Charmaine Demanuele

Sleep spindle deficits in antipsychotic-naïve early course schizophrenia and in non-psychotic first-degree relatives

Introduction: Chronic medicated patients with schizophrenia have marked reductions in sleep spindle activity and a correlated deficit in sleep-dependent memory consolidation. Using archival data, we investigated whether antipsychotic-naïve early course patients with schizophrenia and young non-psychotic first-degree relatives of patients with schizophrenia also show reduced sleep spindle activity and whether spindle activity correlates with cognitive function and symptoms. Method: Sleep spindles during Stage 2 sleep were compared in antipsychotic-naïve adults newly diagnosed with psychosis, young non-psychotic first-degree relatives of schizophrenia patients and two samples of healthy controls matched to the patients and relatives. The relations of spindle parameters with cognitive measures and symptom ratings were examined. Results: Early course schizophrenia patients showed significantly reduced spindle activity relative to healthy controls and to early course patients with other psychotic disorders. Relatives of schizophrenia patients also showed reduced spindle activity compared with controls. Reduced spindle activity correlated with measures of executive function in early course patients, positive symptoms in schizophrenia and IQ estimates across groups. Conclusions: Like chronic medicated schizophrenia patients, antipsychotic-naïve early course schizophrenia patients and young non-psychotic relatives of individuals with schizophrenia have reduced sleep spindle activity. These findings indicate that the spindle deficit is not an antipsychotic side-effect or a general feature of psychosis. Instead, the spindle deficit may predate the onset of schizophrenia, persist throughout its course and be an endophenotype that contributes to cognitive dysfunction.

Information flow between interacting human brains: Identification, validation, and relationship to social expertise

Significance Social interaction is the likely driver of human brain evolution, critical for health, and underlies phenomena as varied as childhood development, stock market behavior, and much of what is studied in the humanities. However, appropriate experimental methods to study the underlying brain processes are still developing and technically challenging. Here, we extend previous pioneering approaches in neuroimaging [functional MRI (fMRI) hyperscanning] to provide a method for studying information flow between interacting humans in a two-person approach. A scan environment enabling synchronized data acquisition and interaction-based fMRI tasks is described. We provide a generally applicable analysis method to identify interacting brain systems. Specific social brain systems are identified as drivers of interaction in humans, and we find a link to a measure of social expertise. Social interactions are fundamental for human behavior, but the quantification of their neural underpinnings remains challenging. Here, we used hyperscanning functional MRI (fMRI) to study information flow between brains of human dyads during real-time social interaction in a joint attention paradigm. In a hardware setup enabling immersive audiovisual interaction of subjects in linked fMRI scanners, we characterize cross-brain connectivity components that are unique to interacting individuals, identifying information flow between the sender’s and receiver’s temporoparietal junction. We replicate these findings in an independent sample and validate our methods by demonstrating that cross-brain connectivity relates to a key real-world measure of social behavior. Together, our findings support a central role of human-specific cortical areas in the brain dynamics of dyadic interactions and provide an approach for the noninvasive examination of the neural basis of healthy and disturbed human social behavior with minimal a priori assumptions.

Hippocampal–Dorsolateral Prefrontal Coupling as a Species-Conserved Cognitive Mechanism: A Human Translational Imaging Study

Hippocampal–prefrontal cortex (HC–PFC) interactions are implicated in working memory (WM) and altered in psychiatric conditions with cognitive impairment such as schizophrenia. While coupling between both structures is crucial for WM performance in rodents, evidence from human studies is conflicting and translation of findings is complicated by the use of differing paradigms across species. We therefore used functional magnetic resonance imaging together with a spatial WM paradigm adapted from rodent research to examine HC–PFC coupling in humans. A PFC–parietal network was functionally connected to hippocampus (HC) during task stages requiring high levels of executive control but not during a matched control condition. The magnitude of coupling in a network comprising HC, bilateral dorsolateral PFC (DLPFC), and right supramarginal gyrus explained one-fourth of the variability in an independent spatial WM task but was unrelated to visual WM performance. HC–DLPFC coupling may thus represent a systems-level mechanism specific to spatial WM that is conserved across species, suggesting its utility for modeling cognitive dysfunction in translational neuroscience.

Characterizing sleep spindles in 11,630 individuals from the National Sleep Research Resource

Sleep spindles are characteristic electroencephalogram (EEG) signatures of stage 2 non-rapid eye movement sleep. Implicated in sleep regulation and cognitive functioning, spindles may represent heritable biomarkers of neuropsychiatric disease. Here we characterize spindles in 11,630 individuals aged 4 to 97 years, as a prelude to future genetic studies. Spindle properties are highly reliable but exhibit distinct developmental trajectories. Across the night, we observe complex patterns of age- and frequency-dependent dynamics, including signatures of circadian modulation. We identify previously unappreciated correlates of spindle activity, including confounding by body mass index mediated by cardiac interference in the EEG. After taking account of these confounds, genetic factors significantly contribute to spindle and spectral sleep traits. Finally, we consider topographical differences and critical measurement issues. Taken together, our findings will lead to an increased understanding of the genetic architecture of sleep spindles and their relation to behavioural and health outcomes, including neuropsychiatric disorders.

Coordination of slow waves with sleep spindles predicts sleep-dependent memory consolidation in schizophrenia

Study Objectives Schizophrenia patients have correlated deficits in sleep spindle density and sleep-dependent memory consolidation. In addition to spindle density, memory consolidation is thought to rely on the precise temporal coordination of spindles with slow waves (SWs). We investigated whether this coordination is intact in schizophrenia and its relation to motor procedural memory consolidation. Methods Twenty-one chronic medicated schizophrenia patients and 17 demographically matched healthy controls underwent two nights of polysomnography, with training on the finger tapping motor sequence task (MST) on the second night and testing the following morning. We detected SWs (0.5-4 Hz) and spindles during non-rapid eye movement (NREM) sleep. We measured SW-spindle phase-amplitude coupling and its relation with overnight improvement in MST performance. Results Patients did not differ from controls in the timing of SW-spindle coupling. In both the groups, spindles peaked during the SW upstate. For patients alone, the later in the SW upstate that spindles peaked and the more reliable this phase relationship, the greater the overnight MST improvement. Regression models that included both spindle density and SW-spindle coordination predicted overnight improvement significantly better than either parameter alone, suggesting that both contribute to memory consolidation. Conclusion Schizophrenia patients show intact spindle-SW temporal coordination, and these timing relationships, together with spindle density, predict sleep-dependent memory consolidation. These relations were seen only in patients suggesting that their memory is more dependent on optimal spindle-SW timing, possibly due to reduced spindle density. Interventions to improve memory may need to increase spindle density while preserving or enhancing the coordination of NREM oscillations.

Area-Specific Information Processing in Prefrontal Cortex during a Probabilistic Inference Task: A Multivariate fMRI BOLD Time Series Analysis

Introduction Discriminating spatiotemporal stages of information processing involved in complex cognitive processes remains a challenge for neuroscience. This is especially so in prefrontal cortex whose subregions, such as the dorsolateral prefrontal (DLPFC), anterior cingulate (ACC) and orbitofrontal (OFC) cortices are known to have differentiable roles in cognition. Yet it is much less clear how these subregions contribute to different cognitive processes required by a given task. To investigate this, we use functional MRI data recorded from a group of healthy adults during a “Jumping to Conclusions” probabilistic reasoning task. Methods We used a novel approach combining multivariate test statistics with bootstrap-based procedures to discriminate between different task stages reflected in the fMRI blood oxygenation level dependent signal pattern and to unravel differences in task-related information encoded by these regions. Furthermore, we implemented a new feature extraction algorithm that selects voxels from any set of brain regions that are jointly maximally predictive about specific task stages. Results Using both the multivariate statistics approach and the algorithm that searches for maximally informative voxels we show that during the Jumping to Conclusions task, the DLPFC and ACC contribute more to the decision making phase comprising the accumulation of evidence and probabilistic reasoning, while the OFC is more involved in choice evaluation and uncertainty feedback. Moreover, we show that in presumably non-task-related regions (temporal cortices) all information there was about task processing could be extracted from just one voxel (indicating the unspecific nature of that information), while for prefrontal areas a wider multivariate pattern of activity was maximally informative. Conclusions/Significance We present a new approach to reveal the different roles of brain regions during the processing of one task from multivariate activity patterns measured by fMRI. This method can be a valuable tool to assess how area-specific processing is altered in psychiatric disorders such as schizophrenia, and in healthy subjects carrying different genetic polymorphisms.

Hippocampal-Dorsolateral Prefrontal Coupling as a Species-Conserved Cognitive Mechanism: A Human Translational Imaging Study.

Abbreviations: CIU/CSU, chronic idiopathic/spontaneous urticaria; BHR, basophil histamine release; fMLP-N, formylmethionine-leucyl-phenylalanine

A statistical approach for segregating cognitive task stages from multivariate fMRI BOLD time series

Multivariate pattern analysis can reveal new information from neuroimaging data to illuminate human cognition and its disturbances. Here, we develop a methodological approach, based on multivariate statistical/machine learning and time series analysis, to discern cognitive processing stages from functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) time series. We apply this method to data recorded from a group of healthy adults whilst performing a virtual reality version of the delayed win-shift radial arm maze (RAM) task. This task has been frequently used to study working memory and decision making in rodents. Using linear classifiers and multivariate test statistics in conjunction with time series bootstraps, we show that different cognitive stages of the task, as defined by the experimenter, namely, the encoding/retrieval, choice, reward and delay stages, can be statistically discriminated from the BOLD time series in brain areas relevant for decision making and working memory. Discrimination of these task stages was significantly reduced during poor behavioral performance in dorsolateral prefrontal cortex (DLPFC), but not in the primary visual cortex (V1). Experimenter-defined dissection of time series into class labels based on task structure was confirmed by an unsupervised, bottom-up approach based on Hidden Markov Models. Furthermore, we show that different groupings of recorded time points into cognitive event classes can be used to test hypotheses about the specific cognitive role of a given brain region during task execution. We found that whilst the DLPFC strongly differentiated between task stages associated with different memory loads, but not between different visual-spatial aspects, the reverse was true for V1. Our methodology illustrates how different aspects of cognitive information processing during one and the same task can be separated and attributed to specific brain regions based on information contained in multivariate patterns of voxel activity.