Hippocampal sclerosis affects fMR-adaptation of lyrics and melodies in songs

Songs constitute a natural combination of lyrics and melodies, but it is unclear whether and how these two song components are integrated during the emergence of a memory trace. Network theories of memory suggest a prominent role of the hippocampus, together with unimodal sensory areas, in the build-up of conjunctive representations. The present study tested the modulatory influence of the hippocampus on neural adaptation to songs in lateral temporal areas. Patients with unilateral hippocampal sclerosis and healthy matched controls were presented with blocks of short songs in which lyrics and/or melodies were varied or repeated in a crossed factorial design. Neural adaptation effects were taken as correlates of incidental emergent memory traces. We hypothesized that hippocampal lesions, particularly in the left hemisphere, would weaken adaptation effects, especially the integration of lyrics and melodies. Results revealed that lateral temporal lobe regions showed weaker adaptation to repeated lyrics as well as a reduced interaction of the adaptation effects for lyrics and melodies in patients with left hippocampal sclerosis. This suggests a deficient build-up of a sensory memory trace for lyrics and a reduced integration of lyrics with melodies, compared to healthy controls. Patients with right hippocampal sclerosis showed a similar profile of results although the effects did not reach significance in this population. We highlight the finding that the integrated representation of lyrics and melodies typically shown in healthy participants is likely tied to the integrity of the left medial temporal lobe. This novel finding provides the first neuroimaging evidence for the role of the hippocampus during repetitive exposure to lyrics and melodies and their integration into a song

Neural codes for one’s own position and direction in a real-world “vista” environment

Humans, like animals, rely on an accurate knowledge of one’s spatial position and facing direction to keep orientated in the surrounding space. Although previous neuroimaging studies demonstrated that scene-selective regions (the parahippocampal place area or PPA, the occipital place area or OPA and the retrosplenial complex or RSC), and the hippocampus (HC) are implicated in coding position and facing direction within small-(room-sized) and large-scale navigational environments, little is known about how these regions represent these spatial quantities in a large open-field environment. Here, we used functional magnetic resonance imaging (fMRI) in humans to explore the neural codes of these navigationally-relevant information while participants viewed images which varied for position and facing direction within a familiar, real-world circular square. We observed neural adaptation for repeated directions in the HC, even if no navigational task was required. Further, we found that the amount of knowledge of the environment interacts with the PPA selectivity in encoding positions: individuals who needed more time to memorize positions in the square during a preliminary training task showed less neural attenuation in this scene-selective region. We also observed adaptation effects, which reflect the real distances between consecutive positions, in scene-selective regions but not in the HC. When examining the multi-voxel patterns of activity we observed that scene-responsive regions and the HC encoded both spatial information and that the RSC classification accuracy for positions was higher in individuals scoring higher to a self-reported questionnaire of spatial abilities. Our findings provide new insight into how the human brain represents a real, large-scale “vista” space, demonstrating the presence of neural codes for position and direction in both scene-selective and hippocampal regions, and revealing the existence, in the former regions, of a map-like spatial representation reflecting real-world distance between consecutive positions

Neural Dynamics of Phonological Processing in the Dorsal Auditory Stream

Neuroanatomical models hypothesize a role for the dorsal auditory pathway in phonological processing as a feedforward efferent system (Davis and Johnsrude, 2007; Rauschecker and Scott, 2009; Hickok et al., 2011). But the functional organization of the pathway, in terms of time course of interactions between auditory, somatosensory, and motor regions, and the hemispheric lateralization pattern is largely unknown. Here, ambiguous duplex syllables, with elements presented dichotically at varying interaural asynchronies, were used to parametrically modulate phonological processing and associated neural activity in the human dorsal auditory stream. Subjects performed syllable and chirp identification tasks, while event-related potentials and functional magnetic resonance images were concurrently collected. Joint independent component analysis was applied to fuse the neuroimaging data and study the neural dynamics of brain regions involved in phonological processing with high spatiotemporal resolution. Results revealed a highly interactive neural network associated with phonological processing, composed of functional fields in posterior temporal gyrus (pSTG), inferior parietal lobule (IPL), and ventral central sulcus (vCS) that were engaged early and almost simultaneously (at 80–100 ms), consistent with a direct influence of articulatory somatomotor areas on phonemic perception. Left hemispheric lateralization was observed 250 ms earlier in IPL and vCS than pSTG, suggesting that functional specialization of somatomotor (and not auditory) areas determined lateralization in the dorsal auditory pathway. The temporal dynamics of the dorsal auditory pathway described here offer a new understanding of its functional organization and demonstrate that temporal information is essential to resolve neural circuits underlying complex behaviors

Revealing Connections in Object and Scene Processing Using Consecutive TMS and fMR-Adaptation

When processing the visual world, our brain must perform many computations that may occur across several regions. It is important to understand communications between regions in order to understand perceptual processes underlying processing of our environment. We sought to determine the connectivity of object and scene processing regions of the cortex, which are not fully established. In order to determine these connections repetitive transcranial magnetic stimulation (rTMS) and functional magnetic resonance-adaptation (fMR-A) were paired together. rTMS was applied to object-selective lateral occipital (LO) and scene-selective transverse occipital sulcus (TOS). Immediately after stimulation, participants underwent fMR-A, and pre- and post-TMS responses were compared. TMS disrupted remote regions revealing connections from LO and TOS to remote object and scene-selective regions in the occipital cortex. In addition, we report important neural correlates regarding the transference of object related information between modalities, from LO to outside the ventral network to parietal and frontal areas

Cortical Representation Underlying the Semantic Processing of Numerical Symbols: Evidence from Adult and Developmental Studies

Humans possess the remarkable ability to process numerical information using numerical symbols such as Arabic digits. A growing body of neuroimaging work has provided new insights into the neural correlates associated with symbolic numerical magnitude processing. However, little is known about the cortical specialization underlying the representation of symbolic numerical magnitude in adults and children. To constrain our current knowledge, I conducted a series of functional Magnetic Resonance Imaging (fMRI) studies that aimed to better understand the functional specialization of symbolic numerical magnitudes representation in the human brain. Using a number line estimation task, the first study contrasted the brain activation associated with processing symbolic numerical magnitude against the brain activation associated with non-numerical magnitude (brightness) processing. Results demonstrated a right lateralized parietal network that was commonly engaged when magnitude dimensions were processed. However, the left intraparietal sulcus (IPS) was additionally activated when symbolic numerical magnitudes were estimated, suggesting that number is a special category amongst magnitude dimensions and that the left hemisphere plays a critical role in representing number. The second study tested a child friendly version of an fMRI-adaptation paradigm in adults. For this participant’s brain response was habituated to a numerical value (i.e., 6) and signal recovery in response to the presentation of numerical deviants was investigated. Across two different brain normalization procedures results showed a replication of previous findings demonstrating that the brain response of the IPS is modulated by the semantic meaning of numbers in the absence of overt response selection. The last study aimed to unravel developmental changes in the cortical representation of symbolic numerical magnitudes in children. Using the paradigm tested in chapter 2, results demonstrated an increase in the signal recovery with age in the left IPS as well as an age-independent signal recovery in the right IPS. This finding indicates that the left IPS becomes increasingly specialized for the representation of symbolic numerical magnitudes over developmental time, while the right IPS may play a different and earlier role in symbolic numerical magnitude representation. Findings of these studies are discussed in relation to our current knowledge about symbolic numerical magnitude representation

A new approach for feature extraction from functional MR images

The functional MR images consist of very high dimensional data containing thousands of voxels, even for a single subject. Data reduction methods are inevitable for the classification of these three-dimensional images. In this study in the first step of the data reduction, the first level statistical analysis was applied to fMRI data and brain maps of each subject were obtained for the feature extraction. In the second step the feature selection was applied to brain maps. According to the feature selection method used in the classification studies of fMRI and which is called as the active method, the intensity values of all brain voxels are ranked from high to low and some of these features are presented to the classifier. However, the location information of the voxels is lost with this method. In this study, a new feature extraction method was presented for use in the classification of fMRI. According to this method, active voxels can be used as features by considering brain maps obtained in three dimensions as slice based. Since the functional MR images have big data sets, the selected features were once again reduced by Principal Component Analysis and the voxel intensity values were presented to the classifiers. As a result; 83.9% classification accuracy was obtained by using kNN classifier with purposed slice-based feature extraction method and it was seen that the slice-based feature extraction method increased the classification.The functional MR images consist of very high dimensional data containing thousands of voxels, even for a single subject. Data reduction methods are inevitable for the classification of these three-dimensional images. In this study in the first step of the data reduction, the first level statistical analysis was applied to fMRI data and brain maps of each subject were obtained for the feature extraction. In the second step the feature selection was applied to brain maps. According to the feature selection method used in the classification studies of fMRI and which is called as the active method, the intensity values of all brain voxels are ranked from high to low and some of these features are presented to the classifier. However, the location information of the voxels is lost with this method. In this study, a new feature extraction method was presented for use in the classification of fMRI. According to this method, active voxels can be used as features by considering brain maps obtained in three dimensions as slice based. Since the functional MR images have big data sets, the selected features were once again reduced by Principal Component Analysis and the voxel intensity values were presented to the classifiers. As a result; 83.9% classification accuracy was obtained by using kNN classifier with purposed slice-based feature extraction method and it was seen that the slice-based feature extraction method increased the classification

Bringing the real world into the fMRI scanner: Repetition effects for pictures versus real objects

Our understanding of the neural underpinnings of perception is largely built upon studies employing 2-dimensional (2D) planar images. Here we used slow event-related functional imaging in humans to examine whether neural populations show a characteristic repetition-related change in haemodynamic response for real-world 3-dimensional (3D) objects, an effect commonly observed using 2D images. As expected, trials involving 2D pictures of objects produced robust repetition effects within classic object-selective cortical regions along the ventral and dorsal visual processing streams. Surprisingly, however, repetition effects were weak, if not absent on trials involving the 3D objects. These results suggest that the neural mechanisms involved in processing real objects may therefore be distinct from those that arise when we encounter a 2D representation of the same items. These preliminary results suggest the need for further research with ecologically valid stimuli in other imaging designs to broaden our understanding of the neural mechanisms underlying human vision