Arasteh ST, Romanowicz J, Pace DF, Golland P, Powell AJ, Maier AK, Truhn D, Brosch T, Weese J, Lotfinia M, van der Geest RJ, Moghari MH. Automated segmentation of 3D cine cardiovascular magnetic resonance imaging. Frontiers in cardiovascular medicine. 2023;10:1167500.

INTRODUCTION: As the life expectancy of children with congenital heart disease (CHD) is rapidly increasing and the adult population with CHD is growing, there is an unmet need to improve clinical workflow and efficiency of analysis. Cardiovascular magnetic resonance (CMR) is a noninvasive imaging modality for monitoring patients with CHD. CMR exam is based on multiple breath-hold 2-dimensional (2D) cine acquisitions that should be precisely prescribed and is expert and institution dependent. Moreover, 2D cine images have relatively thick slices, which does not allow for isotropic delineation of ventricular structures. Thus, development of an isotropic 3D cine acquisition and automatic segmentation method is worthwhile to make CMR workflow straightforward and efficient, as the present work aims to establish.

METHODS: Ninety-nine patients with many types of CHD were imaged using a non-angulated 3D cine CMR sequence covering the whole-heart and great vessels. Automatic supervised and semi-supervised deep-learning-based methods were developed for whole-heart segmentation of 3D cine images to separately delineate the cardiac structures, including both atria, both ventricles, aorta, pulmonary arteries, and superior and inferior vena cavae. The segmentation results derived from the two methods were compared with the manual segmentation in terms of Dice score, a degree of overlap agreement, and atrial and ventricular volume measurements.

RESULTS: The semi-supervised method resulted in a better overlap agreement with the manual segmentation than the supervised method for all 8 structures (Dice score 83.23 ± 16.76% vs. 77.98 ± 19.64%; P-value ≤0.001). The mean difference error in atrial and ventricular volumetric measurements between manual segmentation and semi-supervised method was lower (bias ≤ 5.2 ml) than the supervised method (bias ≤ 10.1 ml).

DISCUSSION: The proposed semi-supervised method is capable of cardiac segmentation and chamber volume quantification in a CHD population with wide anatomical variability. It accurately delineates the heart chambers and great vessels and can be used to accurately calculate ventricular and atrial volumes throughout the cardiac cycle. Such a segmentation method can reduce inter- and intra- observer variability and make CMR exams more standardized and efficient.

Safdar S, Zwick BF, Yu Y, Bourantas GC, Joldes GR, Warfield SK, Hyde DE, Frisken S, Kapur T, Kikinis R, Golby A, Nabavi A, Wittek A, Miller K. SlicerCBM: automatic framework for biomechanical analysis of the brain. International journal of computer assisted radiology and surgery. 2023;18(10):1925–1940.

PURPOSE: Brain shift that occurs during neurosurgery disturbs the brain's anatomy. Prediction of the brain shift is essential for accurate localisation of the surgical target. Biomechanical models have been envisaged as a possible tool for such predictions. In this study, we created a framework to automate the workflow for predicting intra-operative brain deformations.

METHODS: We created our framework by uniquely combining our meshless total Lagrangian explicit dynamics (MTLED) algorithm for computing soft tissue deformations, open-source software libraries and built-in functions within 3D Slicer, an open-source software package widely used for medical research. Our framework generates the biomechanical brain model from the pre-operative MRI, computes brain deformation using MTLED and outputs results in the form of predicted warped intra-operative MRI.

RESULTS: Our framework is used to solve three different neurosurgical brain shift scenarios: craniotomy, tumour resection and electrode placement. We evaluated our framework using nine patients. The average time to construct a patient-specific brain biomechanical model was 3 min, and that to compute deformations ranged from 13 to 23 min. We performed a qualitative evaluation by comparing our predicted intra-operative MRI with the actual intra-operative MRI. For quantitative evaluation, we computed Hausdorff distances between predicted and actual intra-operative ventricle surfaces. For patients with craniotomy and tumour resection, approximately 95% of the nodes on the ventricle surfaces are within two times the original in-plane resolution of the actual surface determined from the intra-operative MRI.

CONCLUSION: Our framework provides a broader application of existing solution methods not only in research but also in clinics. We successfully demonstrated the application of our framework by predicting intra-operative deformations in nine patients undergoing neurosurgical procedures.

Costello H, Yamamori Y, Reeves S, Schrag AE, Howard R, Roiser JP. Longitudinal decline in striatal dopamine transporter binding in Parkinson’s disease: associations with apathy and anhedonia. Journal of neurology, neurosurgery, and psychiatry. 2023;94(10):863–870.

BACKGROUND: Motivational symptoms such as apathy and anhedonia are common in Parkinson's disease (PD), respond poorly to treatment, and are hypothesised to share underlying neural mechanisms. Striatal dopaminergic dysfunction is considered central to motivational symptoms in PD but the association has never been examined longitudinally. We investigated whether progression of dopaminergic dysfunction was associated with emergent apathy and anhedonia symptoms in PD.

METHODS: Longitudinal cohort study of 412 newly diagnosed patients with PD followed over 5 years as part of the Parkinson's Progression Markers Initiative cohort.Apathy and anhedonia were measured using a composite score derived from relevant items of the 15-item Geriatric Depression Scale (GDS-15) and part I of the MDS-Unified Parkinson's Disease Rating Scale. Dopaminergic neurodegeneration was measured using repeated striatal dopamine transporter (DAT) imaging.

RESULTS: Linear mixed-effects modelling across all contemporaneous data points identified a significant negative relationship between striatal DAT specific binding ratio (SBR) and apathy/anhedonia symptoms, which emerged as PD progressed (interaction:β=-0.09, 95% CI (-0.15 to -0.03), p=0.002). Appearance and subsequent worsening of apathy/anhedonia symptoms began on average 2 years after diagnosis and below a threshold striatal DAT SBR level. The interaction between striatal DAT SBR and time was specific to apathy/anhedonia symptoms, with no evidence of a similar interaction for general depressive symptoms from the GDS-15 (excluding apathy/anhedonia items) (β=-0.06, 95% CI (-0.13 to 0.01)) or motor symptoms (β=0.20, 95% CI (-0.25 to 0.65)).

CONCLUSIONS: Our findings support a central role for dopaminergic dysfunction in motivational symptoms in PD. Striatal DAT imaging may be a useful indicator of apathy/anhedonia risk that could inform intervention strategies.

Sullivan JJ, Zekelman LR, Zhang F, Juvekar P, Torio EF, Bunevicius A, Essayed WI, Bastos D, He J, Rigolo L, Golby AJ, O’Donnell LJ. Directionally encoded color track density imaging in brain tumor patients: A potential application to neuro-oncology surgical planning. NeuroImage. Clinical. 2023;38:103412.

BACKGROUND: Diffusion magnetic resonance imaging white matter tractography, an increasingly popular preoperative planning modality used for pre-surgical planning in brain tumor patients, is employed with the goal of maximizing tumor resection while sparing postoperative neurological function. Clinical translation of white matter tractography has been limited by several shortcomings of standard diffusion tensor imaging (DTI), including poor modeling of fibers crossing through regions of peritumoral edema and low spatial resolution for typical clinical diffusion MRI (dMRI) sequences. Track density imaging (TDI) is a post-tractography technique that uses the number of tractography streamlines and their long-range continuity to map the white matter connections of the brain with enhanced image resolution relative to the acquired dMRI data, potentially offering improved white matter visualization in patients with brain tumors. The aim of this study was to assess the utility of TDI-based white matter maps in a neurosurgical planning context compared to the current clinical standard of DTI-based white matter maps.

METHODS: Fourteen consecutive brain tumor patients from a single institution were retrospectively selected for the study. Each patient underwent 3-Tesla dMRI scanning with 30 gradient directions and a b-value of 1000 s/mm2. For each patient, two directionally encoded color (DEC) maps were produced as follows. DTI-based DEC-fractional anisotropy maps (DEC-FA) were generated on the scanner, while DEC-track density images (DEC-TDI) were generated using constrained spherical deconvolution based tractography. The potential clinical utility of each map was assessed by five practicing neurosurgeons, who rated the maps according to four clinical utility statements regarding different clinical aspects of pre-surgical planning. The neurosurgeons rated each map according to their agreement with four clinical utility statements regarding if the map 1 identified clinically relevant tracts, (2) helped establish a goal resection margin, (3) influenced a planned surgical route, and (4) was useful overall. Cumulative link mixed effect modeling and analysis of variance were performed to test the primary effect of map type (DEC-TDI vs. DEC-FA) on rater score. Pairwise comparisons using estimated marginal means were then calculated to determine the magnitude and directionality of differences in rater scores by map type.

RESULTS: A majority of rater responses agreed with the four clinical utility statements, indicating that neurosurgeons found both DEC maps to be useful. Across all four investigated clinical utility statements, the DEC map type significantly influenced rater score. Rater scores were significantly higher for DEC-TDI maps compared to DEC-FA maps. The largest effect size in rater scores in favor of DEC-TDI maps was observed for clinical utility statement 2, which assessed establishing a goal resection margin.

CONCLUSION: We observed a significant neurosurgeon preference for DEC-TDI maps, indicating their potential utility for neurosurgical planning.

Levitt JJ, Zhang F, Vangel M, Nestor PG, Rathi Y, Cetin-Karayumak S, Kubicki M, Coleman MJ, Lewandowski KE, Holt DJ, Keshavan M, Bouix S, Öngür D, Breier A, Shenton ME, O’Donnell LJ. The organization of frontostriatal brain wiring in non-affective early psychosis compared with healthy subjects using a novel diffusion imaging fiber cluster analysis. Molecular psychiatry. 2023;28(6):2301–2311.

BACKGROUND: Alterations in brain connectivity may underlie neuropsychiatric conditions such as schizophrenia. We here assessed the degree of convergence of frontostriatal fiber projections in 56 young adult healthy controls (HCs) and 108 matched Early Psychosis-Non-Affective patients (EP-NAs) using our novel fiber cluster analysis of whole brain diffusion magnetic resonance imaging tractography.

METHODS: Using whole brain tractography and our fiber clustering methodology on harmonized diffusion magnetic resonance imaging data from the Human Connectome Project for Early Psychosis we identified 17 white matter fiber clusters that connect frontal cortex (FCtx) and caudate (Cd) per hemisphere in each group. To quantify the degree of convergence and, hence, topographical relationship of these fiber clusters, we measured the inter-cluster mean distances between the endpoints of the fiber clusters at the level of the FCtx and of the Cd, respectively.

RESULTS: We found (1) in both groups, bilaterally, a non-linear relationship, yielding convex curves, between FCtx and Cd distances for FCtx-Cd connecting fiber clusters, driven by a cluster projecting from inferior frontal gyrus; however, in the right hemisphere, the convex curve was more flattened in EP-NAs; (2) that cluster pairs in the right (p = 0.03), but not left (p = 0.13), hemisphere were significantly more convergent in HCs vs EP-NAs; (3) in both groups, bilaterally, similar clusters projected significantly convergently to the Cd; and, (4) a significant group by fiber cluster pair interaction for 2 right hemisphere fiber clusters (numbers 5, 11; p = .00023; p = .00023) originating in selective PFC subregions.

CONCLUSIONS: In both groups, we found the FCtx-Cd wiring pattern deviated from a strictly topographic relationship and that similar clusters projected significantly more convergently to the Cd. Interestingly, we also found a significantly more convergent pattern of connectivity in HCs in the right hemisphere and that 2 clusters from PFC subregions in the right hemisphere significantly differed in their pattern of connectivity between groups.

Vasung L, Xu J, Abaci-Turk E, Zhou C, Holland E, Barth WH, Barnewolt C, Connolly S, Estroff J, Golland P, Feldman HA, Adalsteinsson E, Grant E. Cross-Sectional Observational Study of Typical in utero Fetal Movements Using Machine Learning. Developmental neuroscience. 2023;45(3):105–114.

Early variations of fetal movements are the hallmark of a healthy developing central nervous system. However, there are no automatic methods to quantify the complex 3D motion of the developing fetus in utero. The aim of this prospective study was to use machine learning (ML) on in utero MRI to perform quantitative kinematic analysis of fetal limb movement, assessing the impact of maternal, placental, and fetal factors. In this cross-sectional, observational study, we used 76 sets of fetal (24-40 gestational weeks [GW]) blood oxygenation level-dependent (BOLD) MRI scans of 52 women (18-45 years old) during typical pregnancies. Pregnant women were scanned for 5-10 min while breathing room air (21% O2) and for 5-10 min while breathing 100% FiO2 in supine and/or lateral position. BOLD acquisition time was 20 min in total with effective temporal resolution approximately 3 s. To quantify upper and lower limb kinematics, we used a 3D convolutional neural network previously trained to track fetal key points (wrists, elbows, shoulders, ankles, knees, hips) on similar BOLD time series. Tracking was visually assessed, errors were manually corrected, and the absolute movement time (AMT) for each joint was calculated. To identify variables that had a significant association with AMT, we constructed a mixed-model ANOVA with interaction terms. Fetuses showed significantly longer duration of limb movements during maternal hyperoxia. We also found a significant centrifugal increase of AMT across limbs and significantly longer AMT of upper extremities <31 GW and longer AMT of lower extremities >35 GW. In conclusion, using ML we successfully quantified complex 3D fetal limb motion in utero and across gestation, showing maternal factors (hyperoxia) and fetal factors (gestational age, joint) that impact movement. Quantification of fetal motion on MRI is a potential new biomarker of fetal health and neuromuscular development.

Wang CJ, Rost NS, Golland P. Spatial-Intensity Transforms for Medical Image-to-Image Translation. IEEE transactions on medical imaging. 2023;42(11):3362–3373.

Image-to-image translation has seen major advances in computer vision but can be difficult to apply to medical images, where imaging artifacts and data scarcity degrade the performance of conditional generative adversarial networks. We develop the spatial-intensity transform (SIT) to improve output image quality while closely matching the target domain. SIT constrains the generator to a smooth spatial transform (diffeomorphism) composed with sparse intensity changes. SIT is a lightweight, modular network component that is effective on various architectures and training schemes. Relative to unconstrained baselines, this technique significantly improves image fidelity, and our models generalize robustly to different scanners. Additionally, SIT provides a disentangled view of anatomical and textural changes for each translation, making it easier to interpret the model's predictions in terms of physiological phenomena. We demonstrate SIT on two tasks: predicting longitudinal brain MRIs in patients with various stages of neurodegeneration, and visualizing changes with age and stroke severity in clinical brain scans of stroke patients. On the first task, our model accurately forecasts brain aging trajectories without supervised training on paired scans. On the second task, it captures associations between ventricle expansion and aging, as well as between white matter hyperintensities and stroke severity. As conditional generative models become increasingly versatile tools for visualization and forecasting, our approach demonstrates a simple and powerful technique for improving robustness, which is critical for translation to clinical settings. Source code is available at

Juvekar P, Dorent R, Kögl F, Torio E, Barr C, Rigolo L, Galvin C, Jowkar N, Kazi A, Haouchine N, Cheema H, Navab N, Pieper S, Wells WM, Bi WL, Golby A, Frisken S, Kapur T. ReMIND: The Brain Resection Multimodal Imaging Database. medRxiv : the preprint server for health sciences. 2023;.

The standard of care for brain tumors is maximal safe surgical resection as the first step. Neuronavigation augments the surgeon's ability to achieve this but loses validity due to brain shift as surgery progresses. Moreover, many gliomas are difficult to distinguish from adjacent healthy brain tissue. Intraoperative MRI (iMRI) is a useful surgical adjunct that can be used to visualize the residual tumor and brain shift. Intraoperative ultrasound (iUS) serves a similar purpose, while also being faster and easier to incorporate into the workflow. However, it provides lower contrast between tumor tissue and normal brain tissue as compared to intraoperative MRI. With the success of data-hungry Artificial Intelligence (AI)/Machine Learning (ML) algorithms in advancing the state of the art in medical image analysis, the benefits of sharing well-curated data can not be overstated. To this end, we provide here the largest publicly-available MRI and intraoperative ultrasound imaging database of surgically treated brain tumors, including gliomas (n=92), metastases (n=11), and others (n=11). This collection contains 369 preoperative MRI series, 320 3D intraoperative ultrasound series, 301 intraoperative MRI series, and 356 segmentations collected from 114 consecutive patients at a single institution. We expect this data to be a resource for computational research in brain shift and image analysis as well as for neurosurgical training in the interpretation of intraoperative ultrasound and iMRI.

He J, Zhang F, Pan Y, Feng Y, Rushmore J, Torio E, Rathi Y, Makris N, Kikinis R, Golby AJ, O’Donnell LJ. Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods. Human brain mapping. 2023;44(17):6055–6073.

The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body. The CST exhibits a somatotopic organization, which means that the motor neurons that control specific body parts are arranged in order within the CST. Diffusion magnetic resonance imaging (MRI) tractography is increasingly used to study the anatomy of the CST. However, despite many advances in tractography algorithms over the past decade, modern, state-of-the-art methods still face challenges. In this study, we compare the performance of six widely used tractography methods for reconstructing the CST and its somatotopic organization. These methods include constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD-Stream) methods, unscented Kalman filter (UKF) tractography methods including multi-fiber (UKF2T) and single-fiber (UKF1T) models, the generalized q-sampling imaging (GQI) based deterministic tractography method, and the TractSeg method. We investigate CST somatotopy by dividing the CST into four subdivisions per hemisphere that originate in the leg, trunk, hand, and face areas of the primary motor cortex. A quantitative and visual comparison is performed using diffusion MRI data (N = 100 subjects) from the Human Connectome Project. Quantitative evaluations include the reconstruction rate of the eight anatomical subdivisions, the percentage of streamlines in each subdivision, and the coverage of the white matter-gray matter (WM-GM) interface. CST somatotopy is further evaluated by comparing the percentage of streamlines in each subdivision to the cortical volumes for the leg, trunk, hand, and face areas. Overall, UKF2T has the highest reconstruction rate and cortical coverage. It is the only method with a significant positive correlation between the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex. However, our experimental results show that all compared tractography methods are biased toward generating many trunk streamlines (ranging from 35.10% to 71.66% of total streamlines across methods). Furthermore, the coverage of the WM-GM interface in the largest motor area (face) is generally low (under 40%) for all compared tractography methods. Different tractography methods give conflicting results regarding the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex, indicating that there is generally no clear relationship, and that reconstruction of CST somatotopy is still a large challenge. Overall, we conclude that while current tractography methods have made progress toward the well-known challenge of improving the reconstruction of the lateral projections of the CST, the overall problem of performing a comprehensive CST reconstruction, including clinically important projections in the lateral (hand and face areas) and medial portions (leg area), remains an important challenge for diffusion MRI tractography.

Costello H, Schrag AE, Howard R, Roiser JP. Dissociable effects of dopaminergic medications on depression symptom dimensions in Parkinson’s disease. medRxiv : the preprint server for health sciences. 2023;.

BACKGROUND: Depression in Parkinson's disease (PD) is common, disabling and responds poorly to standard antidepressant medication. Motivational symptoms of depression, such as apathy and anhedonia, are particularly prevalent in depression in PD and predict poor response to antidepressant treatment. Loss of dopaminergic innervation of the striatum is associated with emergence of motivational symptoms in PD, and mood fluctuations correlate with dopamine availability. Accordingly, optimising dopaminergic treatment for PD can improve depressive symptoms, and dopamine agonists have shown promising effects in improving apathy. However, the differential effect of antiparkinsonian medication on symptom dimensions of depression is not known.

AIMS: We hypothesised that there would be dissociable effects of dopaminergic medications on different depression symptom dimensions. We predicted that dopaminergic medication would specifically improve motivational symptoms, but not other symptoms, of depression. We also hypothesised that antidepressant effects of dopaminergic medications with mechanisms of action reliant on pre-synaptic dopamine neuron integrity would attenuate as pre-synaptic dopaminergic neurodegeneration progresses.

METHODS: We analysed data from a longitudinal study of 412 newly diagnosed PD patients followed over five years in the Parkinson's Progression Markers Initiative cohort. Medication state for individual classes of Parkinson's medications was recorded annually. Previously validated "motivation" and "depression" dimensions were derived from the 15-item geriatric depression scale. Dopaminergic neurodegeneration was measured using repeated striatal dopamine transporter (DAT) imaging.

RESULTS: Linear mixed-effects modelling was performed across all simultaneously acquired data points. Dopamine agonist use was associated with relatively fewer motivation symptoms as time progressed (interaction: β=-0.07, 95%CI [-0.13,-0.01], p=0.015) but had no effect on the depression symptom dimension (p=0.6). In contrast, monoamine oxidase-B (MAO-B) inhibitor use was associated with relatively fewer depression symptoms across all years (β=-0.41, 95%CI [-0.81,-0.01], p=0.047). No associations were observed between either depression or motivation symptoms and levodopa or amantadine use. There was a significant interaction between striatal DAT binding and MAO-B inhibitor use on motivation symptoms: MAO-B inhibitor use was associated with lower motivation symptoms in patients with higher striatal DAT binding (interaction: β=-0.24, 95%CI [-0.43,-0.05], p=0.012). No other medication effects were moderated by striatal DAT binding measures.

CONCLUSIONS: We identified dissociable associations between dopaminergic medications and different dimensions of depression in PD. Dopamine agonists may be effective for treatment of motivational symptoms of depression. In contrast, MAO-B inhibitors may improve both depressive and motivation symptoms, albeit the latter effect appears to be attenuated in patients with more severe striatal dopaminergic neurodegeneration, which may be a consequence of dependence on pre-synaptic dopaminergic neuron integrity.