@article {4458, title = {Data-efficient resting-state functional magnetic resonance imaging brain mapping with deep learning.}, journal = {J Neurosurg}, year = {2023}, month = {04/2023}, pages = {1-12}, abstract = {

OBJECTIVE: Resting-state functional MRI (RS-fMRI) enables the mapping of function within the brain and is emerging as an efficient tool for the presurgical evaluation of eloquent cortex. Models capable of reliable and precise mapping of resting-state networks (RSNs) with a reduced scanning time would lead to improved patient comfort while reducing the cost per scan. The aims of the present study were to develop a deep 3D convolutional neural network (3DCNN) capable of voxel-wise mapping of language (LAN) and motor (MOT) RSNs with minimal quantities of RS-fMRI data.

METHODS: Imaging data were gathered from multiple ongoing studies at Washington University School of Medicine and other thoroughly characterized, publicly available data sets. All study participants (n = 2252 healthy adults) were cognitively screened and completed structural neuroimaging and RS-fMRI. Random permutations of RS-fMRI regions of interest were used to train a 3DCNN. After training, model inferences were compared using varying amounts of RS-fMRI data from the control data set as well as 5 patients with glioblastoma multiforme.

RESULTS: The trained model achieved 96\% out-of-sample validation accuracy on data encompassing a large age range collected on multiple scanner types and varying sequence parameters. Testing on out-of-sample control data showed 97.9\% similarity between results generated using either 50 or 200 RS-fMRI time points, corresponding to approximately 2.5 and 10 minutes, respectively (96.9\% LAN, 96.3\% MOT true-positive rate). In evaluating data from patients with brain tumors, the 3DCNN was able to accurately map LAN and MOT networks despite structural and functional alterations.

CONCLUSIONS: Functional maps produced by the 3DCNN can inform surgical planning in patients with brain tumors in a time-efficient manner. The authors present a highly efficient method for presurgical functional mapping and thus improved functional preservation in patients with brain tumors.

}, issn = {1933-0693}, doi = {10.3171/2023.3.JNS2314}, author = {Luckett, Patrick H and Park, Ki Yun and Lee, John J and Lenze, Eric J and Wetherell, Julie Loebach and Eyler, Lisa T and Snyder, Abraham Z and Ances, Beau M and Shimony, Joshua S and Leuthardt, Eric C} } @article {4449, title = {Resting state network mapping in individuals using deep learning.}, journal = {Front Neurol}, volume = {13}, year = {2023}, month = {01/2023}, pages = {1055437}, abstract = {

INTRODUCTION: Resting state functional MRI (RS-fMRI) is currently used in numerous clinical and research settings. The localization of resting state networks (RSNs) has been utilized in applications ranging from group analysis of neurodegenerative diseases to individual network mapping for pre-surgical planning of tumor resections. Reproducibility of these results has been shown to require a substantial amount of high-quality data, which is not often available in clinical or research settings.

METHODS: In this work, we report voxelwise mapping of a standard set of RSNs using a novel deep 3D convolutional neural network (3DCNN). The 3DCNN was trained on publicly available functional MRI data acquired in = 2010 healthy participants. After training, maps that represent the probability of a voxel belonging to a particular RSN were generated for each participant, and then used to calculate mean and standard deviation (STD) probability maps, which are made publicly available. Further, we compared our results to previously published resting state and task-based functional mappings.

RESULTS: Our results indicate this method can be applied in individual subjects and is highly resistant to both noisy data and fewer RS-fMRI time points than are typically acquired. Further, our results show core regions within each network that exhibit high average probability and low STD.

DISCUSSION: The 3DCNN algorithm can generate individual RSN localization maps, which are necessary for clinical applications. The similarity between 3DCNN mapping results and task-based fMRI responses supports the association of specific functional tasks with RSNs.

}, issn = {1664-2295}, doi = {10.3389/fneur.2022.1055437}, author = {Luckett, Patrick H and Lee, John J and Park, Ki Yun and Raut, Ryan V and Meeker, Karin L and Gordon, Evan M and Snyder, Abraham Z and Ances, Beau M and Leuthardt, Eric C and Shimony, Joshua S} } @article {4459, title = {A somato-cognitive action network alternates with effector regions in motor cortex.}, journal = {Nature}, volume = {617}, year = {2023}, month = {05/2023}, pages = {351-359}, abstract = {

Motor cortex (M1) has been thought to form a continuous somatotopic homunculus extending down~the precentral gyrus from foot to face representations, despite evidence for concentric functional zones and maps of complex actions. Here, using precision functional magnetic resonance imaging (fMRI) methods, we find that the classic homunculus is interrupted by regions with distinct connectivity, structure and function, alternating with effector-specific (foot, hand and mouth) areas. These inter-effector regions exhibit decreased cortical thickness and strong functional connectivity to each other, as well as to the cingulo-opercular network (CON), critical for action and physiological control, arousal, errors and pain. This interdigitation of action control-linked and motor effector regions was verified in the three largest fMRI datasets. Macaque and pediatric (newborn, infant and child) precision fMRI suggested cross-species homologues and developmental precursors of the inter-effector system. A battery of motor and action fMRI tasks documented concentric effector somatotopies, separated by the CON-linked inter-effector regions. The inter-effectors lacked movement specificity and co-activated during action planning (coordination of hands and feet) and axial body movement (such as of the abdomen or eyebrows). These results, together with previous studies demonstrating stimulation-evoked complex actions and connectivity to internal organs such as the adrenal medulla, suggest that M1 is punctuated by a system for whole-body action planning, the somato-cognitive action network (SCAN). In M1, two parallel systems intertwine, forming an integrate-isolate pattern: effector-specific regions (foot, hand and mouth) for isolating fine motor control and the SCAN for integrating goals, physiology and body movement.

}, keywords = {Animals, Brain Mapping, Child, Cognition, Datasets as Topic, Foot, Hand, Humans, Infant, Infant, Newborn, Macaca, Magnetic Resonance Imaging, Motor Cortex, Mouth}, issn = {1476-4687}, doi = {10.1038/s41586-023-05964-2}, author = {Gordon, Evan M and Chauvin, Roselyne J and Van, Andrew N and Rajesh, Aishwarya and Nielsen, Ashley and Newbold, Dillan J and Lynch, Charles J and Seider, Nicole A and Krimmel, Samuel R and Scheidter, Kristen M and Monk, Julia and Miller, Ryland L and Metoki, Athanasia and Montez, David F and Zheng, Annie and Elbau, Immanuel and Madison, Thomas and Nishino, Tomoyuki and Myers, Michael J and Kaplan, Sydney and Badke D{\textquoteright}Andrea, Carolina and Demeter, Damion V and Feigelis, Matthew and Ramirez, Julian S B and Xu, Ting and Barch, Deanna M and Smyser, Christopher D and Rogers, Cynthia E and Zimmermann, Jan and Botteron, Kelly N and Pruett, John R and Willie, Jon T and Brunner, Peter and Shimony, Joshua S and Kay, Benjamin P and Marek, Scott and Norris, Scott A and Gratton, Caterina and Sylvester, Chad M and Power, Jonathan D and Liston, Conor and Greene, Deanna J and Roland, Jarod L and Petersen, Steven E and Raichle, Marcus E and Laumann, Timothy O and Fair, Damien A and Dosenbach, Nico U F} }