Biology subjects

Medaglia, J.

Publications and source records attributed to Medaglia, J..

4 recordsLinked to original sources

bioRxiv2026

Brief Trains of Closed-loop TMS Induce Controllable Power in FM-theta Oscillations

Objective. Frontal-midline theta (FM-theta; 4-8 Hz) oscillations are thought to support cognition. Brief trains of transcranial magnetic stimulation (TMS) can probe these oscillations with high temporal precision but have rarely changed theta power. One possible reason is that prior studies ignored the instantaneous phase of the oscillation, which strongly shapes the cortical response to TMS. We tested whether brief TMS trains triggered at specific FM-theta phases in real time directionally modulate post-train FM-theta power. Approach. Six healthy adults received TMS over dorsomedial prefrontal cortex (Fz) with concurrent electroencephalography during a whole-report working memory task. A beamformer and real-time phase-estimation pipeline triggered four-pulse 6 Hz trains (110% of resting motor threshold) at FM-theta peaks (0 degrees) or troughs (180 degrees), randomized trial by trial. Post-train spectral power was decomposed into total, induced (non-phase-locked) and evoked (phase-locked) components. Main results. Trough-triggered trains transiently increased post-train FM-theta power relative to peak-triggered trains; task performance did not differ. Cluster-based permutation tests showed trough-peak differences in total (4.6-7.1 Hz) and induced (5.0-7.8 Hz) power ~50-440 ms after the train, suggesting amplification of the oscillation . No evoked effect was detected. The total-power effect was robust at the subject level (Cohen's dz = 1.32, p = 0.023). Within-train theta power did not differ between conditions, suggesting a cortical origin. Significance. To our knowledge, this is the first study to lock brief rhythmic TMS trains to the real-time phase of an ongoing oscillation, and to show phase-dependent modulation of FM-theta power after individual trains on timescales relevant to behavior. Open-loop brief-train protocols may have missed such effects by mixing faciliatory and suppressive phases. Phase-locked brief trains offer a precise tool for testing the causal role of oscillations in cognition.

neuroscience↗

bioRxiv2021

Structural Disconnection of the Posterior Medial Frontal Cortex Reduces Speech Error Monitoring

Optimal performance in any task relies on the ability to detect and repair errors. The anterior cingulate cortex and the broader posterior medial frontal cortex (pMFC) are active during error processing. However, it is unclear whether damage to the pMFC impairs error monitoring. We hypothesized that successful error monitoring critically relies on connections between the pMFC and broader cortical networks involved in executive functions and the task being monitored. We tested this hypothesis in the context of speech error monitoring in people with post-stroke aphasia. Diffusion weighted images were collected in 51 adults with chronic left-hemisphere stroke and 37 age-matched control participants. Whole-brain connectomes were derived using constrained spherical deconvolution and anatomically-constrained probabilistic tractography. Support vector regressions identified white matter connections in which lost integrity in stroke survivors related to reduced error detection during confrontation naming. Lesioned connections to the bilateral pMFC were related to reduce error monitoring, including many connections to regions associated with speech production and executive function. We conclude that connections to the pMFC support error monitoring. Error monitoring in speech production is supported by the structural connectivity between the pMFC and regions involved in speech production and executive function. Interactions between pMFC and other taskrelevant processors may similarly be critical for error monitoring in other task contexts.

neuroscience↗

bioRxiv2021

Glass Half Full: Preserved Anatomical Bypasses Predict Variance in Language Functions After Stroke

The severity of post-stroke aphasia is related to damage to white matter connections. However, neural signaling can route not only through direct connections, but also along multi-step network paths. When brain networks are damaged by stroke, paths can bypass around the damage to restore communication. The shortest network paths between regions could be the most efficient routes for mediating bypasses. We examined how shortest-path bypasses after left hemisphere strokes were related to language performance. Regions within and outside of the canonical language network could be important in aphasia recovery. Therefore, we innovated methods to measure the influence of bypasses in the whole brain. Distinguishing bypasses from all residual shortest paths is difficult without pre-stroke imaging. We identified bypasses by finding shortest paths in subjects with stroke that were longer than those observed in the average network of the most reliably observed connections in age-matched controls. We tested whether features of those bypasses predicted scores in four orthogonal dimensions of language performance derived from a factor analysis of a battery of language tasks. The features were the length of each bypass in steps, and how many bypasses overlapped on each individual direct connection. We related these bypass features to language factors using grid-search cross-validated Support Vector Regression, a technique that extracts robust relationships in high-dimensional data analysis. We discovered that the length of bypasses reliably predicted variance in lexical production (R2 = .576) and auditory comprehension scores (R2 = .164). Bypass overlaps reliably predicted variance in Lexical Production scores (R2 = .247). The predictive elongation features revealed that bypass efficiency along the dorsal stream and ventral stream were most related to Lexical Production and Auditory Comprehension, respectively. Among the predictive bypass overlaps, increased bypass routing through the right hemisphere putamen was negatively related to lexical production ability.

neuroscience↗

bioRxiv2017

Improved accuracy of lesion to symptom mapping with multivariate sparse canonical correlations

Lesion to symptom mapping (LSM) is a crucial tool for understanding the causality of brain-behavior relationships. The analyses are typically performed by applying statistical methods on individual brain voxels (VLSM), a method called the mass-univariate approach. Several authors have shown that VLSM suffers from limitations that may decrease the accuracy and reliability of the findings, and have proposed the use of multivariate methods to overcome these limitations. In this study, we propose a multivariate optimization technique known as sparse canonical correlation analysis for neuroimaging (SCCAN) for lesion to symptom mapping. To validate the method and compare it with mass-univariate results, we used data from 131 patients with chronic stroke lesions in the territory of the middle cerebral artery, and created synthetic behavioral scores based on the lesion load of 93 brain regions (putative functional units). LSM analyses were performed with univariate VLSM or SCCAN, and the accuracy of the two methods was compared in terms of both overlap and and displacement from the simulated functional areas. Overall, SCCAN produced more accurate results - higher dice overlap and smaller average displacement - compared to VLSM. This advantage persisted at different sample sizes (N=20-131) and different multiple comparison corrections (false discovery rate, FDR; Bonferroni; permutation-based family wise error rate, FWER). These findings were replicated with a fully automated SCCAN routine that relied on cross-validated predictive accuracy to find the optimal sparseness value. Simulations of one, two, and three brain regions showed a systematic advantage of SCCAN over VLSM; under no circumstance could VLSM exceed the accuracy obtained with SCCAN. When considering functional units composed of multiple brain areas VLSM identified fewer areas than SCCAN. The investigation of real scores of aphasia severity (aphasia quotient and picture naming) showed that SCCAN could accurately identify known language-critical areas, while VLSM either produced diffuse maps (FDR correction) or few scattered voxels (FWER correction). Overall, this study shows that a multivariate method, such as, SCCAN, outperforms VLSM in a number of scenarios, including functional dependency on single or multiple areas, different sample sizes, different multi-area combinations, and different thresholding mechanisms (FWER, Bonferroni, FDR). These results support previous claims that multivariate methods are in general more accurate than mass-univariate approaches, and should be preferred over traditional VLSM approaches. All the methods described in this study are available in the newly developed LESYMAP package for R.

neuroscience↗