Universal automated classification of the acoustic startle reflex using machine learning
Tài liệu tham khảo
Ahmari, 2012, Impaired sensorimotor gating in unmedicated adults with obsessive-compulsive disorder, Neuropsychopharmacology, 37, 1216, 10.1038/npp.2011.308
Allen, 1977, A unified approach to short-time Fourier analysis and synthesis, Proc. IEEE, 65, 1558, 10.1109/PROC.1977.10770
Balki, 2019, Sample-size determination methodologies for machine learning in medical imaging research: a systematic review, Can. Assoc. Radiol. J., 70, 344, 10.1016/j.carj.2019.06.002
Berger, 2013, A novel behavioural approach to detecting tinnitus in the guinea pig, J. Neurosci. Methods, 213, 188, 10.1016/j.jneumeth.2012.12.023
Buse, 2016, Neural correlates of altered sensorimotor gating in boys with Tourette Syndrome: a combined EMG/fMRI study, World J. Biol. Psychiatry, 17, 187, 10.3109/15622975.2015.1112033
Cai, 2018, Feature selection in machine learning: a new perspective, Neurocomputing, 300, 70, 10.1016/j.neucom.2017.11.077
Cassella, 1986, The design and calibration of a startle measurement system, Physiol. Behav., 36, 377, 10.1016/0031-9384(86)90032-6
Cassella, 1986, Habituation, prepulse inhibition, fear conditioning, and drug modulation of the acoustically elicited pinna reflex in rats, Behav. Neurosci., 100, 39, 10.1037/0735-7044.100.1.39
Davis, 1984, 287
Dulawa, 1997, Serotonin1B receptor modulation of startle reactivity, habituation, and prepulse inhibition in wild-type and serotonin1B knockout mice, Psychopharmacology, 132, 125, 10.1007/s002130050328
Fawcett, 2006, An introduction to ROC analysis, Pattern Recognit. Lett., 27, 861, 10.1016/j.patrec.2005.10.010
Fawcett, 2020, Automated classification of acoustic startle reflex waveforms in young CBA/CaJ mice using machine learning, J. Neurosci. Methods, 344, 10.1016/j.jneumeth.2020.108853
Fawcett, 2021, Machine learning, waveform preprocessing and feature extraction methods for classification of acoustic startle waveforms, MethodsX, 8, 10.1016/j.mex.2020.101166
Fendt, 2013, Translational value of startle modulations, Cell Tissue Res., 354, 287, 10.1007/s00441-013-1599-5
Galazyuk, 2015, Gap-prepulse inhibition of the acoustic startle reflex (GPIAS) for tinnitus assessment: current status and future directions, Front. Neurol, 6, 88, 10.3389/fneur.2015.00088
Geyer, 1990, Startle response models of sensorimotor gating and habituation deficits in schizophrenia, Brain Res. Bull., 25, 485, 10.1016/0361-9230(90)90241-Q
Green, 2017, Brief stimulus exposure fully remediates temporal processing deficits induced by early hearing loss, J. Neurosci., 37, 7759, 10.1523/JNEUROSCI.0916-17.2017
Green, 2016, Benefits of stimulus exposure: developmental learning independent of task performance, Front. Neurosci., 10, 263, 10.3389/fnins.2016.00263
Grimsley, 2015, An improved approach to separating startle data from noise, J. Neurosci. Methods, 253, 206, 10.1016/j.jneumeth.2015.07.001
Hastie, 2009
Horlington, 1968, A method for measuring acoustic startle response latency and magnitude in rats: detection of a single stimulus effect using latency measurements, Physiol. Behav., 3, 839, 10.1016/0031-9384(68)90164-9
Ison, 1983, Reflex modification in the domain of startle: II. The anomalous history of a robust and ubiquitous phenomenon, Psychol. Bull., 94, 3, 10.1037/0033-2909.94.1.3
Khan, 2018, Sensorimotor gating deficits in "two-hit" models of schizophrenia risk factors, Schizophr. Res., 198, 68, 10.1016/j.schres.2017.10.009
Koch, 1999, The neurobiology of startle, Prog. Neurobiol., 59, 107, 10.1016/S0301-0082(98)00098-7
Kotsiantis, 2006, Machine learning: a review of classification and combining techniques, Artif. Intell. Rev., 26, 159, 10.1007/s10462-007-9052-3
Kraus, 2011, Relationship between noise-induced hearing-loss, persistent tinnitus and growth-associated protein-43 expression in the rat cochlear nucleus: does synaptic plasticity in ventral cochlear nucleus suppress tinnitus?, Neuroscience, 194, 309, 10.1016/j.neuroscience.2011.07.056
Kuhn, 2013
Lauer, 2017, Acoustic startle modification as a tool for evaluating auditory function of the mouse: progress, pitfalls, and potential, Neurosc. Biobehav. Rev., 77, 194, 10.1016/j.neubiorev.2017.03.009
Lobarinas, 2013, The gap-startle paradigm for tinnitus screening in animal models: limitations and optimization, Hear. Res., 295, 150, 10.1016/j.heares.2012.06.001
Lobarinas, 2004, A novel behavioral paradigm for assessing tinnitus using schedule-induced polydipsia avoidance conditioning (SIP-AC), Hear. Res., 190, 109, 10.1016/S0378-5955(04)00019-X
Longenecker, 2012, Methodological optimization of tinnitus assessment using prepulse inhibition of the acoustic startle reflex, Brain Res., 1485, 54, 10.1016/j.brainres.2012.02.067
Longenecker, 2016, Prepulse inhibition of the acoustic startle reflex vs. auditory brainstem response for hearing assessment, Hear. Res., 339, 80, 10.1016/j.heares.2016.06.006
Longenecker, 2011, Development of tinnitus in CBA/CaJ mice following sound exposure, J. Assoc. Res. Otolaryngol., 12, 647, 10.1007/s10162-011-0276-1
Longenecker, 2018, Addressing variability in the acoustic startle reflex for accurate gap detection assessment, Hear. Res., 363, 119, 10.1016/j.heares.2018.03.013
Lowe, 2015, Alterations in peripheral and central components of the auditory brainstem response: a neural assay of tinnitus, PLoS One, 10, 10.1371/journal.pone.0117228
McKearney, 2019, Objective auditory brainstem response classification using machine learning, Int. J. Audiol., 58, 224, 10.1080/14992027.2018.1551633
NCSS Statistical Software. 2022. One ROC curve and cutoff analysis.
Obuchowski, 2018, Receiver operating characteristic (ROC) curves: review of methods with applications in diagnostic medicine, Phys. Med. Biol., 63, 07TR01, 10.1088/1361-6560/aab4b1
Pantoni, 2020, Quantifying the acoustic startle response in mice using standard digital video, Front. Behav. Neurosci., 14, 83, 10.3389/fnbeh.2020.00083
Preyer, 1900
Schilling, 2017, A new statistical approach for the evaluation of gap-prepulse inhibition of the acoustic startle reflex (GPIAS) for tinnitus assessment, Front. Behav. Neurosci., 11, 198, 10.3389/fnbeh.2017.00198
Sejdić, 2009, Time–frequency feature representation using energy concentration: an overview of recent advances, Digit Signal Process, 19, 153, 10.1016/j.dsp.2007.12.004
Thiele, C., Hirschfeld, G., 2020. cutpointr: improved estimation and validation of optimal cutpoints in R. arXiv preprint arXiv:2002.09209.
Turner, 2006, Gap detection deficits in rats with tinnitus: a potential novel screening tool, Behav. Neurosci., 120, 188, 10.1037/0735-7044.120.1.188
Veer, 2015, Wavelet and short-time Fourier transform comparison-based analysis of myoelectric signals, J. Appl. Stat., 42, 1591, 10.1080/02664763.2014.1001728
Vergara, 2014, A review of feature selection methods based on mutual information, Neural Comput. Applic., 24, 175, 10.1007/s00521-013-1368-0
Virag, 2021, Repurposing a digital kitchen scale for neuroscience research: a complete hardware and software cookbook for PASTA, Sci. Rep., 11, 2963, 10.1038/s41598-021-82710-6
Wake, 2021, Prepulse inhibition predicts subjective hearing in rats, Sci. Rep., 11, 18902, 10.1038/s41598-021-98167-6
Zhang, 2005, Influence of naturally occurring variations in maternal care on prepulse inhibition of acoustic startle and the medial prefrontal cortical dopamine response to stress in adult rats, J. Neurosci., 25, 1493, 10.1523/JNEUROSCI.3293-04.2005