Biology subjects

Magnotti, J.

Publications and source records attributed to Magnotti, J..

5 recordsLinked to original sources

bioRxiv2024

The Noisy Encoding of Disparity Model Predicts Perception of the McGurk Effect in Native Japanese Speakers

The McGurk effect is an illusion that demonstrates the influence of information from the face of the talker on the perception of auditory speech. The diversity of human languages has prompted many intercultural studies of the effect, including in native Japanese speakers. Studies of large samples of native English speakers have shown that the McGurk effect is characterized by high variability, both in the susceptibility of different individuals to the illusion and in the frequency with which different experimental stimuli induce the illusion. The noisy encoding of disparity (NED) model of the McGurk effect uses Bayesian principles to account for this variability by separately estimating the susceptibility and sensory noise for each individual and the strength of each stimulus. To test whether the NED model could account for McGurk perception in a non-Western culture, we applied it to data collected from 80 native Japanese-speaking participants. Fifteen different McGurk stimuli were presented, along with audiovisual congruent stimuli. The McGurk effect was highly variable across stimuli and participants, with the percentage of illusory fusion responses ranging from 3% to 78% across stimuli and from 0% to 91% across participants. Despite this variability, the NED model accurately predicted perception, predicting fusion rates for individual stimuli with 2.1% error and for individual participants with 2.4% error. Stimuli containing the unvoiced pa/ka pairing evoked more fusion responses than the voiced ba/ga pairing. Model estimates of sensory noise was correlated with participant age, with greater sensory noise in older participants. The NED model of the McGurk effect offers a principled way to account for individual and stimulus differences when examining the McGurk effect within and across cultures.

neuroscience↗

bioRxiv2023

YAEL: Your Advanced Electrode Localizer

Intracranial electroencephalography (iEEG) provides a unique opportunity to measure human brain function with implanted electrodes. A key step in neuroscience inference from iEEG is localizing the electrodes relative to individual subject anatomy and identified regions in brain atlases. While there are number of workflows for electrode localization, most suffer from one or more limitations. The first limitation is a lack of integration: scientists must install and use different software packages for each localization step. Second, they are inefficient: while most iEEG analysis steps can be automated, electrode localization is still largely a manual process. Third, most current tools are limited to the localization process itself, leaving users without the ability to create high-quality visualizations for clinical and research purposes. We developed YAEL (Your Advanced Electrode Localizer) to overcome these limitations. First, YAEL is completely integrated: a single easy-to-use graphical user interface (GUI) controls every step of the localization process. Second, YAEL uses a flexible 3D viewer and automation tools to make accurate localization of electrodes quick and easy. Third, after localization is complete, YAEL leverages the same viewer to create high-quality visualizations of electrode data including identified brain areas from atlases; the response to experimental tasks measured with iEEG; and clinical measures such as epileptiform activity or the results of electrical stimulation mapping. YAEL contains more than 30,000 lines of code, is free and open source, and can be installed in minutes on Mac, Windows and Linux platforms from https://yael.wiki. User interactions with YAEL occur through a web browser ensuring a familiar user experience and consistent operation across platforms and whether YAEL is used locally or deployed in the cloud.

neuroscience↗

bioRxiv2022

Using predictive validity to compare associations between brain damage and behavior

Lesion-behavior mapping (LBM) provides a statistical map of the association between voxel-wise brain damage and individual differences in behavior. To understand whether two behaviors are mediated by damage to distinct regions, researchers often compare LBM weight outputs by either the Overlap method or the Correlation method. However, these methods lack statistical criteria to determine whether two LBM are distinct vs. the same and are disconnected from a major goal of LBMs: predicting behavior from brain damage. Without such criteria, researchers may draw conclusions from numeric differences between LBMs that are irrelevant to predicting behavior. We developed and validated a Predictive Validity Comparison method (PVC) that establishes a statistical criterion for comparing two LBMs using predictive accuracy: two LBMs are distinct if and only if they provide unique predictive power for the behaviors being assessed. We applied PVC to two lesion-behavior stroke data sets, demonstrating its utility for determining when behaviors arise from the same vs. different lesion patterns. Using region-of-interest based simulations derived from proportion damage from a large data set (n = 131), PVC accurately detected when behaviors were mediated by different regions (high sensitivity) vs. the same region (high specificity). Both the Overlap method and Correlation method performed poorly on the simulated data. By objectively determining whether two behavioral deficits can be explained by a single vs. distinct patterns of brain damage, PVC provides a critical advance in establishing the brain bases of behavior. We have developed and released a GUI-driven web app to encourage widespread adoption.

neuroscience↗

bioRxiv2019

Molecular profiling predicts meningioma recurrence and reveals loss of DREAM complex repression in aggressive tumors

Meningiomas account for one-third of all primary brain tumors. Although typically benign, about 20% of meningiomas are aggressive, and despite the rigor of the current histopathological classification system, there remains considerable uncertainty in predicting tumor behavior. Here, we analyzed 160 tumors from all three WHO grades (I-III) using clinical, gene expression and sequencing data. Unsupervised clustering analysis identified three molecular types (A, B, and C) that reliably predicted recurrence. These groups did not directly correlate with the WHO grading system, which would classify more than half of the tumors in the most aggressive molecular type as benign. Transcriptional and biochemical analyses revealed that aggressive meningiomas involve loss of the repressor function of the DREAM complex, which results in cell cycle activation; only tumors in this category tend to recur after full resection. These findings should improve our ability to predict recurrence and develop targeted treatments for these clinically challenging tumors.\n\nSignificance StatementMeningiomas are the most common primary brain tumors. Although most of these tumors are benign, one-fifth will recur despite apparently complete resection. Several studies have demonstrated that genomic approaches can yield important insights into the biology of these tumors. We performed RNA sequencing and whole-exome sequencing of 160 tumors from 140 patients, which identified three distinct groups of meningioma that correlate with recurrence better than the current WHO grading system. Our analysis also revealed that the most aggressive type was characterized by loss of the repressive DREAM complex. These findings should improve prognostication for patients and lead to viable therapeutic targets.

cancer biology↗

bioRxiv2018

Published estimates of group differences in multisensory integration are inflated

A common measure of multisensory integration is the McGurk effect, an illusion in which incongruent auditory and visual speech are integrated to produce an entirely different percept. Published studies report that participants who differ in age, gender, culture, native language, or traits related to neurological or psychiatric disorders also differ in their susceptibility to the McGurk effect. These group-level differences are used as evidence for fundamental alterations in sensory processing between populations. However, there is high variability in the McGurk effect within groups: some participants never report the illusion while others always do. High within-group variability means that sample sizes much larger than those in the published literature are necessary to accurately estimate between-group differences. Using empirical data and simulations, we show that a typical study with a sample size of 15 participants per group and a true group difference of 10% will report a group difference of 31%, an effect inflation of three-fold. Reducing the effect inflation by half requires increasing the sample size ten-fold, to 150 participants per group. The combination of within-group variability and small sizes explains two prominent features of the McGurk literature. First, group differences are inflated and highly variable across studies. Second, follow-up studies fail to replicate published reports of large between-group susceptibility differences because initial effect-size estimates are driven by statistical inflation rather than true group differences. These replication failures are especially problematic in studies of clinical populations because they have real-world consequences. Due to the high within-group variability, caution is essential when using the McGurk effect to assess deficits in clinical groups or propose treatment plans, such as training to improve sensory processing.

neuroscience↗