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      Neural Correlates of Single- and Dual-Task Walking in the Real World

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          Abstract

          Recent developments in mobile brain-body imaging (MoBI) technologies have enabled studies of human locomotion where subjects are able to move freely in more ecologically valid scenarios. In this study, MoBI was employed to describe the behavioral and neurophysiological aspects of three different commonly occurring walking conditions in healthy adults. The experimental conditions were self-paced walking, walking while conversing with a friend and lastly walking while texting with a smartphone. We hypothesized that gait performance would decrease with increased cognitive demands and that condition-specific neural activation would involve condition-specific brain areas. Gait kinematics and high density electroencephalography (EEG) were recorded whilst walking around a university campus. Conditions with dual tasks were accompanied by decreased gait performance. Walking while conversing was associated with an increase of theta (θ) and beta (β) neural power in electrodes located over left-frontal and right parietal regions, whereas walking while texting was associated with a decrease of β neural power in a cluster of electrodes over the frontal-premotor and sensorimotor cortices when compared to walking whilst conversing. In conclusion, the behavioral “signatures” of common real-life activities performed outside the laboratory environment were accompanied by differing frequency-specific neural “biomarkers”. The current findings encourage the study of the neural biomarkers of disrupted gait control in neurologically impaired patients.

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          Most cited references54

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          Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis.

          Detecting artifacts produced in EEG data by muscle activity, eye blinks and electrical noise is a common and important problem in EEG research. It is now widely accepted that independent component analysis (ICA) may be a useful tool for isolating artifacts and/or cortical processes from electroencephalographic (EEG) data. We present results of simulations demonstrating that ICA decomposition, here tested using three popular ICA algorithms, Infomax, SOBI, and FastICA, can allow more sensitive automated detection of small non-brain artifacts than applying the same detection methods directly to the scalp channel data. We tested the upper bound performance of five methods for detecting various types of artifacts by separately optimizing and then applying them to artifact-free EEG data into which we had added simulated artifacts of several types, ranging in size from thirty times smaller (-50 dB) to the size of the EEG data themselves (0 dB). Of the methods tested, those involving spectral thresholding were most sensitive. Except for muscle artifact detection where we found no gain of using ICA, all methods proved more sensitive when applied to the ICA-decomposed data than applied to the raw scalp data: the mean performance for ICA was higher and situated at about two standard deviations away from the performance distribution obtained on raw data. We note that ICA decomposition also allows simple subtraction of artifacts accounted for by single independent components, and/or separate and direct examination of the decomposed non-artifact processes themselves.
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            Prefrontal and medial temporal lobe interactions in long-term memory.

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              fNIRS study of walking and walking while talking in young and old individuals.

              Evidence suggests that gait is influenced by higher order cognitive and cortical control mechanisms. However, less is known about the functional correlates of cortical control of gait. Using functional near-infrared spectroscopy, the current study was designed to evaluate whether increased activations in the prefrontal cortex (PFC) were detected in walking while talking (WWT) compared with normal pace walking (NW) in 11 young and 11 old participants. Specifically, the following two hypotheses were evaluated: (a) Activation in the PFC would be increased in WWT compared with NW. (b) The increase in activation in the PFC during WWT as compared with NW would be greater in young than in old participants. Separate linear mixed effects models with age as the two-level between-subject factor, walking condition (NW vs WWT) as the two-level repeated within-subject factor, and HbO2 levels in each of the 16 functional near-infrared spectroscopy channels as the dependent measure revealed significant task effects in 14 channels, indicating a robust bilateral increased activation in the PFC in WWT compared with NW. Furthermore, the group-by-task interaction was significant in 11 channels with young participants showing greater WWT-related increase in HbO2 levels compared with the old participants. This study provided the first evidence that oxygenation levels are increased in the PFC during WWT compared with NW in young and old individuals. This effect was modified by age suggesting that older adults may under-utilize the PFC in attention-demanding locomotion tasks.
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                Author and article information

                Contributors
                Journal
                Front Hum Neurosci
                Front Hum Neurosci
                Front. Hum. Neurosci.
                Frontiers in Human Neuroscience
                Frontiers Media S.A.
                1662-5161
                14 September 2017
                2017
                : 11
                : 460
                Affiliations
                [1] 1Neuroplasticity and Neurorehabilitation Doctoral Training Programme, Neurorehabilitation Unit, School of Health, Sport and Biosscience, University of East London London, United Kingdom
                [2] 2School of Architecture, Computing and Engineering, University of East London London, United Kingdom
                [3] 3UCLPartners Centre for Neurorehabilitation, University College London London, United Kingdom
                Author notes

                Edited by: Klaus Gramann, Technische Universität Berlin, Germany

                Reviewed by: Pierfilippo De Sanctis, Albert Einstein College of Medicine, United States; Johanna Wagner, University of California, San Diego, United States

                *Correspondence: Duncan L. Turner d.l.turner@ 123456uel.ac.uk
                Article
                10.3389/fnhum.2017.00460
                5603763
                28959199
                a31e1154-2744-4971-afd8-eaa5da7e7d0b
                Copyright © 2017 Pizzamiglio, Naeem, Abdalla and Turner.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 09 June 2017
                : 01 September 2017
                Page count
                Figures: 8, Tables: 1, Equations: 0, References: 62, Pages: 12, Words: 8989
                Categories
                Neuroscience
                Original Research

                Neurosciences
                mobile brain-body imaging,eeg,multitasking,neuroimaging,urban environment,gait monitoring

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