Hippocampus dependent and independent theta-networks of working memory maintenance

Working memory is the ability to briefly maintain and manipulate information beyond its transient availability to our senses. This process of short-term stimulus retention has often been proposed to be anatomically distinct from long-term forms of memory. Although it’s been well established that the medial temporal lobe (MTL) is critical to long-term declarative memory, recent evidence has suggested that MTL regions, such as the hippocampus, may also be involved in the working memory maintenance of configural visual relationships. I investigate this possibility in a series of experiments using Magnetoencephalography to record the cortical oscillatory activity within the theta frequency band of patients with bilateral hippocampal sclerosis and normal controls. The results demonstrate that working memory maintenance of configural-relational information is supported by a theta synchronous network coupling frontal, temporal and occipital visual areas, and furthermore that this theta synchrony is critically dependent on the integrity of the hippocampus. Alternate forms of working memory maintenance, that do not require the relational binding of visual configurations, engage dissociable theta synchronous networks functioning independently of the hippocampus. In closing, I will explore the interactions between long-term and short-term forms of memory and demonstrate that through these interactions, memory performance can effectively be improved

A multimodal investigation of dynamic face perception using functional magnetic resonance imaging and magnetoencephalography

Motion is an important aspect of face perception that has been largely neglected to date. Many of the established findings are based on studies that use static facial images, which do not reflect the unique temporal dynamics available from seeing a moving face. In the present thesis a set of naturalistic dynamic facial emotional expressions was purposely created and used to investigate the neural structures involved in the perception of dynamic facial expressions of emotion, with both functional Magnetic Resonance Imaging (fMRI) and Magnetoencephalography (MEG). Through fMRI and connectivity analysis, a dynamic face perception network was identified, which is demonstrated to extend the distributed neural system for face perception (Haxby et al.,2000). Measures of effective connectivity between these regions revealed that dynamic facial stimuli were associated with specific increases in connectivity between early visual regions, such as inferior occipital gyri and superior temporal sulci, along with coupling between superior temporal sulci and amygdalae, as well as with inferior frontal gyri. MEG and Synthetic Aperture Magnetometry (SAM) were used to examine the spatiotemporal profile of neurophysiological activity within this dynamic face perception network. SAM analysis revealed a number of regions showing differential activation to dynamic versus static faces in the distributed face network, characterised by decreases in cortical oscillatory power in the beta band, which were spatially coincident with those regions that were previously identified with fMRI. These findings support the presence of a distributed network of cortical regions that mediate the perception of dynamic facial expressions, with the fMRI data providing information on the spatial co-ordinates paralleled by the MEG data, which indicate the temporal dynamics within this network. This integrated multimodal approach offers both excellent spatial and temporal resolution, thereby providing an opportunity to explore dynamic brain activity and connectivity during face processing

Oszillatorische Gamma-Band-Aktivität bei der Verarbeitung auditorischer Reize im Kurzzeitgedächtnis im MEG

Recent studies have suggested an important role of cortical gamma oscillatory activity (30-100 Hz) as a correlate of encoding, maintaining and retrieving auditory, visual or tactile information in and from memory. It was shown that these cortical stimulus representations were modulated by attention processes. Gamma-band activity (GBA) occurred as an induced response peaking at approximately 200-300 ms after stimulus presentation. Induced cortical responses appear as non-phase-locked activity and are assumed to reflect active cortical processing rather than passive perception. Induced GBA peaking 200-300 ms after stimulus presentation has been assumed to reflect differences between experimental conditions containing various stimuli. By contrast, the relationship between specific oscillatory signals and the representation of individual stimuli has remained unclear. The present study aimed at the identification of such stimulus-specific gamma-band components. We used magnetoencephalography (MEG) to assess gamma activity during an auditory spatial delayed matching-to-sample task. 28 healthy adults were assigned to one of two groups R and L who were presented with only right- or left-lateralized sounds, respectively. Two sample stimuli S1 with lateralization angles of either 15° or 45° deviation from the midsagittal plane were used in each group. Participants had to memorize the lateralization angle of S1 and compare it to a second lateralized sound S2 presented after an 800-ms delay phase. S2 either had the same or a different lateralization angle as S1. After the presentation of S2, subjects had to indicate whether S1 and S2 matched or not. Statistical probability mapping was applied to the signals at sensor level to identify spectral amplitude differences between 15° and 45° stimuli. We found distinct gamma-band components reflecting each sample stimulus with center frequencies ranging between 59 and 72 Hz in different sensors over parieto-occipital cortex contralateral to the side of stimulation. These oscillations showed maximal spectral amplitudes during the middle 200-300 ms of the delay phase and decreased again towards its end. Additionally, we investigated correlations between the activation strength of the gamma-band components and memory task performance. The magnitude of differentiation between oscillatory components representing 'preferred' and 'nonpreferred' stimuli during the final 100 ms of the delay phase correlated positively with task performance. These findings suggest that the observed gamma-band components reflect the activity of neuronal networks tuned to specific auditory spatial stimulus features. The activation of these networks seems to contribute to the maintenance of task-relevant information in short-term memory.Ergebnisse aus aktuellen Studien legen nahe, dass kortikale oszillatorische Aktivität im Gamma-Bereich (30-100 Hz) eine wichtige Rolle für verschiedene kognitive Prozesse spielt. Dazu zählen das Kodieren, die Aufrechterhaltung und der Abruf auditorischer, visueller oder taktiler Informationen in das bzw. aus dem Gedächtnis. Es konnte gezeigt werden, dass diese kortikale Aktivität durch Aufmerksamkeitsprozesse beeinflusst wird. Gamma-Aktivität trat bei vorangegangenen Untersuchungen als induzierte Antwort ca. 200-300 ms nach Stimuluspräsentation auf. Es wird angenommen, dass diese nicht phasengebundenen kortikalen Reizantworten aktive kortikale Verarbeitungs-prozesse widerspiegeln. In früheren Studien wurde induzierte Gamma-Aktivität während der Aufrechterhaltung von Stimulusinformationen über Regionen gefunden, die an der Verarbeitung aufgabenrelevanter Reizmerkmale beteiligt sind. Diese Antworten im Gamma-Bereich spiegelten Unterschiede zwischen verschieden experimentellen Bedingungen wider, jedoch ist wenig über die Repräsentation spezifischer Stimuluseigenschaften durch Gamma-Aktivität bekannt. Mit der vorliegenden Studie haben wir versucht, solche stimulus spezifischen Gamma-Komponenten zu untersuchen. Dafür verwendeten wir Magnetenzephalographie (MEG) und eine auditorische räumliche “delayed matching-to-sample“ Aufgabe. 28 gesunde Erwachsene wurden dabei zwei verschiedenen Gruppen zugeordnet. Gruppe R bekam rechtslateralisierte Stimuli präsentiert, während diese in Gruppe L linkslateralisiert waren. Dabei unterschieden sich die Reize nur in ihrer räumlichen Charakteristik, die Klangmuster blieben unverändert. In beiden Gruppen wurden zwei Beispielstimuli S1 mit Lateralisierungswinkeln von 15° bzw. 45° verwendet. Die Probanden mussten sich den Lateralisierungswinkel von S1 merken und anschließend mit einem zweiten Stimulus S2, der nach einer Verzögerungsphase von 800 ms präsentiert wurde, vergleichen. S2 hatte dabei entweder den gleichen Lateralisierungswinkel wie S1, oder unterschied sich darin von dem ersten Stimulus. Nach der Präsentation von S2 mussten die Probanden signalisieren, ob die Lateralisierungswinkel der beiden Stimuli übereinstimmten oder nicht. Die Signale der einzelnen Sensoren wurden mit einem statistischen Wahrscheinlichkeitsmapping untersucht. Dabei wollten wir Unterschiede in der spektralen Amplitude für Stimuli mit 15° bzw. 45° Lateralisierungswinkel identifizieren. Wir konnten spezifische Gamma-Aktivität für alle Beispielstimuli nachweisen. Die Signale wurden im Bereich von 59-72 Hz gefunden und waren über dem parieto-okzipitalen Kortex jeweils kontralateral zur stimulierten Seite lokalisiert. Die maximalen Spektralamplituden dieser Oszillationen traten während der mittleren 200-300 ms der Verzögerungsphase auf und nahmen zu ihrem Ende hin ab. Zusätzlich haben wir Korrelationen zwischen der Aktivierungsstärke der Gamma-Komponenten und dem Abschneiden bei der Gedächtnisaufgabe untersucht. Dabei zeigte sich, dass der Unterschied der oszillatorischen Antworten auf bevorzugte und nicht-bevorzugte Stimuli während der letzten 100 ms der Verzögerungsphase positiv mit der Leistung in der Gedächtnisaufgabe korrelierte. Diese Ergebnisse sprechen dafür, dass die beobachteten Gamma Komponenten die Aktivität neuronaler Netzwerke, die auf die Verarbeitung räumlicher auditorischer Information spezialisiert sind, widerspiegeln. Die Aktivierung dieser Netzwerke scheint zur Aufrechterhaltung aufgabenbezogener Information im Kurzzeitgedächtnis beizutragen

Modulation of Neural Oscillatory Activity during Dynamic Face Processing

Various neuroimaging and neurophysiological methods have been used to examine neural activation patterns in response to faces. However, much of previous research has relied on static images of faces, which do not allow a complete description of the temporal structure of face-specific neural activities to be made. More recently, insights are emerging from fMRI studies about the neural substrates that underpin our perception of naturalistic dynamic face stimuli, but the temporal and spectral oscillatory activity associated with processing dynamic faces has yet to be fully characterized. Here, we used MEG and beamformer source localization to examine the spatiotemporal profile of neurophysiological oscillatory activity in response to dynamic faces. Source analysis revealed a number of regions showing enhanced activation in response to dynamic relative to static faces in the distributed face network, which were spatially coincident with regions that were previously identified with fMRI. Furthermore, our results demonstrate that perception of realistic dynamic facial stimuli activates a distributed neural network at varying time points facilitated by modulations in low-frequency power within alpha and beta frequency ranges (8-30 Hz). Naturalistic dynamic face stimuli may provide a better means of representing the complex nature of perceiving facial expressions in the real world, and neural oscillatory activity can provide additional insights into the associated neural processes

It's about Time

The purpose of this review/opinion paper is to argue that human cognitive neuroscience has focused too little attention on how the brain may use time and time-based coding schemes to represent, process, and transfer information within and across brain regions. Instead, the majority of cognitive neuroscience studies rest on the assumption of functional localization. Although the functional localization approach has brought us a long way towards a basic characterization of brain functional organization, there are methodological and theoretical limitations of this approach. Further advances in our understanding of neurocognitive function may come from examining how the brain performs computations and forms transient functional neural networks using the rich multi-dimensional information available in time. This approach rests on the assumption that information is coded precisely in time but distributed in space; therefore, measures of rapid neuroelectrophysiological dynamics may provide insights into brain function that cannot be revealed using localization-based approaches and assumptions. Space is not an irrelevant dimension for brain organization; rather, a more complete understanding of how brain dynamics lead to behavior dynamics must incorporate how the brain uses time-based coding and processing schemes

Structural and effective connectivity reveals potential network-based influences on category-sensitive visual areas

Visual category perception is thought to depend on brain areas that respond specifically when certain categories are viewed. These category-sensitive areas are often assumed to be modules (with some degree of processing autonomy) and to act predominantly on feedforward visual input. This modular view can be complemented by a view that treats brain areas as elements within more complex networks and as influenced by network properties. This network-oriented viewpoint is emerging from studies using either diffusion tensor imaging to map structural connections or effective connectivity analyses to measure how their functional responses influence each other. This literature motivates several hypotheses that predict category-sensitive activity based on network properties. Large, long-range fiber bundles such as inferior fronto-occipital, arcuate and inferior longitudinal fasciculi are associated with behavioural recognition and could play crucial roles in conveying backward influences on visual cortex from anterior temporal and frontal areas. Such backward influences could support top-down functions such as visual search and emotion-based visual modulation. Within visual cortex itself, areas sensitive to different categories appear well-connected (e.g., face areas connect to object- and motion sensitive areas) and their responses can be predicted by backward modulation. Evidence supporting these propositions remains incomplete and underscores the need for better integration of DTI and functional imaging

The Functional Role of Neural Oscillations in Non-Verbal Emotional Communication

Effective interpersonal communication depends on the ability to perceive and interpret nonverbal emotional expressions from multiple sensory modalities. Current theoretical models propose that visual and auditory emotion perception involves a network of brain regions including the primary sensory cortices, the superior temporal sulcus (STS), and orbitofrontal cortex (OFC). However, relatively little is known about how the dynamic interplay between these regions gives rise to the perception of emotions. In recent years, there has been increasing recognition of the importance of neural oscillations in mediating neural communication within and between functional neural networks. Here we review studies investigating changes in oscillatory activity during the perception of visual, auditory, and audiovisual emotional expressions, and aim to characterize the functional role of neural oscillations in nonverbal emotion perception. Findings from the reviewed literature suggest that theta band oscillations most consistently differentiate between emotional and neutral expressions. While early theta synchronization appears to reflect the initial encoding of emotionally salient sensory information, later fronto-central theta synchronization may reflect the further integration of sensory information with internal representations. Additionally, gamma synchronization reflects facilitated sensory binding of emotional expressions within regions such as the OFC, STS, and, potentially, the amygdala. However, the evidence is more ambiguous when it comes to the role of oscillations within the alpha and beta frequencies, which vary as a function of modality (or modalities), presence or absence of predictive information, and attentional or task demands. Thus, the synchronization of neural oscillations within specific frequency bands mediates the rapid detection, integration, and evaluation of emotional expressions. Moreover, the functional coupling of oscillatory activity across multiples frequency bands supports a predictive coding model of multisensory emotion perception in which emotional facial and body expressions facilitate the processing of emotional vocalizations

A multimodal investigation of dynamic face perception using functional magnetic resonance imaging and magnetoencephalography

Motion is an important aspect of face perception that has been largely neglected to date. Many of the established findings are based on studies that use static facial images, which do not reflect the unique temporal dynamics available from seeing a moving face. In the present thesis a set of naturalistic dynamic facial emotional expressions was purposely created and used to investigate the neural structures involved in the perception of dynamic facial expressions of emotion, with both functional Magnetic Resonance Imaging (fMRI) and Magnetoencephalography (MEG). Through fMRI and connectivity analysis, a dynamic face perception network was identified, which is demonstrated to extend the distributed neural system for face perception (Haxby et al.,2000). Measures of effective connectivity between these regions revealed that dynamic facial stimuli were associated with specific increases in connectivity between early visual regions, such as inferior occipital gyri and superior temporal sulci, along with coupling between superior temporal sulci and amygdalae, as well as with inferior frontal gyri. MEG and Synthetic Aperture Magnetometry (SAM) were used to examine the spatiotemporal profile of neurophysiological activity within this dynamic face perception network. SAM analysis revealed a number of regions showing differential activation to dynamic versus static faces in the distributed face network, characterised by decreases in cortical oscillatory power in the beta band, which were spatially coincident with those regions that were previously identified with fMRI. These findings support the presence of a distributed network of cortical regions that mediate the perception of dynamic facial expressions, with the fMRI data providing information on the spatial co-ordinates paralleled by the MEG data, which indicate the temporal dynamics within this network. This integrated multimodal approach offers both excellent spatial and temporal resolution, thereby providing an opportunity to explore dynamic brain activity and connectivity during face processing.EThOS - Electronic Theses Online ServiceGBUnited Kingdo