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Dario J. Englot

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5 papers

1 author row

YNIMG Journal 2025 Journal Article

Increased frontoparietal activity related to lower performance in neuropsychological assessment of working memory

August Jurva
Balbir Singh
Helen Qian
Zhengyang Wang
Monica L. Jacobs
Kaltra Dhima
Dario J. Englot
Shawniqua Williams Roberson

Executive functions, including working memory, are typically assessed clinically with neuropsychological instruments. In contrast, computerized tasks are used to test these cognitive functions in laboratory human and animal studies. Little is known of how neural activity captured by laboratory tasks relates to ability measured by clinical instruments and, by extension, clinical diagnoses of pathological conditions. We therefore sought to determine what aspects of neural activity elicited in laboratory tasks are predictive of performance in neuropsychological instruments. We recorded neural activity from intracranial electrodes implanted in human epilepsy patients as they performed laboratory working memory tasks. These patients had completed neuropsychological instruments preoperatively, including the Weschler Adult Intelligent Scale and the Wisconsin Card Sorting test. Our results revealed that increased high-gamma (70-150 Hz) power in the prefrontal and parietal cortex after presentation of visual stimuli to be remembered was indicative of lower performance in the neuropsychological tasks. On the other hand, we observed a positive correlation between high-frequency power amplitude in the delay period of the laboratory tasks and neuropsychological performance. Our results demonstrate how neural activity around task events relates to executive function and may be associated with clinical diagnosis of specific cognitive deficits.

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NeurIPS Conference 2024 Conference Paper

NeuroBOLT: Resting-state EEG-to-fMRI Synthesis with Multi-dimensional Feature Mapping

Yamin Li
Ange Lou
Ziyuan Xu
Shengchao Zhang
Shiyu Wang
Dario J. Englot
Soheil Kolouri
Daniel Moyer

Functional magnetic resonance imaging (fMRI) is an indispensable tool in modern neuroscience, providing a non-invasive window into whole-brain dynamics at millimeter-scale spatial resolution. However, fMRI is constrained by issues such as high operation costs and immobility. With the rapid advancements in cross-modality synthesis and brain decoding, the use of deep neural networks has emerged as a promising solution for inferring whole-brain, high-resolution fMRI features directly from electroencephalography (EEG), a more widely accessible and portable neuroimaging modality. Nonetheless, the complex projection from neural activity to fMRI hemodynamic responses and the spatial ambiguity of EEG pose substantial challenges both in modeling and interpretability. Relatively few studies to date have developed approaches for EEG-fMRI translation, and although they have made significant strides, the inference of fMRI signals in a given study has been limited to a small set of brain areas and to a single condition (i. e. , either resting-state or a specific task). The capability to predict fMRI signals in other brain areas, as well as to generalize across conditions, remain critical gaps in the field. To tackle these challenges, we introduce a novel and generalizable framework: NeuroBOLT, i. e. , Neuro-to-BOLD Transformer, which leverages multi-dimensional representation learning from temporal, spatial, and spectral domains to translate raw EEG data to the corresponding fMRI activity signals across the brain. Our experiments demonstrate that NeuroBOLT effectively reconstructs unseen resting-state fMRI signals from primary sensory, high-level cognitive areas, and deep subcortical brain regions, achieving state-of-the-art accuracy with the potential to generalize across varying conditions and sites, which significantly advances the integration of these two modalities.

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YNIMG Journal 2023 Journal Article

Vigilance associates with the low-dimensional structure of fMRI data

Shengchao Zhang
Sarah E. Goodale
Benjamin P. Gold
Victoria L. Morgan
Dario J. Englot
Catie Chang

The human brain exhibits rich dynamics that reflect ongoing functional states. Patterns in fMRI data, detected in a data-driven manner, have uncovered recurring configurations that relate to individual and group differences in behavioral, cognitive, and clinical traits. However, resolving the neural and physiological processes that underlie such measurements is challenging, particularly without external measurements of brain state. A growing body of work points to underlying changes in vigilance as one driver of time-windowed fMRI connectivity states, calculated on the order of tens of seconds. Here we examine the degree to which the low-dimensional spatial structure of instantaneous fMRI activity is associated with vigilance levels, by testing whether vigilance-state detection can be carried out in an unsupervised manner based on individual BOLD time frames. To investigate this question, we first reduce the spatial dimensionality of fMRI data, and apply Gaussian Mixture Modeling to cluster the resulting low-dimensional data without any a priori vigilance information. Our analysis includes long-duration task and resting-state scans that are conducive to shifts in vigilance. We observe a close alignment between low-dimensional fMRI states (data-driven clusters) and measurements of vigilance derived from concurrent electroencephalography (EEG) and behavior. Whole-brain coactivation analysis revealed cortical anti-correlation patterns that resided primarily during higher behavioral- and EEG-defined levels of vigilance, while cortical activity was more often spatially uniform in states corresponding to lower vigilance. Overall, these findings indicate that vigilance states may be detected in the low-dimensional structure of fMRI data, even within individual time frames.

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YNICL Journal 2022 Journal Article

Deep learning-based automated segmentation of resection cavities on postsurgical epilepsy MRI

T. Campbell Arnold
Ramya Muthukrishnan
Akash R. Pattnaik
Nishant Sinha
Adam Gibson
Hannah Gonzalez
Sandhitsu R. Das
Brian Litt

Accurate segmentation of surgical resection sites is critical for clinical assessments and neuroimaging research applications, including resection extent determination, predictive modeling of surgery outcome, and masking image processing near resection sites. In this study, an automated resection cavity segmentation algorithm is developed for analyzing postoperative MRI of epilepsy patients and deployed in an easy-to-use graphical user interface (GUI) that estimates remnant brain volumes, including postsurgical hippocampal remnant tissue. This retrospective study included postoperative T1-weighted MRI from 62 temporal lobe epilepsy (TLE) patients who underwent resective surgery. The resection site was manually segmented and reviewed by a neuroradiologist (JMS). A majority vote ensemble algorithm was used to segment surgical resections, using 3 U-Net convolutional neural networks trained on axial, coronal, and sagittal slices, respectively. The algorithm was trained using 5-fold cross validation, with data partitioned into training (N = 27) testing (N = 9), and validation (N = 9) sets, and evaluated on a separate held-out test set (N = 17). Algorithm performance was assessed using Dice-Sørensen coefficient (DSC), Hausdorff distance, and volume estimates. Additionally, we deploy a fully-automated, GUI-based pipeline that compares resection segmentations with preoperative imaging and reports estimates of resected brain structures. The cross-validation and held-out test median DSCs were 0. 84 ± 0. 08 and 0. 74 ± 0. 22 (median ± interquartile range) respectively, which approach inter-rater reliability between radiologists (0. 84–0. 86) as reported in the literature. Median 95 % Hausdorff distances were 3. 6 mm and 4. 0 mm respectively, indicating high segmentation boundary confidence. Automated and manual resection volume estimates were highly correlated for both cross-validation (r = 0. 94, p < 0. 0001) and held-out test subjects (r = 0. 87, p < 0. 0001). Automated and manual segmentations overlapped in all 62 subjects, indicating a low false negative rate. In control subjects (N = 40), the classifier segmented no voxels (N = 33), <50 voxels (N = 5), or a small volumes<0. 5 cm3 (N = 2), indicating a low false positive rate that can be controlled via thresholding. There was strong agreement between postoperative hippocampal remnant volumes determined using automated and manual resection segmentations (r = 0. 90, p < 0. 0001, mean absolute error = 6. 3 %), indicating that automated resection segmentations can permit quantification of postoperative brain volumes after epilepsy surgery. Applications include quantification of postoperative remnant brain volumes, correction of deformable registration, and localization of removed brain regions for network modeling.

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YNICL Journal 2020 Journal Article

Temporal lobe epilepsy alters spatio-temporal dynamics of the hippocampal functional network

Victoria L. Morgan
Catie Chang
Dario J. Englot
Baxter P. Rogers

Mesial temporal lobe epilepsy (TLE) is characterized by transient abnormal electrical activity originating in the hippocampus. The objective of this study was to characterize dynamic spatio-temporal fluctuations in hippocampal network connectivity in mTLE using functional connectivity (FC) mapping in 41 unilateral mTLE patients (28 right, 13 left) and 56 healthy control participants using 3T MRI. Dynamic FC was computed across the scan using sliding 60-s windows. This was compared to static FC computed using the whole 10-min functional MRI scan, and to the variance in the hippocampal functional MRI signal. Four states of healthy hippocampal dynamic FC were identified and compared to TLE patients. TLE patients fluctuated between these four states, but the hippocampus ipsilateral to the seizure focus spent more time in a state distinguished by lower prefrontal and parietal FC than the dominant healthy state. Increased time spent in this state was associated with increased impairment in static FC and increased variance in the hippocampal functional MRI signal. Overall, this work provides evidence that increases in variance in signal fluctuations occurring at the seizure focus in the hippocampus in patients with mTLE may contribute to disruptions in healthy FC network dynamics within an fMRI scan that contribute to decreases in static hippocampal FC. These alterations result in decreased hippocampal connectivity to bilateral prefrontal and parietal regions in TLE which may be related to behavior and cognitive impairments in these patients. Therefore, characterization of an individual patient's hippocampal dynamics at different time scales may provide more specific spatio-temporal profiles of network impairment that may be related to hippocampal dysfunction in TLE.

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