1
|
Hu F, Yao P, He K, Yang X, Gouda MA, Zhang L. Effects of Emotional Olfactory Stimuli on Modulating Angry Driving Based on an EEG Connectivity Study. Int J Neural Syst 2024:2450058. [PMID: 39155690 DOI: 10.1142/s0129065724500588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/20/2024]
Abstract
Effectively regulating anger driving has become critical in ensuring road safety. The existing research lacks a feasible exploration of anger-driving regulation. This paper delves into the effect and neural mechanisms of emotional olfactory stimuli (EOS) on regulating anger driving based on EEG. First, this study designed an angry driving regulation experiment based on EOS to record EEG signals. Second, brain activation patterns under various EOS conditions are explored by analyzing functional brain networks (FBNs). Additionally, the paper analyzes dynamic alterations in anger-related characteristics to explore the intensity and persistence of regulating anger driving under different EOS. Finally, the paper studies the frequency energy of EEG changes under EOS through time-frequency analysis. The results indicate that EOS can effectively regulate a driver's anger emotions, especially with the banana odor showing superior effects. Under banana odor stimulus, synchronization between the parietal and temporal lobes significantly decreased. Notably, the regulatory effect of banana odor is optimal and exhibits sustained efficacy. The regulatory effect of banana odor on anger emotions is persistent. Furthermore, the impact of banana odor significantly reduces the distribution of high-energy activation states in the parietal lobe region. Our findings provide new insights into the dynamic characterization of functional connectivity during anger-driving regulation and demonstrate the potential of using EOS as a reliable tool for regulating angry driving.
Collapse
Affiliation(s)
- Fo Hu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, P. R. China
| | - Peipei Yao
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, P. R. China
| | - Kailun He
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, P. R. China
| | - Xusheng Yang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, P. R. China
| | - Mohamed Amin Gouda
- Department of Mechanical Engineering and Automation, Northeastern University, Shenyang, Liaoning 110819, P. R. China
| | - Lekai Zhang
- School of Design and Architecture, Zhejiang University of Technology, Hangzhou Zhejiang 310023, P. R. China
| |
Collapse
|
2
|
Mohamadpour H, Farkhondeh Tale Navi F, Asgharian Asl F, Heysieattalab S, Shakeri E, Karami Isheqlou L. Exploring neural correlates of social dominance: Insights from behavioral, resting- state EEG, and ERP indices. Brain Cogn 2024; 178:106177. [PMID: 38749353 DOI: 10.1016/j.bandc.2024.106177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 04/20/2024] [Accepted: 05/10/2024] [Indexed: 06/14/2024]
Abstract
Numerous studies have explored the concept of social dominance and its implications for leadership within the behavioral and cognitive sciences in recent years. The current study aims to address the gap regarding the neural correlates of social dominance by investigating the associations between psychological measures of social dominance and neural features among a sample of leaders. Thirty healthy male volunteers engaged in a monetary gambling task while their resting-state and task-based electroencephalography data were recorded. The results revealed a positive association between social dominance and resting-state beta oscillations in central electrodes. Furthermore, a negative association was observed between social dominance and task-based reaction time as well as the amplitude of the feedback-related negativity component of the event-related potentials during the gain, but not the loss condition. These findings suggest that social dominance is associated with enhanced reward processing which has implications for social and interpersonal interactions.
Collapse
Affiliation(s)
- Hadi Mohamadpour
- Department of Cognitive Neuroscience, University of Tabriz, Tabriz, Iran
| | | | | | | | - Elmira Shakeri
- Department of Business Management, Allameh Tabataba'i University, Tehran, Iran
| | - Leyla Karami Isheqlou
- Department of Cognitive Neuroscience, University of Tabriz, Tabriz, Iran; Department of Psychology, Utah State University, Utah, USA
| |
Collapse
|
3
|
Luo X, Zhou B, Shi J, Li G, Zhu Y. Effects of gender and age on sleep EEG functional connectivity differences in subjects with mild difficulty falling asleep. Front Psychiatry 2024; 15:1433316. [PMID: 39045546 PMCID: PMC11264056 DOI: 10.3389/fpsyt.2024.1433316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Accepted: 06/17/2024] [Indexed: 07/25/2024] Open
Abstract
Introduction Difficulty falling asleep place an increasing burden on society. EEG-based sleep staging is fundamental to the diagnosis of sleep disorder, and the selection of features for each sleep stage is a key step in the sleep analysis. However, the differences of sleep EEG features in gender and age are not clear enough. Methods This study aimed to investigate the effects of age and gender on sleep EEG functional connectivity through statistical analysis of brain functional connectivity and machine learning validation. The two-overnight sleep EEG data of 78 subjects with mild difficulty falling asleep were categorized into five sleep stages using markers and segments from the "sleep-EDF" public database. First, the 78 subjects were finely grouped, and the mutual information of the six sleep EEG rhythms of δ, θ, α, β, spindle, and sawtooth wave was extracted as a functional connectivity measure. Then, one-way analysis of variance (ANOVA) was used to extract significant differences in functional connectivity of sleep rhythm waves across sleep stages with respect to age and gender. Finally, machine learning algorithms were used to investigate the effects of fine grouping of age and gender on sleep staging. Results and discussion The results showed that: (1) The functional connectivity of each sleep rhythm wave differed significantly across sleep stages, with delta and beta functional connectivity differing significantly across sleep stages. (2) Significant differences in functional connections among young and middle-aged groups, and among young and elderly groups, but no significant difference between middle-aged and elderly groups. (3) Female functional connectivity strength is generally higher than male at the high-frequency band of EEG, but no significant difference in the low-frequency. (4) Finer group divisions based on gender and age can indeed improve the accuracy of sleep staging, with an increase of about 3.58% by using the random forest algorithm. Our results further reveal the electrophysiological neural mechanisms of each sleep stage, and find that sleep functional connectivity differs significantly in both gender and age, providing valuable theoretical guidance for the establishment of automated sleep stage models.
Collapse
Affiliation(s)
- Xiaodong Luo
- Psychiatry Department, The Second Hospital of Jinhua, Jinhua, China
| | - Bin Zhou
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China
| | - Jilong Shi
- College of Engineering, Zhejiang Normal University, Jinhua, China
| | - Gang Li
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China
| | - Yixia Zhu
- Psychiatry Department, The Second Hospital of Jinhua, Jinhua, China
| |
Collapse
|
4
|
González-González E, Requena C, Barbosa F. Examining the influence of self-care practices on brain activity in healthy older adults. Front Aging Neurosci 2024; 16:1420072. [PMID: 39026994 PMCID: PMC11254819 DOI: 10.3389/fnagi.2024.1420072] [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: 04/19/2024] [Accepted: 06/20/2024] [Indexed: 07/20/2024] Open
Abstract
Introduction Studies on the aging brain often occur in active settings, but comparatively few investigate brain activity in resting states. However, exploring brain activity in a resting state offers valuable insights into spontaneous neural processes unaffected by task-specific influences. Objective: To investigate the relationship between self-care practices, cognitive function, and patterns of brain activity in healthy older adults, taking into account predictions from aging brain models. Methodology 77 older adults aged 61 to 87 completing a self-care practices questionnaire, neuropsychological tests, and resting-state electroencephalogram (EEG) recordings. Participants were classified into two groups according to their self-care practices: traditional self-care (T-SC) and developmental self-care (D-SC). Results Although neuropsychological tests did not yield significant differences between the D-SC and T-SC groups, patterns of brain activity revealed distinct behaviors. The T-SC group demonstrated patterns more consistent with established aging brain models, contrasting with the D-SC group, which exhibited brain activity akin to that observed in younger adults. Specifically, the T-SC group displayed hyperactivation related to memory and executive function performance, alongside heightened alpha power in posterior regions. Furthermore, bilateral frontal activation in the beta band was evident. Conclusions The findings suggest a nuanced relationship between self-care practices and brain activity in older adults. While the T-SC group demonstrated brain activity patterns consistent with conservative aging, indicating the preservation of typical aging characteristics, the D-SC group displayed activity suggestive of a potential protective effect. This effect may be linked to self-care strategies that foster development and resilience in cognitive aging.
Collapse
Affiliation(s)
| | - Carmen Requena
- Laboratory of Lab-EEG-Lifespan, University of León, León, Spain
| | - Fernando Barbosa
- Laboratory of Neuropsychophysiology, University of Porto, Porto, Portugal
| |
Collapse
|
5
|
Fehr T, Mehrens S, Haag MC, Amelung A, Gloy K. Changes in Spatiotemporal Dynamics of Default Network Oscillations between 19 and 29 Years of Age. Brain Sci 2024; 14:671. [PMID: 39061412 PMCID: PMC11274777 DOI: 10.3390/brainsci14070671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 06/15/2024] [Accepted: 06/20/2024] [Indexed: 07/28/2024] Open
Abstract
The exploration of functional resting-state brain developmental parameters and measures can help to improve scientific, psychological, and medical applications. The present work focussed on both traditional approaches, such as topographical power analyses at the signal space level, and advanced approaches, such as the exploration of age-related dynamics of source space data. The results confirmed the expectation that the third life decade would show a kind of stability in oscillatory signal and source-space-related parameters. However, from a source dynamics perspective, different frequency ranges appear to develop quite differently, as reflected in age-related sequential network communication profiles. Among other discoveries, the left anterior cingulate source location could be shown to reduce bi-directional network communication in the lower alpha band, whereas it differentiated its uni- and bidirectional communication dynamics to sub-cortical and posterior brain locations. Higher alpha oscillations enhanced communication dynamics between the thalamus and particularly frontal areas. In conclusion, resting-state data appear to be, at least in part, functionally reorganized in the default mode network, while quantitative measures, such as topographical power and regional source activity, did not correlate with age in the third life decade. In line with other authors, we suggest the further development of a multi-perspective approach in biosignal analyses.
Collapse
Affiliation(s)
- Thorsten Fehr
- Institute for Psychology, University of Bremen, 28357 Bremen, Germany (K.G.)
- Center for Advanced Imaging, University of Bremen, 28357 Bremen, Germany
| | - Sophia Mehrens
- Institute for Psychology, University of Bremen, 28357 Bremen, Germany (K.G.)
| | | | - Anneke Amelung
- Institute for Psychology, University of Bremen, 28357 Bremen, Germany (K.G.)
| | - Kilian Gloy
- Institute for Psychology, University of Bremen, 28357 Bremen, Germany (K.G.)
| |
Collapse
|
6
|
Kang JH, Bae JH, Jeon YJ. Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review. Bioengineering (Basel) 2024; 11:418. [PMID: 38790286 PMCID: PMC11118246 DOI: 10.3390/bioengineering11050418] [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: 03/15/2024] [Revised: 04/18/2024] [Accepted: 04/23/2024] [Indexed: 05/26/2024] Open
Abstract
The study of the effects of aging on neural activity in the human brain has attracted considerable attention in neurophysiological, neuropsychiatric, and neurocognitive research, as it is directly linked to an understanding of the neural mechanisms underlying the disruption of the brain structures and functions that lead to age-related pathological disorders. Electroencephalographic (EEG) signals recorded during resting-state conditions have been widely used because of the significant advantage of non-invasive signal acquisition with higher temporal resolution. These advantages include the capability of a variety of linear and nonlinear signal analyses and state-of-the-art machine-learning and deep-learning techniques. Advances in artificial intelligence (AI) can not only reveal the neural mechanisms underlying aging but also enable the assessment of brain age reliably by means of the age-related characteristics of EEG signals. This paper reviews the literature on the age-related features, available analytic methods, large-scale resting-state EEG databases, interpretations of the resulting findings, and recent advances in age-related AI models.
Collapse
Affiliation(s)
- Jae-Hwan Kang
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Jang-Han Bae
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Young-Ju Jeon
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| |
Collapse
|
7
|
Auer T, Goldthorpe R, Peach R, Hebron H, Violante IR. Functionally annotated electrophysiological neuromarkers of healthy ageing and memory function. Hum Brain Mapp 2024; 45:e26687. [PMID: 38651629 PMCID: PMC11036379 DOI: 10.1002/hbm.26687] [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: 09/14/2023] [Revised: 02/22/2024] [Accepted: 04/05/2024] [Indexed: 04/25/2024] Open
Abstract
The unprecedented increase in life expectancy presents a unique opportunity and the necessity to explore both healthy and pathological aspects of ageing. Electroencephalography (EEG) has been widely used to identify neuromarkers of cognitive ageing due to its affordability and richness in information. However, despite the growing volume of data and methodological advancements, the abundance of contradictory and non-reproducible findings has hindered clinical translation. To address these challenges, our study introduces a comprehensive workflow expanding on previous EEG studies and investigates various static and dynamic power and connectivity estimates as potential neuromarkers of cognitive ageing in a large dataset. We also assess the robustness of our findings by testing their susceptibility to band specification. Finally, we characterise our findings using functionally annotated brain networks to improve their interpretability and multi-modal integration. Our analysis demonstrates the effect of methodological choices on findings and that dynamic rather than static neuromarkers are not only more sensitive but also more robust. Consequently, they emerge as strong candidates for cognitive ageing neuromarkers. Moreover, we were able to replicate the most established EEG findings in cognitive ageing, such as alpha oscillation slowing, increased beta power, reduced reactivity across multiple bands, and decreased delta connectivity. Additionally, when considering individual variations in the alpha band, we clarified that alpha power is characteristic of memory performance rather than ageing, highlighting its potential as a neuromarker for cognitive ageing. Finally, our approach using functionally annotated source reconstruction allowed us to provide insights into domain-specific electrophysiological mechanisms underlying memory performance and ageing. HIGHLIGHTS: We provide an open and reproducible pipeline with a comprehensive workflow to investigate static and dynamic EEG neuromarkers. Neuromarkers related to neural dynamics are sensitive and robust. Individualised alpha power characterises cognitive performance rather than ageing. Functional annotation allows cross-modal interpretation of EEG findings.
Collapse
Affiliation(s)
- Tibor Auer
- School of PsychologyUniversity of SurreyGuildfordUK
| | | | | | - Henry Hebron
- School of PsychologyUniversity of SurreyGuildfordUK
| | | |
Collapse
|
8
|
Kaposzta Z, Czoch A, Mukli P, Stylianou O, Liu DH, Eke A, Racz FS. Fingerprints of decreased cognitive performance on fractal connectivity dynamics in healthy aging. GeroScience 2024; 46:713-736. [PMID: 38117421 PMCID: PMC10828149 DOI: 10.1007/s11357-023-01022-x] [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: 09/25/2023] [Accepted: 11/19/2023] [Indexed: 12/21/2023] Open
Abstract
Analysis of brain functional connectivity (FC) could provide insight in how and why cognitive functions decline even in healthy aging (HA). Despite FC being established as fluctuating over time even in the resting state (RS), dynamic functional connectivity (DFC) studies involving healthy elderly individuals and assessing how these patterns relate to cognitive performance are yet scarce. In our recent study we showed that fractal temporal scaling of functional connections in RS is not only reduced in HA, but also predicts increased response latency and reduced task solving accuracy. However, in that work we did not address changes in the dynamics of fractal connectivity (FrC) strength itself and its plausible relationship with mental capabilities. Therefore, here we analyzed RS electroencephalography recordings of the same subject cohort as previously, consisting of 24 young and 19 healthy elderly individuals, who also completed 7 different cognitive tasks after data collection. Dynamic fractal connectivity (dFrC) analysis was carried out via sliding-window detrended cross-correlation analysis (DCCA). A machine learning method based on recursive feature elimination was employed to select the subset of connections most discriminative between the two age groups, identifying 56 connections that allowed for classifying participants with an accuracy surpassing 92%. Mean of DCCA was found generally increased, while temporal variability of FrC decreased in the elderly when compared to the young group. Finally, dFrC indices expressed an elaborate pattern of associations-assessed via Spearman correlation-with cognitive performance scores in both groups, linking fractal connectivity strength and variance to increased response latency and reduced accuracy in the elderly population. Our results provide further support for the relevance of FrC dynamics in understanding age-related cognitive decline and might help to identify potential targets for future intervention strategies.
Collapse
Affiliation(s)
- Zalan Kaposzta
- Department of Physiology, Semmelweis University, 37-47 Tuzolto Street, Budapest, 1094, Hungary
| | - Akos Czoch
- Department of Physiology, Semmelweis University, 37-47 Tuzolto Street, Budapest, 1094, Hungary
| | - Peter Mukli
- Department of Physiology, Semmelweis University, 37-47 Tuzolto Street, Budapest, 1094, Hungary
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Vascular Cognitive Impairment and Neurodegeneration Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- International Training Program in Geroscience, Doctoral School of Basic and Translational Medicine/Department of Public Health, Semmelweis University, Budapest, Hungary
| | - Orestis Stylianou
- Department of Physiology, Semmelweis University, 37-47 Tuzolto Street, Budapest, 1094, Hungary
- Institute of Translational Medicine, Semmelweis University, Budapest, Hungary
- Berlin Institute of Health at Charité, University Hospital Berlin, Charitéplatz 1, 10117, Berlin, Germany
- Department of Neurology with Experimental Neurology, Charité-University Hospital Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Deland Hu Liu
- Chandra Department of Electrical and Computer Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Andras Eke
- Department of Physiology, Semmelweis University, 37-47 Tuzolto Street, Budapest, 1094, Hungary
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Frigyes Samuel Racz
- Department of Physiology, Semmelweis University, 37-47 Tuzolto Street, Budapest, 1094, Hungary.
- Department of Neurology, Dell Medical School, The University of Texas at Austin, 1601 Trinity St, Austin, TX, 78712, USA.
- Mulva Clinic for the Neurosciences, Dell Medical School, The University of Texas at Austin, Austin, TX, USA.
| |
Collapse
|
9
|
Cui Y, Zhang Q. Effects of age on the time course of syntactic priming in Chinese sentence production: an ERP study. Cereb Cortex 2024; 34:bhad503. [PMID: 38282453 DOI: 10.1093/cercor/bhad503] [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: 09/12/2023] [Revised: 12/03/2023] [Accepted: 12/05/2023] [Indexed: 01/30/2024] Open
Abstract
Using a syntactic priming task, we investigated the time course of syntactic encoding in Chinese sentence production and compared encoding patterns between younger and older adults. Participants alternately read sentence descriptions and overtly described pictures, while event-related potentials (ERPs) were recorded. We manipulated the abstract prime structure (active or passive) as well as the lexical overlap of the prime and the target (verb overlap or no overlap). The syntactic choice results replicated classical abstract priming and lexical boost effects in both younger and older adults. However, when production latency was taken into account, the speed benefit from syntactic repetition differed between the two age groups. Meanwhile, preferred priming facilitated production in both age groups, whereas nonpreferred priming inhibited production in the older group. For electroencephalography, an earlier effect of syntactic repetition and a later effect of lexical overlap showed a two-stage pattern of syntactic encoding. Older adults also showed a more delayed and interactive encoding pattern than younger adults, indicating a greater reliance on lexical information. These results are illustrative of the two-stage competition and residual activation models.
Collapse
Affiliation(s)
- Ying Cui
- Department of Psychology, Renmin University of China, 59 Zhongguancun Street, Beijing 100872, China
| | - Qingfang Zhang
- Department of Psychology, Renmin University of China, 59 Zhongguancun Street, Beijing 100872, China
| |
Collapse
|
10
|
Kumar WS, Ray S. Healthy ageing and cognitive impairment alter EEG functional connectivity in distinct frequency bands. Eur J Neurosci 2023; 58:3432-3449. [PMID: 37559505 DOI: 10.1111/ejn.16114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/20/2023] [Accepted: 07/24/2023] [Indexed: 08/11/2023]
Abstract
Functional connectivity (FC) indicates the interdependencies between brain signals recorded from spatially distinct locations in different frequency bands, which is modulated by cognitive tasks and is known to change with ageing and cognitive disorders. Recently, the power of narrow-band gamma oscillations induced by visual gratings have been shown to reduce with both healthy ageing and in subjects with mild cognitive impairment (MCI). However, the impact of ageing/MCI on stimulus-induced gamma FC has not been well studied. We recorded electroencephalogram (EEG) from a large cohort (N = 229) of elderly subjects (>49 years) while they viewed large cartesian gratings to induce gamma oscillations and studied changes in alpha and gamma FC with healthy ageing (N = 218) and MCI (N = 11). Surprisingly, we found distinct differences across age and MCI groups in power and FC. With healthy ageing, alpha power did not change but FC decreased significantly. MCI reduced gamma but not alpha FC significantly compared with age and gender matched controls, even when power was matched between the two groups. Overall, our results suggest distinct effects of ageing and disease on EEG power and FC, suggesting different mechanisms underlying ageing and cognitive disorders.
Collapse
Affiliation(s)
| | - Supratim Ray
- Centre for Neuroscience, Indian Institute of Science, Bengaluru, India
| |
Collapse
|
11
|
Bjerkan J, Lancaster G, Meglič B, Kobal J, Crawford TJ, McClintock PVE, Stefanovska A. Aging affects the phase coherence between spontaneous oscillations in brain oxygenation and neural activity. Brain Res Bull 2023; 201:110704. [PMID: 37451471 DOI: 10.1016/j.brainresbull.2023.110704] [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: 04/25/2023] [Revised: 07/03/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023]
Abstract
The risk of neurodegenerative disorders increases with age, due to reduced vascular nutrition and impaired neural function. However, the interactions between cardiovascular dynamics and neural activity, and how these interactions evolve in healthy aging, are not well understood. Here, the interactions are studied by assessment of the phase coherence between spontaneous oscillations in cerebral oxygenation measured by fNIRS, the electrical activity of the brain measured by EEG, and cardiovascular functions extracted from ECG and respiration effort, all simultaneously recorded. Signals measured at rest in 21 younger participants (31.1 ± 6.9 years) and 24 older participants (64.9 ± 6.9 years) were analysed by wavelet transform, wavelet phase coherence and ridge extraction for frequencies between 0.007 and 4 Hz. Coherence between the neural and oxygenation oscillations at ∼ 0.1 Hz is significantly reduced in the older adults in 46/176 fNIRS-EEG probe combinations. This reduction in coherence cannot be accounted for in terms of reduced power, thus indicating that neurovascular interactions change with age. The approach presented promises a noninvasive means of evaluating the efficiency of the neurovascular unit in aging and disease.
Collapse
Affiliation(s)
- Juliane Bjerkan
- Lancaster University, Department of Physics, LA1 4YB, Lancaster, United Kingdom
| | - Gemma Lancaster
- Lancaster University, Department of Physics, LA1 4YB, Lancaster, United Kingdom
| | - Bernard Meglič
- University of Ljubljana Medical Centre, Department of Neurology, 1525, Ljubljana, Slovenia
| | - Jan Kobal
- University of Ljubljana Medical Centre, Department of Neurology, 1525, Ljubljana, Slovenia
| | - Trevor J Crawford
- Lancaster University, Department of Psychology, LA1 4YF, Lancaster, United Kingdom
| | | | - Aneta Stefanovska
- Lancaster University, Department of Physics, LA1 4YB, Lancaster, United Kingdom.
| |
Collapse
|
12
|
Qi X, Fang J, Sun Y, Xu W, Li G. Altered Functional Brain Network Structure between Patients with High and Low Generalized Anxiety Disorder. Diagnostics (Basel) 2023; 13:diagnostics13071292. [PMID: 37046509 PMCID: PMC10093329 DOI: 10.3390/diagnostics13071292] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/26/2023] [Accepted: 03/27/2023] [Indexed: 04/01/2023] Open
Abstract
To investigate the differences in functional brain network structures between patients with a high level of generalized anxiety disorder (HGAD) and those with a low level of generalized anxiety disorder (LGAD), a resting-state electroencephalogram (EEG) was recorded in 30 LGAD patients and 21 HGAD patients. Functional connectivity between all pairs of brain regions was determined by the Phase Lag Index (PLI) to construct a functional brain network. Then, the characteristic path length, clustering coefficient, and small world were calculated to estimate functional brain network structures. The results showed that the PLI values of HGAD were significantly increased in alpha2, and significantly decreased in the theta and alpha1 rhythms, and the small-world attributes for both HGAD patients and LGAD patients were less than one for all the rhythms. Moreover, the small-world values of HGAD were significantly lower than those of LGAD in the theta and alpha2 rhythms, which indicated that the brain functional network structure would deteriorate with the increase in generalized anxiety disorder (GAD) severity. Our findings may play a role in the development and understanding of LGAD and HGAD to determine whether interventions that target these brain changes may be effective in treating GAD.
Collapse
Affiliation(s)
- Xuchen Qi
- Department of Neurosurgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China
- Department of Neurosurgery, Shaoxing People’s Hospital, Shaoxing 312000, China
| | - Jiaqi Fang
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China
| | - Yu Sun
- Key Laboratory for Biomedical Engineering of Ministry of Education of China, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310000, China
| | - Wanxiu Xu
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China
- Correspondence: (W.X.); (G.L.)
| | - Gang Li
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, China
- Correspondence: (W.X.); (G.L.)
| |
Collapse
|
13
|
Hua W, Li Y. Electroencephalography Based Microstate Functional Connectivity Analysis in Emotional Cognitive Reappraisal Combined with Happy Music. Brain Sci 2023; 13:brainsci13040554. [PMID: 37190519 DOI: 10.3390/brainsci13040554] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 03/22/2023] [Accepted: 03/24/2023] [Indexed: 03/29/2023] Open
Abstract
Currently, research mainly focuses on the effects of happy music on the subjective assessment of cognitive reappraisal, but relevant results of the neural mechanism are lacking. By analysing the functional connectivity of microstates based on electroencephalography (EEG), we investigated the effect of cognitive reappraisal combined with happy music on emotional regulation and the dynamic characteristics of brain functional activities. A total of 52 healthy college students were divided into music group and control group. EEG data and behavioural scores were collected during an experiment of cognitive reappraisal combined with happy music. The dynamic time window of the brain functional network was determined by microstate analysis, and the metrics of functional connectivity, clustering coefficient (Cp) and characteristic path length (Lp), were calculated based on the phase-locked value. The arousal of cognitive reappraisal significantly increased (p = 0.005) in music group, but the valence did not change significantly. This suggested that happy music did not affect emotional regulation from the behavioural perspective. Four microstate global templates (A–D) were determined. With happy music, the duration (p = 0.043) and Lp (p = 0.033) of microstate B increased significantly, indicating that the transfection efficiency of the brain network decreased, reflecting a negative effect on cognitive reappraisal. The duration (p = 0.017) of microstate D decreased and of Cp (p < 0.001) increased significantly, indicating that the local information-processing ability of the brain network increased. We conclude that happy music can change the characteristics of brain functional networks and have a positive effect on cognitive reappraisal in specific period. The research provides a certain electrophysiological basis for applying happy music to cognitive reappraisal.
Collapse
|
14
|
Singh P, Kumar Gandhi T, Kumar L. Reorganization of resting-state brain network functional connectivity across human brain developmental stages. Brain Res 2023; 1800:148196. [PMID: 36463956 DOI: 10.1016/j.brainres.2022.148196] [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: 09/07/2022] [Revised: 11/01/2022] [Accepted: 11/28/2022] [Indexed: 12/02/2022]
Abstract
Cognitive brain aging can either be healthy or degenerative in nature. Multiple alterations occur in brain networks with healthy aging. Much of this has yet to be investigated. This study seeks to understand the typical healthy human brain's developmental stages using a publicly available dataset from the human connectome project. As the human brain's developmental stage varies, we also intend to identify a pattern of reorganization in the resting state functional connectivity of several brain networks. The results are specifically presented based on the resting state BOLD signals of 1096 healthy volunteers between the age group of 7-89 years. The k-means clustering method has been used to determine the various human brain developmental stages. Using the t-SNE technique, the clusters are visually separated. BrainNet Viewer is used to study the changes in resting state functional connectivity of the entire brain as the human brain developmental stages vary. The age-related pattern of change in the resting state functional connectivity of six Dosenbasch brain networks that were grouped using the k-means elbow approach has been additionally presented. For performance evaluation, three metrics of brain network connection including network segregation, between network connectivity, and within-network connectivity are used. As the age cohort changes, a consistent pattern in the variance of these connection indices is seen for different Dosenbasch brain networks. Thus, the study's findings suggest that healthy aging causes a functional reorganization of the resting state brain network connections.
Collapse
Affiliation(s)
- Prerna Singh
- Bharti School of Telecommunication Technology and Management, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110096, India
| | - Tapan Kumar Gandhi
- Cadence Chair Professor of Automation & AI, Convenor, Computer Technology, Department of Electrical Engineering, Hauz Khas, New Delhi 110096, India; Bharti School of Telecommunication, Indian Institute of Technology, Delhi, Hauz Khas, New Delhi 110016, India.
| | - Lalan Kumar
- Department of Electrical Engineering, Bharti School of Telecommunication, New Delhi 110016, India; Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, New Delhi 110016, India
| |
Collapse
|
15
|
Varanasi S, Tuli R, Han F, Chen R, Choa FS. Age Related Functional Connectivity Signature Extraction Using Energy-Based Machine Learning Techniques. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23031603. [PMID: 36772649 PMCID: PMC9920122 DOI: 10.3390/s23031603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Revised: 01/24/2023] [Accepted: 01/29/2023] [Indexed: 05/14/2023]
Abstract
The study of brain connectivity plays an important role in understanding the functional organizations of the brain. It also helps to identify connectivity signatures that can be used for evaluating neural disorders and monitoring treatment efficacy. In this work, age-related changes in brain connectivity are studied to obtain aging signatures based on various modeling techniques. These include an energy-based machine learning technique to identify brain network interaction differences between two age groups with a large (30 years) age gap between them. Disconnectivity graphs and activation maps of the seven prominent resting-state networks (RSN) were obtained from functional MRI data of old and young adult subjects. Two-sample t-tests were performed on the local minimums with Bonferroni correction to control the family-wise error rate. These local minimums are connectivity states showing not only which brain regions but also how strong they are working together. They work as aging signatures that can be used to differentiate young and old groups. We found that the attention network's connectivity signature is a state with all the regions working together and young subjects have a stronger average connectivity among these regions. We have also found a common pattern between young and old subjects where the left and right brain regions of the frontal network are sometimes working separately instead of together. In summary, in this work, we combined machine learning and statistical approaches to extract connectivity signatures, which can be utilized to distinguish aging brains and monitor possible treatment efficacy.
Collapse
Affiliation(s)
- Sravani Varanasi
- Department of Electrical Engineering and Computer Science, University of Maryland Baltimore County, Baltimore, MD 21250, USA
- Correspondence:
| | - Roopan Tuli
- Department of Electrical Engineering, Santa Clara University, Santa Clara, CA 95053, USA
| | - Fei Han
- The Hilltop Institute, University of Maryland Baltimore County, Baltimore, MD 21250, USA
| | - Rong Chen
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Baltimore, Baltimore, MD 21201, USA
| | - Fow-Sen Choa
- Department of Electrical Engineering and Computer Science, University of Maryland Baltimore County, Baltimore, MD 21250, USA
| |
Collapse
|
16
|
Wang J, Fang J, Xu Y, Zhong H, Li J, Li H, Li G. Difference analysis of multidimensional electroencephalogram characteristics between young and old patients with generalized anxiety disorder. Front Hum Neurosci 2022; 16:1074587. [PMID: 36504623 PMCID: PMC9731337 DOI: 10.3389/fnhum.2022.1074587] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 11/08/2022] [Indexed: 11/25/2022] Open
Abstract
Growing evidences indicate that age plays an important role in the development of mental disorders, but few studies focus on the neuro mechanisms of generalized anxiety disorder (GAD) in different age groups. Therefore, this study attempts to reveal the neurodynamics of Young_GAD (patients with GAD under the age of 50) and Old_GAD (patients with GAD over 50 years old) through statistical analysis of multidimensional electroencephalogram (EEG) features and machine learning models. In this study, 10-min resting-state EEG data were collected from 45 Old_GAD and 33 Young_GAD. And multidimensional EEG features were extracted, including absolute power (AP), fuzzy entropy (FE), and phase-lag-index (PLI), on which comparison and analyses were performed later. The results showed that Old_GAD exhibited higher power spectral density (PSD) value and FE value in beta rhythm compared to theta, alpha1, and alpha2 rhythms, and functional connectivity (FC) also demonstrated significant reorganization of brain function in beta rhythm. In addition, the accuracy of machine learning classification between Old_GAD and Young_GAD was 99.67%, further proving the feasibility of classifying GAD patients by age. The above findings provide an objective basis in the field of EEG for the age-specific diagnosis and treatment of GAD.
Collapse
Affiliation(s)
- Jie Wang
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology and Equipment of Zhejiang Province, Zhejiang Normal University, Jinhua, China,College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, China
| | - Jiaqi Fang
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology and Equipment of Zhejiang Province, Zhejiang Normal University, Jinhua, China,College of Engineering, Zhejiang Normal University, Jinhua, China
| | - Yanting Xu
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology and Equipment of Zhejiang Province, Zhejiang Normal University, Jinhua, China,College of Engineering, Zhejiang Normal University, Jinhua, China
| | - Hongyang Zhong
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology and Equipment of Zhejiang Province, Zhejiang Normal University, Jinhua, China,College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, China
| | - Jing Li
- College of Foreign Language, Zhejiang Normal University, Jinhua, China
| | - Huayun Li
- College of Teacher Education, Zhejiang Normal University, Jinhua, China,Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China,*Correspondence: Gang Li,
| | - Gang Li
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology and Equipment of Zhejiang Province, Zhejiang Normal University, Jinhua, China,College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China,Key Laboratory for Biomedical Engineering of Ministry of Education of China, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China,Huayun Li,
| |
Collapse
|
17
|
Soleimani B, Das P, Dushyanthi Karunathilake IM, Kuchinsky SE, Simon JZ, Babadi B. NLGC: Network localized Granger causality with application to MEG directional functional connectivity analysis. Neuroimage 2022; 260:119496. [PMID: 35870697 PMCID: PMC9435442 DOI: 10.1016/j.neuroimage.2022.119496] [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: 03/02/2022] [Revised: 06/21/2022] [Accepted: 07/19/2022] [Indexed: 11/25/2022] Open
Abstract
Identifying the directed connectivity that underlie networked activity between different cortical areas is critical for understanding the neural mechanisms behind sensory processing. Granger causality (GC) is widely used for this purpose in functional magnetic resonance imaging analysis, but there the temporal resolution is low, making it difficult to capture the millisecond-scale interactions underlying sensory processing. Magnetoencephalography (MEG) has millisecond resolution, but only provides low-dimensional sensor-level linear mixtures of neural sources, which makes GC inference challenging. Conventional methods proceed in two stages: First, cortical sources are estimated from MEG using a source localization technique, followed by GC inference among the estimated sources. However, the spatiotemporal biases in estimating sources propagate into the subsequent GC analysis stage, may result in both false alarms and missing true GC links. Here, we introduce the Network Localized Granger Causality (NLGC) inference paradigm, which models the source dynamics as latent sparse multivariate autoregressive processes and estimates their parameters directly from the MEG measurements, integrated with source localization, and employs the resulting parameter estimates to produce a precise statistical characterization of the detected GC links. We offer several theoretical and algorithmic innovations within NLGC and further examine its utility via comprehensive simulations and application to MEG data from an auditory task involving tone processing from both younger and older participants. Our simulation studies reveal that NLGC is markedly robust with respect to model mismatch, network size, and low signal-to-noise ratio, whereas the conventional two-stage methods result in high false alarms and mis-detections. We also demonstrate the advantages of NLGC in revealing the cortical network-level characterization of neural activity during tone processing and resting state by delineating task- and age-related connectivity changes.
Collapse
Affiliation(s)
- Behrad Soleimani
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA; Institute for Systems Research, University of Maryland, College Park, MD, USA.
| | - Proloy Das
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA.
| | - I M Dushyanthi Karunathilake
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA; Institute for Systems Research, University of Maryland, College Park, MD, USA.
| | - Stefanie E Kuchinsky
- Audiology and Speech Pathology Center, Walter Reed National Military Medical Center, Bethesda, MD, USA.
| | - Jonathan Z Simon
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA; Institute for Systems Research, University of Maryland, College Park, MD, USA; Department of Biology, University of Maryland College Park, MD, USA.
| | - Behtash Babadi
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA; Institute for Systems Research, University of Maryland, College Park, MD, USA.
| |
Collapse
|
18
|
Buján A, Sampaio A, Pinal D. Resting-state electroencephalographic correlates of cognitive reserve: Moderating the age-related worsening in cognitive function. Front Aging Neurosci 2022; 14:854928. [PMID: 36185469 PMCID: PMC9521492 DOI: 10.3389/fnagi.2022.854928] [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: 01/14/2022] [Accepted: 08/08/2022] [Indexed: 11/23/2022] Open
Abstract
This exploratory study aimed to investigate the resting-state electroencephalographic (rsEEG) correlates of the cognitive reserve from a life span perspective. Current source density (CSD) and lagged-linear connectivity (LLC) measures were assessed to this aim. We firstly explored the relationship between rsEEG measures for the different frequency bands and a socio-behavioral proxy of cognitive reserve, the Cognitive Reserve Index (CRI). Secondly, we applied moderation analyses to assess whether any of the correlated rsEEG measures showed a moderating role in the relationship between age and cognitive function. Moderate negative correlations were found between the CRI and occipital CSD of delta and beta 2. Moreover, inter- and intrahemispheric LLC measures were correlated with the CRI, showing a negative association with delta and positive associations with alpha 1, beta 1, and beta 2. Among those correlated measures, just two rsEEG variables were significant moderators of the relationship between age and cognition: occipital delta CSD and right hemispheric beta 2 LLC between occipital and limbic regions. The effect of age on cognitive performance was stronger for higher values of both measures. Therefore, lower values of occipital delta CSD and lower beta 2 LLC between right occipital and limbic regions might protect or compensate for the effects of age on cognition. Results of this exploratory study might be helpful to allocate more preventive efforts to curb the progression of cognitive decline in adults with less CR, possibly characterized by these rsEEG parameters at a neural level. However, given the exploratory nature of this study, more conclusive work on these rsEEG measures is needed to firmly establish their role in the cognition-age relationship, for example, verifying if these measures moderate the relationship between brain structure and cognition.
Collapse
Affiliation(s)
- Ana Buján
- Psychological Neuroscience Laboratory (PNL), Research Center in Psychology (CIPsi), School of Psychology, University of Minho, Braga, Portugal
| | | | | |
Collapse
|
19
|
Towards precision sleep medicine: Self-attention GAN as an innovative data augmentation technique for developing personalized automatic sleep scoring classification. Comput Biol Med 2022; 148:105828. [PMID: 35816855 DOI: 10.1016/j.compbiomed.2022.105828] [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: 01/16/2022] [Revised: 05/07/2022] [Accepted: 07/03/2022] [Indexed: 11/03/2022]
Abstract
It is very important to have good quality sleep, which can affect aspects such as memory consolidation, emotional regulation, learning, physical development, and quality of life. Diagnosing human sleep quality and problems quickly and accurately is an important issue for human well-being. Therefore, many automatic sleep scoring methods have been proposed. However, the methods have been developed using sleep data from different individuals or groups. The accuracies of these proposed methods might decrease, due to existing individual differences. In this study, the self-attention generative adversarial network (SAGAN) was applied as an advanced data augmentation technique to propose an improved personalized automatic sleep scoring classification. First, the spectrograms were converted from electroencephalography (EEG). Then, SAGAN was used to generate synthesized spectrograms for each subject. Finally, the real and synthesized spectrograms of each subject were utilized to train a personalized classifier. The averaged accuracy and standard deviation of the proposed method are 95.74% and 3.78%, respectively. Compared to the classifier trained with all subjects' training data, the average accuracy increased by 8.08%. The results proved that the generated spectrograms significantly improved the performance of the personalized automatic sleep scoring classification. The contributions of the proposed method were that made the medical staff and subjects save massive medical resources and time for manual recording and scoring.
Collapse
|
20
|
Van Hoornweder S, Mora DAB, Depestele S, Frieske J, van Dun K, Cuypers K, Verstraelen S, Meesen R. Age and Interlimb Coordination Complexity Modulate Oscillatory Spectral Dynamics and Large-scale Functional Connectivity. Neuroscience 2022; 496:1-15. [PMID: 35691515 DOI: 10.1016/j.neuroscience.2022.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/27/2022] [Accepted: 06/04/2022] [Indexed: 12/12/2022]
Abstract
Interlimb coordination deteriorates as a result of aging. Due to its ubiquity in daily life, a greater understanding of the underlying neurophysiological changes is required. Here, we combined electroencephalography time-frequency spectral power and functional connectivity analyses to provide a comprehensive overview of the neural dynamics underlying the age-related deterioration of interlimb coordination involving all four limbs. Theta, alpha and beta oscillations in the frontal, central and parietal regions were analyzed in twenty younger (18-30 years) and nineteen older adults (65-78 years) during a complex interlimb reaction time task. Reaction time was significantly higher in older adults across all conditions, and the discrepancy between both age groups was largest in the most complex movement condition. Older adults demonstrated enhanced beta event-related desynchronization (i.e., the attenuation of beta power), which further increased along with task complexity and was positively linked to behavioral performance. Theta functional connectivity between frontal, central and parietal regions generally increased with movement complexity, irrespective of age group. In general, frontoparietal alpha band functional connectivity tended to be reduced in older versus younger adults, although these contrasts did not survive multiple comparison corrections. Overall, spectral results suggest that enhanced beta desynchronization in older adults reflects a successful compensatory mechanism to cope with increased difficulty during complex interlimb coordination. Functional connectivity results suggest that theta and alpha band connectivity are prone to respectively task- and age-related modulations. Future work could target these spectral and functional connectivity dynamics through noninvasive brain stimulation to potentially improve interlimb coordination in older adults.
Collapse
Affiliation(s)
- Sybren Van Hoornweder
- REVAL - Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, Diepenbeek, Belgium.
| | | | - Siel Depestele
- REVAL - Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, Diepenbeek, Belgium
| | - Joana Frieske
- REVAL - Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, Diepenbeek, Belgium
| | - Kim van Dun
- REVAL - Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, Diepenbeek, Belgium
| | - Koen Cuypers
- REVAL - Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, Diepenbeek, Belgium; Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
| | - Stefanie Verstraelen
- REVAL - Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, Diepenbeek, Belgium
| | - Raf Meesen
- REVAL - Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, Diepenbeek, Belgium; Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
| |
Collapse
|
21
|
Heise KF, Rueda-Delgado L, Chalavi S, King BR, Monteiro TS, Edden RAE, Mantini D, Swinnen SP. The interaction between endogenous GABA, functional connectivity, and behavioral flexibility is critically altered with advanced age. Commun Biol 2022; 5:426. [PMID: 35523951 PMCID: PMC9076638 DOI: 10.1038/s42003-022-03378-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 04/19/2022] [Indexed: 01/16/2023] Open
Abstract
The flexible adjustment of ongoing behavior challenges the nervous system's dynamic control mechanisms and has shown to be specifically susceptible to age-related decline. Previous work links endogenous gamma-aminobutyric acid (GABA) with behavioral efficiency across perceptual and cognitive domains, with potentially the strongest impact on those behaviors that require a high level of dynamic control. Our analysis integrated behavior and modulation of interhemispheric phase-based connectivity during dynamic motor-state transitions with endogenous GABA concentration in adult human volunteers. We provide converging evidence for age-related differences in the preferred state of endogenous GABA concentration for more flexible behavior. We suggest that the increased interhemispheric connectivity observed in the older participants represents a compensatory neural mechanism caused by phase-entrainment in homotopic motor cortices. This mechanism appears to be most relevant in the presence of a less optimal tuning of the inhibitory tone as observed during healthy aging to uphold the required flexibility of behavioral action. Future work needs to validate the relevance of this interplay between neural connectivity and GABAergic inhibition for other domains of flexible human behavior.
Collapse
Affiliation(s)
- Kirstin-Friederike Heise
- Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium.
- KU Leuven Brain Institute, Leuven, Belgium.
| | - Laura Rueda-Delgado
- Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
- School of Psychology, Trinity College Dublin, Dublin, 2, Ireland
| | - Sima Chalavi
- Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
- KU Leuven Brain Institute, Leuven, Belgium
| | - Bradley R King
- Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
- KU Leuven Brain Institute, Leuven, Belgium
- Department of Health & Kinesiology, College of Health, University of Utah, Salt Lake City, UT, USA
| | - Thiago Santos Monteiro
- Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
- KU Leuven Brain Institute, Leuven, Belgium
| | - Richard A E Edden
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Dante Mantini
- Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
- Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
| | - Stephan P Swinnen
- Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
- KU Leuven Brain Institute, Leuven, Belgium
| |
Collapse
|
22
|
Perinelli A, Assecondi S, Tagliabue CF, Mazza V. Power shift and connectivity changes in healthy aging during resting-state EEG. Neuroimage 2022; 256:119247. [PMID: 35477019 DOI: 10.1016/j.neuroimage.2022.119247] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 04/20/2022] [Accepted: 04/23/2022] [Indexed: 12/15/2022] Open
Abstract
The neural activity of human brain changes in healthy individuals during aging. The most frequent variation in patterns of neural activity are a shift from posterior to anterior areas and a reduced asymmetry between hemispheres. These patterns are typically observed during task execution and by using functional magnetic resonance imaging data. In the present study we investigated whether analogous effects can also be detected during rest and by means of source-space time series reconstructed from electroencephalographic recordings. By analyzing oscillatory power distribution across the brain we indeed found a shift from posterior to anterior areas in older adults. We additionally examined this shift by evaluating connectivity and its changes with age. The findings indicated that inter-area connections among frontal, parietal and temporal areas were strengthened in older individuals. A more complex pattern was shown in intra-area connections, where age-related activity was enhanced in parietal and temporal areas, and reduced in frontal areas. Finally, the resulting network exhibits a loss of modularity with age. Overall, the results extend to resting-state condition the evidence of an age-related shift of brain activity from posterior to anterior areas, thus suggesting that this shift is a general feature of the aging brain rather than being task-specific. In addition, the connectivity results provide new information on the reorganization of resting-state brain activity in aging.
Collapse
Affiliation(s)
- Alessio Perinelli
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Corso Bettini 31, 38068 Rovereto, TN, Italy.
| | - Sara Assecondi
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Corso Bettini 31, 38068 Rovereto, TN, Italy
| | - Chiara F Tagliabue
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Corso Bettini 31, 38068 Rovereto, TN, Italy
| | - Veronica Mazza
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Corso Bettini 31, 38068 Rovereto, TN, Italy
| |
Collapse
|
23
|
Samogin J, Rueda Delgado L, Taberna GA, Swinnen SP, Mantini D. Age-related differences of frequency-dependent functional connectivity in brain networks and their link to motor performance. Brain Connect 2022; 12:686-698. [DOI: 10.1089/brain.2021.0135] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Jessica Samogin
- Research Center for Movement Control and Neuroplasticity, Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | - Laura Rueda Delgado
- Trinity College Institute of Neuroscience, 71434, Dublin, Ireland
- Cumulus Neuroscience, Ltd. , Dublin, Ireland
| | - Gaia Amaranta Taberna
- Research Center for Movement Control and Neuroplasticity, Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | - Stephan P. Swinnen
- Research Center for Movement Control and Neuroplasticity, Department of Movement Sciences, KU Leuven, Leuven, Belgium
- Leuven Brain Institute , Leuven, Belgium
| | - Dante Mantini
- Leuven, Belgium
- Research Center for Movement Control and Neuroplasticity, Department of Movement Sciences, KU Leuven, Leuven, Belgium
| |
Collapse
|
24
|
Javaid H, Kumarnsit E, Chatpun S. Age-Related Alterations in EEG Network Connectivity in Healthy Aging. Brain Sci 2022; 12:brainsci12020218. [PMID: 35203981 PMCID: PMC8870284 DOI: 10.3390/brainsci12020218] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 01/28/2022] [Accepted: 02/01/2022] [Indexed: 02/01/2023] Open
Abstract
Emerging studies have reported that functional brain networks change with increasing age. Graph theory is applied to understand the age-related differences in brain behavior and function, and functional connectivity between the regions is examined using electroencephalography (EEG). The effect of normal aging on functional networks and inter-regional synchronization during the working memory (WM) state is not well known. In this study, we applied graph theory to investigate the effect of aging on network topology in a resting state and during performing a visual WM task to classify aging EEG signals. We recorded EEGs from 20 healthy middle-aged and 20 healthy elderly subjects with their eyes open, eyes closed, and during a visual WM task. EEG signals were used to construct the functional network; nodes are represented by EEG electrodes; and edges denote the functional connectivity. Graph theory matrices including global efficiency, local efficiency, clustering coefficient, characteristic path length, node strength, node betweenness centrality, and assortativity were calculated to analyze the networks. We applied the three classifiers of K-nearest neighbor (KNN), a support vector machine (SVM), and random forest (RF) to classify both groups. The analyses showed the significantly reduced network topology features in the elderly group. Local efficiency, global efficiency, and clustering coefficient were significantly lower in the elderly group with the eyes-open, eyes-closed, and visual WM task states. KNN achieved its highest accuracy of 98.89% during the visual WM task and depicted better classification performance than other classifiers. Our analysis of functional network connectivity and topological characteristics can be used as an appropriate technique to explore normal age-related changes in the human brain.
Collapse
Affiliation(s)
- Hamad Javaid
- Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand;
| | - Ekkasit Kumarnsit
- Physiology Program, Division of Health and Applied Science, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand;
- Biosignal Research Centre for Health, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand
| | - Surapong Chatpun
- Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand;
- Biosignal Research Centre for Health, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand
- Institute of Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand
- Correspondence:
| |
Collapse
|
25
|
Kiani M, Andreu-Perez J, Hagras H, Rigato S, Filippetti ML. Towards Understanding Human Functional Brain Development With Explainable Artificial Intelligence: Challenges and Perspectives. IEEE COMPUT INTELL M 2022. [DOI: 10.1109/mci.2021.3129956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
26
|
Chow R, Rabi R, Paracha S, Hasher L, Anderson CPsych ND, Alain C. Default mode network and neural phase synchronization in healthy aging: A resting state EEG study. Neuroscience 2022; 485:116-128. [PMID: 35051530 DOI: 10.1016/j.neuroscience.2022.01.008] [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] [Received: 05/12/2021] [Revised: 01/07/2022] [Accepted: 01/10/2022] [Indexed: 01/23/2023]
Abstract
Aging is associated with altered brain connectivity within the default mode network (DMN). Although research using functional magnetic resonance imaging has quantified age-related alterations in functional connectivity within this network during resting state, it is less clear how this may be reflected in electrophysiological measures, and how this relates to cognitive performance in older adults. The aim of this study was to quantify age differences in phase synchrony of the DMN during resting state, with particular focus on connectivity between the anterior node (i.e., medial prefrontal cortex, or mPFC) and other associated regions in this network. Electroencephalography was recorded from 55 younger adults (18-30 years, 28 females) and 34 older adults (64-88 years, 16 females) in two resting state conditions (eyes-open and -closed). Source-level functional connectivity was quantified using phase-locking value (PLV) with a spatial filter of six sources of interest, and were subjected to data-driven permutation testing between groups from 1 to 50 Hz. Older adults also completed tests of memory, language, executive functioning, and processing speed. Findings indicated decreased connectivity in the alpha2 range for older than younger adults between the mPFC and other DMN regions including the left angular gyrus and bilateral lateral temporal cortices, the latter of which were associated with lower performance in semantic fluency and executive functioning in older adults. Furthermore, greater PLV in theta and beta bands between the mPFC and posterior cingulate regions was found in older than younger adults. These results suggest age-related changes in DMN functional connectivity are non-uniform and frequency-dependent, and may reflect poorer performance in cognitive domains thought to decline with aging.
Collapse
Affiliation(s)
- Ricky Chow
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario M6A 2E1, Canada
| | - Rahel Rabi
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario M6A 2E1, Canada
| | - Shahier Paracha
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario M6A 2E1, Canada
| | - Lynn Hasher
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario M6A 2E1, Canada; Department of Psychology, University of Toronto, Ontario M5S 3G3, Canada
| | - Nicole D Anderson CPsych
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario M6A 2E1, Canada; Department of Psychology, University of Toronto, Ontario M5S 3G3, Canada; Department of Psychiatry, University of Toronto, Ontario M5T 1R8, Canada
| | - Claude Alain
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario M6A 2E1, Canada; Department of Psychology, University of Toronto, Ontario M5S 3G3, Canada; Institute of Medical Sciences, University of Toronto, Ontario M5S 1A8, Canada.
| |
Collapse
|
27
|
Using EEG to study sensorimotor adaptation. Neurosci Biobehav Rev 2022; 134:104520. [PMID: 35016897 DOI: 10.1016/j.neubiorev.2021.104520] [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: 08/10/2021] [Revised: 12/10/2021] [Accepted: 12/30/2021] [Indexed: 11/23/2022]
Abstract
Sensorimotor adaptation, or the capacity to flexibly adapt movements to changes in the body or the environment, is crucial to our ability to move efficiently in a dynamic world. The field of sensorimotor adaptation is replete with rigorous behavioural and computational methods, which support strong conceptual frameworks. An increasing number of studies have combined these methods with electroencephalography (EEG) to unveil insights into the neural mechanisms of adaptation. We review these studies: discussing EEG markers of adaptation in the frequency and the temporal domain, EEG predictors for successful adaptation and how EEG can be used to unmask latent processes resulting from adaptation, such as the modulation of spatial attention. With its high temporal resolution, EEG can be further exploited to deepen our understanding of sensorimotor adaptation.
Collapse
|
28
|
Korhonen O, Zanin M, Papo D. Principles and open questions in functional brain network reconstruction. Hum Brain Mapp 2021; 42:3680-3711. [PMID: 34013636 PMCID: PMC8249902 DOI: 10.1002/hbm.25462] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 03/11/2021] [Accepted: 04/10/2021] [Indexed: 12/12/2022] Open
Abstract
Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network representation involves often covert theoretical assumptions and methodological choices which affect the way networks are reconstructed from experimental data, and ultimately the resulting network properties and their interpretation. Here, we review some fundamental conceptual underpinnings and technical issues associated with brain network reconstruction, and discuss how their mutual influence concurs in clarifying the organization of brain function.
Collapse
Affiliation(s)
- Onerva Korhonen
- Department of Computer ScienceAalto University, School of ScienceHelsinki
- Centre for Biomedical TechnologyUniversidad Politécnica de MadridPozuelo de Alarcón
| | - Massimiliano Zanin
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC‐UIB), Campus UIBPalma de MallorcaSpain
| | - David Papo
- Fondazione Istituto Italiano di TecnologiaFerrara
- Department of Neuroscience and Rehabilitation, Section of PhysiologyUniversity of FerraraFerrara
| |
Collapse
|
29
|
Özbek Y, Fide E, Yener GG. Resting-state EEG alpha/theta power ratio discriminates early-onset Alzheimer's disease from healthy controls. Clin Neurophysiol 2021; 132:2019-2031. [PMID: 34284236 DOI: 10.1016/j.clinph.2021.05.012] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 03/12/2021] [Accepted: 05/17/2021] [Indexed: 11/17/2022]
Abstract
OBJECTIVES The present study aims to compare early-onset Alzheimer's disease (EOAD) patients with healthy controls (HC), and late-onset Alzheimer's disease (LOAD) patients using resting-state delta, theta, alpha, and beta oscillations and provide a cut-off score of alpha/theta ratio to discriminate individuals with EOAD and young HC. METHODS Forty-seven individuals with EOAD, 51 individuals with LOAD, and demographically-matched 49 young and 51 older controls were included in the study. Spectral-power analysis using Fast-Fourier Transformation (FFT) is performed on resting-state electroencephalography (EEG) data. Delta, theta, alpha, and beta oscillations compared between groups and Receiver Operating Characteristic (ROC) curve analysis was conducted. RESULTS Compared to healthy controls individuals with EOAD showed an increase in slow frequency bands and a decrease in fast frequency bands. Frontal alpha/theta power ratio is the best discriminating value between EOAD and young HC with the sensitivity and specificity greater than 80% with area under the curve (AUC) 0.881. CONCLUSIONS EOAD display more widespread and severe electrophysiological abnormalities than LOAD and HC which may reflect more pronounced pathological burden and cholinergic deficits in EOAD. Additionally, the alpha/theta ratio can discriminate EOAD and young HC successfully. SIGNIFICANCE This study is the first to report that resting-state EEG power can be a promising marker for diagnostic accuracy between EOAD and healthy controls.
Collapse
Affiliation(s)
- Yağmur Özbek
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, Turkey
| | - Ezgi Fide
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, Turkey
| | - Görsev G Yener
- Izmir Biomedicine and Genome Center, Dokuz Eylul University Health Campus, Izmir, Turkey; Izmir University of Economics, Faculty of Medicine, Izmir, Turkey.
| |
Collapse
|
30
|
Shamsi F, Haddad A, Najafizadeh L. Early classification of motor tasks using dynamic functional connectivity graphs from EEG. J Neural Eng 2020; 18. [PMID: 33246319 DOI: 10.1088/1741-2552/abce70] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 11/27/2020] [Indexed: 12/13/2022]
Abstract
OBJECTIVE Classification of electroencephalography (EEG) signals with high accuracy using short recording intervals has been a challenging problem in developing brain computer interfaces (BCIs). This paper presents a novel feature extraction method for EEG recordings to tackle this problem. APPROACH The proposed approach is based on the concept that the brain functions in a dynamic manner, and utilizes dynamic functional connectivity graphs. The EEG data is first segmented into intervals during which functional networks sustain their connectivity. Functional connectivity networks for each identified segment are then localized, and graphs are constructed, which will be used as features. To take advantage of the dynamic nature of the generated graphs, a Long Short Term Memory (LSTM) classifier is employed for classification. MAIN RESULTS Features extracted from various durations of post-stimulus EEG data associated with motor execution and imagery tasks are used to test the performance of the classifier. Results show an average accuracy of 85.32% using features extracted from only 500 ms of the post-stimulus data. SIGNIFICANCE Our results demonstrate, for the first time, that using the proposed feature extraction method, it is possible to classify motor tasks from EEG recordings using a short interval of the data in the order of hundreds of milliseconds (e.g. 500 ms). This duration is considerably shorter than what has been reported before. These results will have significant implications for improving the effectiveness and the speed of BCIs, particularly for those used in assistive technologies.
Collapse
Affiliation(s)
- Foroogh Shamsi
- Electrical and Computer Engineering, Rutgers University, 94 Brett Rd, New Brunswick, New Jersey, NJ 08854, UNITED STATES
| | - Ali Haddad
- Electrical and Computer Engineering, Rutgers University, 94 Brett Rd, New Brunswick, New Jersey, NJ 08854, UNITED STATES
| | - Laleh Najafizadeh
- Electrical and Computer Engineering, Rutgers University, 94 Brett Rd, New Brunswick, New Jersey, 08901-8554, UNITED STATES
| |
Collapse
|
31
|
Ebaid D, Crewther SG. The Contribution of Oculomotor Functions to Rates of Visual Information Processing in Younger and Older Adults. Sci Rep 2020; 10:10129. [PMID: 32576849 PMCID: PMC7311387 DOI: 10.1038/s41598-020-66773-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 05/25/2020] [Indexed: 11/17/2022] Open
Abstract
Oculomotor functions are established surrogate measures of visual attention shifting and rate of information processing, however, the temporal characteristics of saccades and fixations have seldom been compared in healthy educated samples of younger and older adults. Thus, the current study aimed to compare duration of eye movement components in younger (18-25 years) and older (50-81 years) adults during text reading and during object/alphanumeric Rapid Automatic Naming (RAN) tasks. The current study also aimed to examine the contribution of oculomotor functions to threshold time needed for accurate performance on visually-driven cognitive tasks (Inspection Time [IT] and Change Detection [CD]). Results showed that younger adults fixated on individual stimuli for significantly longer than the older participants, while older adults demonstrated significantly longer saccade durations than the younger group. Results also demonstrated that older adults required longer threshold durations (i.e., performed slower) on the visually-driven cognitive tasks, however, the age-group time difference on the CD task was eradicated when the effects of saccade duration were covaried. Thus, these results suggest that age-related cognitive decline is also related to increased duration of saccades and hence, highlights the need to dissociate the age-related motor constraints on the temporal aspects of oculomotor function from visuo-cognitive speed of processing.
Collapse
Affiliation(s)
- Deena Ebaid
- Department of Psychology and Counselling, School of Psychology and Public Health, College of Science, Health and Engineering, La Trobe University, Melbourne, VIC, Australia.
| | - Sheila G Crewther
- Department of Psychology and Counselling, School of Psychology and Public Health, College of Science, Health and Engineering, La Trobe University, Melbourne, VIC, Australia.
| |
Collapse
|
32
|
Rossini PM. Aging and brain connectivity via electroencephalographic recordings. Neuroscience 2019; 422:228-229. [PMID: 31518654 DOI: 10.1016/j.neuroscience.2019.09.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 09/02/2019] [Indexed: 11/16/2022]
Affiliation(s)
- Paolo M Rossini
- Area of Neuroscience, A. Gemelli Foundation-IRCCS, Institute of Neurology, Catholic University, Rome, Italy
| |
Collapse
|