Characterization of Young and Old Adult Brains: An EEG Functional Connectivity Analysis

Moezzi, Bahar; Pratti, Latha Madhuri; Hordacre, Brenton; Graetz, Lynton; Berryman, Carolyn; Lavrencic, Louise; Ridding, Michael C.; Keage, Hannah A.D.; McDonnell, Mark D.; Goldsworthy, Mitchell R.

doi:10.1016/j.neuroscience.2019.08.038

Cited by 49 publications

(33 citation statements)

References 88 publications

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“…Given this, it is unlikely that the longer fixation durations shown by the younger adults are reflective of inefficient visual encoding/requiring longer to embed stimuli into memory as previously suggested 64 . Indeed, as alluded to earlier, it may be the case that a different cognitive strategy was used by the younger individuals during the naming tasks, which did not impact on their behavioral performance 66 (i.e., naming and reading scores were comparable across both groups). However, the precise cognitive strategy used by the two groups remains a subject of debate.…”

Section: Discussionmentioning

confidence: 83%

The Contribution of Oculomotor Functions to Rates of Visual Information Processing in Younger and Older Adults

Ebaid

2020

Sci Rep

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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.

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Section: Discussionmentioning

confidence: 83%

The Contribution of Oculomotor Functions to Rates of Visual Information Processing in Younger and Older Adults

Ebaid

2020

Sci Rep

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“…increased connectivity, and lower sensorimotor performance in older adults, impacting general functionality [11]. Although controversial findings exist, an increased interregional coupling has frequently been observed across imaging methods in older populations during task-free [65, 66] and task-related conditions [67, 68] supporting the hypothesis of age-related dedifferentiation. The question as to whether alterations in GABAergic transmission reflect the cause or the ‘cure’ (i.e.…”

Section: Discussionmentioning

confidence: 99%

The interaction between endogenous GABA, functional connectivity and behavioral flexibility is critically altered with advanced age

Heise

Rueda‐Delgado

Chalavi

et al. 2020

Preprint

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Previous links endogenous gamma-aminobutyric acid (GABA) with behavioral efficiency across perceptual and cognitive domains but with potentially strongest impact on those types of behavior that require a high level of dynamic control. Using a Bayesian moderated mediation analysis, we modeled the age-associated interactions between electroencephalography- and electromyography-derived connectivity metrics and behavior and investigated the specificity of the indirect effect of GABA concentration as measured with magnetic resonance spectroscopy. Only the integrated analysis of all three modalities revealed marked differences between the two age groups. Specifically, relatively lower GABA was more beneficial for the connectivity-behavior association in the young, whereas the older showed a beneficial behavioral effect in the presence of higher GABA given their generally elevated task-related connectivity. Age-related alteration of the preferred state of endogenous GABA concentration may reflect a generic compensatory mechanism allowing for higher temporal precision in neural tuning and maintaining a higher behavioral functioning level.

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“…There has been significant interest in studying task-based or resting-state brain's functional connectivity in healthy and patient subjects, which has resulted in new understanding about the brain function as well as the mechanisms underlying brainrelated disorders [117,118,119,120,121,122,123,124,125,17,126]. In BCIs, functional connectivity measures have been employed as features to discriminate various tasks [28,26,27,22,23].…”

Section: Discussionmentioning

confidence: 99%

Early Classification of Motor Tasks Using Dynamic Functional Connectivity Graphs from EEG

Shamsi

Haddad

Najafizadeh

2020

Preprint

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ObjectiveClassification 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.ApproachThe 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 classificationMain resultsFeatures 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.SignificanceOur results demonstrate, for the first time, that using the proposed feature extraction method, it is possible to classify motor tasks from EEG recordings using only few hundreds of milliseconds of the data. 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.

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Characterization of Young and Old Adult Brains: An EEG Functional Connectivity Analysis

Cited by 49 publications

References 88 publications

The Contribution of Oculomotor Functions to Rates of Visual Information Processing in Younger and Older Adults

The Contribution of Oculomotor Functions to Rates of Visual Information Processing in Younger and Older Adults

The interaction between endogenous GABA, functional connectivity and behavioral flexibility is critically altered with advanced age

Early Classification of Motor Tasks Using Dynamic Functional Connectivity Graphs from EEG

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