Biology subjects

Harvey, D. Y.

Publications and source records attributed to Harvey, D. Y..

3 recordsLinked to original sources

bioRxiv2026

The Role of the Left Inferior Frontal Gyrus in Resolving Immediate and Carryover Semantic Interference During Picture Naming

Naming pictures from the same semantic category can interfere with subsequent word retrieval. Although the left inferior frontal gyrus (LIFG) has been implicated in resolving this semantic interference, its causal role in this process remains unclear. Across three experiments, we investigated the contribution of the LIFG to interference resolution during blocked-cyclic naming. Experiment 1 tested whether continuous theta-burst stimulation over the LIFG increased semantic interference relative to vertex stimulation. Although the overall interference effect did not differ significantly between stimulation sites, exploratory analyses revealed an unexpected pattern based on the initial context (related vs. unrelated) with which items were named, suggesting that LIFG disruption affected retrieval of previously related items even when they were subsequently named in unrelated contexts. This observation motivated two further experiments examining the consequences of prior naming context more directly. Experiment 2 established that, without stimulation, interference observed in related blocks was no longer evident after the items were redistributed into unrelated blocks. Experiment 3 combined this design with cathodal or sham transcranial direct current stimulation (tDCS) over the LIFG. Planned comparisons showed that cathodal stimulation slowed naming of initially related items both within related blocks and after redistribution into unrelated blocks; corresponding comparisons for items named consistently in unrelated blocks were not significant. The similar patterns across two stimulation methods suggest that LIFG-mediated control may influence both selection under current competition and subsequent retrieval of previously competing words. These findings could be accommodated within a hybrid account in which working memory supports context-guided selection and selection-related control helps recalibrate lexical accessibility, limiting the carryover of interference into subsequent naming.

neuroscience↗

bioRxiv2026

Predicting Post-Stroke Aphasia Speech Performance from Multimodal Data with Explainable Machine Learning

Aphasia, an acquired language deficit, is the most common post-stroke focal cognitive impairment, and roughly 60% cases become chronic (duration >6 months). Aphasia therapies could be optimized if clinicians could make personalized predictions of how individual persons with aphasia (PWA) would be likely to perform on particular language tasks. However, current approaches relying on imaging, lesion volume, patient demographics, and clinical scores achieve less than 50% accuracy in predicting performance in PWA. Research algorithms using complex imaging and fMRI can make binary predictions about the presence or absence of aphasia but do not give more clinically relevant information. We aim to predict word-by-word speech accuracy in PWA to better enable personalized speech therapies. To be clinically informative, machine learning models developed for this purpose should use clinically available inputs, explain key features behind a prediction, and generalize to new PWA and previously unseen words. This study combines multimodal input features from clinical testing scores and structural MRI neuroimaging with a novel data source: word-by-word linguistic difficulty. We computed metrics of cognitive burden, such as semantic selection and recall demands, and articulatory burden, such as word length in phonemes and syllables, using naturalistic corpora containing over a billion words of English text. Retrospective training, ten-fold cross validation and 500-run bootstrapping of different machine learning models with various combinations of input features was conducted using 4620 trials. A simplified version of the best model using widely available inputs was deployed clinically through a web app, and prospective generalization was tested on 570 trials with unseen words and different naming tasks in new PWA. We found the best performances with random forest classifiers using linguistic difficulty combined with either clinical information (AUROC {+/-} SEM = 0.87 {+/-} 0.07), or all together with structural imaging connectivity (0.90 {+/-} 0.04). Classifiers using multimodal inputs significantly outperformed others employing single inputs (range 0.66-0.85, p<0.05). Extracting feature importances from the best model showed that Western Aphasia Battery scores, semantic demands, number of phonemes, and syllables were predictive of PWA speech accuracy. Structural integrity in peri-lesional brain regions predicted better language performance whereas higher connectivity of select contralateral homotopes contributed to prediction of worse speech. Without the inclusion of MRI data, lesion volume was a key predictor of PWA speech as well. A simplified, clinically ready, explainable model (publicly available as AphasiaLENS web application) predicted PWA accuracy for any user-entered word, not restricted to a standardized battery. Its prospective generalization performance was not significantly different from the best model using full inputs (AUROC ranges 0.81-0.89, p>0.05). Thus, our research can help inform individualized treatment planning for PWA, while also suggesting research targets through better understanding of brain-behavior relationships.

bioengineering↗

bioRxiv2025

Predicting Neuroplasticity Effects of Continuous Theta Burst Stimulation with Biomarkers from the Motor Evoked Potential TMS Input-Output Curve

The field of neuromodulation lacks predictors of individual differences in plasticity that influence responses to repetitive transcranial magnetic stimulation (rTMS). Continuous theta burst stimulation (cTBS), a form of rTMS known for its inhibitory effects, shows variable responses between individuals, potentially due to differences in neuroplasticity. Predicting individual cTBS effects could vastly enhance its clinical and experimental utility. This study explores whether motor evoked potential (MEP) input-output (IO) parameters measured prior to neuromodulation can predict motor cortex responses to cTBS. IO curves were sampled from healthy adults by recording MEPs over a range of single pulse TMS intensities to obtain parameters including MEPmax and S50 (midpoint intensity). Subjects later received cTBS over the same location of motor cortex and their MEPs before and after stimulation were compared. Both MEPmax and S50 predicted responses, significantly correlating (p<0.05, R2>0.25) with individuals MEP changes at 10, 20, and 30 minutes after cTBS. Further, we introduced and validated an easily implementable biomarker that does not require the time-consuming sampling of full IO curve: MEP130RMT (median of 10 MEPs at 130% RMT). MEP130RMT was also a strong predictor of cTBS response (p<0.005, R2>0.3). Head-to-head comparison against a previously studied genetic biomarker of rTMS responses (BDNF polymorphism) showed that IO based predictors had a superior performance in explaining more response variability. Thus, IO curves derived prior to cTBS administration can reliably predict cTBS-induced changes in cortical excitability. This work points toward an accessible strategy for tailoring stimulation procedures in both diagnostic and therapeutic applications of rTMS, and potentially boosting response rate to other brain stimulation approaches. HIGHLIGHTSO_LIBaseline TMS-MEP Input-Output (IO) Curve parameters significantly predict MEP responses to M1 cTBS. C_LIO_LIHigher MEPmax at baseline predicts more robust inhibitory response to cTBS, while higher midpoint intensity (S50) is associated with less response. C_LIO_LID We developed and validated a new biomarker MEP130RMT, which predicts cTBS response using just 10 baseline MEPs from single TMS pulses of 130% RMT intensity. C_LIO_LIHead to head comparison against BDNF genotyping shows superior performance of IO biomarkers. C_LI

neuroscience↗