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Li H, Han Y, Niu H. Greater up-modulation of intra-individual brain signal variability makes a high-load cognitive task more arduous for older adults. Neuroimage 2024; 290:120577. [PMID: 38490585 DOI: 10.1016/j.neuroimage.2024.120577] [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: 12/01/2023] [Revised: 02/27/2024] [Accepted: 03/12/2024] [Indexed: 03/17/2024] Open
Abstract
The extent to which brain responses are less distinctive across varying cognitive loads in older adults is referred to as neural dedifferentiation. Moment-to-moment brain signal variability, an emerging indicator, reveals not only the adaptability of an individual's brain as an inter-individual trait, but also the allocation of neural resources within an individual due to ever-changing task demands, thus shedding novel insight into the process of neural dedifferentiation. However, how the modulation of intra-individual brain signal variability reflects behavioral differences related to cognitively demanding tasks remains unclear. In this study, we employed functional near-infrared spectroscopy (fNIRS) imaging to capture the variability of brain signals, which was quantified by the standard deviation, during both the resting state and an n-back task (n = 1, 2, 3) in 57 healthy older adults. Using multivariate Partial Least Squares (PLS) analysis, we found that fNIRS signal variability increased from the resting state to the task and increased with working memory load in older adults. We further confirmed that greater fNIRS signal variability generally supported faster and more stable response time in the 2- and 3-back conditions. However, the intra-individual level analysis showed that the greater the up-modulation in fNIRS signal variability with cognitive loads, the more its accuracy decreases and mean response time increases, suggesting that a greater intra-individual brain signal variability up-modulation may reflect decreased efficiency in neural information processing. Taken together, our findings offer new insights into the nature of brain signal variability, suggesting that inter- and intra-individual brain signal variability may index distinct theoretical constructs.
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Affiliation(s)
- Hong Li
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875 China
| | - Ying Han
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, 100053, China; School of Biomedical Engineering, Hainan University, Haikou, 570228, China; Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, 100053, China; National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China; Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, 518132, China.
| | - Haijing Niu
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875 China.
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Jäger AP, Bailey A, Huntenburg JM, Tardif CL, Villringer A, Gauthier CJ, Nikulin V, Bazin P, Steele CJ. Decreased long-range temporal correlations in the resting-state functional magnetic resonance imaging blood-oxygen-level-dependent signal reflect motor sequence learning up to 2 weeks following training. Hum Brain Mapp 2024; 45:e26539. [PMID: 38124341 PMCID: PMC10915743 DOI: 10.1002/hbm.26539] [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: 05/10/2023] [Revised: 11/03/2023] [Accepted: 11/07/2023] [Indexed: 12/23/2023] Open
Abstract
Decreased long-range temporal correlations (LRTC) in brain signals can be used to measure cognitive effort during task execution. Here, we examined how learning a motor sequence affects long-range temporal memory within resting-state functional magnetic resonance imaging signal. Using the Hurst exponent (HE), we estimated voxel-wise LRTC and assessed changes over 5 consecutive days of training, followed by a retention scan 12 days later. The experimental group learned a complex visuomotor sequence while a complementary control group performed tightly matched movements. An interaction analysis revealed that HE decreases were specific to the complex sequence and occurred in well-known motor sequence learning associated regions including left supplementary motor area, left premotor cortex, left M1, left pars opercularis, bilateral thalamus, and right striatum. Five regions exhibited moderate to strong negative correlations with overall behavioral performance improvements. Following learning, HE values returned to pretraining levels in some regions, whereas in others, they remained decreased even 2 weeks after training. Our study presents new evidence of HE's possible relevance for functional plasticity during the resting-state and suggests that a cortical subset of sequence-specific regions may continue to represent a functional signature of learning reflected in decreased long-range temporal dependence after a period of inactivity.
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Affiliation(s)
- Anna‐Thekla P. Jäger
- Department of NeurologyMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Center for Stroke Research Berlin (CSB)Charité—Universitätsmedizin BerlinBerlinGermany
- Brain Language LabFreie Universität BerlinBerlinGermany
| | - Alexander Bailey
- Temerty Faculty of MedicineUniversity of TorontoTorontoOntarioCanada
| | - Julia M. Huntenburg
- Department of NeurologyMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Max Planck Institute for Biological CyberneticsTuebingenGermany
| | - Christine L. Tardif
- Department of Biomedical EngineeringMcGill UniversityMontrealQuébecCanada
- Montreal Neurological InstituteMontrealQuébecCanada
| | - Arno Villringer
- Department of NeurologyMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Center for Stroke Research Berlin (CSB)Charité—Universitätsmedizin BerlinBerlinGermany
- Clinic for Cognitive NeurologyLeipzigGermany
- Leipzig University Medical Centre, IFB Adiposity DiseasesLeipzigGermany
- Collaborative Research Centre 1052‐A5University of LeipzigLeipzigGermany
| | - Claudine J. Gauthier
- Department of Physics/School of HealthConcordia UniversityMontrealQuébecCanada
- Montreal Heart InstituteMontrealQuébecCanada
| | - Vadim Nikulin
- Department of NeurologyMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Pierre‐Louis Bazin
- Department of NeurologyMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Faculty of Social and Behavioral SciencesUniversity of AmsterdamAmsterdamNetherlands
| | - Christopher J. Steele
- Department of NeurologyMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Department of Psychology/School of HealthConcordia UniversityMontrealQuébecCanada
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Choi J, Choi Y, Jung YC, Lee J, Lee J, Park E, Kim IY. Effects of Game-Related Tasks for the Diagnosis and Classification of Gaming Disorder. BIOSENSORS 2024; 14:42. [PMID: 38248419 PMCID: PMC10812970 DOI: 10.3390/bios14010042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/01/2024] [Accepted: 01/11/2024] [Indexed: 01/23/2024]
Abstract
Gaming disorder (GD) is an addictive behavior characterized by an insatiable need to play video games and shares similar symptoms with the failure of self-control due to a decline in cognitive function. Current GD diagnostic and screening tools rely on questionnaires and behavioral observations related to cognitive functions to assess an individual's capacity to maintain self-control in everyday life. However, current GD screening approaches rely on subjective symptoms, and a reliable diagnosis requires long-term clinical follow-up. Recent studies have measured biosignals along with cognitive functional tasks to provide objectivity to GD diagnosis and to acquire immediate results. However, people with GD are hypersensitive to game-related cues, so their responses may vary depending on the type of stimuli, and the difference in response to stimuli might manifest as a difference in the degree of change in the biosignal. Therefore, it is critical to choose the correct stimulus type when performing GD diagnostic tasks. In this study, we investigated the task dependence of cognitive decline in GD by comparing two cognitive functional tasks: a continuous performance task (CPT) and video game play. For this study, 69 young male adults were classified into either the gaming disorder group (GD, n = 39) or a healthy control group (HC, n = 30). CPT score, EEG signal (theta, alpha, and beta), and HRV-HF power were assessed. We observed differences in the left frontal region (LF) of the brain between the GD and HC groups during online video game play. The GD group also showed a significant difference in HF power of HRV between CPT and online video gaming. Furthermore, LF and HRV-HF significantly correlated with Young's Internet Addiction Test (Y-IAT) score, which is positively associated with impulsivity score. The amount of change in theta band activity in LF and HRV-HF-both biomarkers for changes in cognitive function-during online video game play suggests that people with GD express task-dependent cognitive decline compared with HC. Our results demonstrate the feasibility of quantifying individual self-regulation ability for gaming and underscore its importance for GD classification.
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Affiliation(s)
- Jeongbong Choi
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, Republic of Korea
| | - Youngseok Choi
- Department of Electronic Engineering, Hanyang University, Seoul 04763, Republic of Korea
| | - Young-Chul Jung
- Department of Psychiatry, Yonsei University College of Medicine, Seoul 04763, Republic of Korea
| | - Jeyeon Lee
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, Republic of Korea
| | - Jongshill Lee
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, Republic of Korea
| | - Eunkyoung Park
- Department of Biomedical Engineering, Soonchunhyang University, Asan 31538, Republic of Korea
| | - In Young Kim
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, Republic of Korea
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Veillette JP, Lopes P, Nusbaum HC. Temporal Dynamics of Brain Activity Predicting Sense of Agency over Muscle Movements. J Neurosci 2023; 43:7842-7852. [PMID: 37722848 PMCID: PMC10648515 DOI: 10.1523/jneurosci.1116-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 08/07/2023] [Accepted: 09/04/2023] [Indexed: 09/20/2023] Open
Abstract
Our muscles are the primary means through which we affect the external world, and the sense of agency (SoA) over the action through those muscles is fundamental to our self-awareness. However, SoA research to date has focused almost exclusively on agency over action outcomes rather than over the musculature itself, as it was believed that SoA over the musculature could not be manipulated directly. Drawing on methods from human-computer interaction and adaptive experimentation, we use human-in-the-loop Bayesian optimization to tune the timing of electrical muscle stimulation so as to robustly elicit a SoA over electrically actuated muscle movements in male and female human subjects. We use time-resolved decoding of subjects' EEG to estimate the time course of neural activity which predicts reported agency on a trial-by-trial basis. Like paradigms which assess SoA over action consequences, we found that the late (post-conscious) neural activity predicts SoA. Unlike typical paradigms, however, we also find patterns of early (sensorimotor) activity with distinct temporal dynamics predicts agency over muscle movements, suggesting that the "neural correlates of agency" may depend on the level of abstraction (i.e., direct sensorimotor feedback versus downstream consequences) most relevant to a given agency judgment. Moreover, fractal analysis of the EEG suggests that SoA-contingent dynamics of neural activity may modulate the sensitivity of the motor system to external input.SIGNIFICANCE STATEMENT The sense of agency, the feeling of "I did that," when directing one's own musculature is a core feature of human experience. We show that we can robustly manipulate the sense of agency over electrically actuated muscle movements, and we investigate the time course of neural activity that predicts the sense of agency over these actuated movements. We find evidence of two distinct neural processes: a transient sequence of patterns that begins in the early sensorineural response to muscle stimulation and a later, sustained signature of agency. These results shed light on the neural mechanisms by which we experience our movements as volitional.
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Affiliation(s)
- John P Veillette
- Department of Psychology, University of Chicago, Chicago, Illinois 60637
| | - Pedro Lopes
- Department of Computer Science, University of Chicago, Chicago, Illinois 60637
| | - Howard C Nusbaum
- Department of Psychology, University of Chicago, Chicago, Illinois 60637
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Kardan O, Stier AJ, Layden EA, Choe KW, Lyu M, Zhang X, Beilock SL, Rosenberg MD, Berman MG. Improvements in task performance after practice are associated with scale-free dynamics of brain activity. Netw Neurosci 2023; 7:1129-1152. [PMID: 37781143 PMCID: PMC10473260 DOI: 10.1162/netn_a_00319] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 04/11/2023] [Indexed: 10/03/2023] Open
Abstract
Although practicing a task generally benefits later performance on that same task, there are individual differences in practice effects. One avenue to model such differences comes from research showing that brain networks extract functional advantages from operating in the vicinity of criticality, a state in which brain network activity is more scale-free. We hypothesized that higher scale-free signal from fMRI data, measured with the Hurst exponent (H), indicates closer proximity to critical states. We tested whether individuals with higher H during repeated task performance would show greater practice effects. In Study 1, participants performed a dual-n-back task (DNB) twice during MRI (n = 56). In Study 2, we used two runs of n-back task (NBK) data from the Human Connectome Project sample (n = 599). In Study 3, participants performed a word completion task (CAST) across six runs (n = 44). In all three studies, multivariate analysis was used to test whether higher H was related to greater practice-related performance improvement. Supporting our hypothesis, we found patterns of higher H that reliably correlated with greater performance improvement across participants in all three studies. However, the predictive brain regions were distinct, suggesting that the specific spatial H↑ patterns are not task-general.
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Affiliation(s)
- Omid Kardan
- Department of Psychology, University of Chicago, Chicago, IL, USA
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Andrew J. Stier
- Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Elliot A. Layden
- Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Kyoung Whan Choe
- Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Muxuan Lyu
- Department of Psychology, University of Chicago, Chicago, IL, USA
- Department of Management and Marketing, The Hong Kong Polytechnic University, Hong Kong
| | - Xihan Zhang
- Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Sian L. Beilock
- Department of Psychology, University of Chicago, Chicago, IL, USA
- Barnard College, Columbia University, New York, NY, USA
| | | | - Marc G. Berman
- Department of Psychology, University of Chicago, Chicago, IL, USA
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Urrutia GU, Montenegro PG, Pagliarin KC, Keske-Soares M. Development and validation of an experimental verbal Episodic Memory task in Spanish. Codas 2023; 35:e20220067. [PMID: 37729343 PMCID: PMC10546922 DOI: 10.1590/2317-1782/20232022067es] [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: 03/04/2022] [Accepted: 03/16/2023] [Indexed: 09/22/2023] Open
Abstract
PURPOSE To develop and validate an experimental verbal episodic memory task in Spanish. METHODS Six encoding blocks were elaborated, three deep and three superficial, each one with different demands of cognitive effort. The blocks were reviewed by four expert judges and tested in a pilot application. The agreement was assessed on whether the task allowed combined processing level and cognitive effort to be manipulated during incidental encoding of words, as well as clarity of instructions, examples, and workflow. RESULTS Variables such as lexical availability, metrics, and strength of association were useful to differentiate the cognitive effort between each block. The judges agreed that the processing blocks allowed a combined manipulation of the level of processing and cognitive effort and that the instructions are precise. After the pilot, the participants agreed that the instructions, examples, and way of working were easy to understand and perform. CONCLUSION The results provide evidence of validity related to the content for the proposed experimental task, thus becoming a viable alternative to consider in research aimed at identifying environmental factors that contribute to compensating the defects shown by episodic memory with age.
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Affiliation(s)
- Gabriel Urrutia Urrutia
- Departamento de Ciencias de la Fonoaudiología, Facultad de Ciencias de la Salud, Universidad de Talca - UTALCA - Talca (VII Región del Maule), Chile.
| | - Pedro García Montenegro
- Departamento de Ciencias de la Fonoaudiología, Facultad de Ciencias de la Salud, Universidad de Talca - UTALCA - Talca (VII Región del Maule), Chile.
| | - Karina Carlesso Pagliarin
- Programa de Pós-graduação em Distúrbios da Comunicação Humana, Universidade Federal de Santa Maria - UFSM - Santa Maria (RS), Brasil.
| | - Márcia Keske-Soares
- Programa de Pós-graduação em Distúrbios da Comunicação Humana, Universidade Federal de Santa Maria - UFSM - Santa Maria (RS), Brasil.
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7
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Kelty-Stephen DG, Lane E, Bloomfield L, Mangalam M. Multifractal test for nonlinearity of interactions across scales in time series. Behav Res Methods 2023; 55:2249-2282. [PMID: 35854196 DOI: 10.3758/s13428-022-01866-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/27/2022] [Indexed: 01/21/2023]
Abstract
The creativity and emergence of biological and psychological behavior tend to be nonlinear, and correspondingly, biological and psychological measures contain degrees of irregularity. The linear model might fail to reduce these measurements to a sum of independent random factors (yielding a stable mean for the measurement), implying nonlinear changes over time. The present work reviews some of the concepts implicated in nonlinear changes over time and details the mathematical steps involved in their identification. It introduces multifractality as a mathematical framework helpful in determining whether and to what degree the measured series exhibits nonlinear changes over time. These mathematical steps include multifractal analysis and surrogate data production for resolving when multifractality entails nonlinear changes over time. Ultimately, when measurements fail to fit the structures of the traditional linear model, multifractal modeling allows for making those nonlinear excursions explicit, that is, to come up with a quantitative estimate of how strongly events may interact across timescales. This estimate may serve some interests as merely a potentially statistically significant indicator of independence failing to hold, but we suspect that this estimate might serve more generally as a predictor of perceptuomotor or cognitive performance.
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Affiliation(s)
| | - Elizabeth Lane
- Department of Psychiatry, University of California-San Diego, San Diego, CA, USA
| | | | - Madhur Mangalam
- Department of Physical Therapy, Movement and Rehabilitation Sciences, Northeastern University, Boston, MA, USA.
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Pei L, Northoff G, Ouyang G. Comparative analysis of multifaceted neural effects associated with varying endogenous cognitive load. Commun Biol 2023; 6:795. [PMID: 37524883 PMCID: PMC10390511 DOI: 10.1038/s42003-023-05168-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 07/24/2023] [Indexed: 08/02/2023] Open
Abstract
Contemporary neuroscience has firmly established that mental state variation concurs with changes in neural dynamic activity in a complex way that a one-to-one mapping cannot describe. To explore the scenario of the multifaceted changes in neural dynamics associated with simple mental state variation, we took cognitive load - a common cognitive manipulation in psychology - as a venue to characterize how multiple neural dynamic features are simultaneously altered by the manipulation and how their sensitivity differs. Electroencephalogram was collected from 152 participants performing stimulus-free tasks with different demands. The results show that task demand alters wide-ranging neural dynamic features, including band-specific oscillations across broad frequency bands, scale-free dynamics, and cross-frequency phase-amplitude coupling. The scale-free dynamics outperformed others in indexing cognitive load variation. This study demonstrates a complex relationship between cognitive dynamics and neural dynamics, which points to a necessity to integrate multifaceted neural dynamic features when studying mind-brain relationship in the future.
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Affiliation(s)
- Leisi Pei
- Faculty of Education, The University of Hong Kong, Hong Kong, China
| | - Georg Northoff
- Institute of Mental Health Research, Mind, Brain Imaging and Neuroethics Research Unit, University of Ottawa, Ottawa, Canada
| | - Guang Ouyang
- Faculty of Education, The University of Hong Kong, Hong Kong, China.
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9
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Chiew KS. Cognitive Effort Deficits in Depression: Autonomic Correlates and Clues to Potential Rescue. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:683-684. [PMID: 37419608 DOI: 10.1016/j.bpsc.2023.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 05/17/2023] [Indexed: 07/09/2023]
Affiliation(s)
- Kimberly S Chiew
- Department of Psychology, University of Denver, Denver, Colorado.
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10
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Westbrook A, Yang X, Bylsma LM, Daches S, George CJ, Seidman AJ, Jennings JR, Kovacs M. Economic Choice and Heart Rate Fractal Scaling Indicate That Cognitive Effort Is Reduced by Depression and Boosted by Sad Mood. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:687-694. [PMID: 35948258 PMCID: PMC10919246 DOI: 10.1016/j.bpsc.2022.07.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 07/15/2022] [Accepted: 07/19/2022] [Indexed: 05/28/2023]
Abstract
BACKGROUND People with depression typically exhibit diminished cognitive control. Control is subjectively costly, prompting speculation that control deficits reflect reduced cognitive effort. Evidence that people with depression exert less cognitive effort is mixed, however, and motivation may depend on state affect. METHODS We used a cognitive effort discounting task to measure propensity to expend cognitive effort and fractal structure in the temporal dynamics of interbeat intervals to assess on-task effort exertion for 49 healthy control subjects, 36 people with current depression, and 67 people with remitted depression. RESULTS People with depression discounted more steeply, indicating that they were less willing to exert cognitive effort than people with remitted depression and never-depressed control subjects. Also, steeper discounting predicted worse functioning in daily life. Surprisingly, a sad mood induction selectively boosted motivation among participants with depression, erasing differences between them and control subjects. During task performance, depressed participants with the lowest cognitive motivation showed blunted autonomic reactivity as a function of load. CONCLUSIONS Discounting patterns supported the hypothesis that people with current depression would be less willing to exert cognitive effort, and steeper discounting predicted lower global functioning in daily life. Heart rate fractal scaling proved to be a highly sensitive index of cognitive load, and data implied that people with lower motivation for cognitive effort had a diminished physiological capacity to respond to rising cognitive demands. State affect appeared to influence motivation among people with current depression given that they were more willing to exert cognitive effort following a sad mood induction.
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Affiliation(s)
- Andrew Westbrook
- Department of Cognitive, Linguistic & Psychological Sciences, Brown University, Providence, Rhode Island.
| | - Xiao Yang
- Department of Psychology, Old Dominion University, Norfolk, Virginia
| | - Lauren M Bylsma
- Department of Psychiatry, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Shimrit Daches
- Department of Psychology, Bar-Ilan University, Ramat-Gan, Israel
| | - Charles J George
- Department of Psychiatry, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Andrew J Seidman
- Department of Psychiatry, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - J Richard Jennings
- Department of Psychiatry, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Maria Kovacs
- Department of Psychiatry, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
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11
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Ouyang G. A generic neural factor linking resting-state neural dynamics and the brain's response to unexpectedness in multilevel cognition. Cereb Cortex 2023; 33:2931-2946. [PMID: 35739457 DOI: 10.1093/cercor/bhac251] [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: 03/21/2022] [Revised: 05/30/2022] [Accepted: 05/30/2022] [Indexed: 11/12/2022] Open
Abstract
The brain's response to change is fundamental to learning and adaptation; this implies the presence of a universal neural mechanism under various contexts. We hypothesized that this mechanism manifests in neural activity patterns across low and high levels of cognition during task processing as well as in resting-state neural dynamics, because both these elements are different facets of the same dynamical system. We tested our hypothesis by (i) characterizing (a) the neural response to changes in low-level continuous information stream and unexpectedness at different cognitive levels and (b) the spontaneous neural dynamics in resting state, and (ii) examining the associations among the dynamics according to cross-individual variability (n = 200). Our results showed that the brain's response magnitude was monotonically correlated with the magnitude of information fluctuation in a low-level task, forming a simple psychophysical function; moreover, this effect was found to be associated with the brain's response to unexpectedness in high-level cognitive tasks (including language processing). These coherent multilevel neural effects in task processing were also shown to be strongly associated with resting-state neural dynamics characterized by the waxing and waning of Alpha oscillation. Taken together, our results revealed large-scale consistency between the neural dynamic system and multilevel cognition.
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Affiliation(s)
- Guang Ouyang
- Unit of Human Communication, Development, and Information Sciences, Faculty of Education, the University of Hong Kong, Pokfulam road, Hong Kong SAR, 999077, China
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12
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Chen X, Sui L. Alpha band neurofeedback training based on a portable device improves working memory performance of young people. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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13
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Pei L, Zhou X, Leung FKS, Ouyang G. Differential associations between scale-free neural dynamics and different levels of cognitive ability. Psychophysiology 2023; 60:e14259. [PMID: 36700291 DOI: 10.1111/psyp.14259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 12/14/2022] [Accepted: 01/08/2023] [Indexed: 01/27/2023]
Abstract
As indicators of cognitive function, scale-free neural dynamics are gaining increasing attention in cognitive neuroscience. Although the functional relevance of scale-free dynamics has been extensively reported, one fundamental question about its association with cognitive ability remains unanswered: is the association universal across a wide spectrum of cognitive abilities or confined to specific domains? Based on dual-process theory, we designed two categories of tasks to analyze two types of cognitive processes-automatic and controlled-and examined their associations with scale-free neural dynamics characterized from resting-state electroencephalography (EEG) recordings obtained from a large sample of human adults (N = 102). Our results showed that resting-state scale-free neural dynamics did not predict individuals' behavioral performance in tasks that primarily engaged the automatic process but did so in tasks that primarily engaged the controlled process. In addition, by fitting the scale-free parameters separately in different frequency bands, we found that the cognitive association of scale-free dynamics was more strongly manifested in higher-band EEG spectrum. Our findings indicate that resting-state scale-free dynamics are not universal neural indicators for all cognitive abilities but are mainly associated with high-level cognition that entails controlled processes. This finding is compatible with the widely claimed role of scale-free dynamics in reflecting properties of complex dynamic systems.
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Affiliation(s)
- Leisi Pei
- Faculty of Education, The University of Hong Kong, Hong Kong, China
| | - Xinlin Zhou
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | | | - Guang Ouyang
- Faculty of Education, The University of Hong Kong, Hong Kong, China
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14
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Kelty-Stephen DG, Lee J, Cole KR, Shields RK, Mangalam M. Multifractal Nonlinearity Moderates Feedforward and Feedback Responses to Suprapostural Perturbations. Percept Mot Skills 2023; 130:622-657. [PMID: 36600493 DOI: 10.1177/00315125221149147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
An adaptive response to unexpected perturbations requires near-term and long-term adjustments over time. We used multifractal analysis to test how nonlinear interactions across timescales might support an adaptive response following an unpredictable perturbation. We reanalyzed torque data from 44 young and 24 older adults who performed a single-leg squat task challenged by an unexpected mechanical perturbation and a secondary visual-cognitive task. We report three findings: (a) multifractal nonlinearity interacted with pre-perturbation torque production and task error to presage greater pre-voluntary feedforward increases and greater voluntary reductions, respectively, in post-perturbation task error; (b) multifractal nonlinearity presaged relatively smaller task error than standard deviations of both pre-perturbation torques and pre-perturbation task error; and (c) increased task demand (e.g., age-related changes in dexterity and dual-task settings) led to multifractal nonlinearity presaging reduced task error. All these results were consistent with our expectations, except that a pre-perturbation knee torque-dependent increase in post-perturbation task error appeared later for older than for younger participants. This correlational multifractal modeling offered theoretical clarity on the possible roles of nonlinear interactions across timescales, moderating both feedforward and feedback processes, and presaging greater stability when the standard deviation is relatively large and task demands are strong. Thus, multifractal nonlinearity usefully describes movement variability even when paired with classical descriptors like the standard deviation. We discuss potential insights from these findings for understanding suprapostural dexterity and developing rehabilitative interventions.
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Affiliation(s)
- Damian G Kelty-Stephen
- Department of Psychology, 14821State University of New York at New Paltz, New Paltz, NY, USA
| | - Jinhyun Lee
- Department of Physical Therapy and Rehabilitation Sciences, 573932University of Iowa, Iowa City, IA, USA
| | - Keith R Cole
- Department of Health, Human Function, and Rehabilitation Science, 50430George Washington University, Washington, DC, USA
| | - Richard K Shields
- Department of Physical Therapy and Rehabilitation Sciences, 573932University of Iowa, Iowa City, IA, USA
| | - Madhur Mangalam
- Division of Biomechanics and Research Development, Department of Biomechanics, and Center for Research in Human Movement Variability, 14720University of Nebraska at Omaha, Omaha, NE, USA
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15
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Hsu YF, Hämäläinen JA. Load-dependent alpha suppression is related to working memory capacity for numbers. Brain Res 2022; 1791:147994. [DOI: 10.1016/j.brainres.2022.147994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 04/13/2022] [Accepted: 06/23/2022] [Indexed: 11/02/2022]
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16
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Kelty-Stephen DG, Mangalam M. Turing's cascade instability supports the coordination of the mind, brain, and behavior. Neurosci Biobehav Rev 2022; 141:104810. [PMID: 35932950 DOI: 10.1016/j.neubiorev.2022.104810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/09/2022] [Accepted: 08/01/2022] [Indexed: 10/16/2022]
Abstract
Turing inspired a computer metaphor of the mind and brain that has been handy and has spawned decades of empirical investigation, but he did much more and offered behavioral and cognitive sciences another metaphor-that of the cascade. The time has come to confront Turing's cascading instability, which suggests a geometrical framework driven by power laws and can be studied using multifractal formalism and multiscale probability density function analysis. Here, we review a rapidly growing body of scientific investigations revealing signatures of cascade instability and their consequences for a perceiving, acting, and thinking organism. We review work related to executive functioning (planning to act), postural control (bodily poise for turning plans into action), and effortful perception (action to gather information in a single modality and action to blend multimodal information). We also review findings on neuronal avalanches in the brain, specifically about neural participation in body-wide cascades. Turing's cascade instability blends the mind, brain, and behavior across space and time scales and provides an alternative to the dominant computer metaphor.
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Affiliation(s)
- Damian G Kelty-Stephen
- Department of Psychology, State University of New York at New Paltz, New Paltz, NY, USA.
| | - Madhur Mangalam
- Department of Physical Therapy, Movement and Rehabilitation Sciences, Northeastern University, Boston, MA, USA.
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17
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Zhuang C, Meidenbauer KL, Kardan O, Stier AJ, Choe KW, Cardenas-Iniguez C, Huppert TJ, Berman MG. Scale invariance in fNIRS as a measurement of cognitive load. Cortex 2022; 154:62-76. [PMID: 35753183 DOI: 10.1016/j.cortex.2022.05.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 04/29/2022] [Accepted: 05/23/2022] [Indexed: 11/03/2022]
Abstract
Scale invariant neural dynamics are a relatively new but effective means of measuring changes in brain states as a result of varied cognitive load and task difficulty. This study tests whether scale invariance (as measured by the Hurst exponent, H) can be used with functional near-infrared spectroscopy (fNIRS) to quantify cognitive load, paving the way for scale-invariance to be measured in a variety of real-world settings. We analyzed H extracted from the fNIRS time series while participants completed an N-back working memory task. Consistent with what has been demonstrated in fMRI, the current results showed that scale-invariance analysis significantly differentiated between task and rest periods as calculated from both oxy- (HbO) and deoxy-hemoglobin (HbR) concentration changes. Results from both channel-averaged H and a multivariate partial least squares approach (Task PLS) demonstrated higher H during the 1-back task than the 2-back task. These results were stronger for H derived from HbR than from HbO. This suggests that scale-free brain states are a robust signature of cognitive load and not limited by the specific neuroimaging modality employed. Further, as fNIRS is relatively portable and robust to motion-related artifacts, these preliminary results shed light on the promising future of measuring cognitive load in real life settings.
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Affiliation(s)
- Chu Zhuang
- Environmental Neuroscience Lab, Department of Psychology, The University of Chicago, USA
| | - Kimberly L Meidenbauer
- Environmental Neuroscience Lab, Department of Psychology, The University of Chicago, USA.
| | - Omid Kardan
- Environmental Neuroscience Lab, Department of Psychology, The University of Chicago, USA
| | - Andrew J Stier
- Environmental Neuroscience Lab, Department of Psychology, The University of Chicago, USA
| | - Kyoung Whan Choe
- Environmental Neuroscience Lab, Department of Psychology, The University of Chicago, USA; Mansueto Institute for Urban Innovation, The University of Chicago, USA
| | | | - Theodore J Huppert
- Department of Electrical and Computer Engineering, The University of Pittsburgh, USA
| | - Marc G Berman
- Environmental Neuroscience Lab, Department of Psychology, The University of Chicago, USA; Neuroscience Institute, The University of Chicago, USA.
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18
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Steinberg SN, Malins JG, Liu J, King TZ. Within-individual BOLD signal variability in the N-back task and its associations with vigilance and working memory. Neuropsychologia 2022; 173:108280. [PMID: 35662552 DOI: 10.1016/j.neuropsychologia.2022.108280] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 05/03/2022] [Accepted: 05/26/2022] [Indexed: 11/20/2022]
Abstract
In a group of healthy adults (N = 48), this study evaluated how fMRI Blood Oxygen Level-Dependent (BOLD) signal variability differed across letter n-back task load and quantified the extent to which BOLD signal variability was associated with in-scanner accuracy and reaction time as well as out-of-scanner measures of vigilance and working memory (WM). Within-individual BOLD signal variability in regions of interest (ROIs, identified as peak coordinates in an attention/vigilance and WM network using Neurosynth) was differentially modulated across vigilance and WM trials. Within-individual BOLD signal variability was significantly greater across the majority of the ROIs in the working memory trials (2- and 3-back trials) compared to 0-back trials. Notably, this increased variability across the network was accompanied by significantly less variability in the left cingulate gyrus and left inferior temporal lobe during the working memory trials. Significantly fewer differences in within-individual BOLD signal variability were identified for vigilance trials (0- and 1-back trials) compared to crosshair. We hypothesized that increased BOLD signal variability would be associated with n-back task performance and with out-of-scanner measures of vigilance (Digit Span Forward) and WM (Auditory Consonant Trigrams and Digit Span Backward). These results were non-significant after correcting for multiple comparisons. Furthermore, using multivariate analyses (partial least squares regression; PLS-R), within-individual BOLD signal variability in regions associated with a WM-vigilance network did not significantly predict out-of-scanner test performance after appropriate cross validation, yet provided a promising trend for WM trials; greater within-individual BOLD signal variability during WM n-back trials was associated with decreased performance on all included neuropsychological measures, which provides partial support for previous findings. This study demonstrates that patterns of variability differ based on task load in the scanner and illustrates an intriguing association between within-individual BOLD signal variability and out-of-scanner behavioral performance that may be better explored in future studies with a larger sample size.
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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.
| | - Jeffrey G Malins
- Department of Psychology, Georgia State University, Urban Life Building, 11th Floor, 140 Decatur St, Atlanta, GA, 30303, USA; Neuroscience Institute, Georgia State University, PO Box 5030, Atlanta, GA, 30302, USA.
| | - Jingyu Liu
- Neuroscience Institute, Georgia State University, PO Box 5030, Atlanta, GA, 30302, USA; Department of Computer Science, Georgia State University, PO Box 5060, Atlanta, GA, 30302, USA; Center for Translational Research in Neuroimaging and Data Science (TReNDS), 55 Park Place NE, 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, PO Box 5030, Atlanta, GA, 30302, USA.
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19
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Campbell O, Vanderwal T, Weber AM. Fractal-Based Analysis of fMRI BOLD Signal During Naturalistic Viewing Conditions. Front Physiol 2022; 12:809943. [PMID: 35087421 PMCID: PMC8787275 DOI: 10.3389/fphys.2021.809943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 12/14/2021] [Indexed: 01/04/2023] Open
Abstract
Background: Temporal fractals are characterized by prominent scale-invariance and self-similarity across time scales. Monofractal analysis quantifies this scaling behavior in a single parameter, the Hurst exponent (H). Higher H reflects greater correlation in the signal structure, which is taken as being more fractal. Previous fMRI studies have observed lower H during conventional tasks relative to resting state conditions, and shown that H is negatively correlated with task difficulty and novelty. To date, no study has investigated the fractal dynamics of BOLD signal during naturalistic conditions. Methods: We performed fractal analysis on Human Connectome Project 7T fMRI data (n = 72, 41 females, mean age 29.46 ± 3.76 years) to compare H across movie-watching and rest. Results: In contrast to previous work using conventional tasks, we found higher H values for movie relative to rest (mean difference = 0.014; p = 5.279 × 10-7; 95% CI [0.009, 0.019]). H was significantly higher in movie than rest in the visual, somatomotor and dorsal attention networks, but was significantly lower during movie in the frontoparietal and default networks. We found no cross-condition differences in test-retest reliability of H. Finally, we found that H of movie-derived stimulus properties (e.g., luminance changes) were fractal whereas H of head motion estimates were non-fractal. Conclusions: Overall, our findings suggest that movie-watching induces fractal signal dynamics. In line with recent work characterizing connectivity-based brain state dynamics during movie-watching, we speculate that these fractal dynamics reflect the configuring and reconfiguring of brain states that occurs during naturalistic processing, and are markedly different than dynamics observed during conventional tasks.
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Affiliation(s)
- Olivia Campbell
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Tamara Vanderwal
- British Columbia (BC) Children's Hospital Research Institute, UBC, Vancouver, BC, Canada.,Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Alexander Mark Weber
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada.,British Columbia (BC) Children's Hospital Research Institute, UBC, Vancouver, BC, Canada.,Division of Neurology, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada.,Department of Neuroscience, University of British Columbia, Vancouver, BC, Canada
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20
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Cavanagh JF, Gregg D, Light GA, Olguin SL, Sharp RF, Bismark AW, Bhakta SG, Swerdlow NR, Brigman JL, Young JW. Electrophysiological biomarkers of behavioral dimensions from cross-species paradigms. Transl Psychiatry 2021; 11:482. [PMID: 34535625 PMCID: PMC8448772 DOI: 10.1038/s41398-021-01562-w] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 07/20/2021] [Accepted: 08/11/2021] [Indexed: 02/08/2023] Open
Abstract
There has been a fundamental failure to translate preclinically supported research into clinically efficacious treatments for psychiatric disorders. One of the greatest impediments toward improving this species gap has been the difficulty of identifying translatable neurophysiological signals that are related to specific behavioral constructs. Here, we present evidence from three paradigms that were completed by humans and mice using analogous procedures, with each task eliciting candidate a priori defined electrophysiological signals underlying effortful motivation, reinforcement learning, and cognitive control. The effortful motivation was assessed using a progressive ratio breakpoint task, yielding a similar decrease in alpha-band activity over time in both species. Reinforcement learning was assessed via feedback in a probabilistic learning task with delta power significantly modulated by reward surprise in both species. Additionally, cognitive control was assessed in the five-choice continuous performance task, yielding response-locked theta power seen across species, and modulated by difficulty in humans. Together, these successes, and also the teachings from these failures, provide a roadmap towards the use of electrophysiology as a method for translating findings from the preclinical assays to the clinical settings.
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Affiliation(s)
- James F. Cavanagh
- grid.266832.b0000 0001 2188 8502Psychology Department, University of New Mexico, Albuquerque, NM USA
| | - David Gregg
- grid.266832.b0000 0001 2188 8502Department of Neurosciences, University of New Mexico School of Medicine, Albuquerque, NM 87131 USA
| | - Gregory A. Light
- grid.266100.30000 0001 2107 4242Department of Psychiatry, University of California San Diego, 9500 Gilman Drive MC 0804, La Jolla, CA 92093-0804 USA ,grid.410371.00000 0004 0419 2708VISN-22 Mental Illness Research Education and Clinical Center, VA San Diego Healthcare System, San Diego, CA USA
| | - Sarah L. Olguin
- grid.266832.b0000 0001 2188 8502Department of Neurosciences, University of New Mexico School of Medicine, Albuquerque, NM 87131 USA
| | - Richard F. Sharp
- grid.266100.30000 0001 2107 4242Department of Psychiatry, University of California San Diego, 9500 Gilman Drive MC 0804, La Jolla, CA 92093-0804 USA
| | - Andrew W. Bismark
- grid.410371.00000 0004 0419 2708VISN-22 Mental Illness Research Education and Clinical Center, VA San Diego Healthcare System, San Diego, CA USA
| | - Savita G. Bhakta
- grid.266100.30000 0001 2107 4242Department of Psychiatry, University of California San Diego, 9500 Gilman Drive MC 0804, La Jolla, CA 92093-0804 USA
| | - Neal R. Swerdlow
- grid.266100.30000 0001 2107 4242Department of Psychiatry, University of California San Diego, 9500 Gilman Drive MC 0804, La Jolla, CA 92093-0804 USA
| | - Jonathan L. Brigman
- grid.266832.b0000 0001 2188 8502Department of Neurosciences, University of New Mexico School of Medicine, Albuquerque, NM 87131 USA
| | - Jared W. Young
- grid.266100.30000 0001 2107 4242Department of Psychiatry, University of California San Diego, 9500 Gilman Drive MC 0804, La Jolla, CA 92093-0804 USA ,grid.410371.00000 0004 0419 2708VISN-22 Mental Illness Research Education and Clinical Center, VA San Diego Healthcare System, San Diego, CA USA
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21
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Wiesman AI, Christopher-Hayes NJ, Wilson TW. Stairway to memory: Left-hemispheric alpha dynamics index the progressive loading of items into a short-term store. Neuroimage 2021; 235:118024. [PMID: 33836267 PMCID: PMC8354033 DOI: 10.1016/j.neuroimage.2021.118024] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 03/25/2021] [Accepted: 03/27/2021] [Indexed: 01/29/2023] Open
Abstract
The encoding, maintenance, and subsequent retrieval of memories over short time intervals is an essential cognitive function. Load effects on the neural dynamics supporting the maintenance of short-term memories have been well studied, but experimental design limitations have hindered the study of similar effects during the encoding of information into online memory stores. Theoretically, the active encoding of complex visual stimuli into memory must also recruit neural resources in a manner that scales with memory load. Understanding the neural systems supporting this encoding load effect is of particular importance, as some patient populations exhibit difficulties specifically with the encoding, and not the maintenance, of short-term memories. Using magnetoencephalography, a visual sequence memory paradigm, and a novel encoding slope analysis, we provide evidence for a left-lateralized network of regions, oscillating in the alpha frequency range, that exhibit a progressive loading effect of complex visual stimulus information during memory encoding. This progressive encoding load effect significantly tracked the eventual retrieval of item-order memories at the single trial level, and neural activity in these regions was functionally dissociated from that of earlier visual networks. These findings suggest that the active encoding of stimulus information into short-term stores recruits a left-lateralized network of frontal, parietal, and temporal regions, and might be susceptible to modulation (e.g., using non-invasive stimulation) in the alpha band.
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Affiliation(s)
- Alex I Wiesman
- College of Medicine, University of Nebraska Medical Center, Omaha 68198-8422, NE, United States.
| | - Nicholas J Christopher-Hayes
- College of Medicine, University of Nebraska Medical Center, Omaha 68198-8422, NE, United States; Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE, United States
| | - Tony W Wilson
- College of Medicine, University of Nebraska Medical Center, Omaha 68198-8422, NE, United States; Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE, United States
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22
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Meidenbauer KL, Choe KW, Cardenas-Iniguez C, Huppert TJ, Berman MG. Load-dependent relationships between frontal fNIRS activity and performance: A data-driven PLS approach. Neuroimage 2021; 230:117795. [PMID: 33503483 PMCID: PMC8145788 DOI: 10.1016/j.neuroimage.2021.117795] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 01/10/2021] [Accepted: 01/11/2021] [Indexed: 11/19/2022] Open
Abstract
Neuroimaging research frequently demonstrates load-dependent activation in prefrontal and parietal cortex during working memory tasks such as the N-back. Most of this work has been conducted in fMRI, but functional near-infrared spectroscopy (fNIRS) is gaining traction as a less invasive and more flexible alternative to measuring cortical hemodynamics. Few fNIRS studies, however, have examined how working memory load-dependent changes in brain hemodynamics relate to performance. The current study employs a newly developed and robust statistical analysis of task-based fNIRS data in a large sample, and demonstrates the utility of data-driven, multivariate analyses to link brain activation and behavior in this modality. Seventy participants completed a standard N-back task with three N-back levels (N = 1, 2, 3) while fNIRS data were collected from frontal and parietal cortex. Overall, participants showed reliably greater fronto-parietal activation for the 2-back versus the 1-back task, suggesting fronto-parietal fNIRS measurements are sensitive to differences in cognitive load. The results for 3-back were much less consistent, potentially due to poor behavioral performance in the 3-back task. To address this, a multivariate analysis (behavioral partial least squares, PLS) was conducted to examine the interaction between fNIRS activation and performance at each N-back level. Results of the PLS analysis demonstrated differences in the relationship between accuracy and change in the deoxyhemoglobin fNIRS signal as a function of N-back level in eight mid-frontal channels. Specifically, greater reductions in deoxyhemoglobin (i.e., more activation) were positively related to performance on the 3-back task, unrelated to accuracy in the 2-back task, and negatively associated with accuracy in the 1-back task. This pattern of results suggests that the metabolic demands correlated with neural activity required for high levels of accuracy vary as a consequence of task difficulty/cognitive load, whereby more automaticity during the 1-back task (less mid-frontal activity) predicted superior performance on this relatively easy task, and successful engagement of this mid-frontal region was required for high accuracy on a more difficult and cognitively demanding 3-back task. In summary, we show that fNIRS activity can track working memory load and can uncover significant associations between brain activity and performance, thus opening the door for this modality to be used in more wide-spread applications.
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Affiliation(s)
- Kimberly L. Meidenbauer
- Environmental Neuroscience Lab, Department of Psychology, The University of Chicago, 5848 S University Avenue, Chicago, IL 60637, United States
- Corresponding authors: (K.L. Meidenbauer)
| | - Kyoung Whan Choe
- Environmental Neuroscience Lab, Department of Psychology, The University of Chicago, 5848 S University Avenue, Chicago, IL 60637, United States
- Mansueto Institute for Urban Innovation, The University of Chicago, United States
| | - Carlos Cardenas-Iniguez
- Environmental Neuroscience Lab, Department of Psychology, The University of Chicago, 5848 S University Avenue, Chicago, IL 60637, United States
| | - Theodore J. Huppert
- Department of Electrical and Computer Engineering, The University of Pittsburgh, United States
| | - Marc G. Berman
- Environmental Neuroscience Lab, Department of Psychology, The University of Chicago, 5848 S University Avenue, Chicago, IL 60637, United States
- Grossman Institute for Neuroscience, Quantitative Biology and Human Behavior, United States
- Corresponding authors: (K.L. Meidenbauer)
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Beppi C, Ribeiro Violante I, Scott G, Sandrone S. EEG, MEG and neuromodulatory approaches to explore cognition: Current status and future directions. Brain Cogn 2021; 148:105677. [PMID: 33486194 DOI: 10.1016/j.bandc.2020.105677] [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] [Received: 10/04/2020] [Revised: 12/26/2020] [Accepted: 12/27/2020] [Indexed: 01/04/2023]
Abstract
Neural oscillations and their association with brain states and cognitive functions have been object of extensive investigation over the last decades. Several electroencephalography (EEG) and magnetoencephalography (MEG) analysis approaches have been explored and oscillatory properties have been identified, in parallel with the technical and computational advancement. This review provides an up-to-date account of how EEG/MEG oscillations have contributed to the understanding of cognition. Methodological challenges, recent developments and translational potential, along with future research avenues, are discussed.
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Affiliation(s)
- Carolina Beppi
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland; Department of Neurology, University Hospital Zurich and University of Zurich, Zurich, Switzerland; Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
| | - Inês Ribeiro Violante
- Computational, Cognitive and Clinical Neuroscience Laboratory (C3NL), Department of Brain Sciences, Imperial College London, London, United Kingdom; School of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom.
| | - Gregory Scott
- Computational, Cognitive and Clinical Neuroscience Laboratory (C3NL), Department of Brain Sciences, Imperial College London, London, United Kingdom.
| | - Stefano Sandrone
- Computational, Cognitive and Clinical Neuroscience Laboratory (C3NL), Department of Brain Sciences, Imperial College London, London, United Kingdom.
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24
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Jacobson N, Berleman-Paul Q, Mangalam M, Kelty-Stephen DG, Ralston C. Multifractality in postural sway supports quiet eye training in aiming tasks: A study of golf putting. Hum Mov Sci 2021; 76:102752. [PMID: 33468324 DOI: 10.1016/j.humov.2020.102752] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 12/19/2020] [Accepted: 12/22/2020] [Indexed: 01/13/2023]
Abstract
The 'quiet eye' (QE) approach to visually-guided aiming behavior invests fully in perceptual information's potential to organize coordinated action. Sports psychologists refer to QE as the stillness of the eyes during aiming tasks and increasingly into self- and externally-paced tasks. Amidst the 'noisy' fluctuations of the athlete's body, quiet eyes might leave fewer saccadic interruptions to the coupling between postural sway and optic flow. Postural sway exhibits fluctuations whose multifractal structure serves as a robust predictor of visual and haptic perceptual responses. Postural sway generates optic flow centered on an individual's eye height. We predicted that perturbing the eye height by attaching wooden blocks below the feet would perturb the putting more so in QE-trained participants than participants trained technically. We also predicted that QE's efficacy and responses to perturbation would depend on multifractality in postural sway. Specifically, we predicted that less multifractality would predict more adaptive responses to the perturbation and higher putting accuracy. Results showed that lower multifractality led to more accurate putts, and the perturbation of eye height led to less accurate putts, particularly for QE-trained participants. Models of radial error (i.e., the distance between the ball's final position and the hole) indicated that lower estimates of multifractality due to nonlinearity coincided with a more adaptive response to the perturbation. These results suggest that reduced multifractality may act in a context-sensitive manner to restrain motoric degrees of freedom to achieve the task goal.
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Affiliation(s)
- Noah Jacobson
- Department of Psychology, Grinnell College, Grinnell, IA 50112, USA
| | | | - Madhur Mangalam
- Department of Physical Therapy, Movement and Rehabilitation Sciences, Northeastern University, Boston, MA 02115, USA
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25
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Boring MJ, Ridgeway K, Shvartsman M, Jonker TR. Continuous decoding of cognitive load from electroencephalography reveals task-general and task-specific correlates. J Neural Eng 2020; 17:056016. [PMID: 32947265 DOI: 10.1088/1741-2552/abb9bc] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Algorithms to detect changes in cognitive load using non-invasive biosensors (e.g. electroencephalography (EEG)) have the potential to improve human-computer interactions by adapting systems to an individual's current information processing capacity, which may enhance performance and mitigate costly errors. However, for algorithms to provide maximal utility, they must be able to detect load across a variety of tasks and contexts. The current study aimed to build models that capture task-general EEG correlates of cognitive load, which would allow for load detection across variable task contexts. APPROACH Sliding-window support vector machines (SVM) were trained to predict periods of high versus low cognitive load across three cognitively and perceptually distinct tasks: n-back, mental arithmetic, and multi-object tracking. To determine how well these SVMs could generalize to novel tasks, they were trained on data from two of the three tasks and evaluated on the held-out task. Additionally, to better understand task-general and task-specific correlates of cognitive load, a set of models were trained on subsets of EEG frequency features. MAIN RESULTS Models achieved reliable performance in classifying periods of high versus low cognitive load both within and across tasks, demonstrating their generalizability. Furthermore, continuous model outputs correlated with subtle differences in self-reported mental effort and they captured predicted changes in load within individual trials of each task. Additionally, alpha or beta frequency features achieved reliable within- and cross-task performance, suggesting that activity in these frequency bands capture task-general signatures of cognitive load. In contrast, delta and theta frequency features performed considerably worse than the full cross-task models, suggesting that delta and theta activity may be reflective of task-specific differences across cognitive load conditions. SIGNIFICANCE EEG data contains task-general signatures of cognitive load. Sliding-window SVMs can capture these signatures and continuously detect load across multiple task contexts.
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Affiliation(s)
- Matthew J Boring
- Facebook Reality Labs, Redmond, WA, United States of America. Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, United States of America
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Xiong R, Kong F, Yang X, Liu G, Wen W. Pattern Recognition of Cognitive Load Using EEG and ECG Signals. SENSORS 2020; 20:s20185122. [PMID: 32911809 PMCID: PMC7571025 DOI: 10.3390/s20185122] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 09/03/2020] [Accepted: 09/06/2020] [Indexed: 11/16/2022]
Abstract
The matching of cognitive load and working memory is the key for effective learning, and cognitive effort in the learning process has nervous responses which can be quantified in various physiological parameters. Therefore, it is meaningful to explore automatic cognitive load pattern recognition by using physiological measures. Firstly, this work extracted 33 commonly used physiological features to quantify autonomic and central nervous activities. Secondly, we selected a critical feature subset for cognitive load recognition by sequential backward selection and particle swarm optimization algorithms. Finally, pattern recognition models of cognitive load conditions were constructed by a performance comparison of several classifiers. We grouped the samples in an open dataset to form two binary classification problems: (1) cognitive load state vs. baseline state; (2) cognitive load mismatching state vs. cognitive load matching state. The decision tree classifier obtained 96.3% accuracy for the cognitive load vs. baseline classification, and the support vector machine obtained 97.2% accuracy for the cognitive load mismatching vs. cognitive load matching classification. The cognitive load and baseline states are distinguishable in the level of active state of mind and three activity features of the autonomic nervous system. The cognitive load mismatching and matching states are distinguishable in the level of active state of mind and two activity features of the autonomic nervous system.
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Affiliation(s)
- Ronglong Xiong
- School of Electronic and Information Engineering, Southwest University, Chongqing 400715, China; (R.X.); (F.K.); (X.Y.); (G.L.)
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing 400715, China
| | - Fanmeng Kong
- School of Electronic and Information Engineering, Southwest University, Chongqing 400715, China; (R.X.); (F.K.); (X.Y.); (G.L.)
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing 400715, China
| | - Xuehong Yang
- School of Electronic and Information Engineering, Southwest University, Chongqing 400715, China; (R.X.); (F.K.); (X.Y.); (G.L.)
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing 400715, China
| | - Guangyuan Liu
- School of Electronic and Information Engineering, Southwest University, Chongqing 400715, China; (R.X.); (F.K.); (X.Y.); (G.L.)
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing 400715, China
| | - Wanhui Wen
- School of Electronic and Information Engineering, Southwest University, Chongqing 400715, China; (R.X.); (F.K.); (X.Y.); (G.L.)
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing 400715, China
- Correspondence:
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