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Objective: Although extensive insights about the neural mechanisms of reading have been gained via magnetic and electrographic imaging, the temporal evolution of the brain network during sight reading remains unclear. We tested whether the temporal dynamics of the brain functional connectivity involved in sight reading can be tracked using high-density scalp EEG recordings. Approach: Twenty-eight healthy subjects were asked to read words in a rapid serial visual presentation task while recording scalp EEG, and phase locking value was used to estimate the functional connectivity between EEG channels in the theta, alpha, beta, and gamma frequency bands. The resultant networks were then tracked through time. Main results: The network's graph density gradually increases as the task unfolds, peaks 150-250-ms after the appearance of each word, and returns to resting-state values, while the shortest path length between non-adjacent functional areas decreases as the density increases, thus indicating that a progressive integration between regions can be detected at the scalp level. This pattern was independent of the word's type or position in the sentence, occurred in the theta/alpha band but not in beta/gamma band, and peaked earlier in the alpha band compared to the theta band (alpha: 184 +/- 61.48-ms; theta: 237 +/- 65.32-ms, P-value P < 0.01). Nodes in occipital and frontal regions had the highest eigenvector centrality throughout the word's presentation, and no significant lead-lag relationship between frontal/occipital regions and parietal/temporal regions was found, which indicates a consistent pattern in information flow. In the source space, this pattern was driven by a cluster of nodes linked to sensorimotor processing, memory, and semantic integration, with the most central regions being similar across subjects. Significance: These findings indicate that the brain network connectivity can be tracked via scalp EEG as reading unfolds, and EEG-retrieved networks follow highly repetitive patterns lateralized to frontal/occipital areas during reading. 

Abstract Objective.To develop a coil placement optimization pipeline for transcranial magnetic stimulation (TMS) that improves over existing solutions by guaranteeing the feasibility of the solution when double-cone coils are used and/or targets are placed over nonconvex scalp areas like the occipital region.Approach.Our proposed pipeline estimates feasible candidate coil locations by projecting the coil’s geometry over the scalp around the target site and optimizing the coil’s orientation to maximize scalp exposure to coil while avoiding coil-scalp collision. Then, the reciprocity principle is used to select the best position/orientation among candidates and maximize the average electric field (E-field) intensity at the target site. Our pipeline was tested on five magnetic resonance imaging-derived human head models for three different targets (motor cortex, lateral cerebellum, and cerebellar inion) and four coil models (planar coil: MagStim D70; double-cone coils: MagStim DCC, MagVenture Cool-D-B80, and Deymed 120BFV).Main results.Our pipeline returned several feasible solutions for any combination of anatomical target and coil, calculated and screened over 2000 candidates in minutes, and resulted in optimal locations that satisfy the minimum coil-scalp distance, whereas the direct method returned feasible candidates for just one combination of target and coil, i.e. planar coil and convex target over the motor cortex. We also found that, when the objective is to maximize the E-field magnitude, the target-to-scalp extension line is a better axis for coil translation compared to the normal vector at the scalp’s surface, which is commonly used in existing approaches.Significance.We expand the use of numerical optimization for coil placement to double-cone coils, which are rapidly diffusing in research and clinical settings, and novel application domains, e.g. cerebellar TMS and ataxia treatment. 

Abstract Objective.Cerebellar transcranial magnetic stimulation (TMS) has been proposed to suppress limb tremors in essential tremor (ET), but mixed results have been reported so far, both when pulses are applied repetitively TMS (rTMS) and in bursts. We aim to investigate the cellular effects of TMS on the cerebellum under ET through numerical simulations.Approach.A computational model of the olivo-cerebello-thalamocortical pathways exhibiting the main neural biomarkers of ET (i.e. circuit-wide tremor-locked neural oscillations) was expanded to incorporate the effects of TMS-induced electric field (E-field) on Purkinje cells. TMS pulse amplitude, frequency, and temporal pattern were varied, and the resultant effects on ET biomarkers were assessed. Four levels of cellular response to TMS were considered, ranging from low to high cell recruitment underneath the coil, and three stimulation patterns were tested, i.e. rTMS, irregular TMS (ir-TMS, pulses were arranged according to Sobol sequences with average frequency matching rTMS), and phase-locked TMS (PL-TMS).Main results.rTMS can suppress ET oscillations, but its efficacy depends on tremor frequency and recruitment level, with these factors shaping a narrow range of effective settings. The ratio between tremor and rTMS frequencies also affects the neural response and further narrows the span of viable settings, while ir-TMS is ineffective. PL-TMS is highly effective and robust against changes to cell recruitment level and tremor frequency. Across all scenarios, PL-TMS provides a rapid (i.e. within seconds) suppression of tremor oscillations and, when both PL-TMS and rTMS are effective, the time to tremor suppression decreases by 50% or more in PL-TMS versus rTMS. At the cellular level, PL-TMS operates by disrupting the synchronization along the olivo-cerebellar loop, and the preferred phases map onto the mid-region of the silent period between complex spikes of the Purkinje cells.Significance.Cerebellar PL-TMS can provide robust suppression of ET oscillations while operating within safety boundaries. 

Background: Coil placement on the cerebellum lacks accuracy in targeting the intended lobules and limits the efficacy of cerebellar transcranial magnetic stimulation (TMS) in treating movement disorders. Objective: Develop a multiscale computational pipeline and method to rapidly predict the cellular response to cerebellar TMS and optimize the coil placement accordingly for lobule-specific activation. Methods: The pipeline integrates 3T T1/T2-weighted MRI scans of the human cerebellum, lobule parcellation, and finite element models of the TMS-induced electric (E-) fields for figure-of-eight coils (MagStim D70) and double-cone coils (Deymed 120BFV). A constrained optimization method is developed to estimate the fiber bundles from cerebellar cortices to deep nuclei and, for both coil types, find the coil placement and orientation that maximize the E-field intensity in a user-selected lobule. Multicompartmental Purkinje cell models with realistic axon geometries and Gaussian process regression are added to predict the recruitment in the Purkinje layer. Results: Our pipeline was tested in five individuals to target the left lobule VIII and resulted in normalized E-field intensities at the target 49.6±25.6% (D70) and 29.3±17.7% (120BFV) higher compared to standard coil positions (i.e., 3 cm left, 1 cm below the inion), mean±S.D. The minimum pulse intensity to recruit Purkinje cells on a 4 mm2-surface in the target decreased by 21.6% (range: 4.7-55.0%) and 10.7% (range: 7.9-18.2%), and the spillover to adjacent lobules decreased by 70.6±16.3% and 71.7±20.8% compared to standard positions (D70 and 120BFV, respectively). Conclusion: Our tools are effective at targeting specific lobules and pave the way toward patient-specific setups. 

Objective: Although extensive insights about the neural mechanisms of reading have been gained via magnetic and electrographic imaging, the temporal evolution of the brain network during sight reading remains unclear. We tested whether the temporal dynamics of the brain functional connectivity involved in sight reading can be tracked using high-density scalp EEG recordings. Approach: Twenty-eight healthy subjects were asked to read words in rapid serial visual presentation task while recording scalp EEG, and phase locking value was used to estimate the functional connectivity between EEG channels in the theta, alpha, beta, and gamma frequency bands. The resultant networks were then tracked through time. Main results: The network's graph density gradually increases as the task unfolds, peaks 150-250-ms after the appearance of each word, and returns to resting-state values, while the shortest path length between non-adjacent functional areas decreases as the density increases, thus indicating that a progressive integration between regions can be detected at the scalp level. This pattern was independent of the word's type or position in the sentence, occurred in the theta/alpha band but not in beta/gamma range, and peaked earlier in the alpha band compared to the theta band (alpha: 184 ± 61.48-ms; theta: 237 ± 65.32-ms, P-value P<0.01). Nodes in occipital and frontal regions had the highest eigenvector centrality throughout the word's presentation, and no significant lead-lag relationship between frontal/occipital regions and parietal/temporal regions was found, which indicates a consistent pattern in information flow. In the source space, this pattern was driven by a cluster of nodes linked to sensorimotor processing, memory, and semantic integration, with the most central regions being similar across subjects. Significance: These findings indicate that the brain network connectivity can be tracked via scalp EEG as reading unfolds, and EEG-retrieved networks follow highly repetitive patterns lateralized to frontal/occipital areas during reading. 

Abstract Nowadays, multidimensional data are often available from educational testing. One natural issue is to identify whether more dimensional data are useful in fitting the item response data. To address this important issue, we develop a new decomposition of Widely Applicable Information Criterion (WAIC) via the posterior predictive ordinate (PPO) under the joint model for the response, response time and two additional educational testing scores. Based on this decomposition, a new model assessment criterion is then proposed, which allows us to determine which of the response time and two additional scores are most useful in fitting the response data and whether other dimensional data are further needed given that one of these dimensional data is already included in the joint model with the response data. In addition, an efficient Monte Carlo method is developed to compute PPO. An extensive simulation study is conducted to examine the empirical performance of the proposed joint model and the model assessment criterion in the psychological setting. The proposed methodology is further applied to an analysis of a real dataset from a computerized educational assessment program. 

Computerized assessment provides rich multidimensional data including trial-by-trial accuracy and response time (RT) measures. A key question in modeling this type of data is how to incorporate RT data, for example, in aid of ability estimation in item response theory (IRT) models. To address this, we propose a joint model consisting of a two-parameter IRT model for the dichotomous item response data, a log-normal model for the continuous RT data, and a normal model for corresponding paper-and-pencil scores. Then, we reformulate and reparameterize the model to capture the relationship between the model parameters, to facilitate the prior specification, and to make the Bayesian computation more efficient. Further, we propose several new model assessment criteria based on the decomposition of deviance information criterion (DIC) the logarithm of the pseudo-marginal likelihood (LPML). The proposed criteria can quantify the improvement in the fit of one part of the multidimensional data given the other parts. Finally, we have conducted several simulation studies to examine the empirical performance of the proposed model assessment criteria and have illustrated the application of these criteria using a real dataset from a computerized educational assessment program. 

Transcranial direct current stimulation (tDCS) of the cerebellum has rapidly raised interest but the effects of tDCS on cerebellar neurons remain unclear. Assessing the cellular response to tDCS is challenging because of the uneven, highly stratified cytoarchitecture of the cerebellum, within which cellular morphologies, physiological properties, and function vary largely across several types of neurons. In this study, we combine MRI-based segmentation of the cerebellum and a finite element model of the tDCS-induced electric field (EF) inside the cerebellum to determine the field imposed on the cerebellar neurons throughout the region. We then pair the EF with multicompartment models of the Purkinje cell (PC), deep cerebellar neuron (DCN), and granule cell (GrC) and quantify the acute response of these neurons under various orientations, physiological conditions, and sequences of presynaptic stimuli. We show that cerebellar tDCS significantly modulates the postsynaptic spiking precision of the PC, which is expressed as a change in the spike count and timing in response to presynaptic stimuli. tDCS has modest effects, instead, on the PC tonic firing at rest and on the postsynaptic activity of DCN and GrC. In Purkinje cells, anodal tDCS shortens the repolarization phase following complex spikes (-14.7 ± 6.5% of baseline value, mean ± S.D.; max: -22.7%) and promotes burstiness with longer bursts compared to resting conditions. Cathodal tDCS, instead, promotes irregular spiking by enhancing somatic excitability and significantly prolongs the repolarization after complex spikes compared to baseline (+37.0 ± 28.9%, mean ± S.D.; max: +84.3%). tDCS-induced changes to the repolarization phase and firing pattern exceed 10% of the baseline values in Purkinje cells covering up to 20% of the cerebellar cortex, with the effects being distributed along the EF direction and concentrated in the area under the electrode over the cerebellum. Altogether, the acute effects of tDCS on cerebellum mainly focus on Purkinje cells and modulate the precision of the response to synaptic stimuli, thus having the largest impact when the cerebellar cortex is active. Since the spatiotemporal precision of the PC spiking is critical to learning and coordination, our results suggest cerebellar tDCS as a viable therapeutic option for disorders involving cerebellar hyperactivity such as ataxia.