The Experts below are selected from a list of 34512 Experts worldwide ranked by ideXlab platform
Arcangelo Merla - One of the best experts on this subject based on the ideXlab platform.
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fast optical signals in the sensorimotor cortex general linear convolution model applied to multiple source detector distance based data
NeuroImage, 2014Co-Authors: Antonio Maria Chiarelli, Gian Luca Romani, Arcangelo MerlaAbstract:Abstract In this study, we applied the General Linear Convolution Model to detect fast optical signals (FOS) in the somatosensory cortex, and to study their dependence on the source–detector separation distance (2.0 to 3.5 cm) and irradiated light wavelength (690 and 830 nm). We modeled the impulse response function as a rectangular function that lasted 30 ms, with variable time delay with respect to the Stimulus Onset. The model was tested in a cohort of 20 healthy volunteers who underwent supra-motor threshold electrical stimulation of the median nerve. The impulse response function quantified the time delay for the maximal response at 70 ms to 110 ms after Stimulus Onset, in agreement with classical somatosensory-evoked potentials in the literature, previous optical imaging studies based on a grand-average approach, and grand-average based processing. Phase signals at longer wavelength were used to identify FOS for all the source–detector separation distances, but the shortest one. Intensity signals only detected FOS at the greatest distance; i.e., for the largest channel depth. There was no activation for the shorter wavelength light. Correlational analysis between the phase and intensity of FOS further confirmed diffusive rather than optical absorption changes associated with neuronal activity in the activated cortical volume. Our study demonstrates the reliability of our method based on the General Linear Convolution Model for the detection of fast cortical activation through FOS.
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fast optical signal in visual cortex improving detection by general linear convolution model
NeuroImage, 2013Co-Authors: Antonio Maria Chiarelli, Gian Luca Romani, Assunta Di Vacri, Arcangelo MerlaAbstract:Abstract In this study we applied the General Linear Convolution Model to fast optical signals (FOS). We modeled the Impulse Response Function (IRF) as a rectangular function lasting 30 ms, with variable time delay with respect to the Stimulus Onset. Simulated data confirmed the feasibility of this approach and its capability of detecting simulated activations in case of very unfavorable Signal to Noise Ratio (SNR), providing better results than the grand average method. The model was tested in a cohort of 10 healthy volunteers who underwent to hemi-field visual stimulation. Experimental data quantified the IRF time delay at 80–100 ms after the Stimulus Onset, in agreement with classical visual evoked potential literature and previous optical imaging studies based on grand average approach and a larger number of trails. FOS confirmed the expected contralateral activation in the occipital region. Correlational analysis between hemodynamic intensity signal, phase and intensity FOS supports diffusive rather than optical absorption changes associated with neuronal activity in the activated cortical volume. Our study provides a feasible method for detecting fast cortical activations by means of FOS.
Antonio Maria Chiarelli - One of the best experts on this subject based on the ideXlab platform.
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fast optical signals in the sensorimotor cortex general linear convolution model applied to multiple source detector distance based data
NeuroImage, 2014Co-Authors: Antonio Maria Chiarelli, Gian Luca Romani, Arcangelo MerlaAbstract:Abstract In this study, we applied the General Linear Convolution Model to detect fast optical signals (FOS) in the somatosensory cortex, and to study their dependence on the source–detector separation distance (2.0 to 3.5 cm) and irradiated light wavelength (690 and 830 nm). We modeled the impulse response function as a rectangular function that lasted 30 ms, with variable time delay with respect to the Stimulus Onset. The model was tested in a cohort of 20 healthy volunteers who underwent supra-motor threshold electrical stimulation of the median nerve. The impulse response function quantified the time delay for the maximal response at 70 ms to 110 ms after Stimulus Onset, in agreement with classical somatosensory-evoked potentials in the literature, previous optical imaging studies based on a grand-average approach, and grand-average based processing. Phase signals at longer wavelength were used to identify FOS for all the source–detector separation distances, but the shortest one. Intensity signals only detected FOS at the greatest distance; i.e., for the largest channel depth. There was no activation for the shorter wavelength light. Correlational analysis between the phase and intensity of FOS further confirmed diffusive rather than optical absorption changes associated with neuronal activity in the activated cortical volume. Our study demonstrates the reliability of our method based on the General Linear Convolution Model for the detection of fast cortical activation through FOS.
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fast optical signal in visual cortex improving detection by general linear convolution model
NeuroImage, 2013Co-Authors: Antonio Maria Chiarelli, Gian Luca Romani, Assunta Di Vacri, Arcangelo MerlaAbstract:Abstract In this study we applied the General Linear Convolution Model to fast optical signals (FOS). We modeled the Impulse Response Function (IRF) as a rectangular function lasting 30 ms, with variable time delay with respect to the Stimulus Onset. Simulated data confirmed the feasibility of this approach and its capability of detecting simulated activations in case of very unfavorable Signal to Noise Ratio (SNR), providing better results than the grand average method. The model was tested in a cohort of 10 healthy volunteers who underwent to hemi-field visual stimulation. Experimental data quantified the IRF time delay at 80–100 ms after the Stimulus Onset, in agreement with classical visual evoked potential literature and previous optical imaging studies based on grand average approach and a larger number of trails. FOS confirmed the expected contralateral activation in the occipital region. Correlational analysis between hemodynamic intensity signal, phase and intensity FOS supports diffusive rather than optical absorption changes associated with neuronal activity in the activated cortical volume. Our study provides a feasible method for detecting fast cortical activations by means of FOS.
Gian Luca Romani - One of the best experts on this subject based on the ideXlab platform.
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fast optical signals in the sensorimotor cortex general linear convolution model applied to multiple source detector distance based data
NeuroImage, 2014Co-Authors: Antonio Maria Chiarelli, Gian Luca Romani, Arcangelo MerlaAbstract:Abstract In this study, we applied the General Linear Convolution Model to detect fast optical signals (FOS) in the somatosensory cortex, and to study their dependence on the source–detector separation distance (2.0 to 3.5 cm) and irradiated light wavelength (690 and 830 nm). We modeled the impulse response function as a rectangular function that lasted 30 ms, with variable time delay with respect to the Stimulus Onset. The model was tested in a cohort of 20 healthy volunteers who underwent supra-motor threshold electrical stimulation of the median nerve. The impulse response function quantified the time delay for the maximal response at 70 ms to 110 ms after Stimulus Onset, in agreement with classical somatosensory-evoked potentials in the literature, previous optical imaging studies based on a grand-average approach, and grand-average based processing. Phase signals at longer wavelength were used to identify FOS for all the source–detector separation distances, but the shortest one. Intensity signals only detected FOS at the greatest distance; i.e., for the largest channel depth. There was no activation for the shorter wavelength light. Correlational analysis between the phase and intensity of FOS further confirmed diffusive rather than optical absorption changes associated with neuronal activity in the activated cortical volume. Our study demonstrates the reliability of our method based on the General Linear Convolution Model for the detection of fast cortical activation through FOS.
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fast optical signal in visual cortex improving detection by general linear convolution model
NeuroImage, 2013Co-Authors: Antonio Maria Chiarelli, Gian Luca Romani, Assunta Di Vacri, Arcangelo MerlaAbstract:Abstract In this study we applied the General Linear Convolution Model to fast optical signals (FOS). We modeled the Impulse Response Function (IRF) as a rectangular function lasting 30 ms, with variable time delay with respect to the Stimulus Onset. Simulated data confirmed the feasibility of this approach and its capability of detecting simulated activations in case of very unfavorable Signal to Noise Ratio (SNR), providing better results than the grand average method. The model was tested in a cohort of 10 healthy volunteers who underwent to hemi-field visual stimulation. Experimental data quantified the IRF time delay at 80–100 ms after the Stimulus Onset, in agreement with classical visual evoked potential literature and previous optical imaging studies based on grand average approach and a larger number of trails. FOS confirmed the expected contralateral activation in the occipital region. Correlational analysis between hemodynamic intensity signal, phase and intensity FOS supports diffusive rather than optical absorption changes associated with neuronal activity in the activated cortical volume. Our study provides a feasible method for detecting fast cortical activations by means of FOS.
Angelika Peer - One of the best experts on this subject based on the ideXlab platform.
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Third example of selective stimulation of the shallower nerve fibre N1.
2019Co-Authors: Gloria Araiza Illan, Heiko Stüber, Ken E. Friedl, Ian R. Summers, Angelika PeerAbstract:a) shows the electrode currents. b) and c) illustrate the membrane potential Vn of N1 and N3, respectively, 1 ms after Stimulus Onset. d) and e) correspond to the time courses of the excitations; the region indicated by dotted lines in d) demonstrates that an excitation (shown in red) propagates towards the nerve ending (CNS) in N1 (thus considered activated), but not in N3, illustrated in e) (thus considered inhibited).
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Second example of selective stimulation of the deeper nerve fibre N3.
2019Co-Authors: Gloria Araiza Illan, Heiko Stüber, Ken E. Friedl, Ian R. Summers, Angelika PeerAbstract:a) shows the electrode currents. b) and c) illustrate the membrane potential Vn of N1 and N3, respectively, 1 ms after Stimulus Onset. d) and e) correspond to the time courses of the excitations; the region indicated by dotted lines in d) shows that an excitation (in red) propagates towards the nerve ending (CNS) in N1 (thus considered activated), but not in N3, depicted in e) (thus considered inhibited).
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Second example of selective stimulation of the shallower nerve fibre N1.
2019Co-Authors: Gloria Araiza Illan, Heiko Stüber, Ken E. Friedl, Ian R. Summers, Angelika PeerAbstract:a) shows the electrode currents. b) and c) illustrate the membrane potential Vn of N1 and N3, respectively, 1 ms after Stimulus Onset. d) and e) correspond to the time courses of the excitations; it is shown in the region indicated by dotted lines in d), that an excitation (shown in red) propagates towards the nerve ending (CNS) in N1 (thus considered activated), but not in N3, illustrated in e) (thus considered inhibited).
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First example of selective stimulation of the deeper nerve fibre N3.
2019Co-Authors: Gloria Araiza Illan, Heiko Stüber, Ken E. Friedl, Ian R. Summers, Angelika PeerAbstract:a) shows the electrode currents. b) and c) illustrate the membrane potential Vn of N1 and N3, respectively, 1 ms after Stimulus Onset. d) and e) correspond to the time courses of the excitations; it can be seen (region indicated by dotted lines) that an excitation (shown in red) propagates towards the nerve ending (CNS) in N3 as shown in e) (thus considered activated), but not in N1 illustrated in d) (thus considered inhibited).
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First example of selective stimulation of the shallower nerve fibre N1.
2019Co-Authors: Gloria Araiza Illan, Heiko Stüber, Ken E. Friedl, Ian R. Summers, Angelika PeerAbstract:a) shows the electrode currents. b) and c) illustrate the membrane potential Vn of N1 and N3, respectively, 1 ms after Stimulus Onset. d) and e) correspond to the time courses of the excitations; it can be seen (region indicated by dotted lines) that an excitation (shown in red) propagates towards the nerve ending (CNS) in N1 (thus considered activated), as shown in d), but not in N3 illustrated in e) (thus considered inhibited).
Arcadio Gotor - One of the best experts on this subject based on the ideXlab platform.
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associative and semantic priming effects occur at very short Stimulus Onset asynchronies in lexical decision and naming
Cognition, 1997Co-Authors: Manuel Perea, Arcadio GotorAbstract:Abstract Prior research has found significant associative/semantic priming effects at very short Stimulus-Onset asynchronies (SOAs) in experimental tasks such as lexical decision, but not in naming tasks (however, see Lukatela and Turvey, 1994 ). In this paper, the time course of associative priming effects was analyzed at several very short SOAs (33, 50, and 67 ms), using the masked priming paradigm ( Forster and Davis, 1984 ), both in lexical decision (Experiment 1) and naming (Experiment 2). The results show small—but significant—associative priming effects in both tasks. Additionally, using the masked priming procedure at the 67 ms SOA, Experiments 3 and 4, shows facilitatory priming effects for both associatively and semantically (unassociative) related pairs in lexical decision and naming tasks. That is, automatic priming can be semantic. Taken together, our data appear to support interactive models of word recognition in which semantic activation may influence the early stages of word processing. © 1997 Elsevier Science B.V.
