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Amos TJ, Guragai B, Rao Q, Li W, Jin Z, Zhang J, Li L. Task functional networks predict individual differences in the speed of emotional facial discrimination. Neuroimage 2024; 297:120715. [PMID: 38945182 DOI: 10.1016/j.neuroimage.2024.120715] [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: 09/30/2023] [Revised: 04/21/2024] [Accepted: 06/28/2024] [Indexed: 07/02/2024] Open
Abstract
Every individual experiences negative emotions, such as fear and anger, significantly influencing how external information is perceived and processed. With the gradual rise in brain-behavior relationship studies, analyses investigating individual differences in negative emotion processing and a more objective measure such as the response time (RT) remain unexplored. This study aims to address this gap by establishing that the individual differences in the speed of negative facial emotion discrimination can be predicted from whole-brain functional connectivity when participants were performing a face discrimination task. Employing the connectome predictive modeling (CPM) framework, we demonstrated this in the young healthy adult group from the Human Connectome Project-Young Adults (HCP-YA) dataset and the healthy group of the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA) dataset. We identified distinct network contributions in the adult and adolescent predictive models. The highest represented brain networks involved in the adult model predictions included representations from the motor, visual association, salience, and medial frontal networks. Conversely, the adolescent predictive models showed substantial contributions from the cerebellum-frontoparietal network interactions. Finally, we observed that despite the successful within-dataset prediction in healthy adults and adolescents, the predictive models failed in the cross-dataset generalization. In conclusion, our study shows that individual differences in the speed of emotional facial discrimination can be predicted in healthy adults and adolescent samples using their functional connectivity during negative facial emotion processing. Future research is needed in the derivation of more generalizable models.
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Affiliation(s)
- Toluwani Joan Amos
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China
| | - Bishal Guragai
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China
| | - Qianru Rao
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China
| | - Wenjuan Li
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China
| | - Zhenlan Jin
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China
| | - Junjun Zhang
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China.
| | - Ling Li
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China.
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2
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Brown T, Kim K, Gehring WJ, Lustig C, Bohnen NI. Sensitivity to and Control of Distraction: Distractor-Entrained Oscillation and Frontoparietal EEG Gamma Synchronization. Brain Sci 2024; 14:609. [PMID: 38928609 PMCID: PMC11202030 DOI: 10.3390/brainsci14060609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/13/2024] [Accepted: 06/13/2024] [Indexed: 06/28/2024] Open
Abstract
While recent advancements have been made towards a better understanding of the involvement of the prefrontal cortex (PFC) in the context of cognitive control, the exact mechanism is still not fully understood. Successful behavior requires the correct detection of goal-relevant cues and resisting irrelevant distractions. Frontal parietal networks have been implicated as important for maintaining cognitive control in the face of distraction. The present study investigated the role of gamma-band power in distraction resistance and frontoparietal networks, as its increase is linked to cholinergic activity. We examined changes in gamma activity and their relationship to frontoparietal top-down modulation for distractor challenges and to bottom-up distractor processing. Healthy young adults were tested using a modified version of the distractor condition sustained attention task (dSAT) while wearing an EEG. The modified distractor was designed so that oscillatory activities could be entrained to it, and the strength of entrainment was used to assess the degree of distraction. Increased top-down control during the distractor challenge increased gamma power in the left parietal regions rather than the right prefrontal regions predicted from rodent studies. Specifically, left parietal gamma power increased in response to distraction where the amount of this increase was negatively correlated with the neural activity reflecting bottom-up distractor processing in the visual area. Variability in gamma power in right prefrontal regions was associated with increased response time variability during distraction. This may suggest that the right prefrontal region may contribute to the signaling needed for top-down control rather than its implementation.
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Affiliation(s)
- Taylor Brown
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Kamin Kim
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA; (K.K.); (W.J.G.); (C.L.)
| | - William J. Gehring
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA; (K.K.); (W.J.G.); (C.L.)
| | - Cindy Lustig
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA; (K.K.); (W.J.G.); (C.L.)
| | - Nicolaas I. Bohnen
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA;
- Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA
- Neurology Service and GRECC, VA Ann Arbor Healthcare System, Ann Arbor, MI 48105, USA
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3
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Baror S, Baumgarten TJ, He BJ. Neural Mechanisms Determining the Duration of Task-free, Self-paced Visual Perception. J Cogn Neurosci 2024; 36:756-775. [PMID: 38357932 DOI: 10.1162/jocn_a_02131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2024]
Abstract
Humans spend hours each day spontaneously engaging with visual content, free from specific tasks and at their own pace. Currently, the brain mechanisms determining the duration of self-paced perceptual behavior remain largely unknown. Here, participants viewed naturalistic images under task-free settings and self-paced each image's viewing duration while undergoing EEG and pupillometry recordings. Across two independent data sets, we observed large inter- and intra-individual variability in viewing duration. However, beyond an image's presentation order and category, specific image content had no consistent effects on spontaneous viewing duration across participants. Overall, longer viewing durations were associated with sustained enhanced posterior positivity and anterior negativity in the ERPs. Individual-specific variations in the spontaneous viewing duration were consistently correlated with evoked EEG activity amplitudes and pupil size changes. By contrast, presentation order was selectively correlated with baseline alpha power and baseline pupil size. Critically, spontaneous viewing duration was strongly predicted by the temporal stability in neural activity patterns starting as early as 350 msec after image onset, suggesting that early neural stability is a key predictor for sustained perceptual engagement. Interestingly, neither bottom-up nor top-down predictions about image category influenced spontaneous viewing duration. Overall, these results suggest that individual-specific factors can influence perceptual processing at a surprisingly early time point and influence the multifaceted ebb and flow of spontaneous human perceptual behavior in naturalistic settings.
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Affiliation(s)
- Shira Baror
- New York University Grossman School of Medicine
- Hebrew University of Jerusalem
| | - Thomas J Baumgarten
- New York University Grossman School of Medicine
- Heinrich Heine University, Düsseldorf
| | - Biyu J He
- New York University Grossman School of Medicine
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4
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Jones HM, Yoo K, Chun MM, Rosenberg MD. Edge-Based General Linear Models Capture Moment-to-Moment Fluctuations in Attention. J Neurosci 2024; 44:e1543232024. [PMID: 38316565 PMCID: PMC10993033 DOI: 10.1523/jneurosci.1543-23.2024] [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: 08/15/2023] [Revised: 12/18/2023] [Accepted: 01/15/2024] [Indexed: 02/07/2024] Open
Abstract
Although we must prioritize the processing of task-relevant information to navigate life, our ability to do so fluctuates across time. Previous work has identified fMRI functional connectivity (FC) networks that predict an individual's ability to sustain attention and vary with attentional state from 1 min to the next. However, traditional dynamic FC approaches typically lack the temporal precision to capture moment-to-moment network fluctuations. Recently, researchers have "unfurled" traditional FC matrices in "edge cofluctuation time series" which measure timepoint-by-timepoint cofluctuations between regions. Here we apply event-based and parametric fMRI analyses to edge time series to capture moment-to-moment fluctuations in networks related to attention. In two independent fMRI datasets examining young adults of both sexes in which participants performed a sustained attention task, we identified a reliable set of edges that rapidly deflects in response to rare task events. Another set of edges varies with continuous fluctuations in attention and overlaps with a previously defined set of edges associated with individual differences in sustained attention. Demonstrating that edge-based analyses are not simply redundant with traditional regions-of-interest-based approaches, up to one-third of reliably deflected edges were not predicted from univariate activity patterns alone. These results reveal the large potential in combining traditional fMRI analyses with edge time series to identify rapid reconfigurations in networks across the brain.
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Affiliation(s)
- Henry M Jones
- Department of Psychology, The University of Chicago, Chicago, Illinois 60637
- Institute for Mind and Biology, The University of Chicago, Chicago, Illinois 60637
| | - Kwangsun Yoo
- Department of Psychology, Yale University, New Haven, Connecticut 06520
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul 06355, Korea
- Data Science Research Institute, Research Institute for Future Medicine, Samsung Medical Center, Seoul 06351, Korea
| | - Marvin M Chun
- Department of Psychology, Yale University, New Haven, Connecticut 06520
- Wu Tsai Institute, Yale University, New Haven, Connecticut 06520
- Department of Neuroscience, Yale University, New Haven, Connecticut 06520
| | - Monica D Rosenberg
- Department of Psychology, The University of Chicago, Chicago, Illinois 60637
- Institute for Mind and Biology, The University of Chicago, Chicago, Illinois 60637
- Neuroscience Institute, The University of Chicago, Chicago, Illinois 60637
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5
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Wamsley EJ, Collins M. Effect of cognitive load on time spent offline during wakefulness. Cereb Cortex 2024; 34:bhae022. [PMID: 38300213 DOI: 10.1093/cercor/bhae022] [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: 09/21/2023] [Revised: 12/13/2023] [Accepted: 01/12/2024] [Indexed: 02/02/2024] Open
Abstract
Humans continuously alternate between online attention to the current environment and offline attention to internally generated thought and imagery. This may be a fundamental feature of the waking brain, but remains poorly understood. Here, we took a data-driven approach to defining online and offline states of wakefulness, using machine learning methods applied to measures of sensory responsiveness, subjective report, electroencephalogram (EEG), and pupil diameter. We tested the effect of cognitive load on the structure and prevalence of online and offline states, hypothesizing that time spent offline would increase as cognitive load of an ongoing task decreased. We also expected that alternation between online and offline states would persist even in the absence of a cognitive task. As in prior studies, we arrived at a three-state model comprised of one online state and two offline states. As predicted, when cognitive load was high, more time was spent online. Also as predicted, the same three states were present even when participants were not performing a task. These observations confirm our method is successful at isolating seconds-long periods of offline time. Varying cognitive load may be a useful way to manipulate time spent in at least one of these offline states in future experimental studies.
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Affiliation(s)
- Erin J Wamsley
- Department of Psychology and Program in Neuroscience, Furman University, 3300 Poinsett Highway, Johns Hall 206K, Greenville, SC 29613, United States
| | - Megan Collins
- Department of Psychology and Program in Neuroscience, Furman University, 3300 Poinsett Highway, Johns Hall 206K, Greenville, SC 29613, United States
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Yang S, Dong H, Albitos PJ, Wang Y, Fang Y, Cao L, Wang J, Sun L, Zhang H. Low-frequency variability in theta activity modulates the attention-fluctuation across task and resting states. Neuropsychologia 2024; 193:108757. [PMID: 38103680 DOI: 10.1016/j.neuropsychologia.2023.108757] [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: 08/14/2023] [Revised: 11/05/2023] [Accepted: 12/11/2023] [Indexed: 12/19/2023]
Abstract
Sustained attention is not constant but fluctuates influencing our task performance. Albeit intensive investigations, it remains unclear whether the attention-fluctuation during tasks is derived from its spontaneous fluctuation in the resting state. Here, we addressed this issue by investigating the attention-fluctuation in both task and resting states, through the EEG measurement of theta-variability. We found significant rest-task modulation of theta-variability, i.e., reduced theta-variability in the task state compared to the resting state. This task and rest modulation was manifested in the low-frequency of theta-variability (<0.1 Hz). Furthermore, the low-frequency theta-variability exhibited a significant rest-task correlation, however, only the low-frequency theta-variability in the task state but not in the resting state was correlated with the behavioral performance. These findings shed light on the low-frequency feature of attention-fluctuation, and advanced our understanding of sustained attention by suggesting that the theta-variability in low-frequencies was relevant to attention level in task state.
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Affiliation(s)
- Shiyou Yang
- Centre for Cognition and Brain Disorders, The Affiliated Hospital, Hangzhou Normal University, Hangzhou, Zhejiang, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairment, Hangzhou, Zhejiang, China; School of Psychology, Northeast Normal University, Changchun, Jilin, China
| | - Huimei Dong
- Centre for Cognition and Brain Disorders, The Affiliated Hospital, Hangzhou Normal University, Hangzhou, Zhejiang, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairment, Hangzhou, Zhejiang, China
| | - Princess Jane Albitos
- Centre for Cognition and Brain Disorders, The Affiliated Hospital, Hangzhou Normal University, Hangzhou, Zhejiang, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairment, Hangzhou, Zhejiang, China
| | - Yaoyao Wang
- Centre for Cognition and Brain Disorders, The Affiliated Hospital, Hangzhou Normal University, Hangzhou, Zhejiang, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairment, Hangzhou, Zhejiang, China
| | - Yantong Fang
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairment, Hangzhou, Zhejiang, China
| | - Longfei Cao
- Centre for Cognition and Brain Disorders, The Affiliated Hospital, Hangzhou Normal University, Hangzhou, Zhejiang, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairment, Hangzhou, Zhejiang, China
| | - Jinghua Wang
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairment, Hangzhou, Zhejiang, China; Department of Neurology the Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Li Sun
- Peking University Sixth Hospital, Institute of Mental Health, Beijing, China; NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Hang Zhang
- Centre for Cognition and Brain Disorders, The Affiliated Hospital, Hangzhou Normal University, Hangzhou, Zhejiang, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairment, Hangzhou, Zhejiang, China.
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7
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Kindler J, Ishida T, Michel C, Klaassen AL, Stüble M, Zimmermann N, Wiest R, Kaess M, Morishima Y. Aberrant brain dynamics in individuals with clinical high risk of psychosis. SCHIZOPHRENIA BULLETIN OPEN 2024; 5:sgae002. [PMID: 38605980 PMCID: PMC7615822 DOI: 10.1093/schizbullopen/sgae002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
Background Resting-state network (RSN) functional connectivity analyses have profoundly influenced our understanding of the pathophysiology of psychoses and their clinical high risk (CHR) states. However, conventional RSN analyses address the static nature of large-scale brain networks. In contrast, novel methodological approaches aim to assess the momentum state and temporal dynamics of brain network interactions. Methods Fifty CHR individuals and 33 healthy controls (HC) completed a resting-state functional MRI scan. We performed an Energy Landscape analysis, a data-driven method using the pairwise maximum entropy model, to describe large-scale brain network dynamics such as duration and frequency of, and transition between, different brain states. We compared those measures between CHR and HC, and examined the association between neuropsychological measures and neural dynamics in CHR. Results Our main finding is a significantly increased duration, frequency, and higher transition rates to an infrequent brain state with coactivation of the salience, limbic, default mode and somatomotor RSNs in CHR as compared to HC. Transition of brain dynamics from this brain state was significantly correlated with processing speed in CHR. Conclusion In CHR, temporal brain dynamics are attracted to an infrequent brain state, reflecting more frequent and longer occurrence of aberrant interactions of default mode, salience, and limbic networks. Concurrently, more frequent and longer occurrence of the brain state is associated with core cognitive dysfunctions, predictors of future onset of full-blown psychosis.
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Affiliation(s)
- Jochen Kindler
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Takuya Ishida
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Kimiidera, Japan
| | - Chantal Michel
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Arndt-Lukas Klaassen
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Miriam Stüble
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
| | - Nadja Zimmermann
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Roland Wiest
- University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland
| | - Michael Kaess
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
- Department of Child and Adolescent Psychiatry, Center for Psychosocial Medicine, University Hospital Heidelberg, Heidelberg, Germany
| | - Yosuke Morishima
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
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8
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Jones HM, Yoo K, Chun MM, Rosenberg MD. Edge-based general linear models capture high-frequency fluctuations in attention. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.06.547966. [PMID: 37503244 PMCID: PMC10369861 DOI: 10.1101/2023.07.06.547966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Although we must prioritize the processing of task-relevant information to navigate life, our ability to do so fluctuates across time. Previous work has identified fMRI functional connectivity (FC) networks that predict an individual's ability to sustain attention and vary with attentional state from one minute to the next. However, traditional dynamic FC approaches typically lack the temporal precision to capture moment-by-moment network fluctuations. Recently, researchers have 'unfurled' traditional FC matrices in 'edge cofluctuation time series' which measure time point-by-time point cofluctuations between regions. Here we apply event-based and parametric fMRI analyses to edge time series to capture high-frequency fluctuations in networks related to attention. In two independent fMRI datasets in which participants performed a sustained attention task, we identified a reliable set of edges that rapidly deflects in response to rare task events. Another set of edges varies with continuous fluctuations in attention and overlaps with a previously defined set of edges associated with individual differences in sustained attention. Demonstrating that edge-based analyses are not simply redundant with traditional regions-of-interest based approaches, up to one-third of reliably deflected edges were not predicted from univariate activity patterns alone. These results reveal the large potential in combining traditional fMRI analyses with edge time series to identify rapid reconfigurations in networks across the brain.
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Affiliation(s)
| | | | - Marvin M Chun
- Department of Psychology, Yale University
- Wu Tsai Institute, Yale University
| | - Monica D Rosenberg
- Department of Psychology, The University of Chicago
- Neuroscience Institute, The University of Chicago
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9
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Rao B, Wang S, Yu M, Chen L, Miao G, Zhou X, Zhou H, Liao W, Xu H. Suboptimal states and frontoparietal network-centered incomplete compensation revealed by dynamic functional network connectivity in patients with post-stroke cognitive impairment. Front Aging Neurosci 2022; 14:893297. [PMID: 36003999 PMCID: PMC9393744 DOI: 10.3389/fnagi.2022.893297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 07/21/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundNeural reorganization occurs after a stroke, and dynamic functional network connectivity (dFNC) pattern is associated with cognition. We hypothesized that dFNC alterations resulted from neural reorganization in post-stroke cognitive impairment (PSCI) patients, and specific dFNC patterns characterized different pathological types of PSCI.MethodsResting-state fMRI data were collected from 16 PSCI patients with hemorrhagic stroke (hPSCI group), 21 PSCI patients with ischemic stroke (iPSCI group), and 21 healthy controls (HC). We performed the dFNC analysis for the dynamic connectivity states, together with their topological and temporal features.ResultsWe identified 10 resting-state networks (RSNs), and the dFNCs could be clustered into four reoccurring states (modular, regional, sparse, and strong). Compared with HC, the hPSCI and iPSCI patients showed lower standard deviation (SD) and coefficient of variation (CV) in the regional and modular states, respectively (p < 0.05). Reduced connectivities within the primary network (visual, auditory, and sensorimotor networks) and between the primary and high-order cognitive control domains were observed (p < 0.01).ConclusionThe transition trend to suboptimal states may play a compensatory role in patients with PSCI through redundancy networks. The reduced exploratory capacity (SD and CV) in different suboptimal states characterized cognitive impairment and pathological types of PSCI. The functional disconnection between the primary and high-order cognitive control network and the frontoparietal network centered (FPN-centered) incomplete compensation may be the pathological mechanism of PSCI. These results emphasize the flexibility of neural reorganization during self-repair.
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Affiliation(s)
- Bo Rao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Sirui Wang
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Minhua Yu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Linglong Chen
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Guofu Miao
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xiaoli Zhou
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Hong Zhou
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Weijing Liao
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
- *Correspondence: Weijing Liao,
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
- Haibo Xu,
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10
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Evans TC, Alonso MR, Jagger-Rickels A, Rothlein D, Zuberer A, Bernstein J, Fortier CB, Fonda JR, Villalon A, Jorge R, Milberg W, McGlinchey R, DeGutis J, Esterman M. PTSD symptomatology is selectively associated with impaired sustained attention ability and dorsal attention network synchronization. Neuroimage Clin 2022; 36:103146. [PMID: 36055063 PMCID: PMC9437905 DOI: 10.1016/j.nicl.2022.103146] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 07/03/2022] [Accepted: 08/03/2022] [Indexed: 12/14/2022]
Abstract
Posttraumatic Stress Disorder (PTSD) symptomatology is associated with dysregulated sustained attention, which produces functional impairments. Performance on sustained attention paradigms such as continuous performance tasks are influenced by both the ability to sustain attention and response strategy. However, previous studies have not dissociated PTSD-related associations with sustained attention ability and strategy, which limits characterization of neural circuitry underlying PTSD-related attentional impairments. Therefore, we characterized and replicated PTSD-related associations with sustained attention ability and response strategy in trauma-exposed Veterans, which guided characterization of PTSD-related differences in neural circuit function. In Study 1, PTSD symptoms were selectively associated with reduced sustained attention ability, but not more impulsive response strategies. In Study 2, we utilized task and resting-state fMRI to characterize neural circuitry underlying PTSD-related differences in sustained attention ability. Both PTSD symptomatology and sustained attention ability exhibited converging associations with reduced dorsal attention network (DAN) synchronization to endogeneous attentional fluctuations. Post-hoc time course analyses demonstrated that PTSD symptoms were most accurately characterized by delayed, rather than globally reduced, DAN synchronization to endogenous attentional fluctuations. Together, these findings suggest that PTSD symptomatology may selectively impair sustained attention ability by disrupting proactive engagement of attentional control circuitry.
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Affiliation(s)
- Travis C. Evans
- Boston Attention and Learning Lab (BALLAB), VA Boston Healthcare System, USA,Department of Psychiatry, Boston University School of Medicine, USA,Corresponding author at: VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130, USA.
| | | | - Audreyana Jagger-Rickels
- Boston Attention and Learning Lab (BALLAB), VA Boston Healthcare System, USA,National Center for PTSD, VA Boston Healthcare System, USA
| | - David Rothlein
- Boston Attention and Learning Lab (BALLAB), VA Boston Healthcare System, USA,National Center for PTSD, VA Boston Healthcare System, USA
| | - Agnieszka Zuberer
- Boston Attention and Learning Lab (BALLAB), VA Boston Healthcare System, USA,Department of Psychiatry and Psychotherapy, University Hospital Jena, Germany,Department of Psychiatry and Psychotherapy, University of Tübingen, Germany
| | - John Bernstein
- Translational Research Center for TBI and Stress Disorders (TRACTS), VA Boston Healthcare System, USA
| | - Catherine B. Fortier
- Translational Research Center for TBI and Stress Disorders (TRACTS), VA Boston Healthcare System, USA,Department of Psychiatry, Harvard Medical School, USA,Geriatric Research, Education, and Clinical Center (GRECC), VA Boston Healthcare System, USA
| | - Jennifer R. Fonda
- Department of Psychiatry, Boston University School of Medicine, USA,Translational Research Center for TBI and Stress Disorders (TRACTS), VA Boston Healthcare System, USA,Department of Psychiatry, Harvard Medical School, USA,Geriatric Research, Education, and Clinical Center (GRECC), VA Boston Healthcare System, USA
| | - Audri Villalon
- Translational Research Center for TBI and Stress Disorders (TRACTS), Michael E. DeBakey VA Medical Center, Houston, TX, USA,Beth K. and Stuart C. Yudofsky Division of Neuropsychiatry, Baylor College of Medicine, USA
| | - Ricardo Jorge
- Translational Research Center for TBI and Stress Disorders (TRACTS), Michael E. DeBakey VA Medical Center, Houston, TX, USA,Beth K. and Stuart C. Yudofsky Division of Neuropsychiatry, Baylor College of Medicine, USA
| | - William Milberg
- Translational Research Center for TBI and Stress Disorders (TRACTS), VA Boston Healthcare System, USA,Department of Psychiatry, Harvard Medical School, USA,Geriatric Research, Education, and Clinical Center (GRECC), VA Boston Healthcare System, USA
| | - Regina McGlinchey
- Translational Research Center for TBI and Stress Disorders (TRACTS), VA Boston Healthcare System, USA,Department of Psychiatry, Harvard Medical School, USA,Geriatric Research, Education, and Clinical Center (GRECC), VA Boston Healthcare System, USA
| | - Joseph DeGutis
- Boston Attention and Learning Lab (BALLAB), VA Boston Healthcare System, USA,Department of Psychiatry, Harvard Medical School, USA,Geriatric Research, Education, and Clinical Center (GRECC), VA Boston Healthcare System, USA
| | - Michael Esterman
- Boston Attention and Learning Lab (BALLAB), VA Boston Healthcare System, USA,Department of Psychiatry, Boston University School of Medicine, USA,National Center for PTSD, VA Boston Healthcare System, USA,Neuroimaging Research for Veterans (NeRVe) Center, VA Boston Healthcare System, USA
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Ogihara T, Tanioka K, Hiroyasu T, Hiwa S. Predicting the Degree of Distracted Driving Based on fNIRS Functional Connectivity: A Pilot Study. FRONTIERS IN NEUROERGONOMICS 2022; 3:864938. [PMID: 38235448 PMCID: PMC10790849 DOI: 10.3389/fnrgo.2022.864938] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Accepted: 06/21/2022] [Indexed: 01/19/2024]
Abstract
Distracted driving is one of the main causes of traffic accidents. By predicting the attentional state of drivers, it is possible to prevent distractions and promote safe driving. In this study, we developed a model that could predict the degree of distracted driving based on brain activity. Changes in oxyhemoglobin concentrations were measured in drivers while driving a real car using functional near-infrared spectroscopy (fNIRS). A regression model was constructed for each participant using functional connectivity as an explanatory variable and brake reaction time to random beeps while driving as an objective variable. As a result, we were able to construct a prediction model with the mean absolute error of 5.58 × 102 ms for the BRT of the 12 participants. Furthermore, the regression model with the highest prediction accuracy for each participant was analyzed to gain a better understanding of the neural basis of distracted driving. The 11 of 12 models that showed significant accuracy were classified into five clusters by hierarchical clustering based on their functional connectivity edges used in each cluster. The results showed that the combinations of the dorsal attention network (DAN)-sensory-motor network (SMN) and DAN-ventral attention network (VAN) connections were common in all clusters and that these networks were essential to predict the degree of distraction in complex multitask driving. They also confirmed the existence of multiple types of prediction models with different within- and between-network connectivity patterns. These results indicate that it is possible to predict the degree of distracted driving based on the driver's brain activity during actual driving. These results are expected to contribute to the development of safe driving systems and elucidate the neural basis of distracted driving.
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Affiliation(s)
- Takahiko Ogihara
- Graduate School of Life and Medical Sciences, Doshisha University, Kyoto, Japan
| | - Kensuke Tanioka
- Department of Biomedical Sciences and Informatics, Doshisha University, Kyoto, Japan
| | - Tomoyuki Hiroyasu
- Department of Biomedical Sciences and Informatics, Doshisha University, Kyoto, Japan
| | - Satoru Hiwa
- Department of Biomedical Sciences and Informatics, Doshisha University, Kyoto, Japan
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12
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Jagger-Rickels A, Rothlein D, Stumps A, Evans TC, Bernstein J, Milberg W, McGlinchey R, DeGutis J, Esterman M. An executive function subtype of PTSD with unique neural markers and clinical trajectories. Transl Psychiatry 2022; 12:262. [PMID: 35760805 PMCID: PMC9237057 DOI: 10.1038/s41398-022-02011-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 05/19/2022] [Accepted: 05/27/2022] [Indexed: 11/18/2022] Open
Abstract
Previous work identified a cognitive subtype of PTSD with impaired executive function (i.e., impaired EF-PTSD subtype) and aberrant resting-state functional connectivity between frontal parietal control (FPCN) and limbic (LN) networks. To better characterize this cognitive subtype of PTSD, this study investigated (1) alterations in specific FPCN and LN subnetworks and (2) chronicity of PTSD symptoms. In a post-9/11 veteran sample (N = 368, 89% male), we identified EF subgroups using a standardized neuropsychological battery and a priori cutoffs for impaired, average, and above-average EF performance. Functional connectivity between two subnetworks of the FPCN and three subnetworks of the LN was assessed using resting-state fMRI (n = 314). PTSD chronicity over a 1-2-year period was assessed using a reliable change index (n = 175). The impaired EF-PTSD subtype had significantly reduced negative functional connectivity between the FPCN subnetwork involved in top-down control of emotion and two LN subnetworks involved in learning/memory and social/emotional processing. This impaired EF-PTSD subtype had relatively chronic PTSD, while those with above-average EF and PTSD displayed greater symptom reduction. Lastly, FPCN-LN subnetworks partially mediated the relationship between EF and PTSD chronicity (n = 121). This study reveals (1) that an impaired EF-PTSD subtype has a specific pattern of FPCN-LN subnetwork connectivity, (2) a novel above-average EF-PTSD subtype displays reduced PTSD chronicity, and (3) both cognitive and neural functioning predict PTSD chronicity. The results indicate a need to investigate how individuals with this impaired EF-PTSD subtype respond to treatment, and how they might benefit from personalized and novel approaches that target these neurocognitive systems.
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Affiliation(s)
- Audreyana Jagger-Rickels
- National Center for PTSD (NCPTSD), VA Boston Healthcare System, Boston, MA, USA. .,Boston Attention and Learning Lab (BALLAB), VA Boston Healthcare System, Boston, MA, USA. .,Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA.
| | - David Rothlein
- grid.410370.10000 0004 4657 1992National Center for PTSD (NCPTSD), VA Boston Healthcare System, Boston, MA USA ,grid.410370.10000 0004 4657 1992Boston Attention and Learning Lab (BALLAB), VA Boston Healthcare System, Boston, MA USA
| | - Anna Stumps
- grid.410370.10000 0004 4657 1992Boston Attention and Learning Lab (BALLAB), VA Boston Healthcare System, Boston, MA USA ,grid.33489.350000 0001 0454 4791Department of Psychological and Brain Sciences, University of Delaware, Newark, DE USA ,grid.410370.10000 0004 4657 1992Translational Research Center for TBI and Stress Disorders (TRACTS), VA Boston Healthcare System, Boston, MA USA
| | - Travis Clark Evans
- grid.410370.10000 0004 4657 1992Boston Attention and Learning Lab (BALLAB), VA Boston Healthcare System, Boston, MA USA ,grid.189504.10000 0004 1936 7558Department of Psychiatry, Boston University School of Medicine, Boston, MA USA
| | - John Bernstein
- grid.410370.10000 0004 4657 1992Translational Research Center for TBI and Stress Disorders (TRACTS), VA Boston Healthcare System, Boston, MA USA
| | - William Milberg
- grid.410370.10000 0004 4657 1992Translational Research Center for TBI and Stress Disorders (TRACTS), VA Boston Healthcare System, Boston, MA USA ,grid.38142.3c000000041936754XDepartment of Psychiatry, Harvard Medical School, Boston, MA USA ,grid.410370.10000 0004 4657 1992Geriatric Research, Education and Clinical Center (GRECC), VA Boston Healthcare System, Boston, MA USA
| | - Regina McGlinchey
- grid.410370.10000 0004 4657 1992Translational Research Center for TBI and Stress Disorders (TRACTS), VA Boston Healthcare System, Boston, MA USA ,grid.38142.3c000000041936754XDepartment of Psychiatry, Harvard Medical School, Boston, MA USA ,grid.410370.10000 0004 4657 1992Geriatric Research, Education and Clinical Center (GRECC), VA Boston Healthcare System, Boston, MA USA
| | - Joseph DeGutis
- grid.410370.10000 0004 4657 1992Boston Attention and Learning Lab (BALLAB), VA Boston Healthcare System, Boston, MA USA ,grid.410370.10000 0004 4657 1992Translational Research Center for TBI and Stress Disorders (TRACTS), VA Boston Healthcare System, Boston, MA USA ,grid.38142.3c000000041936754XDepartment of Psychiatry, Harvard Medical School, Boston, MA USA
| | - Michael Esterman
- grid.410370.10000 0004 4657 1992National Center for PTSD (NCPTSD), VA Boston Healthcare System, Boston, MA USA ,grid.410370.10000 0004 4657 1992Boston Attention and Learning Lab (BALLAB), VA Boston Healthcare System, Boston, MA USA ,grid.189504.10000 0004 1936 7558Department of Psychiatry, Boston University School of Medicine, Boston, MA USA ,grid.410370.10000 0004 4657 1992Translational Research Center for TBI and Stress Disorders (TRACTS), VA Boston Healthcare System, Boston, MA USA ,grid.410370.10000 0004 4657 1992Neuroimaging Research for Veterans (NeRVe) Center, VA Boston Healthcare System, Boston, MA USA
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13
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Gbadeyan O, Teng J, Prakash RS. Predicting response time variability from task and resting-state functional connectivity in the aging brain. Neuroimage 2022; 250:118890. [PMID: 35007719 PMCID: PMC9063711 DOI: 10.1016/j.neuroimage.2022.118890] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 12/23/2021] [Accepted: 01/06/2022] [Indexed: 12/22/2022] Open
Abstract
Aging is associated with declines in a host of cognitive functions, including attentional control, inhibitory control, episodic memory, processing speed, and executive functioning. Theoretical models attribute the age-related decline in cognitive functioning to deficits in goal maintenance and attentional inhibition. Despite these well-documented declines in executive control resources, older adults endorse fewer episodes of mind-wandering when assessed using task-embedded thought probes. Furthermore, previous work on the neural basis of mind-wandering has mostly focused on young adults with studies predominantly focusing on the activity and connectivity of a select few canonical networks. However, whole-brain functional networks associated with mind-wandering in aging have not yet been characterized. In this study, using response time variability-the trial-to-trial fluctuations in behavioral responses-as an indirect marker of mind-wandering or an "out-of-the-zone" attentional state representing suboptimal behavioral performance, we show that brain-based predictive models of response time variability can be derived from whole-brain task functional connectivity. In contrast, models derived from resting-state functional connectivity alone did not predict individual response time variability. Finally, we show that despite successful within-sample prediction of response time variability, our models did not generalize to predict response time variability in independent cohorts of older adults with resting-state connectivity. Overall, our findings provide evidence for the utility of task-based functional connectivity in predicting individual response time variability in aging. Future research is needed to derive more robust and generalizable models.
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Affiliation(s)
- Oyetunde Gbadeyan
- Department of Psychology, The Ohio State University, 139 Psychology Building, 1835 Neil Avenue, Columbus, OH 43210, USA
| | - James Teng
- Department of Psychology, The Ohio State University, 139 Psychology Building, 1835 Neil Avenue, Columbus, OH 43210, USA
| | - Ruchika Shaurya Prakash
- Department of Psychology, The Ohio State University, 139 Psychology Building, 1835 Neil Avenue, Columbus, OH 43210, USA; Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, OH, USA.
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14
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Groot JM, Csifcsák G, Wientjes S, Forstmann BU, Mittner M. Catching Wandering Minds with Tapping Fingers: Neural and Behavioral Insights into Task-unrelated Cognition. Cereb Cortex 2022; 32:4447-4463. [PMID: 35034114 PMCID: PMC9574234 DOI: 10.1093/cercor/bhab494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 12/03/2021] [Accepted: 12/04/2021] [Indexed: 11/30/2022] Open
Abstract
When the human mind wanders, it engages in episodes during which attention is focused on self-generated thoughts rather than on external task demands. Although the sustained attention to response task is commonly used to examine relationships between mind wandering and executive functions, limited executive resources are required for optimal task performance. In the current study, we aimed to investigate the relationship between mind wandering and executive functions more closely by employing a recently developed finger-tapping task to monitor fluctuations in attention and executive control through task performance and periodical experience sampling during concurrent functional magnetic resonance imaging (fMRI) and pupillometry. Our results show that mind wandering was preceded by increases in finger-tapping variability, which was correlated with activity in dorsal and ventral attention networks. The entropy of random finger-tapping sequences was related to activity in frontoparietal regions associated with executive control, demonstrating the suitability of this paradigm for studying executive functioning. The neural correlates of behavioral performance, pupillary dynamics, and self-reported attentional state diverged, thus indicating a dissociation between direct and indirect markers of mind wandering. Together, the investigation of these relationships at both the behavioral and neural level provided novel insights into the identification of underlying mechanisms of mind wandering.
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Affiliation(s)
- Josephine M Groot
- Department of Psychology, UiT – The Arctic University of Norway, Tromsø 9037 , Norway
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam 1018 WB , The Netherlands
| | - Gábor Csifcsák
- Department of Psychology, UiT – The Arctic University of Norway, Tromsø 9037 , Norway
| | - Sven Wientjes
- Department of Experimental Psychology, University of Ghent, Ghent 9000 , Belgium
| | - Birte U Forstmann
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam 1018 WB , The Netherlands
| | - Matthias Mittner
- Address correspondence to Matthias Mittner, Department of Psychology, UiT – The Arctic University of Norway, Huginbakken 32, 9037 Tromsø, Norway.
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15
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Machner B, Braun L, Imholz J, Koch PJ, Münte TF, Helmchen C, Sprenger A. Resting-State Functional Connectivity in the Dorsal Attention Network Relates to Behavioral Performance in Spatial Attention Tasks and May Show Task-Related Adaptation. Front Hum Neurosci 2022; 15:757128. [PMID: 35082607 PMCID: PMC8784839 DOI: 10.3389/fnhum.2021.757128] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 12/10/2021] [Indexed: 11/13/2022] Open
Abstract
Between-subject variability in cognitive performance has been related to inter-individual differences in functional brain networks. Targeting the dorsal attention network (DAN) we questioned (i) whether resting-state functional connectivity (FC) within the DAN can predict individual performance in spatial attention tasks and (ii) whether there is short-term adaptation of DAN-FC in response to task engagement. Twenty-seven participants first underwent resting-state fMRI (PRE run), they subsequently performed different tasks of spatial attention [including visual search (VS)] and immediately afterwards received another rs-fMRI (POST run). Intra- and inter-hemispheric FC between core hubs of the DAN, bilateral intraparietal sulcus (IPS) and frontal eye field (FEF), was analyzed and compared between PRE and POST. Furthermore, we investigated rs-fMRI-behavior correlations between the DAN-FC in PRE/POST and task performance parameters. The absolute DAN-FC did not change from PRE to POST. However, different significant rs-fMRI-behavior correlations were revealed for intra-/inter-hemispheric connections in the PRE and POST run. The stronger the FC between left FEF and IPS before task engagement, the better was the learning effect (improvement of reaction times) in VS (r = 0.521, p = 0.024). And the faster the VS (mean RT), the stronger was the FC between right FEF and IPS after task engagement (r = −0.502, p = 0.032). To conclude, DAN-FC relates to the individual performance in spatial attention tasks supporting the view of functional brain networks as priors for cognitive ability. Despite a high inter- and intra-individual stability of DAN-FC, the change of FC-behavior correlations after task performance possibly indicates task-related adaptation of the DAN, underlining that behavioral experiences may shape intrinsic brain activity. However, spontaneous state fluctuations of the DAN-FC over time cannot be fully ruled out as an alternative explanation.
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Affiliation(s)
- Björn Machner
- Department of Neurology, University Hospital Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, Lübeck, Germany
- *Correspondence: Björn Machner, ; orcid.org/0000-0001-7981-2906
| | - Lara Braun
- Department of Neurology, University Hospital Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Jonathan Imholz
- Department of Neurology, University Hospital Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Philipp J. Koch
- Department of Neurology, University Hospital Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, Lübeck, Germany
| | - Thomas F. Münte
- Department of Neurology, University Hospital Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, Lübeck, Germany
| | - Christoph Helmchen
- Department of Neurology, University Hospital Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, Lübeck, Germany
| | - Andreas Sprenger
- Department of Neurology, University Hospital Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, Lübeck, Germany
- Department of Psychology II, University of Lübeck, Lübeck, Germany
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16
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Song H, Park BY, Park H, Shim WM. Cognitive and Neural State Dynamics of Narrative Comprehension. J Neurosci 2021; 41:8972-8990. [PMID: 34531284 PMCID: PMC8549535 DOI: 10.1523/jneurosci.0037-21.2021] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 09/03/2021] [Accepted: 09/07/2021] [Indexed: 11/21/2022] Open
Abstract
Narrative comprehension involves a constant interplay of the accumulation of incoming events and their integration into a coherent structure. This study characterizes cognitive states during narrative comprehension and the network-level reconfiguration occurring dynamically in the functional brain. We presented movie clips of temporally scrambled sequences to human participants (male and female), eliciting fluctuations in the subjective feeling of comprehension. Comprehension occurred when processing events that were highly causally related to the previous events, suggesting that comprehension entails the integration of narratives into a causally coherent structure. The functional neuroimaging results demonstrated that the integrated and efficient brain state emerged during the moments of narrative integration with the increased level of activation and across-modular connections in the default mode network. Underlying brain states were synchronized across individuals when comprehending novel narratives, with increased occurrences of the default mode network state, integrated with sensory processing network, during narrative integration. A model based on time-resolved functional brain connectivity predicted changing cognitive states related to comprehension that are general across narratives. Together, these results support adaptive reconfiguration and interaction of the functional brain networks on causal integration of the narratives.SIGNIFICANCE STATEMENT The human brain can integrate temporally disconnected pieces of information into coherent narratives. However, the underlying cognitive and neural mechanisms of how the brain builds a narrative representation remain largely unknown. We showed that comprehension occurs as the causally related events are integrated to form a coherent situational model. Using fMRI, we revealed that the large-scale brain states and interaction between brain regions dynamically reconfigure as comprehension evolves, with the default mode network playing a central role during moments of narrative integration. Overall, the study demonstrates that narrative comprehension occurs through a dynamic process of information accumulation and causal integration, supported by the time-varying reconfiguration and brain network interaction.
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Affiliation(s)
- Hayoung Song
- Center for Neuroscience Imaging Research, IBS, Suwon, Korea, 16419
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Korea, 16419
- Department of Psychology, University of Chicago, Chicago, Illinois, 60637
| | - Bo-Yong Park
- Center for Neuroscience Imaging Research, IBS, Suwon, Korea, 16419
- Department of Electronic, Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea, 16419
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec Canada, H3A 2B4
- Department of Data Science, Inha University, Incheon, Korea, 22201
| | - Hyunjin Park
- Center for Neuroscience Imaging Research, IBS, Suwon, Korea, 16419
- School of Electronics and Electrical Engineering, Sungkyunkwan University, Suwon, Korea, 16419
| | - Won Mok Shim
- Center for Neuroscience Imaging Research, IBS, Suwon, Korea, 16419
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Korea, 16419
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea, 16419
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17
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Song H, Finn ES, Rosenberg MD. Neural signatures of attentional engagement during narratives and its consequences for event memory. Proc Natl Acad Sci U S A 2021; 118:e2021905118. [PMID: 34385312 PMCID: PMC8379980 DOI: 10.1073/pnas.2021905118] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
As we comprehend narratives, our attentional engagement fluctuates over time. Despite theoretical conceptions of narrative engagement as emotion-laden attention, little empirical work has characterized the cognitive and neural processes that comprise subjective engagement in naturalistic contexts or its consequences for memory. Here, we relate fluctuations in narrative engagement to patterns of brain coactivation and test whether neural signatures of engagement predict subsequent memory. In behavioral studies, participants continuously rated how engaged they were as they watched a television episode or listened to a story. Self-reported engagement was synchronized across individuals and driven by the emotional content of the narratives. In functional MRI datasets collected as different individuals watched the same show or listened to the same story, engagement drove neural synchrony, such that default mode network activity was more synchronized across individuals during more engaging moments of the narratives. Furthermore, models based on time-varying functional brain connectivity predicted evolving states of engagement across participants and independent datasets. The functional connections that predicted engagement overlapped with a validated neuromarker of sustained attention and predicted recall of narrative events. Together, our findings characterize the neural signatures of attentional engagement in naturalistic contexts and elucidate relationships among narrative engagement, sustained attention, and event memory.
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Affiliation(s)
- Hayoung Song
- Department of Psychology, University of Chicago, Chicago, IL 60637;
| | - Emily S Finn
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755
| | - Monica D Rosenberg
- Department of Psychology, University of Chicago, Chicago, IL 60637;
- Neuroscience Institute, University of Chicago, Chicago, IL 60637
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18
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Variable rather than extreme slow reaction times distinguish brain states during sustained attention. Sci Rep 2021; 11:14883. [PMID: 34290318 PMCID: PMC8295386 DOI: 10.1038/s41598-021-94161-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 07/07/2021] [Indexed: 02/03/2023] Open
Abstract
A common behavioral marker of optimal attention focus is faster responses or reduced response variability. Our previous study found two dominant brain states during sustained attention, and these states differed in their behavioral accuracy and reaction time (RT) variability. However, RT distributions are often positively skewed with a long tail (i.e., reflecting occasional slow responses). Therefore, a larger RT variance could also be explained by this long tail rather than the variance around an assumed normal distribution (i.e., reflecting pervasive response instability based on both faster and slower responses). Resolving this ambiguity is important for better understanding mechanisms of sustained attention. Here, using a large dataset of over 20,000 participants who performed a sustained attention task, we first demonstrated the utility of the exGuassian distribution that can decompose RTs into a strategy factor, a variance factor, and a long tail factor. We then investigated which factor(s) differed between the two brain states using fMRI. Across two independent datasets, results indicate unambiguously that the variance factor differs between the two dominant brain states. These findings indicate that ‘suboptimal’ is different from ‘slow’ at the behavior and neural level, and have implications for theoretically and methodologically guiding future sustained attention research.
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