Biology subjects

Coslett, H. B.

Publications and source records attributed to Coslett, H. B..

5 recordsLinked to original sources

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

Reorganization of motor functions within visuomotor networks subsequent to somatosensory cortical damage

Somatosensory inputs are critical to motor control. Animal studies have shown that primary somatosensory lesions cause sensorimotor deficits along with disrupted organization in primary motor cortex (M1). How does damage in primary somatosensory cortex (S1) influence motor networks in humans? Using fMRI, we examined two individuals with extensive damage to left somatosensory cortex, but primarily intact motor cortex and preserved motor abilities. Given left S1 damage, tactile detection and localization were impaired for the contralesional hand in both individuals. When moving the contralesional hand, LS, with near complete damage to the S1 hand area, showed increased activation in ipsilesional putamen and deactivation in contralesional cerebellum relative to age-matched controls. These findings demonstrate influences of S1 damage to subcortical sensorimotor areas that are distant from the lesion site, and a potential reweighting of the motor network with increased action selection in putamen and inhibition of sensory prediction in cerebellum in the face of sensory loss. In contrast, RF, who had a small island of spared S1 in the hand area, showed greater activation in contralesional S1 for movement versus rest. This same region was also activated by pure somatosensory stimulation in a second experiment, suggesting that the spared S1 area in RF still subserves sensorimotor processing. Finally, the right middle occipital gyrus was more strongly activated in both individuals compared with controls, suggesting a potential reliance on visual imagery in the face of degraded sensory feedback.

neuroscience↗

bioRxiv2022

Disruption of anterior temporal lobe reduces distortions in memory from category knowledge

Memory retrieval does not provide a perfect recapitulation of past events, but instead an imperfect reconstruction of event-specific details and general knowledge. However, it remains unclear whether this reconstruction relies on mixtures of signals from different memory systems, including one supporting general knowledge. Here, we investigate whether the anterior temporal lobe (ATL) distorts new memories due to prior category knowledge. In this experiment (N=36), participants encoded and retrieved image-location associations. Most images locations were clustered according to their category, but some were in random locations. With this protocol, we previously demonstrated that randomly located images were retrieved closer to their category cluster relative to their encoded locations, suggesting an influence of category knowledge. We combined this procedure with transcranial magnetic stimulation (TMS) delivered to the left ATL before retrieval. We separately examined event-specific details (error) and category knowledge (bias) to identify distinct signals attributable to different memory systems. We found that TMS to ATL attenuated bias in location memory, but only for atypical category members. The magnitude of error was not impacted, suggesting that a memorys fidelity can be decoupled from its distortion by category knowledge. This raises the intriguing possibility that retrieval is jointly supported by separable memory systems.

neuroscience↗

bioRxiv2017

Causal role of beta oscillations in time estimation

The neural mechanisms underlying time perception are of vital importance to a comprehensive understanding of behavior and cognition. Recent work has pointed to a supramodal role for beta oscillations in coordinating endogenous timing mechanisms for the purpose of measuring temporal intervals. However, the majority of this work has employed paradigms relying on timed motor responses, which are confounded by betas established role in motor movement. Further, no study to date has tested if the alteration of beta oscillations subsequently impacts timing. Here, we address these concerns and demonstrate for the first time a causal connection between beta oscillations and timing. To accomplish this, we first re-analyzed two, separate EEG datasets from psychophysical experiments (Wiener, et al. 2012; 2015) demonstrating that beta oscillations are associated with the retention and comparison of a memory standard for duration, and that transcranial magnetic stimulation (TMS) of the right supramarginal gyrus leads to an increase in midline beta power during the encoding of a temporal interval, corresponding with a longer perceived interval of time. Next, we conducted a study of 20 healthy human participants using transcranial alternating current stimulation (tACS), over frontocentral cortex, at alpha (10Hz) and beta (20Hz) frequencies, during a visual temporal bisection task, demonstrating that beta stimulation exclusively shifts the perception of time such that stimuli are reported as longer in length, while preserving precision. Finally, we decomposed trial-by-trial choice data with a drift diffusion model of decision making and temporal encoding that reveals the shift in timing is caused by a change in the starting point of accumulation, rather than the drift rate or threshold. Our results provide causal evidence of betas involvement in the perception of time, and point to a specific role for beta oscillations in the encoding and retention of memory for temporal intervals.

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↗