Global brain-wide signals in functional magnetic resonance imaging (fMRI) are influenced by temporal variations in vigilance, peripheral physiological processes, head motion, and other potential neuronal and non-neuronal sources. These effects are challenging to disentangle as fluctuations in vigilance and peripheral physiology are difficult to detect with fMRI alone. In this study, we leveraged multimodal neuroimaging data (simultaneous fMRI, EEG, respiratory, and cardiac recordings) to investigate the ability of dimensionality reduction techniques to separate influences of vigilance, physiology, and other global effects in fMRI. Our study included resting-state fMRI from 30 subjects, parcellated into 317 brain regions. Two different methods, temporal independent component analysis (tICA) and a fully connected autoencoder, were used to project the atlas-based data into a lower dimensional latent space. The correlation of each latent component with the EEG alpha/theta power ratio (a marker of vigilance), physiological signals (respiratory volume and heart rate), and the global fMRI signal was computed. LASSO regression was additionally employed to reconstruct the alpha/theta ratio from the latent components. Our results showed that tICA, but not the autoencoder, was able to disentangle a vigilance-related component from other global effects. Both the vigilance and global components exhibited a moderate relationship with physiological activity. Therefore, tICA is useful for isolating vigilance-related influences in fMRI, which may aid in discovering novel clinical biomarkers linked to vigilance dysregulation as well as assist in explaining intersubject variability due to in-scanner state.
Sleep disturbances are commonly reported among patients with Alzheimer’s Disease (AD). Further, the disruption of subcortical areas such as the Basal Forebrain (BF) and its constituent Nucleus Basalis of Meynert (NBM), which play an important role in maintaining wakefulness or alertness (also known as vigilance), occurs early in AD. In this study, we delineate vigilance-linked fMRI patterns in an aging population and determine how these patterns relate to subcortical integrity and cognition. We used fMRI data from the Vanderbilt Memory and Aging Project dataset, consisting of 49 MCI patients and 75 healthy controls. Since external measures of vigilance are not present during fMRI, we used a data-driven technique for extracting vigilance information directly from fMRI data. With this approach, we derived subject-specific spatial maps reflecting a whole-brain activity pattern that is correlated with vigilance. We first assessed the relationships between cognitive measures (subject memory composite and executive function scores) and structural measures (BF and NBM volumes obtained from subject-specific segmentation methods) using Pearson correlations. BF and NBM volumes were found to be significantly correlated with memory composite in MCI subjects and with executive function in HCs. We then performed a mediation analysis to evaluate how NBM volume may mediate fMRI-derived vigilance effects on memory composite scores in MCI subjects. fMRI vigilance activity and memory composite were significantly associated in the hippocampus, posterior cingulate cortex, and anterior cingulate cortex, regions involved in the default-mode and salience networks. These results suggest that cognitive decline in AD may be linked with both subcortical structural changes and vigilance-related fMRI signals, opening new directions for potential functional biomarkers in pathological aging populations.
Selective amygdalohippocampectomy (SelAH) for mesial temporal lobe epilepsy (mTLE) involves the resection of the anterior hippocampus and the amygdala. A recent study related to SelAH reports that among 168 patients for whom two-year Engel outcomes data were available, 73% had Engel I outcomes (free of disabling seizure); 16.6% had Engel II outcomes (rare disabling seizures); 4.7% had Engel III outcomes (worthwhile improvement); and 5.3% had Engel IV outcomes (no worthwhile improvement). Success rate among sites also varies greatly. Possible explanations for variability in outcomes are the resected volume and/or the subregion of the hippocampus and amygdala that have been resected. To explore this hypothesis, the accurate segmentation of the resected cavity needs to be performed on a large scale. This is, however, a difficult and time-consuming task that requires expertise. Here we explore using a nnUNET to perform the task. Inspired by Youngeun, a level set loss is used in addition to the original DICE and cross-entropy loss in nnUNET to capture the cavity boundaries better. We show that, even with a modest-sized training set (25 volumes), the median DICE value between automated and manual segmentations is 0.88, which suggests that the automatic and accurate segmentation of the resection cavity is achievable.
This paper advances a new paradigm of minimally invasive neurosurgical interventions through skull foramina, which promise to improve patient outcomes by reducing postoperative pain and recovery times, and perhaps even complication rates. The foramen ovale, a small opening in the base of the skull, is currently used to insert recording electrodes into the brain for diagnosing epilepsy and as a pathway for ablating the trigeminal nerve for facial pain. An MRI-compatible robotic platform to position neurosurgical tools along a prescribed trajectory through the foramen ovale can enable access to deep brain targets for diagnosis or intervention. In this paper, we describe design goals and constraints, determined both heuristically and empirically, for such a robotic system. These include the space available within the scanner around the patient, the set of possible needle angles of approach to the foramen ovale, patient positioning options within the scanner, and the force needed to tilt the needle to desired angles. These design considerations can be used to inform future work on the design of MRI-conditional robots to access the brain through the foramen ovale.
KEYWORDS: Distortion, Magnetic resonance imaging, Functional magnetic resonance imaging, Brain, Neuroimaging, Visualization, Signal attenuation, Image processing, Deep learning
Functional MRI (fMRI) suffers from susceptibility-induced geometric distortions. Current state-of-the-art distortion correction methods require reverse phase encoded images or additional field maps, but not all imaging protocols include these additional scans. We propose SynBOLD-DisCo to enable state-of-the-art distortion correction of single phase encoded fMRI using only an additional structural image. We use a 3D U-net to synthesize undistorted fMRI images from the structural image and use this undistorted synthetic image as an anatomical target for distortion correction with state-of-the-art methods. We demonstrate SynBOLD-DisCo produces results qualitatively and quantitatively equivalent to state-of-the-art correction methods without the need for additional calibration scans.
KEYWORDS: Thalamus, Epilepsy, Data modeling, Image registration, Brain stimulation, 3D acquisition, Detection and tracking algorithms, Brain, Algorithm development, 3D modeling
Epilepsy is the fourth most common neurological disorder and affects people of all ages worldwide. Deep Brain Stimulation (DBS) has emerged as an alternative treatment option when anti-epileptic drugs or respective surgery cannot lead to satisfactory outcomes. To facilitate the planning of the procedure and for its standardization, it is desirable to develop an algorithm to automatically localize the DBS stimulation target, i.e., Anterior Nucleus of Thalamus (ANT), which is a challenging target to plan. In this work, we perform an extensive comparative study by benchmarking various localization methods for ANT-DBS. Specifically, the methods involved in this study include traditional registration method and deep-learning-based methods including heatmap matching and differentiable spatial to numerical transform (DSNT). Our experimental results show that the deep-learning (DL)- based localization methods that are trained with pseudo labels can achieve a performance that is comparable to the inter-rater and intra-rater variability and that they are orders of magnitude faster than traditional methods
Deep brain stimulation (DBS) has been recently approved by the FDA to treat epilepsy patients with refractory seizures, i.e., patients for whom medications are not effective. It involves stimulating the anterior nucleus of the thalamus (ANT) with electric impulses using permanently placed electrodes. One main challenge with the procedure is to determine a trajectory to place the implant at the proper location while avoiding sensitive structures. In this work, we focus on one category of sensitive structures, i.e., brain vessels, and we propose a method to segment them in clinically acquired contrast-enhanced T1-weighted (T1CE) MRI images. We develop a deep-learning-based 3D U-Net model that we train/test on a set of images for which we have created the ground truth. We compare this approach to a traditional vesselness-based technique and we show that our method produces significantly better results (Dice score: 0.794), especially for small vessels.
In pre- and post-surgical surface shape analysis, establishing shape correspondence is necessary to investigate the postoperative surface changes. However, structural absence after the operation accompanies focal non-rigid changes, which leads to challenges in existing surface registration methods. In this paper, we present a fully automatic particle-based method to establish surface correspondence that can handle partial structural abnormality in the temporal lobe resection. Our method optimizes the coordinates of points which are modeled as particles on surfaces in a hierarchical way to reduce a chance of being trapped in a local minimum during the optimization. In the experiments, we evaluate the effectiveness of our method in comparison with conventional spherical registration (FreeSurfer) on two scenarios: cortical thickness changes in healthy controls within a short scan-rescan time window and patients with temporal lobe resection. The postsurgical scan is acquired at least 1 year after the presurgical scan. In region of interest-wise (ROI-wise) analysis, no changes on cortical thickness are found in both methods on the healthy control group. In patients, since there is no ground truth available, we instead investigated the disagreement between our method and FreeSurfer. We see poorly matched ROIs and large cortical thickness changes using FreeSurfer. On the contrary, our method shows well-matched ROIs and subtle cortical thickness changes. This suggests that the proposed method can establish a stable shape correspondence, which is not fully captured in a conventional spherical registration.
Responsive neurostimulation (RNS) is a novel surgical intervention for treating medically refractory epilepsy. A neurostimulator implanted under the skull monitors brain activity in one or two seizure foci and provides direct electrical stimulation using implanted electrodes to prevent partial onset seizures. Despite significant successes in reducing seizure frequency over time, outcomes are less than optimal in a number of patients. To maximize treatment efficacy, it is critical to identify the factors that contribute to the variance in outcomes, including accurate knowledge of the final electrode location. However, there is as yet no automated algorithm to localize the RNS electrodes in the brain. Currently, physicians manually demarcate the positions of the leads in postoperative images, a method that is affected by rater bias and is impractical for largescale studies. In this paper, we propose an intensity feature based algorithm that can automatically identify the electrode positions in postoperative CT images. We also validate the performance of our algorithm on a multicenter dataset of 13 implanted patients and test how it compares with expert raters.
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