Biology subjects

Rosen, B. Q.

Publications and source records attributed to Rosen, B. Q..

3 recordsLinked to original sources

bioRxiv2017

Simulating human sleep spindle MEG and EEG from ion channel and circuit level dynamics

BackgroundAlthough they form a unitary phenomenon, the relationship between extracranial M/EEG and transmembrane ion flows is understood only as a general principle rather than as a well-articulated and quantified causal chain.\n\nMethodWe present an integrated multiscale model, consisting of a neural simulation of thalamus and cortex during stage N2 sleep and a biophysical model projecting cortical current densities to M/EEG fields. Sleep spindles were generated through the interactions of local and distant network connections and intrinsic currents within thalamocortical circuits. 32,652 cortical neurons were mapped onto the cortical surface reconstructed from subjects MRI, interconnected based on geodesic distances, and scaled-up to current dipole densities based on laminar recordings in humans. MRIs were used to generate a quasi-static electromagnetic model enabling simulated cortical activity to be projected to the M/EEG sensors.\n\nResultsThe simulated M/EEG spindles were similar in amplitude and topography to empirical examples in the same subjects. Simulated spindles with more core-dominant activity were more MEG weighted.\n\nComparison with Existing MethodsPrevious models lacked either spindle-generating thalamic neural dynamics or whole head biophysical modeling; the framework presented here is the first to simultaneously capture these disparate scales simultaneously.\n\nConclusionsThis multiscale model provides a platform for the principled quantitative integration of existing information relevant to the generation of sleep spindles, and allows the implications of future findings to be explored. It provides a proof of principle for a methodological framework allowing large-scale integrative brain oscillations to be understood in terms of their underlying channels and synapses.

neuroscience↗

bioRxiv2017

Thalamocortical and Intracortical Laminar Connectivity Determines Sleep Spindle Properties

Spindle oscillations are brief oscillatory activity during non-rapid eye movement (NREM) sleep. Spindle density and synchronization properties are different in MEG versus EEG recordings in humans and also vary with learning performance, suggesting spindle involvement in memory consolidation. Using computational models, we identified network mechanisms that may explain differences in spindle properties across cortical structures. First, we report that differences in spindle occurrence between MEG and EEG data may arise from the properties of the core vs. matrix thalamocortical systems. The matrix system, projecting superficially, has wider thalamocortical fanout compared to the core system, projecting to the middle layers, and requires the recruitment of a larger population of neurons to initiate a spindle. Our model demonstrates that this property is sufficient to explain lower spindle density and higher spatial synchrony of spindles in the superficial cortical layers, as observed in the EEG signal. In contrast, spindles in the core system occurred more frequently but less synchronously, as observed in the MEG recordings. Futhermore, consistent with human recordings, in the model, spindles occurred independently in the core system but matrix system spindles commonly co-occurred with core one. We found that the intracortical excitatory connections from layer III/IV to layer V promote spindle propagation from the core to the matrix system, leading to widespread spindle activity. Our study predicts that plasticity of the intra and inter cortical connectivity can potentially be a mechanism for increasing in spindle density as observed during learning.\n\nAuthor summaryThe density of sleep spindles has been shown to correlate with memory consolidation. Further, sleep spindles occur more often in human MEG than EEG. We developed thalamocortical network model that is capable of spontaneous generation of spindles across cortical layers and that captures the essential statistical features of spindles observed in experiments. We predict that differences in thalamo-cortical connectivity, known from anatomical studies, lead to the differences in the spindle properties between EEG and MEG as observed in human recordings. Further, we predict that the intracortical connectivity between cortical layers, a property influenced by sleep preceding learning, increases spindle density. Results from our study highlight the role of cortical and thalamic projections on the occurrence and properties of spindles.

neuroscience↗

bioRxiv2017

1/f neural noise is a better predictor of schizophrenia than neural oscillations

Diagnosis and symptom severity in schizophrenia are associated with irregularities across neural oscillatory frequency bands, including theta, alpha, beta, and gamma. However, electroencephalographic signals consist of both periodic and aperiodic activity characterized by the (1/fX) shape in the power spectrum. In this paper we investigated oscillatory and aperiodic activity differences between patients with schizophrenia and healthy controls during a target detection task. Separation into periodic and aperiodic components revealed that the steepness of the power spectrum better predicted group status than traditional band-limited oscillatory power in a classification analysis. Aperiodic activity also outperformed the predictions made using participants behavioral responses. Additionally, the differences in aperiodic activity were highly consistent across all electrodes. In sum, compared to oscillations the aperiodic activity appears to be a more accurate and more robust way to differentiate patients with schizophrenia from healthy controls. Significance statementUnderstanding the neurobiological origins of schizophrenia and identifying reliable and consistent biomarkers are of critical importance to improving treatment of that disease. Numerous studies have reported disruptions to neural oscillations in patients with schizophrenia. This has, in part, led to schizophrenia being characterized as a disease of disrupted neural coordination, reflected by changes in frequency band power. We report however that changes in the aperiodic signal can also predict clinical status. Unlike band-limited power though, aperiodic activity predicts status better than participants own behavioral performance and acts as a consistent predictor across all electrodes. Alterations in the aperiodic signal are consistent with well-established inhibitory neuron dysfunctions associated with schizophrenia, allowing for a direct link between noninvasive EEG and chronic, widespread, neurobiological deficits.

neuroscience↗