Explore the vast range of titles available.

Measurements of the peripheral sensory adaptation response were compared to a simple mathematical relationship involving the spontaneous, peak, and steady-state activities. This relationship is based on the geometric mean and is found to be obeyed to good approximation in peripheral sensory units showing a sustained response to prolonged stimulation. From an extensive review of past studies, the geometric mean relationship is shown to be independent of modality and is satisfied in a wide range of animal species. The consilience of evidence, from nearly 100 years of experiments beginning with the work of Edgar Adrian, suggests that this is a fundamental result of neurophysiology.

Perimetry, or visual field test, estimates differential light sensitivity thresholds across many locations in the visual field (e.g., 54 locations in the 24–2 grid). Recent developments have shown that an entire visual field may be relatively accurately reconstructed from measurements of a subset of these locations using a linear regression model. Here, we show that incorporating a dimensionality reduction layer can improve the robustness of this reconstruction. Specifically, we propose to use principal component analysis to transform the training dataset to a lower dimensional representation and then use this representation to reconstruct the visual field. We named our new reconstruction method the transformed-target principal component regression (TTPCR). When trained on a large dataset, our new method yielded results comparable with the original linear regression method, demonstrating that there is no underfitting associated with parameter reduction. However, when trained on a small dataset, our new method used on average 22% fewer trials to reach the same error. Our results suggest that dimensionality reduction techniques can improve the robustness of visual field testing reconstruction algorithms.

People with type 2 diabetes (T2D) have increased cancer risk. Liver cancer (LC) has a high prevalence in East Asia and is one of the leading causes of cancer death globally. Diagnosis of LC at early stage carries good prognosis. We used stored serum from patients of Hong Kong Diabetes Register before cancer diagnosis to extract RNA to screen for microRNA markers for early detection of LC in T2D. After screening with Affymetrix GeneChip microarray with serum RNA from 19 incident T2D LC (T2D-LC), 20 T2D cancer free (T2D-CF) and 20 non-T2D non-cancer patients, top signals were validated in a 3-group comparison including 1888 T2D-CF, 127 T2D-LC, and 487 T2D patients with non-liver cancer patients using qPCR. We detected 2.55-fold increase in miR-122-5p and 9.21-fold increase in miR-455-3p in the T2D-LC group. Using ROC analysis, miR-122-5p and miR-455-3p jointly predicted LC with an area under the curve of 0.770. After adjustment for confounders, each unit increase of miR-455-3p increased the odds ratio for liver cancer by 1.022. Increased serum levels of miR-122-5p and miR-455-3p were independently associated with increased risk of incident LC in T2D and may serve as potential biomarkers for early detection of LC in T2D.

Speech acquisition involves complex coordination of articulatory structures, primarily the jaw, lips, and tongue. Typically developing children acquire speech sounds in a hierarchical sequence governed by progressive neuromotor maturation. However, disruptions in speech motor control can lead to systematic phonological error patterns, commonly attributed to cognitive-linguistic deficits. This study explores the articulatory foundations of phonological error patterns in preschool-aged children diagnosed with moderate-to-severe speech sound disorders. Using data from 48 children who participated in a randomized controlled trial, we employed Mutual Information Analysis and Random Forest Models to quantify associations between specific speech motor limitations and phonological error patterns identified via the Diagnostic Evaluation of Articulation and Phonology assessment. Results showed moderate associations between phonological error patterns in particular cluster reduction, final consonant deletion, stopping, gliding, and atypical errors and limitations in jaw, labial-facial, and lingual control. Gliding, unexpectedly, demonstrated a negative relationship with speech motor errors, being more prevalent among older and milder cases, suggesting it may reflect a compensatory developmental stage rather than purely phonological simplification. These findings highlight the significance of speech motor constraints in phonological error patterns, challenging traditional cognitive-linguistic explanations. The results support theoretical frameworks such as Articulatory Phonology, emphasizing that phonological error patterns are influenced substantially by articulatory and neuromotor development. This study underscores the need for integrating motor considerations into clinical assessments and interventions for speech sound disorders in children, fostering a comprehensive approach bridging cognitive-linguistic and motor speech perspectives.

A fundamental inequality governing the spike activity of peripheral neurons is derived and tested against auditory data. This inequality states that the steady-state firing rate must lie between the arithmetic and geometric means of the spontaneous and peak activities during adaptation. Implications towards the development of auditory mechanistic models are explored.

Therapeutic seizures may work for treatment-resistant depression (TRD) by producing neuroplasticity. We evaluated whether magnetic seizure therapy (MST) produces changes in suicidal ideation and neuroplasticity as indexed through transcranial magnetic stimulation and electroencephalography (TMS-EEG) of the dorsolateral prefrontal cortex (DLPFC). Twenty-three patients with TRD were treated with MST. Changes in suicidal ideation was assessed through the Scale for Suicidal Ideation (SSI). Before and after the treatment course, neuroplasticity in excitatory and inhibitory circuits was assessed with TMS-EEG measures of cortical-evoked activity (CEA) and long-interval cortical inhibition (LICI) from the left DLPFC, and the left motor cortex as a control condition. As in our previous report, the relationship between TMS-EEG measures and suicidal ideation was examined with the SSI. Results show that 44.4% of patients experienced resolution of suicidal ideation. Based on DLPFC assessment, MST produced significant CEA increase over the frontal central electrodes (cluster p < 0.05), but did not change LICI on a group level. MST also reduced the SSI scores (p < 0.005) and the amount of reduction correlated with the decrease in LICI over the right frontal central electrodes (cluster p < 0.05; rho = 0.73 for Cz). LICI change identified patients who were resolved of suicidal ideation with 90% sensitivity and 88% specificity (AUC = 0.9, p = 0.004). There was no significant finding with motor cortex assessment. Overall, MST produced significant rates of resolution of suicidal ideation. MST also produced neuroplasticity in the frontal cortex, likely through long-term potentiation (LTP)-like mechanisms. The largest reduction in suicidal ideation was demonstrated in patients showing concomitant decreases in cortical inhibition—a mechanism linked to enhanced LTP-like plasticity. These findings provide insights into the mechanisms through which patients experience resolution of suicidal ideation following seizure treatments in depression.

The rate coding response of a single peripheral sensory neuron in the asymptotic, near-equilibrium limit can be derived using information theory, asymptotic Bayesian statistics and a theory of complex systems. Almost no biological knowledge is required. The theoretical expression shows good agreement with spike-frequency adaptation data across different sensory modalities and animal species. The approach permits the discovery of a new neurophysiological equation and shares similarities with statistical physics.

In this study, we used electrocorticographic (ECoG) signals to extract the onset of arm movement as well as the velocity of the hand as a function of time. ECoG recordings were obtained from three individuals while they performed reaching tasks in the left, right and forward directions. The ECoG electrodes were placed over the motor cortex contralateral to the moving arm. Movement onset was detected from gamma activity with near perfect accuracy (> 98%), and a multiple linear regression model was used to predict the trajectory of the reaching task in three-dimensional space with an accuracy exceeding 85%. An adaptive selection of frequency bands was used for movement classification and prediction. This demonstrates the efficacy of developing a real-time brain-machine interface for arm movements with as few as eight ECoG electrodes.

Concurrent recording of electroencephalography (EEG) during transcranial magnetic stimulation (TMS) is an emerging and powerful tool for studying brain health and function. Despite a growing interest in adaptation of TMS-EEG across neuroscience disciplines, its widespread utility is limited by signal processing challenges. These challenges arise due to the nature of TMS and the sensitivity of EEG to artifacts that often mask TMS-evoked potentials (TEP)s. With an increase in the complexity of data processing methods and a growing interest in multi-site data integration, analysis of TMS-EEG data requires the development of a standardized method to recover TEPs from various sources of artifacts. This article introduces TMSEEG, an open-source MATLAB application comprised of multiple algorithms organized to facilitate a step-by-step procedure for TMS-EEG signal processing. Using a modular design and interactive graphical user interface (GUI), this toolbox aims to streamline TMS-EEG signal processing for both novice and experienced users. Specifically, TMSEEG provides: (i) targeted removal of TMS-induced and general EEG artifacts; (ii) a step-by-step modular workflow with flexibility to modify existing algorithms and add customized algorithms; (iii) a comprehensive display and quantification of artifacts; (iv) quality control check points with visual feedback of TEPs throughout the data processing workflow; and (v) capability to label and store a database of artifacts. In addition to these features, the software architecture of TMSEEG ensures minimal user effort in initial setup and configuration of parameters for each processing step. This is partly accomplished through a close integration with EEGLAB, a widely used open-source toolbox for EEG signal processing. In this article, we introduce TMSEEG, validate its features and demonstrate its application in extracting TEPs across several single- and multi-pulse TMS protocols. As the first open-source GUI-based pipeline for TMS-EEG signal processing, this toolbox intends to promote the widespread utility and standardization of an emerging technology in brain research.

Subsequent to global initiatives in mapping the human brain and investigations of neurobiological markers for brain disorders, the number of multi-site studies involving the collection and sharing of large volumes of brain data, including electroencephalography (EEG), has been increasing. Among the complexities of conducting multi-site studies and increasing the shelf life of biological data beyond the original study are timely standardization and documentation of relevant study parameters. We present the insights gained and guidelines established within the EEG working group of the Canadian Biomarker Integration Network in Depression (CAN-BIND). CAN-BIND is a multi-site, multi-investigator, and multi-project network supported by the Ontario Brain Institute with access to Brain-CODE, an informatics platform that hosts a multitude of biological data across a growing list of brain pathologies. We describe our approaches and insights on documenting and standardizing parameters across the study design, data collection, monitoring, analysis, integration, knowledge-translation, and data archiving phases of CAN-BIND projects. We introduce a custom-built EEG toolbox to track data preprocessing with open-access for the scientific community. We also evaluate the impact of variation in equipment setup on the accuracy of acquired data. Collectively, this work is intended to inspire establishing comprehensive and standardized guidelines for multi-site studies.