Journal Of Neurophysiology Research Articles

The use of blood flow restriction does not enhance adaptations of corticospinal excitability or intracortical inhibition after acute resistance training

Resistance exercise with blood flow restriction (BFR) is considered a safe and powerful training tool. Low-load (LL) resistance training with BFR results in comparable hypertrophy compared to conventional heavy-load (HL) resistance training (Centner et al., 2019). Additionally, LL training with BFR (LL-BFR) induces comparable (Grønfeldt et al., 2020) or slightly lower (Centner et al., 2019) effects on muscle strength than HL training. It is generally accepted that increases in maximum strength are mediated by muscular hypertrophy and/or neural adaptations. However, the effect of BFR on neural adaptations is scarcely investigated. The aim of this study was therefore to investigate the effects of BFR on acute neural adaptations following a single bout of resistance training.
 Fifteen male participants (21-35 years) volunteered to participate in this study. On three separate days, participants performed a strength training session of the right elbow flexors with either LL, LL-BFR or HL. Neuromuscular adaptations were investigated before and after each resistance training session by means of transcranial magnetic stimulation (single- and paired-pulse TMS) and peripheral nerve stimulation (Mmax). Data were acquired during submaximal sustained isometric contractions. During LL-BFR, the cuff was inflated to 50% of the individual arterial occlusion pressure to restrict venous outflow in the working musculature during exercise.
 The preliminary data analysis with mixed linear models revealed that, compared to baseline values, normalized MEPs (% of Mmax) from the trained biceps brachii were enhanced for HL (Cohen’s d = 0.51) but not LL-BFR (Cohen’s d = 0.03) when compared to LL. None of the groups showed relevant changes in short-interval intracortical inhibition (SICI) and intracortical facilitation (ICF). The size of Mmax decreased slightly for HL (Cohen’s d = -0.18) but not LL-BFR (Cohen’s d = 0.06) when compared to LL.
 This is the first study that investigated acute neural adaptations in response to resistance training with and without BFR. We observed increased MEP amplitudes after HL but not LL what is in line with previous evidence (Mason et al., 2019). Since training with BFR results in a rapid increase in motoneuronal excitability (Copithorne et al., 2020), the increased MEP are less likely caused by changes at the cortical site.
 Centner, C., Wiegel, P., Gollhofer, A., & König, D. (2019). Effects of blood flow restriction training on muscular strength and hypertrophy in older individuals: A systematic review and meta-analysis. Sports Medicine, 49, 95-108. https://doi.org/10.1007/s40279-018-0994-1
 Copithorne, D. B., Rice, C. L., & McNeil, C. J. (2020). Effect of blood flow occlusion on corticospinal excitability during sustained low-intensity isometric elbow flexion. Journal of Neurophysiology, 123(3), 1113-1119. https://doi.org/10.1152/jn.00644.2019
 Grønfeldt, B. M., Lindberg Nielsen, J., Mieritz, R. M., Lund, H., & Aagaard, P. (2020). Effect of blood-flow restricted vs heavy-load strength training on muscle strength: Systematic review and meta-analysis. Scandinavian Journal of Medicine & Science in Sports, 30(5), 837-848. https://doi.org/10.1111/sms.13632
 Mason, J., Howatson, G., Frazer, A. K., Pearce, A. J., Jaberzadeh, S., Avela, J., & Kidgell, D. J. (2019). Modulation of intracortical inhibition and excitation in agonist and antagonist muscles following acute strength training. European Journal of Applied Physiology, 119, 2185-2199. https://doi.org/10.1007/s00421-019-04203-9

This Month in Anesthesiology

This Month in Anesthesiology

What transcranial direct current stimulation intensity is best for cognitive enhancement?

In a recent study published in The Journal of Neurophysiology, Ehrardt et al. (Ehrhardt SE, Filmer HL, Wards Y, Mattingley JB, Dux PE. J Neurophysiol 125: 385-397, 2021) report that moderate intensity (1 mA/25 cm2) transcranial direct current stimulation (tDCS) is optimal for improving performance on a stimulus-response matching task, as opposed to a lower 0.7 mA/25 cm2 or higher 2 mA/25 cm2 dose. This result suggests that behavioral effects of tDCS do not follow a linear dose-response curve. Potential neurobiological and neurocognitive implications of these findings, as well as suggested directions for future research, are discussed.

Contrasting time and frequency domains: ERN and induced theta oscillations differentially predict post-error behavior.

The present study investigated the neural dynamics of error processing in both the time and frequency domains, as well as associated behavioral phenomena, at the single-trial level. We used a technique that enabled us to separately investigate the evoked and induced aspects of the EEG signal (Cohen & Donner, 2013, Journal of Neurophysiology, 110[12], 2752-2763). We found that at the single-trial level, while the (evoked) error-related negativity (ERN) predicted only post-error slowing (PES)-and only when errors occurred on incongruent trials-induced frontal midline theta power served as a robust predictor of both PES and post-error accuracy (PEA) regardless of stimulus congruency. Mediation models of both electrophysiological indices demonstrated that although the relationship between theta and PEA was mediated by PES, there was not a relationship between the ERN and PEA. Our data suggest that although the ERN and frontal midline theta index functionally related underlying cognitive processes, they are not simply the same process manifested in different domains. In addition, our findings are consistent with the adaptive theory of post-error slowing, as PES was positively associated with post-error accuracy at the single-trial level. More generally, our study provides additional support for the inclusion of a time-frequency approach to better understand the role of medial frontal cortex in action monitoring.

Evidence for a Functional Hierarchy of Association Networks.

Patient lesion and neuroimaging studies have identified a rostral-to-caudal functional gradient in the lateral frontal cortex (LFC) corresponding to higher-order (complex or abstract) to lower-order (simple or concrete) cognitive control. At the same time, monkey anatomical and human functional connectivity studies show that frontal regions are reciprocally connected with parietal and temporal regions, forming parallel and distributed association networks. Here, we investigated the link between the functional gradient of LFC regions observed during control tasks and the parallel, distributed organization of association networks. Whole-brain fMRI task activity corresponding to four orders of hierarchical control [Badre, D., & D'Esposito, M. Functional magnetic resonance imaging evidence for a hierarchical organization of the prefrontal cortex. Journal of Cognitive Neuroscience, 19, 2082-2099, 2007] was compared with a resting-state functional connectivity MRI estimate of cortical networks [Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106, 1125-1165, 2011]. Critically, at each order of control, activity in the LFC and parietal cortex overlapped onto a common association network that differed between orders. These results are consistent with a functional organization based on separable association networks that are recruited during hierarchical control. Furthermore, corticostriatal functional connectivity MRI showed that, consistent with their participation in functional networks, rostral-to-caudal LFC and caudal-to-rostral parietal regions had similar, order-specific corticostriatal connectivity that agreed with a striatal gating model of hierarchical rule use. Our results indicate that hierarchical cognitive control is subserved by parallel and distributed association networks, together forming multiple localized functional gradients in different parts of association cortex. As such, association networks, while connectionally organized in parallel, may be functionally organized in a hierarchy via dynamic interaction with the striatum.

Validation of a new double-coil TMS method to assess corticospinal excitability bilaterally

Validation of a new double-coil TMS method to assess corticospinal excitability bilaterally

Synchrony changes on different time-scales during in vitro neuronal network development

Synchrony changes on different time-scales during in vitro neuronal network development

Collection of Simulated Data from a Thalamocortical Network Model

A major challenge in experimental data analysis is the validation of analytical methods in a fully controlled scenario where the justification of the interpretation can be made directly and not just by plausibility. In some sciences, this could be a mathematical proof, yet biological systems usually do not satisfy assumptions of mathematical theorems. One solution is to use simulations of realistic models to generate ground truth data. In neuroscience, creating such data requires plausible models of neural activity, access to high performance computers, expertise and time to prepare and run the simulations, and to process the output. To facilitate such validation tests of analytical methods we provide rich data sets including intracellular voltage traces, transmembrane currents, morphologies, and spike times. Moreover, these data can be used to study the effects of different tissue models on the measurement. The data were generated using the largest publicly available multicompartmental model of thalamocortical network (Traub et al., Journal of Neurophysiology, 93(4), 2194–2232 (Traub et al. 2005)), with activity evoked by different thalamic stimuli.

Strategies to Increase Rigor and Reproducibility of Data in Manuscripts: Reply to Héroux.

reply: In a Letter to the Editor, Dr. Martin Heroux presents an analysis of compliance with statistical guidelines ([Curran-Everett and Benos 2004][1]) in manuscripts published by the Journal of Neurophysiology in 2015. These guidelines ([Curran-Everett and Benos 2004][1]) are referenced in the “

Extrastriate Visual Areas Integrate Form Features over Space and Time to Construct Representations of Stationary and Rigidly Rotating Objects.

When an object moves behind a bush, for example, its visible fragments are revealed at different times and locations across the visual field. Nonetheless, a whole moving object is perceived. Unlike traditional modal and amodal completion mechanisms known to support spatial form integration when all parts of a stimulus are simultaneously visible, relatively little is known about the neural substrates of the spatiotemporal form integration (STFI) processes involved in generating coherent object representations from a succession visible fragments. We used fMRI to identify brain regions involved in two mechanisms supporting the representation of stationary and rigidly rotating objects whose form features are shown in succession: STFI and position updating. STFI allows past and present form cues to be integrated over space and time into a coherent object even when the object is not visible in any given frame. STFI can occur whether or not the object is moving. Position updating allows us to perceive a moving object, whether rigidly rotating or translating, even when its form features are revealed at different times and locations in space. Our results suggest that STFI is mediated by visual regions beyond V1 and V2. Moreover, although widespread cortical activation has been observed for other motion percepts derived solely from form-based analyses [Tse, P. U. Neural correlates of transformational apparent motion. Neuroimage, 31, 766-773, 2006; Krekelberg, B., Vatakis, A., & Kourtzi, Z. Implied motion from form in the human visual cortex. Journal of Neurophysiology, 94, 4373-4386, 2005], increased responses for the position updating that lead to rigidly rotating object representations were only observed in visual areas KO and possibly hMT+, indicating that this is a distinct and highly specialized type of processing.

Orientation-tuned surround suppression improves computational models of human visual cortex

In recent years, population receptive field models fit to fMRI data have been used to characterize responses in human visual cortex (Dumoulin and Wandell, 2008, Neuroimage). Initial population receptive field models represented stimuli as binary contrast masks, but recent models have incorporated a larger range of visual computations in order to predict responses directly from the image pixels. The model-based approach summarizes diverse findings from visual neuroscience and elucidates the visual computations performed in each area. FMRI has the particular advantage of enabling quick and effective exploration of computational properties throughout the entire visual system. A recent two-stage cascade model was shown to account for much of the observed variance of BOLD responses in early visual areas in response to a wide range of band-limited grayscale images (Kay et al. 2013, PLoS CB). However, the existing model still exhibits certain systematic failures. Response amplitudes to contrast patterns with a single orientation are systematically overpredicted, while amplitudes to contrast patterns with extended curved edges are systematically underpredicted. Here we asked whether updating the model to reflect additional findings about the visual response properties of neurons could improve model performance. Physiology, psychophysics, and theory support the hypothesis that neuronal responses are suppressed by an orientation-tuned surround (Schwartz and Simoncelli, 2001, Nature Neuroscience; Cavanaugh, Bair, and Movshon, 2002, Journal of Neurophysiology). We revisited data from Kay et al. and implemented an additional computation: a spatially extended, orientation-tuned divisive normalization step. The updated model produced systematically better predictions in V1/V2/V3, without introducing additional parameters or degrees of freedom. These results show steady progress in building increasingly accurate models of the computations performed in the human visual pathways. Meeting abstract presented at VSS 2015

Inappropriate interpretation of surface EMG signals and muscle fiber characteristics impedes understanding of the control of neuromuscular function

as the final common pathway from the nervous system to muscle, the motor unit transmits an activation signal generated by the nervous system to engage the contractile proteins and produce the muscle forces needed for reflex responses, automatic behaviors, and voluntary actions ([11][1]). The net

Brain Machine Interface for forces and motion based on musculo-skeletal model

Event Abstract Back to Event Brain Machine Interface for forces and motion based on musculo-skeletal model Yasuharu Koike1*, Hiroyuki Kambara1 and Natsue Yoshimura1 1 Tokyo Institute of Technology, Japan Non-invasive measurement method, such as EEG, fMRI or NIRS, has been used for Brain Computer Interface. EEG has nice temporal resolution, and it is used for BCI, such as amplitudes of different frequency bands; imagining movement of different parts of the body; slow cortical potentials and gamma band rhythms. Recently, Electrocorticography (ECoG) is an alternative approach to less invasive BMIs. Since ECoG records directly from neuronal activities on the cortical surface, ECoG has higher spatio- temporal resolution with better signal-to-noise ratio than scalp EEG. ECoG has also shown potential as a stable long-term recording method. Several studies using ECoG have already succeeded in the classification of movement direction, grasp type, and prediction of hand trajectory. Despite these successes, however, there still remains considerable work for the realization of ECoG- based prosthesis. The human neuromuscular system naturally modulates mechanical stiffness and vis- cosity to achieve proper interaction with the environment. Current rehabilitation robots can perform sophisticated operations including stiffness control. Our previous works suggested that the angle, torque, and stiffness of joints can be predicted from muscle activity. Therefore, decoding muscle activity is an important component for realizing BMI systems capable of controlling interaction force or stiffness. In this talk, we introduce a reconstruction of muscle activity time series using a simple mathematical musculoskeletal model (1,2). Then we introduce BMI using this musculoskeletal model from EEG, ECoG recordings (3-6). Reference (1) Hiroyuki Kambara, Duk Shin, and Yasuharu Koike. A computational model for optimal muscle activity considering muscle viscoelasticity in wrist movements. Journal of neurophysiology, 109:2145–60, 2013. (2) Duk Shin, Jaehyo Kim, and Yasuharu Koike. A myokinetic arm model for estimating joint torque and stiffness from EMG signals during maintained posture. Journal of neurophysiology, 101:387–401, 2009. (3) N Yoshimura, C S Dasalla, T Hanakawa, M A Sato, and Y Koike. Reconstruction of flexor and extensor muscle activities from electroencephalography cortical currents. Neuroimage, 59(2):1324–1337, 2012. (4) Duk Shin, Hidenori Watanabe, Hiroyuki Kambara, Atsushi Nambu, Tadashi Isa, Yukio Nishimura, and Yasuharu Koike. Prediction of Muscle Activities from Electrocorticograms in Primary Motor Cortex of Primates. PLoS ONE, 7, 2012. (5) Yasuhiko Nakanishi, Takufumi Yanagisawa, Duk Shin, Ryohei Fukuma, Chao Chen, Hiroyuki Kambara, Nat- sue Yoshimura, Masayuki Hirata, Toshiki Yoshimine, and Yasuharu Koike. Prediction of Three-Dimensional Arm Trajectories Based on ECoG Signals Recorded from Human Sensorimotor Cortex. PLoS ONE, 8, 2013. (6) Chao Chen, Duk Shin, Hidenori Watanabe, Yasuhiko Nakanishi, Hiroyuki Kambara, Natsue Yoshimura, Atsushi Nambu, Tadashi Isa, Yukio Nishimura, and Yasuharu Koike. Decoding grasp force profile from electrocorticography signals in non-human primate sensorimotor cortex. Neuroscience research, 83:1–7, 2014. Keywords: Brain machine interface (BMI), ECoG signals, fMRI, NIRS, Musculo-skeletal model Conference: 2015 International Workshop on Clinical Brain-Machine Interfaces (CBMI2015), Tokyo, Japan, 13 Mar - 15 Mar, 2015. Presentation Type: Oral presentation / lecture Topic: Clinical Brain-Machine Interfaces Citation: Koike Y, Kambara H and Yoshimura N (2015). Brain Machine Interface for forces and motion based on musculo-skeletal model. Conference Abstract: 2015 International Workshop on Clinical Brain-Machine Interfaces (CBMI2015). doi: 10.3389/conf.fnhum.2015.218.00013 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 23 Apr 2015; Published Online: 29 Apr 2015. * Correspondence: Dr. Yasuharu Koike, Tokyo Institute of Technology, Meguro, Tokyo, Japan, koike@pi.titech.ac.jp Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Yasuharu Koike Hiroyuki Kambara Natsue Yoshimura Google Yasuharu Koike Hiroyuki Kambara Natsue Yoshimura Google Scholar Yasuharu Koike Hiroyuki Kambara Natsue Yoshimura PubMed Yasuharu Koike Hiroyuki Kambara Natsue Yoshimura Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Improving BCI decoding performance through analysis of cortical traveling waves

Event Abstract Back to Event Improving BCI decoding performance through analysis of cortical traveling waves Kevin Vanbecelaere1*, Nikolay Chumerin2, Cees Van Leeuwen1 and David M. Alexander1 1 KU Leuven, Belgium 2 KU Leuven, Belgium An assumption in nearly all cognitive neuroscience research is that of space-time separability. It assumes that the mechanisms constituting the cortical signal are functions of space and time, and that these dimensions of signal can be treated separately. This implies that part of the signal -that which is space-time inseparable- is noise. A growing literature focuses on the spatiotemporal dynamics of cortical activity in the form of traveling waves. Traveling waves have been shown to be functionally significant at the single-trial level (Alexander et al., 2006; Alexander et al., 2009) and at multiple spatial scales of cortex (Klimesch et al., 2007; Nauhaus et al., 2009; Takahashi et al., 2011). The steady-state visual evoked potential (SSVEP) data presented in this paper come from a brain-computer interface (BCI) setup. The aim of a BCI system is to decode brain activity in order to enable subjects to interact with the world through computers, bypassing the need of muscular activity. Participants were shown three aligned LED stimuli flickering at different frequencies and at different phase shifts. When flickering in the alpha band, the stimulus induces an EEG response that possesses a spectral component narrowly centered around the frequency of presentation. The BCI apparatus uses this response to decide what stimulus the subject is looking at. Therefore, maximizing the phase accuracy of the stimulus-induced SSVEP signal is crucial for efficient decoding. Typically, phase-locking to the mean phase of the stimulus is relatively poor (i.e. circular variance relatively large). In this paper, we hypothesize that the BCI signal is not space-time separable and much of phase jitter can be explained by taking traveling wave components into account, hence improving the BCI performance. Figure 1 References Alexander, D. M., Flynn, G. J., Wong, W., Whitford, T. J., Harris, A. W. F., Galletly, C. a., & Silverstein, S. M. (2009). Spatio-temporal EEG waves in first episode schizophrenia. Clinical Neurophysiology, 120(9), 1667–1682. doi:10.1016/j.clinph.2009.06.020 Alexander, D. M., Trengove, C., Wright, J. J., Boord, P. R., & Gordon, E. (2006). Measurement of phase gradients in the EEG. Journal of Neuroscience Methods, 156(1-2), 111–28. doi:10.1016/j.jneumeth.2006.02.016 Klimesch, W., Hanslmayr, S., Sauseng, P., Gruber, W. R., & Doppelmayr, M. (2007). P1 and traveling alpha waves: evidence for evoked oscillations. Journal of Neurophysiology, 97(2), 1311–1318. doi:10.1152/jn.00876.2006 Nauhaus, I., Busse, L., Carandini, M., & Ringach, D. L. (2009). Stimulus contrast modulates functional connectivity in visual cortex. Nature Neuroscience, 12(1), 70–6. doi:10.1038/nn.2232 Takahashi, K., Saleh, M., Penn, R. D., & Hatsopoulos, N. G. (2011). Propagating waves in human motor cortex. Frontiers in Human Neuroscience, 5, 40. doi:10.3389/fnhum.2011.00040 Keywords: traveling waves, BCI, SSVEP, Space-time separability, EEG Conference: Second Belgian Neuroinformatics Congress, Leuven, Belgium, 4 Dec - 4 Dec, 2015. Presentation Type: Poster Presentation Topic: Methods and Modeling Citation: Vanbecelaere K, Chumerin N, Van Leeuwen C and Alexander DM (2015). Improving BCI decoding performance through analysis of cortical traveling waves. Front. Neuroinform. Conference Abstract: Second Belgian Neuroinformatics Congress. doi: 10.3389/conf.fninf.2015.19.00038 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 16 Oct 2015; Published Online: 17 Nov 2015. * Correspondence: Mr. Kevin Vanbecelaere, KU Leuven, B- 3000, Vlaams-Brabant, Leuven, Belgium, kevin.vanbecelaere@ppw.kuleuven.be Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Kevin Vanbecelaere Nikolay Chumerin Cees Van Leeuwen David M Alexander Google Kevin Vanbecelaere Nikolay Chumerin Cees Van Leeuwen David M Alexander Google Scholar Kevin Vanbecelaere Nikolay Chumerin Cees Van Leeuwen David M Alexander PubMed Kevin Vanbecelaere Nikolay Chumerin Cees Van Leeuwen David M Alexander Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Trial history effects in the ventral attentional network.

The ventral attentional network (VAN) is thought to drive "stimulus driven attention" [e.g., Asplund, C. L., Todd, J. J., Snyder, A. P., & Marois, R. A central role for the lateral prefrontal cortex in goal-directed and stimulus-driven attention. Nature Neuroscience, 13, 507-512, 2010; Shulman, G. L., McAvoy, M. P., Cowan, M. C., Astafiev, S. V., Tansy, A. P., D' Avossa, G., et al. Quantitative analysis of attention and detection signals during visual search. Journal of Neurophysiology, 90, 3384-3397, 2003]; in other words, it instantiates within the current stimulus environment the top-down attentional biases maintained by the dorsal attention network [e.g., Kincade, J. M., Abrams, R. A., Astafiev, S. V., Shulman, G. L., & Corbetta, M. An event-related functional magnetic resonance imaging study of voluntary and stimulus-driven orienting of attention. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 25, 4593-4604, 2005]. Previous work has shown that the dorsal attentional network is sensitive to trial history, such that it is challenged by changes in task goals and facilitated by repetition thereof [e.g., Kristjánsson, A., Vuilleumier, P., Schwartz, S., Macaluso, E., & Driver, J. Neural basis for priming of pop-out during visual search revealed with fMRI. Cerebral Cortex, 17, 1612-1624, 2007]. Here, we investigate whether the VAN also preserves information across trials such that it is challenged when previously rejected stimuli become task relevant. We used fMRI to investigate the sensitivity of the ventral attentional system to prior history effects as measured by the distractor preview effect. This behavioral phenomenon reflects a bias against stimuli that have historically not supported task performance. We found regions traditionally considered to be part of the VAN (right middle frontal gyrus, inferior frontal gyrus and right supramarginal gyrus) [Shulman, G. L., McAvoy, M. P., Cowan, M. C., Astafiev, S. V., Tansy, A. P., D' Avossa, G., et al. Quantitative analysis of attention and detection signals during visual search. Journal of Neurophysiology, 90, 3384-3397, 2003] to be more active when task-relevant stimuli had not supported task performance in a previous trial than when they had. Investigations of the ventral visual system suggest that this effect is more reliably driven by trial history preserved within the VAN than that preserved within the visual system per se. We conclude that VAN maintains its interactions with top-down stimulus biases and bottom-up stimulation across time, allowing previous experience with the stimulus environment to influence attentional biases under current circumstances.

Corrigendum

CorrigendaCorrigendumPublished Online:15 May 2014https://doi.org/10.1152/japplphysiol.zdg-1052-corr.2014Original articleMoreSectionsPDF (58 KB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations ShareShare onFacebookTwitterLinkedInEmailWeChat Janssen I, Heymsfield SB, Wang Z, Ross R. Skeletal muscle mass and distribution in 468 men and women aged 18–88 yr. J Appl Physiol 89: 81–88, 2000; doi: 10.1152/jappl.2000.89.1.81.—In Fig. 4, the regression formulas for the men were incorrectly attributed to the women, and the regression formulas for the women were incorrectly attributed to the men. The correct formulas should be as follows:Panel A: Men: SM mass (kg) = −0.003 (weight2) + 0.772 (weight) − 10.100, SEE = 3.7. Women: SM mass (kg) = −0.001 (weight2) + 0.28 (weight) + 6.2, SEE = 2.9.Panel B: Men: SM mass (%) = −0.169 (weight) + 53.1, SEE = 4.3. Women: SM mass (%) = −0.208 (weight) + 43.4, SEE = 4.1.This article has no references to display. Download PDF Previous Back to Top Next FiguresReferencesRelatedInformationRelated ArticlesSkeletal muscle mass and distribution in 468 men and women aged 18–88 yr 01 Jul 2000Journal of Applied PhysiologyCited ByMotor plan differs for young and older adults during similar movementsAgostina Casamento-Moran, Yen-Ting Chen, Neha Lodha, Basma Yacoubi, and Evangelos A. Christou1 April 2017 | Journal of Neurophysiology, Vol. 117, No. 4Targeted Lipidomic Analysis of Myoblasts by GC-MS and LC-MS/MS26 August 2017Senescence in the aging process25 July 2017 | F1000Research, Vol. 6 More from this issue > Volume 116Issue 10May 2014Pages 1342-1342 Copyright & PermissionsCopyright © 2014 the American Physiological Societyhttps://doi.org/10.1152/japplphysiol.zdg-1052-corr.2014History Published online 15 May 2014 Published in print 15 May 2014 Metrics

NeuroElectro: A database describing the electrophysiology properties of different neuron types

Event Abstract Back to Event NeuroElectro: A database describing the electrophysiology properties of different neuron types Shreejoy Tripathy1*, Judith Savitskaya1, Richard Gerkin1 and Nathaniel Urban1 1 Carnegie Mellon University, United States Brains achieve efficient function through implementing a division of labor, in which different neurons serve distinct computational roles. One of the most striking ways in which neuron types differ is in their electrophysiology properties. These properties arise through combinations of ion channels that collectively define the computations that a neuron performs on its inputs. Though the electrophysiology of many neuron types has been previously characterized, these data exist across thousands of journal articles, making cross-study neuron-to-neuron comparisons difficult. Here, using a combination of manual and automated methods, we describe a methodology to curate neuron electrophysiology information into a centralized database. We developed methods to extract neuron electrophysiology information from formatted data tables contained within the journals Journal of Neuroscience and Journal of Neurophysiology, which contain the majority of the this published information. Using web searching and html parsing tools in Python, we found and stored 1600 electrophysiology data tables across 500 articles. Because authors often use different terms to refer to the same electrophysiology concept, e.g. "Vrest" and "RMP" both refer to a neuron's resting membrane potential, we found the need to develop a basic electrophysiology ontology. Similarly, we used Neurolex's existing neuron ontology ( http://neurolex.org ) to map different terminology to neuron concepts. We validated our automated methods through manual inspection of a subset of the data. While electrophysiology concept identification was highly reliable (>80%), we found that identifying the correct neuron was less accurate (< 50%), in part because of the incompleteness of the neuron ontology, suggesting the need for a two-stage automatic and manual approach. We hope that this database will be of use to those interested in validating their own measurements on neuron electrophysiology. Furthermore, we plan to integrate this database with existing neuron databases on morphology or gene expression. We hope that these databases will lead to a quantitative understanding of the computational function of different neuron types. Keywords: Electrophysiology, computational neuroscience, Database development, ontology, Characterization methods Conference: 5th INCF Congress of Neuroinformatics, Munich, Germany, 10 Sep - 12 Sep, 2012. Presentation Type: Poster Topic: Neuroinformatics Citation: Tripathy S, Savitskaya J, Gerkin R and Urban N (2014). NeuroElectro: A database describing the electrophysiology properties of different neuron types. Front. Neuroinform. Conference Abstract: 5th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2014.08.00099 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 21 Mar 2013; Published Online: 27 Feb 2014. * Correspondence: Dr. Shreejoy Tripathy, Carnegie Mellon University, Pittsburgh, United States, stripathy@cmu.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Shreejoy Tripathy Judith Savitskaya Richard Gerkin Nathaniel Urban Google Shreejoy Tripathy Judith Savitskaya Richard Gerkin Nathaniel Urban Google Scholar Shreejoy Tripathy Judith Savitskaya Richard Gerkin Nathaniel Urban PubMed Shreejoy Tripathy Judith Savitskaya Richard Gerkin Nathaniel Urban Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

The perception of object versus objectless motion

Wertheimer, M. (Zeitschrift für Psychologie und Physiologie der Sinnesorgane, 61:161-265, 1912) classical distinction between beta (object) and phi (objectless) motion is elaborated here in a series of experiments concerning competition between two qualitatively different motion percepts, induced by sequential changes in luminance for two-dimensional geometric objects composed of rectangular surfaces. One of these percepts is of spreading-luminance motion that continuously sweeps across the entire object; it exhibits shape invariance and is perceived most strongly for fast speeds. Significantly for the characterization of phi as objectless motion, the spreading luminance does not involve surface boundaries or any other feature; the percept is driven solely by spatiotemporal changes in luminance. Alternatively, and for relatively slow speeds, a discrete series of edge motions can be perceived in the direction opposite to spreading-luminance motion. Akin to beta motion, the edges appear to move through intermediate positions within the object's changing surfaces. Significantly for the characterization of beta as object motion, edge motion exhibits shape dependence and is based on the detection of oppositely signed changes in contrast (i.e., counterchange) for features essential to the determination of an object's shape, the boundaries separating its surfaces. These results are consistent with area MT neurons that differ with respect to speed preference Newsome et al (Journal of Neurophysiology, 55:1340-1351, 1986) and shape dependence Zeki (Journal of Physiology, 236:549-573, 1974).

Man or Monkey? Empathy predicts species category boundary judgements

Event Abstract Back to Event Man or Monkey? Empathy predicts species category boundary judgements Anna Brooks1* and Natalie Doring1 1 Southern Cross University, Psychology, Australia Empathic capacity – the extent to which an individual responds vicariously to the emotional status of non-self agents – relates to perceived similarity. Specifically, stronger empathic responses are associated with higher levels of perceived similarity (Gallese 2001). In line with this, we tested whether empathy and performance on a vision-based category boundary task are related. Participants judged species membership (or not) of individual images taken from a human/monkey continuum (Sigala et al 2011). They then completed an empathy scale. Data show that for female participants, empathic capacity predicted performance on the perceptual task. In particular, high empathy women accepted images with low levels of target signal as belonging to the species of interest. Implications of this first demonstration of a systematic relationship between empathic capacity and vision-based category boundary judgements are discussed. References Gallese V. The 'Shared Manifold' Hypothesis: From Mirror Neurons to Empathy, Journal of Consciousness Studies, 8(5-7), 33-50. Sigala R., Logothetis N.K. & Rainer G. (2011) Own-species bias in the representations of monkey and human face categories in the primate temporal lobe, Journal of Neurophysiology, 105, 2749-2752. Keywords: Empathy, category, Visual Perception, Social Behavior, judgement Conference: ACNS-2013 Australasian Cognitive Neuroscience Society Conference, Clayton, Melbourne, Australia, 28 Nov - 1 Dec, 2013. Presentation Type: Poster Topic: Emotion and Social Citation: Brooks A and Doring N (2013). Man or Monkey? Empathy predicts species category boundary judgements. Conference Abstract: ACNS-2013 Australasian Cognitive Neuroscience Society Conference. doi: 10.3389/conf.fnhum.2013.212.00098 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 25 Sep 2013; Published Online: 25 Nov 2013. * Correspondence: Dr. Anna Brooks, Southern Cross University, Psychology, Coffs Harbour, NSW, 2450, Australia, anna.brooks@scu.edu.au Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Anna Brooks Natalie Doring Google Anna Brooks Natalie Doring Google Scholar Anna Brooks Natalie Doring PubMed Anna Brooks Natalie Doring Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Influence of vigilance on resting state brain activity

Aims: Despite task absence the brain remains functionally and metabolically active exhibiting coherent activity in characteristic sets of brain regions, called resting state networks (RSN). RSN manifest as correlation patterns in “spontaneous” BOLD signal fluctuations with 10–100 s period [1]. Their origins remain obscure. Typically, in a wake resting state experiment data stems from subjects resting with eyes closed in the MRI scanner for ~10 min [2]. Hypothesis: We hypothesized that in resting state experiments subjects do not maintain constant wakefulness but that (1) the temporal profiles of RSN are influenced by fluctuations in vigilance and (2) cortical activity fluctuations by subcortical brain activity [3]. Method: We studied 55 non sleep-deprived German subjects with EEG-fMRI under above-mentioned conditions. Vigilance throughout the experiment was scored [4]. Next, a support vector machine classifier was trained to perform sleep staging [5] based on RSN functional connectivity matrices and applied to an independent resting state fMRI data set of 76 Chinese subjects [6]. Results: 50% of the 55 subjects did not maintain steady wakefulness until minute 5, vigilance state switches occurred up to a rate of 1/min. Sleep stage classifier performance was 87%, and results could be reproduced in the independent data set (“Chinese”). Excluding the thalamus from the classification procedure resulted in poorer (<70%) sleep scoring performance. Conclusion: 1. changes in vigilance occur at a similar temporal scale as that of RSN fluctuations. 2. RSN configuration is sleep stage specific and highly dependent on thalamic activity. 4. In classical resting state experiments up to 50% of the subjects do not maintain steady wakefulness for beyond 5 minutes. When proposing RSN differences as biomarkers for diseases [7], vigilance bias between patient (e.g. resulting from drugs, encephalopathy, age) and control groups needs to be controlled for. Literatur: 1. Fox, M.D. and M.E. Raichle, Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci, 2007. 8(9): p. 700-11. 2. Van Dijk, K.R., et al., Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. Journal of neurophysiology, 2010. 103(1): p. 297-321. 3. Friston, K., Functional and Effective Connectivity in Neuroimaging: A Synthesis. Hum Brain Mapp, 1996. 2: p. 56-68. 4. AASM, The AASM Manual for the Scoring of Sleep and Associated Events- Rules, Terminology and Technical Specifications. 2007, Chicago: American Academy of Sleep Medicine. 5. Crisler, S., et al., Sleep-stage scoring in the rat using a support vector machine. Journal of neuroscience methods, 2008. 168(2): p. 524-34. 6. Biswal, B.B., et al., Toward discovery science of human brain function. Proceedings of the National Academy of Sciences of the United States of America, 2010. 107(10): p. 4734-9. 7. Greicius, M., Resting-state functional connectivity in neuropsychiatric disorders. Curr Opin Neurol, 2008. 21(4): p. 424-30. Funding: Bundesministerium fur Bildung und Forschung (grant 01 EV 0703) and Landes-Offensive zur Entwicklung Wissenschaftlich-okonomischer Exzellenz (LOEWE, Neuronale Koordination Forschungsschwerpunkt Frankfurt).