The hypothesis that the N1, the major negative component of the cortical evoked response to auditory stimuli, originates from the primary auditory cortex has been supported by several studies. In a previous study we showed that, when monaural stimulation with pure tones is used, the distribution of the N1 peak over the scalp could be accounted for by successive activation of adjacent sources on the floor of the Sylvian fissure. In an attempt to establish the generality of the phenomenon, in this study we investigated further the generation of the N1 component using a variety of auditory stimuli, including pure tones, complex sounds (musical notes), and words, as well as binaural stimulus presentation. Additionally, we used a new recording system which allows recording of the distribution of the magnetic flux over the entire head simultaneously, thus eliminating the need for multiple recording sessions and the related problems of habituation and of changes in attention level. We found that a series of single dipolar sources could account for the entire duration of the N1m component. The location of the sources fell within the primary auditory cortex and, during the evolution of the component, they followed a posterior-anterior, medial-lateral, superior-inferior trajectory, bilaterally, along the superior surface of the temporal lobes. Additionally, the distribution of N1 sources on the two hemispheres showed a marked asymmetry, with the right hemisphere sources covering a larger area. The established consistency of successive source excitation across subjects, studies, types of stimuli, and recording systems, as well as the newly demonstrated hemispheric asymmetry of source extent, suggest the presence of a reliable phenomenon indicative of the functional organization of the auditory cortex.
"In particular, three basic components have been identified which give rise to a composite N1. There is also MEG and EEG evidence that the auditory cortex is a prime contributor to the N1 component (Zouridakis et al., 1998; Knoth and Lippe, 2012). N1 itself is modulated by the pitch and intensity of auditory stimuli (Beagley and Knight, 1967; Butler, 1968; Pantev et al., 1988; Alain et al., 1997; Butler and Trainor, 2012), and is sensitive to attention effects (Naatanen and Picton, 1987; Luck, 2005; Naatanen et al., 2011a). "
[Show abstract][Hide abstract] ABSTRACT: Fragile X syndrome (FXS) is an inherited form of intellectual disability and autism. Among other symptoms, FXS patients demonstrate abnormalities in sensory processing and communication. Clinical, behavioral, and electrophysiological studies consistently show auditory hypersensitivity in humans with FXS. Consistent with observations in humans, the Fmr1 KO mouse model of FXS also shows evidence of altered auditory processing and communication deficiencies. A well-known and commonly used phenotype in pre-clinical studies of FXS is audiogenic seizures. In addition, increased acoustic startle response is seen in the Fmr1 KO mice. In vivo electrophysiological recordings indicate hyper-excitable responses, broader frequency tuning, and abnormal spectrotemporal processing in primary auditory cortex of Fmr1 KO mice. Thus, auditory hyper-excitability is a robust, reliable, and translatable biomarker in Fmr1 KO mice. Abnormal auditory evoked responses have been used as outcome measures to test therapeutics in FXS patients. Given that similarly abnormal responses are present in Fmr1 KO mice suggests that cellular mechanisms can be addressed. Sensory cortical deficits are relatively more tractable from a mechanistic perspective than more complex social behaviors that are typically studied in autism and FXS. The focus of this review is to bring together clinical, functional, and structural studies in humans with electrophysiological and behavioral studies in mice to make the case that auditory hypersensitivity provides a unique opportunity to integrate molecular, cellular, circuit level studies with behavioral outcomes in the search for therapeutics for FXS and other autism spectrum disorders.
"Different approaches have been used in previous studies to account for the generators of the auditory N100/N100m response in EEG/MEG observations. Zouridakis and colleagues  found that using a single moving dipole within the primary auditory cortex could account for the entire duration of the N100m (from about 70 ms to 150 ms after stimulus) and that during the evolution of the component, it followed a bilateral posterior-anterior, medial-lateral, superior-inferior trajectory, extending about 2 cm into the superior surface of the temporal lobes. This finding was confirmed by several other MEG studies , . "
[Show abstract][Hide abstract] ABSTRACT: IN THIS WORK WE PROPOSE A BIOLOGICALLY REALISTIC LOCAL CORTICAL CIRCUIT MODEL (LCCM), BASED ON NEURAL MASSES, THAT INCORPORATES IMPORTANT ASPECTS OF THE FUNCTIONAL ORGANIZATION OF THE BRAIN THAT HAVE NOT BEEN COVERED BY PREVIOUS MODELS: (1) activity dependent plasticity of excitatory synaptic couplings via depleting and recycling of neurotransmitters and (2) realistic inter-laminar dynamics via laminar-specific distribution of and connections between neural populations. The potential of the LCCM was demonstrated by accounting for the process of auditory habituation. The model parameters were specified using Bayesian inference. It was found that: (1) besides the major serial excitatory information pathway (layer 4 to layer 2/3 to layer 5/6), there exists a parallel "short-cut" pathway (layer 4 to layer 5/6), (2) the excitatory signal flow from the pyramidal cells to the inhibitory interneurons seems to be more intra-laminar while, in contrast, the inhibitory signal flow from inhibitory interneurons to the pyramidal cells seems to be both intra- and inter-laminar, and (3) the habituation rates of the connections are unsymmetrical: forward connections (from layer 4 to layer 2/3) are more strongly habituated than backward connections (from Layer 5/6 to layer 4). Our evaluation demonstrates that the novel features of the LCCM are of crucial importance for mechanistic explanations of brain function. The incorporation of these features into a mass model makes them applicable to modeling based on macroscopic data (like EEG or MEG), which are usually available in human experiments. Our LCCM is therefore a valuable building block for future realistic models of human cognitive function.
PLoS ONE 10/2013; 8(10):e77876. DOI:10.1371/journal.pone.0077876 · 3.23 Impact Factor
"In line with this prediction, our results show both greater GFP for chosen than non-chosen stimuli in the N1 time window and greater N1-P2 differences for chosen stimuli, driven by the larger N1 mean amplitude to the sound perceived as different. The N1 wave is generated in the superior temporal gyrus (auditory cortex) in response to sound  and has been suggested to reflect stimulus encoding . It is subject to adaptation, reducing in amplitude with stimulus repetition [45,46], as we have also observed. "
[Show abstract][Hide abstract] ABSTRACT: Perceptual decision making is prone to errors, especially near threshold. Physiological, behavioural and modeling studies suggest this is due to the intrinsic or 'internal' noise in neural systems, which derives from a mixture of bottom-up and top-down sources. We show here that internal noise can form the basis of perceptual decision making when the external signal lacks the required information for the decision. We recorded electroencephalographic (EEG) activity in listeners attempting to discriminate between identical tones. Since the acoustic signal was constant, bottom-up and top-down influences were under experimental control. We found that early cortical responses to the identical stimuli varied in global field power and topography according to the perceptual decision made, and activity preceding stimulus presentation could predict both later activity and behavioural decision. Our results suggest that activity variations induced by internal noise of both sensory and cognitive origin are sufficient to drive discrimination judgments.
PLoS ONE 07/2013; 8(7):e68928. DOI:10.1371/journal.pone.0068928 · 3.23 Impact Factor
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