Literature DB >> 23867556

Fast optical signals in the sensorimotor cortex: General Linear Convolution Model applied to multiple source-detector distance-based data.

Antonio Maria Chiarelli1, Gian Luca Romani, Arcangelo Merla.   

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.
© 2013 Elsevier Inc. All rights reserved.

Keywords:  Fast optical signal; General Linear Model; Somatosensory cortex

Mesh:

Substances:

Year:  2013        PMID: 23867556     DOI: 10.1016/j.neuroimage.2013.07.021

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  10 in total

1.  Fiberless, Multi-Channel fNIRS-EEG System Based on Silicon Photomultipliers: Towards Sensitive and Ecological Mapping of Brain Activity and Neurovascular Coupling.

Authors:  Antonio Maria Chiarelli; David Perpetuini; Pierpaolo Croce; Giuseppe Greco; Leonardo Mistretta; Raimondo Rizzo; Vincenzo Vinciguerra; Mario Francesco Romeo; Filippo Zappasodi; Arcangelo Merla; Pier Giorgio Fallica; Günter Edlinger; Rupert Ortner; Giuseppe Costantino Giaconia
Journal:  Sensors (Basel)       Date:  2020-05-16       Impact factor: 3.576

2.  Characterization of a fiber-less, multichannel optical probe for continuous wave functional near-infrared spectroscopy based on silicon photomultipliers detectors: in-vivo assessment of primary sensorimotor response.

Authors:  Antonio M Chiarelli; Sebania Libertino; Filippo Zappasodi; Massimo Mazzillo; Francesco Di Pompeo; Arcangelo Merla; Salvatore Lombardo; Giorgio Fallica
Journal:  Neurophotonics       Date:  2017-09-27       Impact factor: 3.593

Review 3.  From brain to blood vessels and back: a noninvasive optical imaging approach.

Authors:  Gabriele Gratton; Antonio M Chiarelli; Monica Fabiani
Journal:  Neurophotonics       Date:  2017-04-07       Impact factor: 3.593

Review 4.  Simultaneous functional near-infrared spectroscopy and electroencephalography for monitoring of human brain activity and oxygenation: a review.

Authors:  Antonio M Chiarelli; Filippo Zappasodi; Francesco Di Pompeo; Arcangelo Merla
Journal:  Neurophotonics       Date:  2017-08-22       Impact factor: 3.593

5.  Prediction of state anxiety by machine learning applied to photoplethysmography data.

Authors:  David Perpetuini; Antonio Maria Chiarelli; Daniela Cardone; Chiara Filippini; Sergio Rinella; Simona Massimino; Francesco Bianco; Valentina Bucciarelli; Vincenzo Vinciguerra; Piero Fallica; Vincenzo Perciavalle; Sabina Gallina; Sabrina Conoci; Arcangelo Merla
Journal:  PeerJ       Date:  2021-01-15       Impact factor: 2.984

6.  Evidence of Neurovascular Un-Coupling in Mild Alzheimer's Disease through Multimodal EEG-fNIRS and Multivariate Analysis of Resting-State Data.

Authors:  Antonio M Chiarelli; David Perpetuini; Pierpaolo Croce; Chiara Filippini; Daniela Cardone; Ludovica Rotunno; Nelson Anzoletti; Michele Zito; Filippo Zappasodi; Arcangelo Merla
Journal:  Biomedicines       Date:  2021-03-26

7.  Distinct effects of prematurity on MRI metrics of brain functional connectivity, activity, and structure: Univariate and multivariate analyses.

Authors:  Antonio M Chiarelli; Carlo Sestieri; Riccardo Navarra; Richard G Wise; Massimo Caulo
Journal:  Hum Brain Mapp       Date:  2021-05-06       Impact factor: 5.038

8.  Multi-Site Photoplethysmographic and Electrocardiographic System for Arterial Stiffness and Cardiovascular Status Assessment.

Authors:  David Perpetuini; Antonio Maria Chiarelli; Lidia Maddiona; Sergio Rinella; Francesco Bianco; Valentina Bucciarelli; Sabina Gallina; Vincenzo Perciavalle; Vincenzo Vinciguerra; Arcangelo Merla; Giorgio Fallica
Journal:  Sensors (Basel)       Date:  2019-12-17       Impact factor: 3.576

9.  Tomographic Task-Related Functional Near-Infrared Spectroscopy in Acute Sport-Related Concussion: An Observational Case Study.

Authors:  Mario Forcione; Antonio Maria Chiarelli; David Perpetuini; David James Davies; Patrick O'Halloran; David Hacker; Arcangelo Merla; Antonio Belli
Journal:  Int J Mol Sci       Date:  2020-08-29       Impact factor: 5.923

10.  MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer.

Authors:  Andrea Delli Pizzi; Antonio Maria Chiarelli; Piero Chiacchiaretta; Martina d'Annibale; Pierpaolo Croce; Consuelo Rosa; Domenico Mastrodicasa; Stefano Trebeschi; Doenja Marina Johanna Lambregts; Daniele Caposiena; Francesco Lorenzo Serafini; Raffaella Basilico; Giulio Cocco; Pierluigi Di Sebastiano; Sebastiano Cinalli; Antonio Ferretti; Richard Geoffrey Wise; Domenico Genovesi; Regina G H Beets-Tan; Massimo Caulo
Journal:  Sci Rep       Date:  2021-03-08       Impact factor: 4.996

  10 in total

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