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The retinogeniculate visual pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus. Identification and visualization of the RGVP are important in studying the anatomy of the visual system and can inform the treatment of related brain diseases. Diffusion MRI (dMRI) tractography is an advanced imaging method that uniquely enables in vivo mapping of the 3D trajectory of the RGVP. Currently, identification of the RGVP from tractography data relies on expert (manual) selection of tractography streamlines, which is time‐consuming, has high clinical and expert labor costs, and is affected by inter‐observer variability. In this paper, we present a novel deep learning framework, DeepRGVP, to enable fast and accurate identification of the RGVP from dMRI tractography data. We design a novel microstructure‐informed supervised contrastive learning method that leverages both streamline label and tissue microstructure information to determine positive and negative pairs. We propose a new streamline‐level data augmentation method to address highly imbalanced training data, where the number of RGVP streamlines is much lower than that of non‐RGVP streamlines. In the experiments, we perform comparisons with several state‐of‐the‐art deep learning methods that were designed for tractography parcellation. Furthermore, to assess the generalizability of the proposed RGVP method, we apply our method to dMRI tractography data from neurosurgical patients with pituitary tumors. In comparison with the state‐of‐the‐art methods, we show superior RGVP identification results using DeepRGVP with significantly higher accuracy and F1 scores. In the patient data experiment, we show DeepRGVP can successfully identify RGVPs despite the effect of lesions affecting the RGVPs. Overall, our study shows the high potential of using deep learning to automatically identify the RGVP. [ABSTRACT FROM AUTHOR]

Presurgical planning prior to brain tumor resection is critical for the preservation of neurologic function post‐operatively. Neurosurgeons increasingly use advanced brain mapping techniques pre‐ and intra‐operatively to delineate brain regions which are "eloquent" and should be spared during resection. Functional MRI (fMRI) has emerged as a commonly used non‐invasive modality for individual patient mapping of critical cortical regions such as motor, language, and visual cortices. To map motor function, patients are scanned using fMRI while they perform various motor tasks to identify brain networks critical for motor performance, but it may be difficult for some patients to perform tasks in the scanner due to pre‐existing deficits. Connectome fingerprinting (CF) is a machine‐learning approach that learns associations between resting‐state functional networks of a brain region and the activations in the region for specific tasks; once a CF model is constructed, individualized predictions of task activation can be generated from resting‐state data. Here we utilized CF to train models on high‐quality data from 208 subjects in the Human Connectome Project (HCP) and used this to predict task activations in our cohort of healthy control subjects (n = 15) and presurgical patients (n = 16) using resting‐state fMRI (rs‐fMRI) data. The prediction quality was validated with task fMRI data in the healthy controls and patients. We found that the task predictions for motor areas are on par with actual task activations in most healthy subjects (model accuracy around 90%–100% of task stability) and some patients suggesting the CF models can be reliably substituted where task data is either not possible to collect or hard for subjects to perform. We were also able to make robust predictions in cases in which there were no task‐related activations elicited. The findings demonstrate the utility of the CF approach for predicting activations in out‐of‐sample subjects, across sites and scanners, and in patient populations. This work supports the feasibility of the application of CF models to presurgical planning, while also revealing challenges to be addressed in future developments. Practitioner Points: Precision motor network prediction using connectome fingerprinting.Carefully trained models' performance limited by stability of task‐fMRI data.Successful cross‐scanner predictions and motor network mapping in patients with tumor. [ABSTRACT FROM AUTHOR]

The unambiguous localization of eloquent functional areas is necessary to decrease the neurological morbidity of neurosurgical procedures. We explored the minimum spatial resolution requirements for functional magnetic resonance imaging (fMRI) data acquisition when brain mapping is used in neurosurgical planning and navigation. Using a 1.5 Tesla clinical MRI scanner, eight patients with brain tumors underwent fMRI scans using spatial resolution of approximately 4 x 4 x 4 mm(3) to map the eloquent motor and language areas during the performance of cognitive/sensorimotor tasks. The fMRI results were then used intra-operatively in an open MRI system to delineate eloquent areas. Retrospectively, activation patterns were visually inspected by a neurosurgeon to determine qualitatively whether ambiguity with respect to the activation boundaries, due to low spatial resolution, could be of potential significance for surgical guidance. A significant degree of ambiguity in both the extent and shape of activation was judged to be present in data from six of the eight patients. Analysis of fMRI data at multiple resolutions from a normal volunteer showed that at 3 mm isotropic resolution, eloquent areas were better localized within the gray matter although there was still some potential for ambiguity caused by activations appearing to cross a sulcus. The data acquired with 2-mm isotropic voxels significantly enhanced the spatial localization of activation to within the gray matter. Thus, isotropic spatial resolution on the order of 2 x 2 x 2 mm(3), which is much higher than the resolutions used in typical fMRI examinations, may be needed for the unambiguous identification of cortical activation with respect to tumors and important anatomical landmarks., (Copyright 2003 Wiley-Liss, Inc.)