node in patients across conditions. Overall, the results revealed high sensitivity of FC indices to task conditions, and suggest relatively small brain network-level disturbances after clinically mild strokes.
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Affiliation(s)
- Erlend S. Dørum
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
- Department of Psychology, University of Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Tobias Kaufmann
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Dag Alnæs
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Geneviève Richard
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Knut K. Kolskår
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
- Department of Psychology, University of Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Andreas Engvig
- Department of Internal Medicine, Oslo University Hospital, Norway
| | - Anne-Marthe Sanders
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
- Department of Psychology, University of Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Kristine Ulrichsen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
- Department of Psychology, University of Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Hege Ihle-Hansen
- Department of Geriatric Medicine, Oslo University Hospital, Norway
| | | | - Lars T. Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
- Department of Psychology, University of Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Norway
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Camacho MC, Quiñones-Camacho LE, Perlman SB. Does the child brain rest?: An examination and interpretation of resting cognition in developmental cognitive neuroscience. Neuroimage 2020; 212:116688. [PMID: 32114148 PMCID: PMC7190083 DOI: 10.1016/j.neuroimage.2020.116688] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 02/04/2020] [Accepted: 02/25/2020] [Indexed: 02/02/2023] Open
Abstract
In cognitive neuroscience, measurements of "resting baseline" are often considered stable across age and used as a reference point against which to judge cognitive state. The task-based approach-comparing resting baseline to task conditions-implies that resting baseline is an equalizer across participants and-in the case of studies of developmental changes in cognition-across age groups. In contrast, network neuroscience explicitly examines the development of "resting state" networks across age, at odds with the idea of a consistent resting baseline. Little attention has been paid to how cognition during rest may shift across development, particularly in children under the age of eight. Childhood is marked by striking maturation of neural systems, including a protracted developmental period for cognitive control systems. To grow and shape these cognitive systems, children have a developmental imperative to engage their neural circuitry at every possible opportunity. Thus, periods of "rest" without specific instructions may require additional control for children as they fight against developmental expectation to move, speak, or otherwise engage. We therefore theorize that the child brain does not rest in a manner consistent with the adult brain as longer rest periods may represent increased cognitive control. To shape this theory, we first review the extant literature on neurodevelopment across early childhood within the context of cognitive development. Next, we present nascent evidence for a destabilized baseline for comparisons across age. Finally, we present recommendations for designing, analyzing, and interpreting tasks conducted with young children as well as for resting state. Future work must aim to tease apart the cognitive context under which we examine functional brain development in young children and take considerations into account unique to each age.
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Affiliation(s)
- M Catalina Camacho
- Division of Biology and Biomedical Sciences (Neurosciences), Washington University in St. Louis, St. Louis, MO, USA.
| | | | - Susan B Perlman
- Division of Biology and Biomedical Sciences (Neurosciences), Washington University in St. Louis, St. Louis, MO, USA; Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
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Richard G, Kolskår K, Sanders AM, Kaufmann T, Petersen A, Doan NT, Monereo Sánchez J, Alnæs D, Ulrichsen KM, Dørum ES, Andreassen OA, Nordvik JE, Westlye LT. Assessing distinct patterns of cognitive aging using tissue-specific brain age prediction based on diffusion tensor imaging and brain morphometry. PeerJ 2018; 6:e5908. [PMID: 30533290 PMCID: PMC6276592 DOI: 10.7717/peerj.5908] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Accepted: 10/10/2018] [Indexed: 01/26/2023] Open
Abstract
Multimodal imaging enables sensitive measures of the architecture and integrity of the human brain, but the high-dimensional nature of advanced brain imaging features poses inherent challenges for the analyses and interpretations. Multivariate age prediction reduces the dimensionality to one biologically informative summary measure with potential for assessing deviations from normal lifespan trajectories. A number of studies documented remarkably accurate age prediction, but the differential age trajectories and the cognitive sensitivity of distinct brain tissue classes have yet to be adequately characterized. Exploring differential brain age models driven by tissue-specific classifiers provides a hitherto unexplored opportunity to disentangle independent sources of heterogeneity in brain biology. We trained machine-learning models to estimate brain age using various combinations of FreeSurfer based morphometry and diffusion tensor imaging based indices of white matter microstructure in 612 healthy controls aged 18-87 years. To compare the tissue-specific brain ages and their cognitive sensitivity, we applied each of the 11 models in an independent and cognitively well-characterized sample (n = 265, 20-88 years). Correlations between true and estimated age and mean absolute error (MAE) in our test sample were highest for the most comprehensive brain morphometry (r = 0.83, CI:0.78-0.86, MAE = 6.76 years) and white matter microstructure (r = 0.79, CI:0.74-0.83, MAE = 7.28 years) models, confirming sensitivity and generalizability. The deviance from the chronological age were sensitive to performance on several cognitive tests for various models, including spatial Stroop and symbol coding, indicating poorer performance in individuals with an over-estimated age. Tissue-specific brain age models provide sensitive measures of brain integrity, with implications for the study of a range of brain disorders.
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Affiliation(s)
- Geneviève Richard
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Knut Kolskår
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Anne-Marthe Sanders
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Tobias Kaufmann
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Anders Petersen
- Center for Visual Cognition, Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Nhat Trung Doan
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Jennifer Monereo Sánchez
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dag Alnæs
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Kristine M. Ulrichsen
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Erlend S. Dørum
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Ole A. Andreassen
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | | | - Lars T. Westlye
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
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Mindful breath awareness meditation facilitates efficiency gains in brain networks: A steady-state visually evoked potentials study. Sci Rep 2018; 8:13687. [PMID: 30209327 PMCID: PMC6135840 DOI: 10.1038/s41598-018-32046-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Accepted: 08/31/2018] [Indexed: 01/11/2023] Open
Abstract
The beneficial effects of mindfulness-based therapeutic interventions have stimulated a rapidly growing body of scientific research into underlying psychological processes. Resulting evidence indicates that engaging with mindfulness meditation is associated with increased performance on a range of cognitive tasks. However, the mechanisms promoting these improvements require further investigation. We studied changes in behavioural performance of 34 participants during a multiple object tracking (MOT) task that taps core cognitive processes, namely sustained selective visual attention and spatial working memory. Concurrently, we recorded the steady-state visually evoked potential (SSVEP), an EEG signal elicited by the continuously flickering moving objects, and indicator of attentional engagement. Participants were tested before and after practicing eight weeks of mindful breath awareness meditation or progressive muscle relaxation as active control condition. The meditation group improved their MOT-performance and exhibited a reduction of SSVEP amplitudes, whereas no such changes were observed in the relaxation group. Neither group changed in self-reported positive affect and mindfulness, while a marginal increase in negative affect was observed in the mindfulness group. This novel way of combining MOT and SSVEP provides the important insight that mindful breath awareness meditation may lead to refinements of attention networks, enabling more efficient use of attentional resources.
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Key Brain Network Nodes Show Differential Cognitive Relevance and Developmental Trajectories during Childhood and Adolescence. eNeuro 2018; 5:eN-NWR-0092-18. [PMID: 30073200 PMCID: PMC6071203 DOI: 10.1523/eneuro.0092-18.2018] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 05/14/2018] [Accepted: 05/31/2018] [Indexed: 11/21/2022] Open
Abstract
Human adolescence is a period of rapid changes in cognition and goal-directed behavior, and it constitutes a major transitional phase towards adulthood. One of the mechanisms suggested to underlie the protracted maturation of functional brain networks, is the increased network integration and segregation enhancing neural efficiency. Importantly, the increasing coordinated network interplay throughout development is mediated through functional hubs, which are highly connected brain areas suggested to be pivotal nodes for the regulation of neural activity. To elucidate brain hub development during childhood and adolescence, we estimated voxel-wise eigenvector centrality (EC) using functional magnetic resonance imaging (fMRI) data from two different psychological contexts (resting state and a working memory task), in a large cross-sectional sample (n = 754) spanning the age from 8 to 22 years, and decomposed the maps using independent component analysis (ICA). Our results reveal significant age-related centrality differences in cingulo-opercular, visual, and sensorimotor network nodes during both rest and task performance, suggesting that common neurodevelopmental processes manifest across different mental states. Supporting the functional significance of these developmental patterns, the centrality of the cingulo-opercular node was positively associated with task performance. These findings provide evidence for protracted maturation of hub properties in specific nodes of the brain connectome during the course of childhood and adolescence and suggest that cingulo-opercular centrality is a key factor supporting neurocognitive development.
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Geng X, Xu J, Liu B, Shi Y. Multivariate Classification of Major Depressive Disorder Using the Effective Connectivity and Functional Connectivity. Front Neurosci 2018; 12:38. [PMID: 29515348 PMCID: PMC5825897 DOI: 10.3389/fnins.2018.00038] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Accepted: 01/16/2018] [Indexed: 12/29/2022] Open
Abstract
Major depressive disorder (MDD) is a mental disorder characterized by at least 2 weeks of low mood, which is present across most situations. Diagnosis of MDD using rest-state functional magnetic resonance imaging (fMRI) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability. To our best knowledge, no studies aim at classification with effective connectivity and functional connectivity measures between MDD patients and healthy controls. In this study, we performed a data-driving classification analysis using the whole brain connectivity measures which included the functional connectivity from two brain templates and effective connectivity measures created by the default mode network (DMN), dorsal attention network (DAN), frontal-parietal network (FPN), and silence network (SN). Effective connectivity measures were extracted using spectral Dynamic Causal Modeling (spDCM) and transformed into a vectorial feature space. Linear Support Vector Machine (linear SVM), non-linear SVM, k-Nearest Neighbor (KNN), and Logistic Regression (LR) were used as the classifiers to identify the differences between MDD patients and healthy controls. Our results showed that the highest accuracy achieved 91.67% (p < 0.0001) when using 19 effective connections and 89.36% when using 6,650 functional connections. The functional connections with high discriminative power were mainly located within or across the whole brain resting-state networks while the discriminative effective connections located in several specific regions, such as posterior cingulate cortex (PCC), ventromedial prefrontal cortex (vmPFC), dorsal cingulate cortex (dACC), and inferior parietal lobes (IPL). To further compare the discriminative power of functional connections and effective connections, a classification analysis only using the functional connections from those four networks was conducted and the highest accuracy achieved 78.33% (p < 0.0001). Our study demonstrated that the effective connectivity measures might play a more important role than functional connectivity in exploring the alterations between patients and health controls and afford a better mechanistic interpretability. Moreover, our results showed a diagnostic potential of the effective connectivity for the diagnosis of MDD patients with high accuracies allowing for earlier prevention or intervention.
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Affiliation(s)
- Xiangfei Geng
- Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, China
| | - Junhai Xu
- Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, China
- Laboratory of Neural Imaging, Keck School of Medicine, USC Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Baolin Liu
- Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, China
- State Key Laboratory of Intelligent Technology and Systems, National Laboratory for Information Science and Technology, Tsinghua University, Beijing, China
| | - Yonggang Shi
- Laboratory of Neural Imaging, Keck School of Medicine, USC Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
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de Vos F, Koini M, Schouten TM, Seiler S, van der Grond J, Lechner A, Schmidt R, de Rooij M, Rombouts SARB. A comprehensive analysis of resting state fMRI measures to classify individual patients with Alzheimer's disease. Neuroimage 2017; 167:62-72. [PMID: 29155080 DOI: 10.1016/j.neuroimage.2017.11.025] [Citation(s) in RCA: 103] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Revised: 11/06/2017] [Accepted: 11/13/2017] [Indexed: 01/24/2023] Open
Abstract
Alzheimer's disease (AD) patients show altered patterns of functional connectivity (FC) on resting state functional magnetic resonance imaging (RSfMRI) scans. It is yet unclear which RSfMRI measures are most informative for the individual classification of AD patients. We investigated this using RSfMRI scans from 77 AD patients (MMSE = 20.4 ± 4.5) and 173 controls (MMSE = 27.5 ± 1.8). We calculated i) FC matrices between resting state components as obtained with independent component analysis (ICA), ii) the dynamics of these FC matrices using a sliding window approach, iii) the graph properties (e.g., connection degree, and clustering coefficient) of the FC matrices, and iv) we distinguished five FC states and administered how long each subject resided in each of these five states. Furthermore, for each voxel we calculated v) FC with 10 resting state networks using dual regression, vi) FC with the hippocampus, vii) eigenvector centrality, and viii) the amplitude of low frequency fluctuations (ALFF). These eight measures were used separately as predictors in an elastic net logistic regression, and combined in a group lasso logistic regression model. We calculated the area under the receiver operating characteristic curve plots (AUC) to determine classification performance. The AUC values ranged between 0.51 and 0.84 and the highest were found for the FC matrices (0.82), FC dynamics (0.84) and ALFF (0.82). The combination of all measures resulted in an AUC of 0.85. We show that it is possible to obtain moderate to good AD classification using RSfMRI scans. FC matrices, FC dynamics and ALFF are most discriminative and the combination of all the resting state measures improves classification accuracy slightly.
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Affiliation(s)
- Frank de Vos
- Leiden University, Institute of Psychology, the Netherlands, Wassenaarseweg 52, 2333 AK, Leiden, The Netherlands; Leiden University Medical Center, Department of Radiology, the Netherlands, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands; Leiden Institute for Brain and Cognition, the Netherlands, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
| | - Marisa Koini
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, 8036, Graz, Austria
| | - Tijn M Schouten
- Leiden University, Institute of Psychology, the Netherlands, Wassenaarseweg 52, 2333 AK, Leiden, The Netherlands; Leiden University Medical Center, Department of Radiology, the Netherlands, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands; Leiden Institute for Brain and Cognition, the Netherlands, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Stephan Seiler
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, 8036, Graz, Austria
| | - Jeroen van der Grond
- Leiden University Medical Center, Department of Radiology, the Netherlands, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Anita Lechner
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, 8036, Graz, Austria
| | - Reinhold Schmidt
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, 8036, Graz, Austria
| | - Mark de Rooij
- Leiden University, Institute of Psychology, the Netherlands, Wassenaarseweg 52, 2333 AK, Leiden, The Netherlands; Leiden Institute for Brain and Cognition, the Netherlands, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Serge A R B Rombouts
- Leiden University, Institute of Psychology, the Netherlands, Wassenaarseweg 52, 2333 AK, Leiden, The Netherlands; Leiden University Medical Center, Department of Radiology, the Netherlands, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands; Leiden Institute for Brain and Cognition, the Netherlands, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
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11
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Racz FS, Mukli P, Nagy Z, Eke A. Increased prefrontal cortex connectivity during cognitive challenge assessed by fNIRS imaging. BIOMEDICAL OPTICS EXPRESS 2017; 8:3842-3855. [PMID: 28856054 PMCID: PMC5560845 DOI: 10.1364/boe.8.003842] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 05/05/2017] [Accepted: 05/30/2017] [Indexed: 05/24/2023]
Abstract
In this study, functional near-infrared spectroscopy (fNIRS) and the graph theory approach were used to access the functional connectivity (FC) of the prefrontal cortex (PFC) in a resting state and during increased mental workload. For this very purpose, a pattern recognition-based test was developed, which elicited a strong response throughout the PFC during the test condition. FC parameters obtained during stimulation were found increased compared to those in a resting state after correlation based signal improvement (CBSI), which can attenuate those components of fNIRS signals which are unrelated to neural activity. These results indicate that the cognitive challenge increased the FC in the PFC and suggests a great potential in investigating FC in various cognitive states.
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Affiliation(s)
- Frigyes Samuel Racz
- Institute of Clinical Experimental Research, Semmelweis University, 37-43 Tűzoltó Street, Budapest 1094, Hungary
- Department of Physiology, 37-43 Tűzoltó Street, Budapest 1094, Hungary
| | - Peter Mukli
- Institute of Clinical Experimental Research, Semmelweis University, 37-43 Tűzoltó Street, Budapest 1094, Hungary
- Department of Physiology, 37-43 Tűzoltó Street, Budapest 1094, Hungary
| | - Zoltan Nagy
- Institute of Clinical Experimental Research, Semmelweis University, 37-43 Tűzoltó Street, Budapest 1094, Hungary
| | - Andras Eke
- Institute of Clinical Experimental Research, Semmelweis University, 37-43 Tűzoltó Street, Budapest 1094, Hungary
- Department of Physiology, 37-43 Tűzoltó Street, Budapest 1094, Hungary
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12
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Doan NT, Engvig A, Zaske K, Persson K, Lund MJ, Kaufmann T, Cordova-Palomera A, Alnæs D, Moberget T, Brækhus A, Barca ML, Nordvik JE, Engedal K, Agartz I, Selbæk G, Andreassen OA, Westlye LT. Distinguishing early and late brain aging from the Alzheimer's disease spectrum: consistent morphological patterns across independent samples. Neuroimage 2017; 158:282-295. [PMID: 28666881 DOI: 10.1016/j.neuroimage.2017.06.070] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Revised: 05/12/2017] [Accepted: 06/23/2017] [Indexed: 11/30/2022] Open
Abstract
Alzheimer's disease (AD) is a debilitating age-related neurodegenerative disorder. Accurate identification of individuals at risk is complicated as AD shares cognitive and brain features with aging. We applied linked independent component analysis (LICA) on three complementary measures of gray matter structure: cortical thickness, area and gray matter density of 137 AD, 78 mild (MCI) and 38 subjective cognitive impairment patients, and 355 healthy adults aged 18-78 years to identify dissociable multivariate morphological patterns sensitive to age and diagnosis. Using the lasso classifier, we performed group classification and prediction of cognition and age at different age ranges to assess the sensitivity and diagnostic accuracy of the LICA patterns in relation to AD, as well as early and late healthy aging. Three components showed high sensitivity to the diagnosis and cognitive status of AD, with different relationships with age: one reflected an anterior-posterior gradient in thickness and gray matter density and was uniquely related to diagnosis, whereas the other two, reflecting widespread cortical thickness and medial temporal lobe volume, respectively, also correlated significantly with age. Repeating the LICA decomposition and between-subject analysis on ADNI data, including 186 AD, 395 MCI and 220 age-matched healthy controls, revealed largely consistent brain patterns and clinical associations across samples. Classification results showed that multivariate LICA-derived brain characteristics could be used to predict AD and age with high accuracy (area under ROC curve up to 0.93 for classification of AD from controls). Comparison between classifiers based on feature ranking and feature selection suggests both common and unique feature sets implicated in AD and aging, and provides evidence of distinct age-related differences in early compared to late aging.
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Affiliation(s)
- Nhat Trung Doan
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway.
| | - Andreas Engvig
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Medicine, Diakonhjemmet Hospital, Oslo, Norway
| | - Krystal Zaske
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Karin Persson
- Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway; Department of Geriatric Medicine, The Memory Clinic, Oslo University Hospital, Oslo, Norway
| | - Martina Jonette Lund
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Tobias Kaufmann
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Aldo Cordova-Palomera
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Dag Alnæs
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Torgeir Moberget
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Anne Brækhus
- Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway; Department of Geriatric Medicine, The Memory Clinic, Oslo University Hospital, Oslo, Norway
| | - Maria Lage Barca
- Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway; Department of Geriatric Medicine, The Memory Clinic, Oslo University Hospital, Oslo, Norway
| | | | - Knut Engedal
- Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway; Department of Geriatric Medicine, The Memory Clinic, Oslo University Hospital, Oslo, Norway
| | - Ingrid Agartz
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Geir Selbæk
- Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway; Centre for Old Age Psychiatric Research, Innlandet Hospital Trust, Ottestad, Norway
| | - Ole A Andreassen
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Lars T Westlye
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
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