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Introduction

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  1. Rapid Brain Discrimination of Sounds of ObjectsMicah M. Murray,1,2 * Christian Camen,3 * Sara L. Gonzalez Andino,4 Pierre Bovet,3 and Stephanie Clarke1 1Neuropsychology Division and 2Radiology Service, The Functional Electrical Neuroimaging Laboratory, Hôpital Nestlé, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland, 3Faculté de Psychologie et des Sciences de l’Éducation, Geneva University, 1211 Geneva, Switzerland, and 4The Functional Brain Mapping Laboratory, Neurology Department, Geneva University Hospital, 1211 Geneva, SwitzerlandThe Journal of Neuroscience, January 25, 2006

  2. Introduction • How fast can the brain discriminate sounds? • Related studies indicate that recognition and categorization of faces and objects can be achieved within ~150ms after stimulus onset. • Superior temporal sulcus shown to be involved in speech/ voice recognition. • May be motor related or so called mirror neuron system. • Temporal information thought to play role in language acquisition and proficiency. • Evidence that specialization within auditory network might differentiate sound categories into tools vs. animals.

  3. The study • Used electrical neuroimaging (EEG) • Examined speed and the neurophysiological mechanism by which sounds of living and man-made objects are first differentiated.

  4. Materials • 9 healthy subjects – 21-34 years of age • 6 females • 3 males • All right handed • No history of illness • Used complex meaningful sounds from an on-line library • 16 bit stereo; 22,500Hz digitization • Used a set of 120 sounds as a database for selecting the sounds used for the EEG portion of the study.

  5. Methods • In a pretest session, a separate group of 18 subjects listened to, identified, and gave a familiarity rating of the sound using a 1-7 Likert scale. • The sounds that were most often correctly identified were used: • 20 living/ 20 man-made • Used audio editing to account for discrepancies between the sounds including: • Each sound made to 500ms in length • 50ms decay time applied to end of sound file to minimize clicks @ offset • Compared for acoustic differences and time frequencies • Harmonics-to-noise ratio( no significant differences) • Results showed that only significant differences were shown after ~125ms of sound and for frequencies above ~4000Hz because an additional 15-20ms is required for signal transmission into the human auditory cortex.

  6. Stimulus list

  7. Methods (Cont.) • As an additional control a second group of subjects were selected: • 10 healthy subjects • 5 male, 5 female • 20-32 years of age • 8 right handed, 3 left • Listened to final set of 120 sounds. • Identified living vs. man-made and using 1-7 Likert scale rated their confidence, and familiarity with the sound.

  8. Procedure and task • Living vs. non-living oddball paradigm. • Target stimuli to which subjects pressed a response button occurred 10% of the time, 90% were distracters. • 300 trials with intervals of 3.4s. • Each subject completed 4 blocks of trials (2man-made, 2 living). • Peristimulus epochs of continuous EEG averaged from each subject separatly for each condition to compute auditory evoked potentials (AEPs). • Baseline defined as 100ms prestimulus period. • Trials with blinks or eye movements were rejected.

  9. Procedure and task • AEPs were submitted to two independent studies: • 1) a topographic pattern analysis for defining time periods • 2) instantaneous global field power (GFP) done to minimize observer bias and paired with t-tests • The first point where the t-test exceeded 0.05 criterion for 11 consecutive data points was labeled as onset of an AEP modulation. • Estimated sources in brain underlying AEPs in response to the sounds using local autoregressive average (LAURA).

  10. Results • Behavioral: • Subjects performed task with no significant difference between sensitivity measures based on signal detection theory when sounds served as targets. • Reaction times did not differ • Time-frequency analysis showed significant living vs. man-made differences only after ~125ms, no differences between mean harmonics to noise ratio indicating they cannot account for AEP effects before ~125ms.

  11. Results • Electrophysiological: • Topographic pattern analysis determined whether different configurations of brain generates accounted for responses to the sounds (7 different topographies). • In the two experimental conditions identical electric field topographies were shown at 0-69, 70-119, 120-154, 258-355, 356-500ms. • Two topographies at 155-257ms. • Neither effect reached 0.05 significance. • Differences in period over N1 component at frontocentral scalp. • Tested using 4 electrodes ( AFz, FCz, CPz, POz ). • Larger response to man-made sounds only at electrode FCz and over 70-119ms period. • 155-257ms revealed significant differences with man-made having peaked ~12ms earlier.

  12. Results • LAURA estimations reveal stronger responses to man-made sounds in right posterior temporal cortex. • Because differences in electrical fields predominated AEPs estimations were calculated for specific periods: • Living 155-211 and 212-257ms • Man-made 155-199 and 200-257ms. • Both bilateral sources within posterior portion of superior and middle temporal cortices and premotor cortices with weaker activity in left inferior frontal cortex. • Stronger in premotor in response to man-made.

  13. LAURA SOURCE ESTIMATIONS living 155-211ms 212-257ms Man-made 155-199ms 200-257ms

  14. Discussion/ Conclusion • Not linked to behavioural differences, time-frequency, or harmonics-to-noise ratios. • Common network of brain areas involved in auditory what pathway, response more strongly to man-made objects in regions of right posterior, superior and middle temporal cortices and left inferior frontal cortex. • Differential processing of categories of sound initiates predominantly in strucures of the right hemisphere • The contrast of animals vs. tools yielded stronger responses with middle superior temporal gyrus • Stronger differentiation in tools because they might include richer multisensory and action related associations.

  15. Cheryl Bush • Cognitive Neuroscience 3680N

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