Projects per year
Human mismatch negativity (MMN) is modelled in rodents and other non-human species to examine its underlying neurological mechanisms, primarily described in terms of deviance-detection and adaptation. Using the mouse model, we aim to elucidate subtle dependencies between the mismatch response (MMR) and different physical properties of sound. Epidural field potentials were recorded from urethane anaesthetised and conscious mice during oddball and many-standards control paradigms with stimuli varying in duration, frequency, intensity, and inter-stimulus interval. Resulting auditory evoked potentials, classical MMR (oddball – standard), and controlled MMR (oddball – control) waveforms were analysed. Stimulus duration correlated with stimulus-off response peak latency, whereas frequency, intensity, and inter-stimulus interval correlated with stimulus-on N1 and P1 (conscious only) peak amplitudes. These relationships were instrumental in shaping classical MMR morphology in both anaesthetised and conscious animals, suggesting these waveforms reflect modification of normal auditory processing by different physical properties of sound. Controlled MMR waveforms appeared to exhibit habituation to auditory stimulation over time, which was equally observed in response to oddball and standard stimuli. These findings are inconsistent with the mechanisms thought to underlie human MMN, which currently do not address differences due to specific physical features of sound. Thus, no evidence was found to objectively support the deviance-detection or adaptation hypotheses of MMN in relation to anaesthetised or conscious mice. These findings highlight the potential risk of mischaracterising difference waveform components that are principally influenced by physical sensitivities and habituation of the auditory system.
- mismatch response
1/10/11 → 27/07/17
Project: Research Studentship - Internally Allocated
Characterising mismatch negativity biomarker signatures in preclinical models relevant to schizophreniaAuthor: O'Reilly, J., 27 Jul 2017
Student thesis: Doctoral Thesis