Spatiotemporal precision of neuroimaging in psychiatry

Aberrant patterns of cognition, perception, and behaviour seen in psychiatric disorders are thought to be driven by a complex interplay of neural processes that evolve at a rapid temporal scale. Understanding these dynamic processes in vivo in humans has been hampered by a trade-off between the spatial and temporal resolution inherent to current neuroimaging technology. A recent trend in psychiatric research has been the use of high temporal resolution imaging, particularly magnetoencephalography (MEG), often in conjunction with sophisticated machine learning decoding techniques. Developments here promise novel insights into the spatiotemporal dynamics of cognitive phenomena, including domains relevant to psychiatric illness such as reward and avoidance learning, memory, and planning. This review considers recent advances afforded by exploiting this increased spatiotemporal precision, with specific reference to applications the seek to drive a mechanistic understanding of psychopathology and the realisation of preclinical translation

Building Mental Experiences: From Scenes to Events

Mental events are central to everyday cognition, be it our continuous perception of the world, recalling autobiographical memories, or imagining the future. Little is known about the fine-grained temporal dynamics of these processes. Given the apparent predominance of scene imagery across cognition, in this thesis I used magnetoencephalography to investigate whether and how activity in the hippocampus and ventromedial prefrontal cortex (vmPFC) supports the mental construction of scenes and the events to which they give rise. In the first experiment, participants gradually imagined scenes and also closely matched non-scene arrays; this allowed me to assess whether any brain regions showed preferential responses to scene imagery. The anterior hippocampus and vmPFC were particularly engaged by the construction of scene imagery, with the vmPFC driving hippocampal activity. In the second experiment, I found that certain objects – those that were space-defining – preferentially engaged the vmPFC and superior temporal gyrus during scene construction, providing insight into how objects affect the creation of scene representations. The third experiment involved boundary extension during scene perception, permitting me to examine how single scenes might be prepared for inclusion into events. I observed changes in evoked responses just 12.5-58 ms after scene onset over fronto-temporal sensors, with again the vmPFC exerting a driving influence on other brain regions, including the hippocampus. In the final experiment, participants watched brief movies of events built from a series of scenes or non-scene patterns. A difference in evoked responses between the two event types emerged during the first frame of the movies, the primary source of which was shown to be the hippocampus. The enduring theme of the results across experiments was scene-specific engagement of the hippocampus and vmPFC, with the latter being the driving influence. Overall, this thesis provides insights into the neural dynamics of how scenes are built, made ready for inclusion into unfolding mental episodes, and then linked to produce our seamless experience of the world

Mapping the Spatiotemporal Evolution of Emotional Processing: An MEG Study Across Arousal and Valence Dimensions

Electrophysiological and functional neuroimaging findings indicate that the neural mechanisms underlying the processing of emotional dimensions (i.e., valence, arousal) constitute a spatially and temporally distributed emotional network, modulated by the arousal and/or valence of the emotional stimuli. We examined the time course and source distribution of gamma time-locked magnetoencephalographic activity in response to a series of emotional stimuli viewed by healthy adults. We used a beamformer and a sliding window analysis to generate a succession of spatial maps of event-related brain responses across distinct levels of valence (pleasant/unpleasant) and arousal (high/low) in 30–100 Hz. Our results show parallel emotion-related responses along specific temporal windows involving mainly dissociable neural pathways for valence and arousal during emotional picture processing. Pleasant valence was localized in the left inferior frontal gyrus, while unpleasant valence in the right occipital gyrus, the precuneus, and the left caudate nucleus. High arousal was processed by the left orbitofrontal cortex, amygdala, and inferior frontal gyrus, as well as the right middle temporal gyrus, inferior parietal lobule, and occipital gyrus. Pleasant by high arousal interaction was localized in the left inferior and superior frontal gyrus, as well as the right caudate nucleus, putamen, and gyrus rectus. Unpleasant by high arousal interaction was processed by the right superior parietal gyrus. Valence was prioritized (onset at ∼60 ms) to all other effects, while pleasant valence was short lived in comparison to unpleasant valence (offsets at ∼110 and ∼320 ms, respectively). Both arousal and valence × arousal interactions emerged relatively early (onset at ∼150 ms, and ∼170 ms, respectively). Our findings support the notion that brain regions differentiate between valence and arousal, and demonstrate, for the first time, that these brain regions may also respond to distinct combinations of these two dimensions within specific time windows

Direct brain recordings reveal continuous encoding of structure in random stimuli

The brain excels at processing sensory input, even in rich or chaotic environments. Mounting evidence attributes this to the creation of sophisticated internal models of the environment that draw on statistical structures in the unfolding sensory input. Understanding how and where this modeling takes place is a core question in statistical learning and predictive processing. In this context, we address the role of transitional probabilities as an implicit structure supporting the encoding of a random auditory stream. Leveraging information-theoretical principles and the high spatiotemporal resolution of intracranial electroencephalography, we analyzed the trial-by-trial high-frequency activity representation of transitional probabilities. This unique approach enabled us to demonstrate how the brain continuously encodes structure in random stimuli and revealed the involvement of a network outside of the auditory system, including hippocampal, frontal, and temporal regions. Linking the frame-works of statistical learning and predictive processing, our work illuminates an implicit process that can be crucial for the swift detection of patterns and unexpected events in the environment.Fil: Fuhrer, Julian. University of Oslo; NoruegaFil: Kyrre, Glette. University of Oslo; NoruegaFil: Ivanovic, Jugoslav. University of Oslo; NoruegaFil: Gunnar Larsson, Pål. University of Oslo; NoruegaFil: Bekinschtein, Tristán Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. University of Cambridge; Reino UnidoFil: Kochen, Sara Silvia. Universidad Nacional Arturo Jauretche. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos; ArgentinaFil: Knight, Robert T.. University of California at Berkeley; Estados UnidosFil: Tørresen, Jim. University of Oslo; NoruegaFil: Solbakk, Anne Kristin. University of Oslo; Noruega. Helgeland Hospital; NoruegaFil: Endestad, Tor. University of Oslo; Noruega. Helgeland Hospital; NoruegaFil: Blenkmann, Alejandro Omar. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. University of Oslo; Norueg

Episodic Memory, Theta-Activity and Schizophrenia

People with schizophrenia are known to have difficulties with episodic memory (EM). The purpose of this investigation was to examine the relationship between theta-power and: i) behavioural measures of EM performance, ii) event- related potential (ERP) indices of recollection and, iii) measures of schizophrenia symptomatology. In doing so, the aim was to gain a better understanding of the basic neural mechanisms that contribute to successful EM performance, and how these may differ for people with schizophrenia. The present investigation adopted an endophenotypic approach and collected measures of schizotypy from student participants to minimise patient factors that can confound interpretations. Fifty- four participants were asked to complete a reality-monitoring exclusion EM paradigm whilst electroencephalogram (EEG) data were collected. Measures of theta-power and ERPs were time-locked to words presented during the retrieval phase. There was a significant positive correlation between theta-power over Fz between 600-1000ms post-stimulus presentation and estimates of recollection in the imagine condition as well as a significant negative correlation between these measures of theta-power for perceive items and ERP indices of recollection for imagine items. There was also a significant positive correlation between measures of frontal theta-power in the imagine condition and negative schizotypy. The epoch employed means it is likely these measures of theta- power reflect processes contributing to the content-specific retrieval of imagined items, and post-retrieval processes acting in service of differentiating imagined items in EM. Results are discussed in terms of suggestions for interventions and directions for future research

The integration of bottom-up and top-down signals in human perception in health and disease

To extract a meaningful visual experience from the information falling on the retina, the visual system must integrate signals from multiple levels. Bottom-up signals provide input relating to local features while top-down signals provide contextual feedback and reflect internal states of the organism. In this thesis I will explore the nature and neural basis of this integration in two key areas. I will examine perceptual filling-in of artificial scotomas to investigate the bottom-up signals causing changes in perception when filling-in takes place. I will then examine how this perceptual filling-in is modified by top-down signals reflecting attention and working memory. I will also investigate hemianopic completion, an unusual form of filling-in, which may reflect a breakdown in top-down feedback from higher visual areas. The second part of the thesis will explore a different form of top-down control of visual processing. While the effects of cognitive mechanisms such as attention on visual processing are well-characterised, other types of top-down signal such as reward outcome are less well explored. I will therefore study whether signals relating to reward can influence visual processing. To address these questions, I will employ a range of methodologies including functional MRI, magnetoencephalography and behavioural testing in healthy participants and patients with cortical damage. I will demonstrate that perceptual filling-in of artificial scotomas is largely a bottom-up process but that higher cognitive functions can modulate the phenomenon. I will also show that reward modulates activity in higher visual areas in the absence of concurrent visual stimulation and that receiving reward leads to enhanced activity in primary visual cortex on the next trial. These findings reveal that integration occurs across multiple levels even for processes rooted in early retinotopic regions, and that higher cognitive processes such as reward can influence the earliest stages of cortical visual processing

Music in the brain

Music is ubiquitous across human cultures — as a source of affective and pleasurable experience, moving us both physically and emotionally — and learning to play music shapes both brain structure and brain function. Music processing in the brain — namely, the perception of melody, harmony and rhythm — has traditionally been studied as an auditory phenomenon using passive listening paradigms. However, when listening to music, we actively generate predictions about what is likely to happen next. This enactive aspect has led to a more comprehensive understanding of music processing involving brain structures implicated in action, emotion and learning. Here we review the cognitive neuroscience literature of music perception. We show that music perception, action, emotion and learning all rest on the human brain’s fundamental capacity for prediction — as formulated by the predictive coding of music model. This Review elucidates how this formulation of music perception and expertise in individuals can be extended to account for the dynamics and underlying brain mechanisms of collective music making. This in turn has important implications for human creativity as evinced by music improvisation. These recent advances shed new light on what makes music meaningful from a neuroscientific perspective