My PhD thesis consists of two neuroimaging studies in the field of cognitive neuroscience of music and sound as following described. (1) In the first research, entitled “Moving soundscape: an fMRI study on auditory self- and object-motion perception”, I investigated the auditory perception related to the discrimination between self- and object-motion, using an fMRI technique. Among scientific literature, no previous imaging studies researched about these two different types of motion perception in the auditory domain. I explored brain activation induced by complex auditory scenes including 64 ecological and binaurally spatialized sound sources moving either coherently or randomly, thus mimicking ecological stimulation during self and object motion respectively. The moving soundscape stimulus was designed in a similar way as the optical “flowfields” which are routinely used to map visual regions specialized for self motion such as V6+ (Pitzalis et al., 2010) and consisting of white dots moving across different trajectories and directions, either coherently or randomly. HYPOTHESES: I investigated if this novel kind of auditory stimulation is able to elicit activation in the visual motion region V6+ and whether it is able to engage supposed functional analogues areas in the auditory network. A further hypothesis concerns the cross-modal sensitivity of para- hippocampal place area (PPA), a specialized visual area for scene recognition (Epstein and Kanwisher, 1998; Sulpizio et al., 2013). METHOD: 16 participants underwent six distinct 3T Allegra-fMR acquisition scans: 4 of moving soundscape and 2 of flowfield localizer. For the main experiment, the initial analysis aimed at selecting a set of brain regions more implicated in at least one experimental condition (coherent, random, static) as compared to the baseline (silence condition). The resulting estimates entered a repeated-measures analysis of variance (RM-ANOVA) comparing activations between the three conditions. The same analysis was also applied to the V6+, as individually defined on the basis of the flowfield localizer by comparing the flowfields and the random motion conditions. The PPA region was probabilistically defined in Sulpizio and colleagues (Sulpizio et al. 2013) by contrasting passive viewing localizer blocks of scenes vs. faces pictures in a group of 34 subjects separate from the participants of the current study; this region also entered a RM-ANOVA. RESULTS: The main experiment allowed me to observe the involvement of several areas inside and – surprisingly – outside auditory cortex such as: planum temporale (PT:Griffith et al., 2002) in the auditory pathway; hMT/V5+ (Braddick et al., 2001), lateral occipital region (LOR: Tootell et al., 2001), V3A and V3B (Larsson et al., 2006), in the dorsal visual motion pathway. Moreover I observed activations in regions involved in visual object recognition such as lateral occipital cortex (LOC: Kanwisher et al., 2001) and fusiform gyrus (FUS: Weiner and Zilles, 2015). The auditory motion coding could seem to have its core area in the right PT able to distinguish between moving and static stimuli, but unable to discriminate between coherent and random motion. V6+ and the other dorsal visual motion areas surprisingly appeared more responsive to static sound rather than to moving sound stimuli. PPA, LOC and FUS showed the same trend. I supposed that the ecological fitness of moving soundscape has triggered a healthy synesthetic process: visual mental imagery induced by auditory stimuli. Referring to the synesthesia models (Ramachandran & Hubbard, 2001; Esterman et al. 2006; Hubbard et al. 2007; Hubbard et al. 2011), I supposed an inhibition process to motion induced by strongly overlapping frequencies (also caused by psycho-acoustic indexes) that could explain the greater responsiveness to static rather than to moving stimuli of the dorsal visual pathway areas. Data about PPA, LOC and FUS revealed the cross-modal sensitivity of these regions for object and scene recognition. (2) The second study was entitled “Different brain processes between musicians and non-musicians during preparation, perception and action in visual and auditory decision-making”. I investigated brain and behavioral plasticity induced by musical training and transferred to decision-making processes, using an ERP technique. From a theoretical perspective, I referenced to an adaptation of Moreno’s transfer-effect model (2013). Musical training and skills transfer-effects have been conceptualized as a multidimensional continuum defined by two orthogonal dimensions: 1) the level of affected processing (low-level: sensory; high-level: cognitive) and 2) the transfer domain’s distance (near = auditory; far = visual). Among perceptual decision-making tasks, the Go/No-go task has been chosen to compare musicians and non- musicians, because it requires inhibitory control, an executive function that could represent the link to transfer-effects induced by musical training; moreover I administered the Go/No-go task in two different modalities (visual and auditory) in order to verify the near and far skill’s transfer. HYPOTHESES: I hypothesized that musicians could show differences from non-musicians in premotor, early and late ERPs components and in behavioral performances, proportionally to the transfer-effect distance and to the level of affected processes. Therefore I expected that musicians could show smaller premotor components (reflecting less effort) and higher amplitude both in sensory and cognitive components, above all in the auditory task, with a mirrored behavioral performance. METHOD: 31 participants underwent two separate auditory and visual Go/No-go tasks each made by 10 runs during a 64-channel EEG recording. The visual stimuli consisted of four squared configurations made by vertical and horizontal bars; the two Go and No-go stimuli were created by coupling line orientations with configuration complexity. The auditory stimuli consisted in four complex tones created by coupling two notes (F-sharp and C) with two ranges of harmonic octaves. Behavioral data analysis entered RM-ANOVAs aiming to detect differences between groups, modalities and conditions about response time (RT), intra-individual coefficient of variation (ICV), false alarms (FA), anticipations and omissions. ERP data analysis entered RM-ANOVAs aiming to detect differences between group, conditions, modalities and electrode sites in an wide window of components: BP, pN, P1, N1, P2, N2, P3. RESULTS: I found several differences between musicians and non- musicians. Musicians showed less effort in motor preparation (smaller BP component), faster and more accurate performances in the auditory- domain (decreased false alarm rate, increased N1 amplitude, increased P3 amplitude). However, musicians were less accurate than non- musicians in the visual task (increased false alarm rate and decreased P3 amplitude). Interpreted by means of the Moreno’s pyramidal model (2013), these results appeared only partly in line with this conceptualization, showing that the brain plasticity induced by musical training could transfer to a far domain (such as visual domain), not only in terms of advantages, but also as disadvantages.
My PhD thesis consists of two neuroimaging studies in the field of cognitive neuroscience of music and sound as following described. (1) In the first research, entitled “Moving soundscape: an fMRI study on auditory self- and object-motion perception”, I investigated the auditory perception related to the discrimination between self- and object-motion, using an fMRI technique. Among scientific literature, no previous imaging studies researched about these two different types of motion perception in the auditory domain. I explored brain activation induced by complex auditory scenes including 64 ecological and binaurally spatialized sound sources moving either coherently or randomly, thus mimicking ecological stimulation during self and object motion respectively. The moving soundscape stimulus was designed in a similar way as the optical “flowfields” which are routinely used to map visual regions specialized for self motion such as V6+ (Pitzalis et al., 2010) and consisting of white dots moving across different trajectories and directions, either coherently or randomly. HYPOTHESES: I investigated if this novel kind of auditory stimulation is able to elicit activation in the visual motion region V6+ and whether it is able to engage supposed functional analogues areas in the auditory network. A further hypothesis concerns the cross-modal sensitivity of para- hippocampal place area (PPA), a specialized visual area for scene recognition (Epstein and Kanwisher, 1998; Sulpizio et al., 2013). METHOD: 16 participants underwent six distinct 3T Allegra-fMR acquisition scans: 4 of moving soundscape and 2 of flowfield localizer. For the main experiment, the initial analysis aimed at selecting a set of brain regions more implicated in at least one experimental condition (coherent, random, static) as compared to the baseline (silence condition). The resulting estimates entered a repeated-measures analysis of variance (RM-ANOVA) comparing activations between the three conditions. The same analysis was also applied to the V6+, as individually defined on the basis of the flowfield localizer by comparing the flowfields and the random motion conditions. The PPA region was probabilistically defined in Sulpizio and colleagues (Sulpizio et al. 2013) by contrasting passive viewing localizer blocks of scenes vs. faces pictures in a group of 34 subjects separate from the participants of the current study; this region also entered a RM-ANOVA. RESULTS: The main experiment allowed me to observe the involvement of several areas inside and – surprisingly – outside auditory cortex such as: planum temporale (PT:Griffith et al., 2002) in the auditory pathway; hMT/V5+ (Braddick et al., 2001), lateral occipital region (LOR: Tootell et al., 2001), V3A and V3B (Larsson et al., 2006), in the dorsal visual motion pathway. Moreover I observed activations in regions involved in visual object recognition such as lateral occipital cortex (LOC: Kanwisher et al., 2001) and fusiform gyrus (FUS: Weiner and Zilles, 2015). The auditory motion coding could seem to have its core area in the right PT able to distinguish between moving and static stimuli, but unable to discriminate between coherent and random motion. V6+ and the other dorsal visual motion areas surprisingly appeared more responsive to static sound rather than to moving sound stimuli. PPA, LOC and FUS showed the same trend. I supposed that the ecological fitness of moving soundscape has triggered a healthy synesthetic process: visual mental imagery induced by auditory stimuli. Referring to the synesthesia models (Ramachandran & Hubbard, 2001; Esterman et al. 2006; Hubbard et al. 2007; Hubbard et al. 2011), I supposed an inhibition process to motion induced by strongly overlapping frequencies (also caused by psycho-acoustic indexes) that could explain the greater responsiveness to static rather than to moving stimuli of the dorsal visual pathway areas. Data about PPA, LOC and FUS revealed the cross-modal sensitivity of these regions for object and scene recognition. (2) The second study was entitled “Different brain processes between musicians and non-musicians during preparation, perception and action in visual and auditory decision-making”. I investigated brain and behavioral plasticity induced by musical training and transferred to decision-making processes, using an ERP technique. From a theoretical perspective, I referenced to an adaptation of Moreno’s transfer-effect model (2013). Musical training and skills transfer-effects have been conceptualized as a multidimensional continuum defined by two orthogonal dimensions: 1) the level of affected processing (low-level: sensory; high-level: cognitive) and 2) the transfer domain’s distance (near = auditory; far = visual). Among perceptual decision-making tasks, the Go/No-go task has been chosen to compare musicians and non- musicians, because it requires inhibitory control, an executive function that could represent the link to transfer-effects induced by musical training; moreover I administered the Go/No-go task in two different modalities (visual and auditory) in order to verify the near and far skill’s transfer. HYPOTHESES: I hypothesized that musicians could show differences from non-musicians in premotor, early and late ERPs components and in behavioral performances, proportionally to the transfer-effect distance and to the level of affected processes. Therefore I expected that musicians could show smaller premotor components (reflecting less effort) and higher amplitude both in sensory and cognitive components, above all in the auditory task, with a mirrored behavioral performance. METHOD: 31 participants underwent two separate auditory and visual Go/No-go tasks each made by 10 runs during a 64-channel EEG recording. The visual stimuli consisted of four squared configurations made by vertical and horizontal bars; the two Go and No-go stimuli were created by coupling line orientations with configuration complexity. The auditory stimuli consisted in four complex tones created by coupling two notes (F-sharp and C) with two ranges of harmonic octaves. Behavioral data analysis entered RM-ANOVAs aiming to detect differences between groups, modalities and conditions about response time (RT), intra-individual coefficient of variation (ICV), false alarms (FA), anticipations and omissions. ERP data analysis entered RM-ANOVAs aiming to detect differences between group, conditions, modalities and electrode sites in an wide window of components: BP, pN, P1, N1, P2, N2, P3. RESULTS: I found several differences between musicians and non- musicians. Musicians showed less effort in motor preparation (smaller BP component), faster and more accurate performances in the auditory- domain (decreased false alarm rate, increased N1 amplitude, increased P3 amplitude). However, musicians were less accurate than non- musicians in the visual task (increased false alarm rate and decreased P3 amplitude). Interpreted by means of the Moreno’s pyramidal model (2013), these results appeared only partly in line with this conceptualization, showing that the brain plasticity induced by musical training could transfer to a far domain (such as visual domain), not only in terms of advantages, but also as disadvantages.
AUDITORY PERCEPTION AND BRAIN PLASTICITY IN MUSICIANS: NEUROIMAGING STUDIES / Gigante, Elena. - ELETTRONICO. - (2016).
AUDITORY PERCEPTION AND BRAIN PLASTICITY IN MUSICIANS: NEUROIMAGING STUDIES
GIGANTE, ELENA
01/01/2016
Abstract
My PhD thesis consists of two neuroimaging studies in the field of cognitive neuroscience of music and sound as following described. (1) In the first research, entitled “Moving soundscape: an fMRI study on auditory self- and object-motion perception”, I investigated the auditory perception related to the discrimination between self- and object-motion, using an fMRI technique. Among scientific literature, no previous imaging studies researched about these two different types of motion perception in the auditory domain. I explored brain activation induced by complex auditory scenes including 64 ecological and binaurally spatialized sound sources moving either coherently or randomly, thus mimicking ecological stimulation during self and object motion respectively. The moving soundscape stimulus was designed in a similar way as the optical “flowfields” which are routinely used to map visual regions specialized for self motion such as V6+ (Pitzalis et al., 2010) and consisting of white dots moving across different trajectories and directions, either coherently or randomly. HYPOTHESES: I investigated if this novel kind of auditory stimulation is able to elicit activation in the visual motion region V6+ and whether it is able to engage supposed functional analogues areas in the auditory network. A further hypothesis concerns the cross-modal sensitivity of para- hippocampal place area (PPA), a specialized visual area for scene recognition (Epstein and Kanwisher, 1998; Sulpizio et al., 2013). METHOD: 16 participants underwent six distinct 3T Allegra-fMR acquisition scans: 4 of moving soundscape and 2 of flowfield localizer. For the main experiment, the initial analysis aimed at selecting a set of brain regions more implicated in at least one experimental condition (coherent, random, static) as compared to the baseline (silence condition). The resulting estimates entered a repeated-measures analysis of variance (RM-ANOVA) comparing activations between the three conditions. The same analysis was also applied to the V6+, as individually defined on the basis of the flowfield localizer by comparing the flowfields and the random motion conditions. The PPA region was probabilistically defined in Sulpizio and colleagues (Sulpizio et al. 2013) by contrasting passive viewing localizer blocks of scenes vs. faces pictures in a group of 34 subjects separate from the participants of the current study; this region also entered a RM-ANOVA. RESULTS: The main experiment allowed me to observe the involvement of several areas inside and – surprisingly – outside auditory cortex such as: planum temporale (PT:Griffith et al., 2002) in the auditory pathway; hMT/V5+ (Braddick et al., 2001), lateral occipital region (LOR: Tootell et al., 2001), V3A and V3B (Larsson et al., 2006), in the dorsal visual motion pathway. Moreover I observed activations in regions involved in visual object recognition such as lateral occipital cortex (LOC: Kanwisher et al., 2001) and fusiform gyrus (FUS: Weiner and Zilles, 2015). The auditory motion coding could seem to have its core area in the right PT able to distinguish between moving and static stimuli, but unable to discriminate between coherent and random motion. V6+ and the other dorsal visual motion areas surprisingly appeared more responsive to static sound rather than to moving sound stimuli. PPA, LOC and FUS showed the same trend. I supposed that the ecological fitness of moving soundscape has triggered a healthy synesthetic process: visual mental imagery induced by auditory stimuli. Referring to the synesthesia models (Ramachandran & Hubbard, 2001; Esterman et al. 2006; Hubbard et al. 2007; Hubbard et al. 2011), I supposed an inhibition process to motion induced by strongly overlapping frequencies (also caused by psycho-acoustic indexes) that could explain the greater responsiveness to static rather than to moving stimuli of the dorsal visual pathway areas. Data about PPA, LOC and FUS revealed the cross-modal sensitivity of these regions for object and scene recognition. (2) The second study was entitled “Different brain processes between musicians and non-musicians during preparation, perception and action in visual and auditory decision-making”. I investigated brain and behavioral plasticity induced by musical training and transferred to decision-making processes, using an ERP technique. From a theoretical perspective, I referenced to an adaptation of Moreno’s transfer-effect model (2013). Musical training and skills transfer-effects have been conceptualized as a multidimensional continuum defined by two orthogonal dimensions: 1) the level of affected processing (low-level: sensory; high-level: cognitive) and 2) the transfer domain’s distance (near = auditory; far = visual). Among perceptual decision-making tasks, the Go/No-go task has been chosen to compare musicians and non- musicians, because it requires inhibitory control, an executive function that could represent the link to transfer-effects induced by musical training; moreover I administered the Go/No-go task in two different modalities (visual and auditory) in order to verify the near and far skill’s transfer. HYPOTHESES: I hypothesized that musicians could show differences from non-musicians in premotor, early and late ERPs components and in behavioral performances, proportionally to the transfer-effect distance and to the level of affected processes. Therefore I expected that musicians could show smaller premotor components (reflecting less effort) and higher amplitude both in sensory and cognitive components, above all in the auditory task, with a mirrored behavioral performance. METHOD: 31 participants underwent two separate auditory and visual Go/No-go tasks each made by 10 runs during a 64-channel EEG recording. The visual stimuli consisted of four squared configurations made by vertical and horizontal bars; the two Go and No-go stimuli were created by coupling line orientations with configuration complexity. The auditory stimuli consisted in four complex tones created by coupling two notes (F-sharp and C) with two ranges of harmonic octaves. Behavioral data analysis entered RM-ANOVAs aiming to detect differences between groups, modalities and conditions about response time (RT), intra-individual coefficient of variation (ICV), false alarms (FA), anticipations and omissions. ERP data analysis entered RM-ANOVAs aiming to detect differences between group, conditions, modalities and electrode sites in an wide window of components: BP, pN, P1, N1, P2, N2, P3. RESULTS: I found several differences between musicians and non- musicians. Musicians showed less effort in motor preparation (smaller BP component), faster and more accurate performances in the auditory- domain (decreased false alarm rate, increased N1 amplitude, increased P3 amplitude). However, musicians were less accurate than non- musicians in the visual task (increased false alarm rate and decreased P3 amplitude). Interpreted by means of the Moreno’s pyramidal model (2013), these results appeared only partly in line with this conceptualization, showing that the brain plasticity induced by musical training could transfer to a far domain (such as visual domain), not only in terms of advantages, but also as disadvantages.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
