Mckinneyirwin4994

Dynamic causal modeling (DCM) is a widely used tool to estimate the effective connectivity of specified models of a brain network. Finding the model explaining measured data is one of the most important outstanding problems in Bayesian modeling. Using heuristic model search algorithms enables us to find an optimal model without having to define a model set a priori. However, the development of such methods is cumbersome in the case of large model-spaces. We aimed to utilize commonly used graph theoretical search algorithms for DCM to create a framework for characterizing them, and to investigate relevance of such methods for single-subject and group-level studies. Because of the enormous computational demand of DCM calculations, we separated the model estimation procedure from the search algorithm by providing a database containing the parameters of all models in a full model-space. For test data a publicly available fMRI dataset of 60 subjects was used. First, we reimplemented the deterministic bilinear DCM still considered the recommended approach. We envision the freely available database of estimated model-spaces to help further studies of the DCM model-space, and the ReDCM package to be a useful contribution for Bayesian inference within and beyond the field of neuroscience.Due to the point-like nature of neuronal spiking, efficient neural network simulators often employ event-based simulation schemes for synapses. Yet many types of synaptic plasticity rely on the membrane potential of the postsynaptic cell as a third factor in addition to pre- and postsynaptic spike times. In some learning rules membrane potentials not only influence synaptic weight changes at the time points of spike events but in a continuous manner. In these cases, synapses therefore require information on the full time course of membrane potentials to update their strength which a priori suggests a continuous update in a time-driven manner. The latter hinders scaling of simulations to realistic cortical network sizes and relevant time scales for learning. Here, we derive two efficient algorithms for archiving postsynaptic membrane potentials, both compatible with modern simulation engines based on event-based synapse updates. We theoretically contrast the two algorithms with a time-driven synapse update scheme to analyze advantages in terms of memory and computations. We further present a reference implementation in the spiking neural network simulator NEST for two prototypical voltage-based plasticity rules the Clopath rule and the Urbanczik-Senn rule. For both rules, the two event-based algorithms significantly outperform the time-driven scheme. Depending on the amount of data to be stored for plasticity, which heavily differs between the rules, a strong performance increase can be achieved by compressing or sampling of information on membrane potentials. Our results on computational efficiency related to archiving of information provide guidelines for the design of learning rules in order to make them practically usable in large-scale networks.The Approximate Number System (ANS) allows humans and non-human animals to estimate large quantities without counting. It is most commonly studied in visual contexts (i.e., with displays containing different numbers of dots), although the ANS may operate on all approximate quantities regardless of modality (e.g., estimating the number of a series of auditory tones). Previous research has shown that there is a link between ANS and mathematics abilities, and that this link is resilient to differences in visual experience (Kanjlia et al., 2018). However, little is known about the function of the ANS and its relationship to mathematics abilities in the absence of other types of sensory input. Here, we investigated the acuity of the ANS and its relationship with mathematics abilities in a group of students from the Sichuan Province in China, half of whom were deaf. We found, consistent with previous research, that ANS acuity improves with age. We found that mathematics ability was predicted by Non-verbal IQ and Inhibitory Control, but not visual working memory capacity or Attention Network efficiencies. Even above and beyond these predictors, ANS ability still accounted for unique variance in mathematics ability. Notably, there was no interaction with hearing, which indicates that the role played by the ANS in explaining mathematics competence is not modulated by hearing capacity. Finally, we found that age, Non-verbal IQ and Visual Working Memory capacity were predictive of ANS performance when controlling for other factors. In fact, although students with hearing loss performed slightly worse than students with normal hearing on the ANS task, hearing was no longer significantly predictive of ANS performance once other factors were taken into account. These results indicate that the ANS is able to develop at a consistent pace with other cognitive abilities in the absence of auditory experience, and that its relationship with mathematics ability is not contingent on sensory input from hearing.

As a complication-prone operation, deep brain stimulation (DBS) has become the first-line surgical approach for patients with advanced Parkinson's disease (PD). This study aimed to evaluate the incidence and risk factors of DBS-associated complications.

We have reviewed a consecutive series of patients with PD undergoing DBS procedures to describe the type, severity, management, and outcome of postoperative complications from January 2011 to December 2018. Both univariate and multivariate analyses were performed to identify statistically significant risk factors. We also described our surgical strategies to minimize the adverse events.

A total of 225 patients underwent 229 DBS implantation procedures (440 electrodes), of whom 20 patients experienced 23 DBS-associated complications, including ten operation-related complications and 13 hardware-related ones. PFK158 chemical structure Univariate analysis elucidated that comorbid medical conditions (

= 0.024), hypertension (

= 0.003), early-stage operation (

< 0.001), and uunplanned readmission.

Dystonic opisthotonus is defined as a backward arching of the neck and trunk, which ranges in severity from mild backward jerks to life-threatening prolonged severe muscular spasms. It can be associated with generalized dystonic syndromes or, rarely, present as a form of axial truncal dystonia. The etiologies vary from idiopathic, genetic, tardive, hereditary-degenerative, or associated with parkinsonism. We report clinical cases of dystonic opisthotonus associated with adult-onset dystonic syndromes, that benefitted from globus pallidus internus (GPi) deep brain stimulation (DBS).

Clinical data from patients with dystonic syndromes who underwent comprehensive medical review, multidisciplinary assessment, and tailored medical and neurosurgical managements were prospectively analyzed. Quantification of dystonia severity pre- and postoperatively was performed using the Burke-Fahn-Marsden Dystonia Rating Scale and quantification of overall pain severity was performed using the Visual Analog Scale.

Three maed and tailored provided symptomatic control in this cohort and may be considered in other carefully selected cases.

We quantitatively analyzed high-frequency oscillations (HFOs) using scalp electroencephalography (EEG) in patients with infantile spasms (IS).

We enrolled 60 children with IS hospitalized from January 2019 to August 2020. Sixty healthy age-matched children comprised the control group. Time-frequency analysis was used to quantify γ, ripple, and fast ripple (FR) oscillation energy changes.

γ, ripple, and FR oscillations dominated in the temporal and frontal lobes. The average HFO energy of the sleep stage is lower than that of the wake stage in the same frequency bands in both the normal control (NC) and IS groups (

< 0.05). The average HFO energy of the IS group was significantly higher than that of the NC group in γ band during sleep stage (

< 0.01). The average HFO energy of S and Post-S stage were higher than that of sleep stage in γ band (

< 0.05). In the ripple band, the average HFO energy of Pre-S, S, and Post-S stage was higher than that of sleep stage (

< 0.05). Before treatmey than frequency. On scalp EEG, γ oscillations can better detect susceptibility to epilepsy than ripple and FR oscillations. HFOs can trigger spasms. The analysis of average HFO energy can be used as a predictor of the effectiveness of epilepsy treatment.Introduction The present study was conducted to verify a promising experimental setup which demonstrated an inversed Stroop-effect (much faster responses for incongruent relative to congruent Stroop trials) following a mismatching tone. In the matching condition, which was an almost exact replication of the original study, participants were required to indicate whether word color and word meaning were matching, whereas in the response conflict condition, instruction was the same as in a classical Stroop task and required the participants to respond to the word color. As in the original study, each trial was preceded by a sine tone which was deviant in pitch in 20% of the trials. Results The main result was that the Stroop effect was not inversed after deviant tones, neither under the matching task instruction nor under the response conflict task instruction. The Stroop effect was unaffected by the previous "conceptual mismatch." Conclusion The current study failed to replicate the astonishing concept of "conflict priming" reported in previous work and does not open the doors for a new window on sequences of conflicts. Nevertheless, the failed replication is valuable for future research, since it demonstrated that "Conflict Priming" as a facilitation of processing of conflict trials following deviant tones, is not an confirmed finding.Prior behavioral work has shown that selective restudy of some studied items leaves recall of the other studied items unaffected when lag between study and restudy is short, but improves recall of the other items when lag is prolonged. The beneficial effect has been attributed to context retrieval, assuming that selective restudy reactivates the context at study and thus provides a retrieval cue for the other items (Bäuml, 2019). Here the results of two experiments are reported, in each of which subjects studied a list of items and then, after a short 2-min or a prolonged 10-min lag, restudied some of the list items. Participants' electroencephalography (EEG) was recorded during both the study and restudy phases. In Experiment 2, but not in Experiment 1, subjects engaged in a mental context reinstatement task immediately before the restudy phase started, trying to mentally reinstate the study context. Results of Experiment 1 revealed a theta/alpha power increase from study to restudy after short lag and an alpha/beta power decrease after long lag. Engagement in the mental context reinstatement task in Experiment 2 eliminated the decrease in alpha/beta power. The results are consistent with the view that the observed alpha/beta decrease reflects context retrieval, which became obsolete when there was preceding mental context reinstatement.