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Zhang H, Meng C, Di X, Wu X, Biswal B. Static and dynamic functional connectome reveals reconfiguration profiles of whole-brain network across cognitive states. Netw Neurosci 2023; 7:1034-1050. [PMID: 37781145 PMCID: PMC10473282 DOI: 10.1162/netn_a_00314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 03/21/2023] [Indexed: 10/03/2023] Open
Abstract
Assessment of functional connectivity (FC) has revealed a great deal of knowledge about the macroscale spatiotemporal organization of the brain network. Recent studies found task-versus-rest network reconfigurations were crucial for cognitive functioning. However, brain network reconfiguration remains unclear among different cognitive states, considering both aggregate and time-resolved FC profiles. The current study utilized static FC (sFC, i.e., long timescale aggregate FC) and sliding window-based dynamic FC (dFC, i.e., short timescale time-varying FC) approaches to investigate the similarity and alterations of edge weights and network topology at different cognitive loads, particularly their relationships with specific cognitive process. Both dFC/sFC networks showed subtle but significant reconfigurations that correlated with task performance. At higher cognitive load, brain network reconfiguration displayed increased functional integration in the sFC-based aggregate network, but faster and larger variability of modular reorganization in the dFC-based time-varying network, suggesting difficult tasks require more integrated and flexible network reconfigurations. Moreover, sFC-based network reconfigurations mainly linked with the sensorimotor and low-order cognitive processes, but dFC-based network reconfigurations mainly linked with the high-order cognitive process. Our findings suggest that reconfiguration profiles of sFC/dFC networks provide specific information about cognitive functioning, which could potentially be used to study brain function and disorders.
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Affiliation(s)
- Heming Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chun Meng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Xiao Wu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Bharat Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
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2
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Shen Y, Cai H, Mo F, Yao S, Yu Y, Zhu J. Functional connectivity gradients of the cingulate cortex. Commun Biol 2023; 6:650. [PMID: 37337086 DOI: 10.1038/s42003-023-05029-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 06/08/2023] [Indexed: 06/21/2023] Open
Abstract
Heterogeneity of the cingulate cortex is evident in multiple dimensions including anatomy, function, connectivity, and involvement in networks and diseases. Using the recently developed functional connectivity gradient approach and resting-state functional MRI data, we found three functional connectivity gradients that captured distinct dimensions of cingulate hierarchical organization. The principal gradient exhibited a radiating organization with transitions from the middle toward both anterior and posterior parts of the cingulate cortex and was related to canonical functional networks and corresponding behavioral domains. The second gradient showed an anterior-posterior axis across the cingulate cortex and had prominent geometric distance dependence. The third gradient displayed a marked differentiation of subgenual and caudal middle with other parts of the cingulate cortex and was associated with cortical morphology. Aside from providing an updated framework for understanding the multifaceted nature of cingulate heterogeneity, the observed hierarchical organization of the cingulate cortex may constitute a novel research agenda with potential applications in basic and clinical neuroscience.
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Affiliation(s)
- Yuhao Shen
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022, Hefei, China
- Research Center of Clinical Medical Imaging, Anhui Province, 230032, Hefei, China
- Anhui Provincial Institute of Translational Medicine, 230032, Hefei, China
| | - Huanhuan Cai
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022, Hefei, China
- Research Center of Clinical Medical Imaging, Anhui Province, 230032, Hefei, China
- Anhui Provincial Institute of Translational Medicine, 230032, Hefei, China
| | - Fan Mo
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022, Hefei, China
- Research Center of Clinical Medical Imaging, Anhui Province, 230032, Hefei, China
- Anhui Provincial Institute of Translational Medicine, 230032, Hefei, China
| | - Shanwen Yao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022, Hefei, China
- Research Center of Clinical Medical Imaging, Anhui Province, 230032, Hefei, China
- Anhui Provincial Institute of Translational Medicine, 230032, Hefei, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022, Hefei, China.
- Research Center of Clinical Medical Imaging, Anhui Province, 230032, Hefei, China.
- Anhui Provincial Institute of Translational Medicine, 230032, Hefei, China.
| | - Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022, Hefei, China.
- Research Center of Clinical Medical Imaging, Anhui Province, 230032, Hefei, China.
- Anhui Provincial Institute of Translational Medicine, 230032, Hefei, China.
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3
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Yu R, Han B, Wu X, Wei G, Zhang J, Ding M, Wen X. Dual-functional network regulation underlies the central executive system in working memory. Neuroscience 2023:S0306-4522(23)00245-2. [PMID: 37286158 DOI: 10.1016/j.neuroscience.2023.05.025] [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: 11/09/2022] [Revised: 04/24/2023] [Accepted: 05/27/2023] [Indexed: 06/09/2023]
Abstract
The frontoparietal network (FPN) and cingulo-opercular network (CON) may exert top-down regulation corresponding to the central executive system (CES) in working memory (WM); however, contributions and regulatory mechanisms remain unclear. We examined network interaction mechanisms underpinning the CES by depicting CON- and FPN-mediated whole-brain information flow in WM. We used datasets from participants performing verbal and spatial working memory tasks, divided into encoding, maintenance, and probe stages. We used general linear models to obtain task-activated CON and FPN nodes to define regions of interest (ROI); an online meta-analysis defined alternative ROIs for validation. We calculated whole-brain functional connectivity (FC) maps seeded by CON and FPN nodes at each stage using beta sequence analysis. We used Granger causality analysis to obtain the connectivity maps and assess task-level information flow patterns. For verbal working memory, the CON functionally connected positively and negatively to task-dependent and task-independent networks, respectively, at all stages. FPN FC patterns were similar only in the encoding and maintenance stages. The CON elicited stronger task-level outputs. Main effects were: stable CON→FPN, CON→DMN, CON→visual areas, FPN→visual areas, and phonological areas→FPN. The CON and FPN both up-regulated task-dependent and down-regulated task-independent networks during encoding and probing. Task-level output was slightly stronger for the CON. CON→FPN, CON→DMN, visual areas→CON, and visual areas→FPN showed consistent effects. The CON and FPN might together underlie the CES's neural basis and achieve top-down regulation through information interaction with other large-scale functional networks, and the CON may be a higher-level regulatory core in WM.
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Affiliation(s)
- Renshu Yu
- Department of Psychology, Renmin University of China, Beijing, China, 100872; Laboratory of the Department of Psychology, Renmin University of China, Beijing, China, 100872
| | - Bukui Han
- Department of Psychology, Renmin University of China, Beijing, China, 100872; Laboratory of the Department of Psychology, Renmin University of China, Beijing, China, 100872
| | - Xia Wu
- School of Artificial Intelligence, Beijing Normal University, Beijing, China, 100093
| | - Guodong Wei
- Department of Psychology, Renmin University of China, Beijing, China, 100872; Laboratory of the Department of Psychology, Renmin University of China, Beijing, China, 100872
| | - Junhui Zhang
- Department of Psychology, Renmin University of China, Beijing, China, 100872; Laboratory of the Department of Psychology, Renmin University of China, Beijing, China, 100872
| | - Mingzhou Ding
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville FL, USA, 32611
| | - Xiaotong Wen
- Department of Psychology, Renmin University of China, Beijing, China, 100872; Laboratory of the Department of Psychology, Renmin University of China, Beijing, China, 100872; Interdisciplinary Platform of Philosophy and Cognitive Science, Renmin University of China, China, 100872.
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4
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Chen P, Ban W, Wang W, You Y, Yang Z. The Devastating Effects of Sleep Deprivation on Memory: Lessons from Rodent Models. Clocks Sleep 2023; 5:276-294. [PMID: 37218868 DOI: 10.3390/clockssleep5020022] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/11/2023] [Accepted: 05/11/2023] [Indexed: 05/24/2023] Open
Abstract
In this narrative review article, we discuss the role of sleep deprivation (SD) in memory processing in rodent models. Numerous studies have examined the effects of SD on memory, with the majority showing that sleep disorders negatively affect memory. Currently, a consensus has not been established on which damage mechanism is the most appropriate. This critical issue in the neuroscience of sleep remains largely unknown. This review article aims to elucidate the mechanisms that underlie the damaging effects of SD on memory. It also proposes a scientific solution that might explain some findings. We have chosen to summarize literature that is both representative and comprehensive, as well as innovative in its approach. We examined the effects of SD on memory, including synaptic plasticity, neuritis, oxidative stress, and neurotransmitters. Results provide valuable insights into the mechanisms by which SD impairs memory function.
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Affiliation(s)
- Pinqiu Chen
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, China
- Key Laboratory of Molecular Pharmacology and Drug Evaluation, Ministry of Education, School of Pharmacy, Yantai University, Yantai 264005, China
| | - Weikang Ban
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, China
| | - Wenyan Wang
- Key Laboratory of Molecular Pharmacology and Drug Evaluation, Ministry of Education, School of Pharmacy, Yantai University, Yantai 264005, China
| | - Yuyang You
- School of Automation, Beijing Institute of Technology, Beijing 100081, China
| | - Zhihong Yang
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, China
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5
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Zhang H, Di X, Rypma B, Yang H, Meng C, Biswal B. Interaction Between Memory Load and Experimental Design on Brain Connectivity and Network Topology. Neurosci Bull 2023; 39:631-644. [PMID: 36565381 PMCID: PMC10073362 DOI: 10.1007/s12264-022-00982-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 08/18/2022] [Indexed: 12/25/2022] Open
Abstract
The conventional approach to investigating functional connectivity in the block-designed study usually concatenates task blocks or employs residuals of task activation. While providing many insights into brain functions, the block design adds more manipulation in functional network analysis that may reduce the purity of the blood oxygenation level-dependent signal. Recent studies utilized one single long run for task trials of the same condition, the so-called continuous design, to investigate functional connectivity based on task functional magnetic resonance imaging. Continuous brain activities associated with the single-task condition can be directly utilized for task-related functional connectivity assessment, which has been examined for working memory, sensory, motor, and semantic task experiments in previous research. But it remains unclear how the block and continuous design influence the assessment of task-related functional connectivity networks. This study aimed to disentangle the separable effects of block/continuous design and working memory load on task-related functional connectivity networks, by using repeated-measures analysis of variance. Across 50 young healthy adults, behavioral results of accuracy and reaction time showed a significant main effect of design as well as interaction between design and load. Imaging results revealed that the cingulo-opercular, fronto-parietal, and default model networks were associated with not only task activation, but significant main effects of design and load as well as their interaction on intra- and inter-network functional connectivity and global network topology. Moreover, a significant behavior-brain association was identified for the continuous design. This work has extended the evidence that continuous design can be used to study task-related functional connectivity and subtle brain-behavioral relationships.
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Affiliation(s)
- Heming Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, 07102, USA
| | - Bart Rypma
- Department of Psychology, University of Texas at Dallas, Dallas, 75390, USA
| | - Hang Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Chun Meng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
| | - Bharat Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, 07102, USA.
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6
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Das SK, Sao AK, Biswal BB. Estimation of neuronal task information in fMRI using zero frequency resonator. Neuroimage 2023; 267:119865. [PMID: 36610681 PMCID: PMC10635735 DOI: 10.1016/j.neuroimage.2023.119865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 12/14/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023] Open
Abstract
In functional magnetic resonance imaging (fMRI), temporal onsets of BOLD events contain crucial information on activity-inducing signals and make a significant impact in the analysis of functional connectivity (FC). In literature, the estimation of the onsets of the BOLD events from the acquired blood oxygen level-dependent (BOLD) signal using fMRI is mostly performed by choosing locations with a high value of the BOLD signal. This approach may give false onset points because it can incorporate redundant onsets which could be due to non-neuronal activity or can exclude true low-valued BOLD signals. In this study, we present a novel approach to estimating the temporal onsets of the BOLD events using a zero frequency resonator (ZFR) without necessitating information regarding the experimental paradigm (EP). The proposed approach exploits the impulse-like characteristic of activity-inducing signal to estimate the temporal onset points of BOLD events using ZFR which has been widely studied in the area of speech signal processing to estimate the glottal closure instances. The idea behind the approach is that an ideal neuronal impulse has, in principle, equal energy at all frequencies, including around the zero frequency, and will preserve the information of the temporal onsets of the BOLD events at its output. The ZFR-based approach estimates two important features, namely: 1) task-induced temporal onsets of the BOLD events in the fMRI time course and 2) high SNR (HSNR) regions around the estimated BOLD events. Both the estimated features are used to obtain the FC. Results are demonstrated using both the synthetic and experimental (event-related finger tapping and block design working memory) data. We show that a small number of plausible time points, estimated by ZFR, can convey sufficient information indicating the associated activation pattern. The method also illustrates its significance over the conventional correlation and threshold-based conditional rate analysis to estimate FC. The study demonstrates that ZFR-estimated BOLD events and HSNR regions can produce sufficient functionality of the brain in the task paradigm.
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Affiliation(s)
- Sukesh Kumar Das
- Indian Institute of Technology Mandi, Mandi, HP 175005, Himachal Pradesh, India.
| | - Anil K Sao
- Indian Institute of Technology Bhilai, Bhilai, Chhattisgarh 492015, Chhattisgarh, India.
| | - Bharat B Biswal
- New Jersey Institute of Technology, Newark, NJ 07102, New Jersey, USA.
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7
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Di X, Biswal BB. A functional MRI pre-processing and quality control protocol based on statistical parametric mapping (SPM) and MATLAB. FRONTIERS IN NEUROIMAGING 2023; 1:1070151. [PMID: 37555150 PMCID: PMC10406300 DOI: 10.3389/fnimg.2022.1070151] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 12/19/2022] [Indexed: 08/10/2023]
Abstract
Functional MRI (fMRI) has become a popular technique to study brain functions and their alterations in psychiatric and neurological conditions. The sample sizes for fMRI studies have been increasing steadily, and growing studies are sourced from open-access brain imaging repositories. Quality control becomes critical to ensure successful data processing and valid statistical results. Here, we outline a simple protocol for fMRI data pre-processing and quality control based on statistical parametric mapping (SPM) and MATLAB. The focus of this protocol is not only to identify and remove data with artifacts and anomalies, but also to ensure the processing has been performed properly. We apply this protocol to the data from fMRI Open quality control (QC) Project, and illustrate how each quality control step can help to identify potential issues. We also show that simple steps such as skull stripping can improve coregistration between the functional and anatomical images.
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Affiliation(s)
- Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Bharat B. Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
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8
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Kulkarni M, Covey TJ. Examination of the temporal-spatial dynamics of working memory training-induced neuroplasticity. Brain Res 2023; 1798:148135. [DOI: 10.1016/j.brainres.2022.148135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 10/24/2022] [Accepted: 10/26/2022] [Indexed: 11/16/2022]
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9
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Wang L, Wu H, Dai C, Peng Z, Song T, Xu L, Xu M, Shao Y, Li S, Fu W. Dynamic hippocampal functional connectivity responses to varying working memory loads following total sleep deprivation. J Sleep Res 2022; 32:e13797. [PMID: 36528854 DOI: 10.1111/jsr.13797] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 10/26/2022] [Accepted: 11/11/2022] [Indexed: 12/23/2022]
Abstract
Sleep loss with work overload can impact human cognitive performance. However, the brain's response to an increased working memory load following total sleep deprivation (TSD) remains unclear. In the present study, we focussed on the dynamic response of the hippocampus to increased working memory load before and after total sleep deprivation of 36 h. A total of 16 male participants completed a verbal working memory task under functional magnetic resonance imaging. After whole-brain activation analysis and region of interest analysis of the hippocampus, the generalised form of context-dependent psychophysiological interactions (gPPI) was used to analyse the hippocampal functional connectivity with the whole brain. The results revealed that as the working memory load increased within a small range, from 0-back to 1-back task, the left hippocampal functional connectivity decreased with the left supplementary motor area, left pars opercularis, left rolandic operculum, right superior frontal gyrus, bilateral precentral gyrus, and left middle cingulate cortex following total sleep deprivation compared with that observed in resting wakefulness. When the working memory load further increased from 1-back to 2-back task, the connectivity increased between the left hippocampus and the left superior parietal lobule as well as between the left hippocampus and right lingual gyrus after total sleep deprivation compared with that observed in resting wakefulness. Moreover, the left hippocampus gPPI effect on the left middle cingulate cortex and left superior parietal lobule could predict the behavioural test accuracy in 1-back and 2-back task, respectively, following total sleep deprivation. These findings indicated that increased working memory load after total sleep deprivation disrupts working memory processes. The brain reacts to these disruptions in a dynamic and flexible manner, involving not only brain activation but also hippocampus-related functional network connections.
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Affiliation(s)
- Letong Wang
- School of Psychology Beijing Sport University Beijing China
| | - Haijing Wu
- Department of Gynecologic Oncology Sichuan Cancer Hospital Chengdu China
| | - Cimin Dai
- School of Psychology Beijing Sport University Beijing China
| | - Ziyi Peng
- School of Psychology Beijing Sport University Beijing China
| | - Tao Song
- School of Psychology Beijing Sport University Beijing China
| | - Lin Xu
- School of Psychology Beijing Sport University Beijing China
| | - Mengmeng Xu
- School of Psychology Beijing Sport University Beijing China
| | - Yongcong Shao
- School of Psychology Beijing Sport University Beijing China
- School of Biological Science and Medical Engineering Beihang University Beijing China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences Suzhou China
| | - Shijun Li
- Department of Radiology, First Medical Center Chinese PLA General Hospital Beijing China
| | - Weiwei Fu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences Suzhou China
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10
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Fernández-Rubio G, Carlomagno F, Vuust P, Kringelbach ML, Bonetti L. Associations between abstract working memory abilities and brain activity underlying long-term recognition of auditory sequences. PNAS NEXUS 2022; 1:pgac216. [PMID: 36714830 PMCID: PMC9802106 DOI: 10.1093/pnasnexus/pgac216] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 09/26/2022] [Indexed: 02/01/2023]
Abstract
Memory is a complex cognitive process composed of several subsystems, namely short- and long-term memory and working memory (WM). Previous research has shown that adequate interaction between subsystems is crucial for successful memory processes such as encoding, storage, and manipulation of information. However, few studies have investigated the relationship between different subsystems at the behavioral and neural levels. Thus, here we assessed the relationship between individual WM abilities and brain activity underlying the recognition of previously memorized auditory sequences. First, recognition of previously memorized versus novel auditory sequences was associated with a widespread network of brain areas comprising the cingulate gyrus, hippocampus, insula, inferior temporal cortex, frontal operculum, and orbitofrontal cortex. Second, we observed positive correlations between brain activity underlying auditory sequence recognition and WM. We showed a sustained positive correlation in the medial cingulate gyrus, a brain area that was widely involved in the auditory sequence recognition. Remarkably, we also observed positive correlations in the inferior temporal, temporal-fusiform, and postcentral gyri, brain areas that were not strongly associated with auditory sequence recognition. In conclusion, we discovered positive correlations between WM abilities and brain activity underlying long-term recognition of auditory sequences, providing new evidence on the relationship between memory subsystems. Furthermore, we showed that high WM performers recruited a larger brain network including areas associated with visual processing (i.e., inferior temporal, temporal-fusiform, and postcentral gyri) for successful auditory memory recognition.
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Affiliation(s)
- Gemma Fernández-Rubio
- To whom correspondence should be addressed: Building 1710, Universitetsbyen 3, 8000 Aarhus C, Denmark:
| | | | - Peter Vuust
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music Aarhus/Aalborg, 8000 Aarhus, Denmark
| | - Morten L Kringelbach
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music Aarhus/Aalborg, 8000 Aarhus, Denmark,Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford OX3 9BX, UK,Department of Psychiatry, University of Oxford, Oxford OX1 2JD, UK
| | - Leonardo Bonetti
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music Aarhus/Aalborg, 8000 Aarhus, Denmark,Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford OX3 9BX, UK,Department of Psychiatry, University of Oxford, Oxford OX1 2JD, UK
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11
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Zhang H, Zhao R, Hu X, Guan S, Margulies DS, Meng C, Biswal BB. Cortical connectivity gradients and local timescales during cognitive states are modulated by cognitive loads. Brain Struct Funct 2022; 227:2701-2712. [PMID: 36098843 DOI: 10.1007/s00429-022-02564-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 08/29/2022] [Indexed: 11/02/2022]
Abstract
Although resting-state fMRI studies support that human brain is topographically organized regarding localized and distributed processes, it is still unclear about the task-modulated cortical hierarchy in terms of distributed functional connectivity and localized timescales. To address, current study investigated the effect of cognitive load on cortical connectivity gradients and local timescales in the healthy brain using resting state fMRI as well as 1- and 2-back working memory task fMRI. The results demonstrated that (1) increased cognitive load was associated with lower principal gradient in transmodal cortices, higher principal gradient in primary cortices, decreased decay rate and reduced timescale variability; (2) global properties including gradient variability, timescale decay rate, timescale variability and network topology were all modulated by cognitive load, with timescale variability related to behavioral performance; and (3) at 2-back state, the timescale variability was indirectly and negatively linked with global network integration, which was mediated by gradient variability. In conclusion, current study provides novel evidence for load-modulated cortical connectivity gradients and local timescales during cognitive states, which could contribute to better understanding about cognitive load theory and brain disorders with cognitive dysfunction.
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Affiliation(s)
- Heming Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Chengdu, 611731, China
| | - Rong Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Chengdu, 611731, China
| | - Xin Hu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Chengdu, 611731, China
| | - Sihai Guan
- Key Laboratory of Electronic and Information Engineering (Southwest Minzu University), State Ethnic Affairs Commission. College of Electronic and Information, Southwest Minzu University, Chengdu, 610225, China
| | - Daniel S Margulies
- Max Planck Research Group for Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, 04103, Leipzig, Germany
| | - Chun Meng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Chengdu, 611731, China.
| | - Bharat B Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Chengdu, 611731, China. .,Department of Biomedical Engineering, New Jersey Institute of Technology, University Height, 607 Fenster Hall, Newark, NJ, 07102, USA.
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12
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Northoff G, Vatansever D, Scalabrini A, Stamatakis EA. Ongoing Brain Activity and Its Role in Cognition: Dual versus Baseline Models. Neuroscientist 2022:10738584221081752. [PMID: 35611670 DOI: 10.1177/10738584221081752] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
What is the role of the brain's ongoing activity for cognition? The predominant perspectives associate ongoing brain activity with resting state, the default-mode network (DMN), and internally oriented mentation. This triad is often contrasted with task states, non-DMN brain networks, and externally oriented mentation, together comprising a "dual model" of brain and cognition. In opposition to this duality, however, we propose that ongoing brain activity serves as a neuronal baseline; this builds upon Raichle's original search for the default mode of brain function that extended beyond the canonical default-mode brain regions. That entails what we refer to as the "baseline model." Akin to an internal biological clock for the rest of the organism, the ongoing brain activity may serve as an internal point of reference or standard by providing a shared neural code for the brain's rest as well as task states, including their associated cognition. Such shared neural code is manifest in the spatiotemporal organization of the brain's ongoing activity, including its global signal topography and dynamics like intrinsic neural timescales. We conclude that recent empirical evidence supports a baseline model over the dual model; the ongoing activity provides a global shared neural code that allows integrating the brain's rest and task states, its DMN and non-DMN, and internally and externally oriented cognition.
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From Shorter to Longer Timescales: Converging Integrated Information Theory (IIT) with the Temporo-Spatial Theory of Consciousness (TTC). ENTROPY 2022; 24:e24020270. [PMID: 35205564 PMCID: PMC8871397 DOI: 10.3390/e24020270] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/19/2022] [Accepted: 02/10/2022] [Indexed: 02/01/2023]
Abstract
Time is a key element of consciousness as it includes multiple timescales from shorter to longer ones. This is reflected in our experience of various short-term phenomenal contents at discrete points in time as part of an ongoing, more continuous, and long-term ‘stream of consciousness.’ Can Integrated Information Theory (IIT) account for this multitude of timescales of consciousness? According to the theory, the relevant spatiotemporal scale for consciousness is the one in which the system reaches the maximum cause-effect power; IIT currently predicts that experience occurs on the order of short timescales, namely, between 100 and 300 ms (theta and alpha frequency range). This can well account for the integration of single inputs into a particular phenomenal content. However, such short timescales leave open the temporal relation of specific phenomenal contents to others during the course of the ongoing time, that is, the stream of consciousness. For that purpose, we converge the IIT with the Temporo-spatial Theory of Consciousness (TTC), which, assuming a multitude of different timescales, can take into view the temporal integration of specific phenomenal contents with other phenomenal contents over time. On the neuronal side, this is detailed by considering those neuronal mechanisms driving the non-additive interaction of pre-stimulus activity with the input resulting in stimulus-related activity. Due to their non-additive interaction, the single input is not only integrated with others in the short-term timescales of 100–300 ms (alpha and theta frequencies) (as predicted by IIT) but, at the same time, also virtually expanded in its temporal (and spatial) features; this is related to the longer timescales (delta and slower frequencies) that are carried over from pre-stimulus to stimulus-related activity. Such a non-additive pre-stimulus-input interaction amounts to temporo-spatial expansion as a key mechanism of TTC for the constitution of phenomenal contents including their embedding or nesting within the ongoing temporal dynamic, i.e., the stream of consciousness. In conclusion, we propose converging the short-term integration of inputs postulated in IIT (100–300 ms as in the alpha and theta frequency range) with the longer timescales (in delta and slower frequencies) of temporo-spatial expansion in TTC.
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Gallardo-Moreno GB, Alvarado-Rodríguez FJ, Romo-Vázquez R, Vélez-Pérez H, González-Garrido AA. Type 1 diabetes affects the brain functional connectivity underlying working memory processing. Psychophysiology 2021; 59:e13969. [PMID: 34762737 DOI: 10.1111/psyp.13969] [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: 07/16/2021] [Revised: 10/22/2021] [Accepted: 10/26/2021] [Indexed: 11/29/2022]
Abstract
Visuospatial working memory (VSWM) deficits have been demonstrated to occur during the development of type-1-diabetes (T1D). Despite confirming the early appearance of distinct task-related brain activation patterns in T1D patients compared to healthy controls, the effect of VSWM load on functional brain connectivity during task performance is still unknown. Using electroencephalographic methods, the present study evaluated this topic in clinically well-controlled T1D young patients and healthy individuals, while they performed a VSWM task with different memory load levels during two main VSWM processing phases: encoding and maintenance. The results showed a significantly lower number of correct responses and longer reaction times in T1D while performing the task. Besides, higher and progressively increasing functional connectivity indices were found for T1D patients in response to cumulative degrees of VSWM load, from the beginning of the VSWM encoding phase, without notably affecting the VSWM maintenance phase. In contrast, healthy controls managed to solve the task, showing lower functional brain connectivity during the initial VSWM processing steps with more gradual task-related adjustments. Present results suggest that T1D patients anticipate high VSWM load demands by early recruiting supplementary processing resources as the probable expression of a more inefficient, though paradoxically better adjusted to task demands cognitive strategy.
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Affiliation(s)
| | - Francisco J Alvarado-Rodríguez
- División de Electrónica y Computación, CUCEI, Universidad de Guadalajara, Guadalajara, Mexico.,Dpto. de Electromecánica, Universidad Autónoma de Guadalajara, Guadalajara, Mexico
| | - Rebeca Romo-Vázquez
- División de Electrónica y Computación, CUCEI, Universidad de Guadalajara, Guadalajara, Mexico
| | - Hugo Vélez-Pérez
- División de Electrónica y Computación, CUCEI, Universidad de Guadalajara, Guadalajara, Mexico
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15
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Fietz J, Pöhlchen D, Binder FP, Czisch M, Sämann PG, Spoormaker VI. Pupillometry tracks cognitive load and salience network activity in a working memory functional magnetic resonance imaging task. Hum Brain Mapp 2021; 43:665-680. [PMID: 34622518 PMCID: PMC8720183 DOI: 10.1002/hbm.25678] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 08/16/2021] [Accepted: 09/16/2021] [Indexed: 01/29/2023] Open
Abstract
The diameter of the human pupil tracks working memory processing and is associated with activity in the frontoparietal network. At the same time, recent neuroimaging research has linked human pupil fluctuations to activity in the salience network. In this combined functional magnetic resonance imaging (fMRI)/pupillometry study, we recorded the pupil size of healthy human participants while they performed a blockwise organized working memory task (N‐back) inside an MRI scanner in order to monitor the pupil fluctuations associated neural activity during working memory processing. We first confirmed that mean pupil size closely followed working memory load. Combining this with fMRI data, we focused on blood oxygen level dependent (BOLD) correlates of mean pupil size modeled onto the task blocks as a parametric modulation. Interrogating this modulated task regressor, we were able to retrieve the frontoparietal network. Next, to fully exploit the within‐block dynamics, we divided the blocks into 1 s time bins and filled these with corresponding pupil change values (first‐order derivative of pupil size). We found that pupil change within N‐back blocks was positively correlated with BOLD amplitudes in the areas of the salience network (namely bilateral insula, and anterior cingulate cortex). Taken together, fMRI with simultaneous measurement of pupil parameters constitutes a valuable tool to dissect working memory subprocesses related to both working memory load and salience of the presented stimuli.
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Affiliation(s)
- Julia Fietz
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany.,International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Max Planck Institute of Psychiatry, Munich, Germany
| | - Dorothee Pöhlchen
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany.,International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Max Planck Institute of Psychiatry, Munich, Germany
| | - Florian P Binder
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany.,International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Max Planck Institute of Psychiatry, Munich, Germany
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- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany.,Max Planck Institute of Psychiatry, Munich, Germany
| | | | | | - Victor I Spoormaker
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
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16
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Capacity differences in working memory based on resting state brain networks. Sci Rep 2021; 11:19502. [PMID: 34593909 PMCID: PMC8484281 DOI: 10.1038/s41598-021-98848-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 08/25/2021] [Indexed: 02/08/2023] Open
Abstract
Herein, we compared the connectivity of resting-state networks between participants with high and low working memory capacity groups. Brain network connectivity was assessed under both resting and working memory task conditions. Task scans comprised dual-task (reading sentences while memorizing target words) and single-task (reading sentences) conditions. The low capacity group showed relatively stronger connectivity during resting-state in most brain regions, and the high capacity group showed a stronger connectivity between the medial prefrontal and posterior parietal cortices. During task performance, the dorsal attention and salience networks were relatively strongly connected in the high capacity group. In the comparison between dual- and single-task conditions, increased coupling between the anterior cingulate cortex and other attentional control-related areas were noted in the high capacity group. These findings suggest that working memory differences are related with network connectivity variations in attentional control-associated regions during both resting and task performance conditions.
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Carr SJA, Chen W, Fondran J, Friel H, Sanchez-Gonzalez J, Zhang J, Tatsuoka C. Early Stopping in Experimentation With Real-Time Functional Magnetic Resonance Imaging Using a Modified Sequential Probability Ratio Test. Front Neurosci 2021; 15:643740. [PMID: 34803577 PMCID: PMC8600259 DOI: 10.3389/fnins.2021.643740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 10/13/2021] [Indexed: 11/22/2022] Open
Abstract
Introduction: Functional magnetic resonance imaging (fMRI) often involves long scanning durations to ensure the associated brain activity can be detected. However, excessive experimentation can lead to many undesirable effects, such as from learning and/or fatigue effects, discomfort for the subject, excessive motion artifacts and loss of sustained attention on task. Overly long experimentation can thus have a detrimental effect on signal quality and accurate voxel activation detection. Here, we propose dynamic experimentation with real-time fMRI using a novel statistically driven approach that invokes early stopping when sufficient statistical evidence for assessing the task-related activation is observed. Methods: Voxel-level sequential probability ratio test (SPRT) statistics based on general linear models (GLMs) were implemented on fMRI scans of a mathematical 1-back task from 12 healthy teenage subjects and 11 teenage subjects born extremely preterm (EPT). This approach is based on likelihood ratios and allows for systematic early stopping based on target statistical error thresholds. We adopt a two-stage estimation approach that allows for accurate estimates of GLM parameters before stopping is considered. Early stopping performance is reported for different first stage lengths, and activation results are compared with full durations. Finally, group comparisons are conducted with both early stopped and full duration scan data. Numerical parallelization was employed to facilitate completion of computations involving a new scan within every repetition time (TR). Results: Use of SPRT demonstrates the feasibility and efficiency gains of automated early stopping, with comparable activation detection as with full protocols. Dynamic stopping of stimulus administration was achieved in around half of subjects, with typical time savings of up to 33% (4 min on a 12 min scan). A group analysis produced similar patterns of activity for control subjects between early stopping and full duration scans. The EPT group, individually, demonstrated more variability in location and extent of the activations compared to the normal term control group. This was apparent in the EPT group results, reflected by fewer and smaller clusters. Conclusion: A systematic statistical approach for early stopping with real-time fMRI experimentation has been implemented. This dynamic approach has promise for reducing subject burden and fatigue effects.
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Affiliation(s)
- Sarah J. A. Carr
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- Department of Neurology, Case Western Reserve University, Cleveland, OH, United States
| | - Weicong Chen
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Jeremy Fondran
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Harry Friel
- Philips Healthcare, Highland Heights, OH, United States
| | | | - Jing Zhang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Curtis Tatsuoka
- Department of Neurology, Case Western Reserve University, Cleveland, OH, United States
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States
- *Correspondence: Curtis Tatsuoka,
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Kupis L, Romero C, Dirks B, Hoang S, Parladé MV, Beaumont AL, Cardona SM, Alessandri M, Chang C, Nomi JS, Uddin LQ. Evoked and intrinsic brain network dynamics in children with autism spectrum disorder. Neuroimage Clin 2020; 28:102396. [PMID: 32891039 PMCID: PMC7479441 DOI: 10.1016/j.nicl.2020.102396] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Revised: 07/26/2020] [Accepted: 08/19/2020] [Indexed: 11/27/2022]
Abstract
OBJECTIVE Brain dynamics underlie flexible cognition and behavior, yet little is known regarding this relationship in autism spectrum disorder (ASD). We examined time-varying changes in functional co-activation patterns (CAPs) across rest and task-evoked brain states to characterize differences between children with ASD and typically developing (TD) children and identify relationships with severity of social behaviors and restricted and repetitive behaviors. METHOD 17 children with ASD and 27 TD children ages 7-12 completed a resting-state fMRI scan and four runs of a non-cued attention switching task. Metrics indexing brain dynamics were generated from dynamic CAPs computed across three major large-scale brain networks: midcingulo-insular (M-CIN), medial frontoparietal (M-FPN), and lateral frontoparietal (L-FPN). RESULTS Five time-varying CAPs representing dynamic co-activations among network nodes were identified across rest and task fMRI datasets. Significant Diagnosis × Condition interactions were observed for the dwell time of CAP 3, representing co-activation between nodes of the M-CIN and L-FPN, and the frequency of CAP 1, representing co-activation between nodes of the L-FPN. A significant brain-behavior association between dwell time of CAP 5, representing co-activation between nodes of the M-FPN, and social abilities was also observed across both groups of children. CONCLUSION Analysis of brain co-activation patterns reveals altered dynamics among three core networks in children with ASD, particularly evident during later stages of an attention task. Dimensional analyses demonstrating relationships between M-FPN dwell time and social abilities suggest that metrics of brain dynamics may index individual differences in social cognition and behavior.
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Affiliation(s)
- Lauren Kupis
- Department of Psychology, University of Miami, Coral Gables, FL, USA.
| | - Celia Romero
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Bryce Dirks
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Stephanie Hoang
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Meaghan V Parladé
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Amy L Beaumont
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Sandra M Cardona
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | | | - Catie Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Jason S Nomi
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL, USA; Neuroscience Program, University of Miami Miller School of Medicine, Miami, FL, USA.
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19
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Zhao W, Chen X, Zhang Q, Du B, Deng X, Ji F, Xiang YT, Wang C, Dong Q, Chen C, Li J. Effect of ZNF804A gene polymorphism (rs1344706) on the plasticity of the functional coupling between the right dorsolateral prefrontal cortex and the contralateral hippocampal formation. NEUROIMAGE-CLINICAL 2020; 27:102279. [PMID: 32544854 PMCID: PMC7301231 DOI: 10.1016/j.nicl.2020.102279] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 05/06/2020] [Accepted: 05/07/2020] [Indexed: 12/30/2022]
Abstract
ZNF804A has now been recognized as a schizophrenia risk gene by multiple genome-wide association studies with its intronic polymorphism rs1344706 being reported as the first genome-wide significant risk variant for schizophrenia. Although the functional impact of this gene is still unknown, rs1344706's contribution to the functional coupling between the right dorsolateral prefrontal cortex (DLPFC) and the contralateral hippocampal formation (HF) has been reported by several studies. The current study tested whether the right DLPFC-left HF functional coupling showed plasticity during cognitive training (Study I) and whether rs1344706 affected the plasticity (Study II). In Study I, we conducted a randomized controlled trial with 30 subjects receiving 20 sessions of adaptive training on a memory span task (the training group) and 30 subjects practicing on a non-adaptive easy version of the same memory span task for 20 sessions (the control group). All subjects were scanned using fMRI before and after the training. Analyses of resting-state and task-state fMRI data consistently showed that the adaptive memory span training significantly strengthened the right DLPFC-left HF functional coupling. In Study II, we conducted a genetic association study with 101 subjects (combining the data from the training group in Study I with those from an additional subsequent sample of 71 subjects who received the same training and fMRI scans). Results showed that rs1344706 was significantly associated with training-induced changes in functional coupling. Subjects carrying the non-risk allele (C) of rs1344706 showed greater training-induced plasticity than the risk allele (A) homozygotes. These findings expanded our current understanding of the functional impact of the schizophrenia risk variant of ZNF804A gene and suggested that the ZNF804A gene could be used as a prospective target for future antipsychotic drugs and clinical research.
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Affiliation(s)
- Wan Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, PR China
| | - Xiongying Chen
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders & Beijing Institute for Brain Disorders Center of Schizophrenia, Beijing Anding Hospital, Capital Medical University, Beijing 100088, PR China
| | - Qiumei Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, PR China; School of Mental Health, Jining Medical University, 45# Jianshe South Road, Jining 272013, Shandong Province, PR China
| | - Boqi Du
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, PR China
| | - Xiaoxiang Deng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, PR China
| | - Feng Ji
- School of Mental Health, Jining Medical University, 45# Jianshe South Road, Jining 272013, Shandong Province, PR China
| | - Yu-Tao Xiang
- Faculty of Health Sciences, University of Macau, Avenida da Universidade, Taipa, Macau, PR China
| | - Chuanyue Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders & Beijing Institute for Brain Disorders Center of Schizophrenia, Beijing Anding Hospital, Capital Medical University, Beijing 100088, PR China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, PR China
| | - Chuansheng Chen
- Department of Psychological Science, University of California, Irvine, CA 92697, United States
| | - Jun Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, PR China.
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20
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Di X, Zhang H, Biswal BB. Anterior cingulate cortex differently modulates frontoparietal functional connectivity between resting-state and working memory tasks. Hum Brain Mapp 2020; 41:1797-1805. [PMID: 31904907 PMCID: PMC7268054 DOI: 10.1002/hbm.24912] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 12/09/2019] [Accepted: 12/16/2019] [Indexed: 12/20/2022] Open
Abstract
The brain frontoparietal regions and the functional communications between them are critical in supporting working memory and other executive functions. The functional connectivity between frontoparietal regions are modulated by working memory loads, and are shown to be modulated by a third brain region in resting-state. However, it is largely unknown whether the third-region modulations remain the same during working memory tasks or were largely modulated by task demands. In the current study, we collected functional MRI (fMRI) data when the subjects were performing n-back tasks and in resting-state. We first used a block-designed localizer to define the frontoparietal regions that showed higher activations in the 2-back than the 1-back condition. Next, we performed physiophysiological interaction (PPI) analysis using left and right middle frontal gyrus (MFG) and superior parietal lobule (SPL) regions, respectively, in three continuous-designed runs of resting-state, 1-back, and 2-back conditions. No regions showed consistent modulatory interactions with the seed pairs in the three conditions. Instead, the anterior cingulate cortex (ACC) showed different modulatory interactions with the right MFG and SPL among the three conditions. While the increased activity of the ACC was associated with decreased functional coupling between the right MFG and SPL in resting-state, it was associated with increased functional coupling in the 2-back condition. The observed task modulations support the functional significance of the modulations of the ACC on frontoparietal connectivity.
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Affiliation(s)
- Xin Di
- School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey
| | - Heming Zhang
- School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Bharat B Biswal
- School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey
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