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Conventional clustering techniques for neuroimaging applications usually focus on capturing differences between given subjects, while neglecting arising differences between features and the potential bias caused by degraded data quality. In practice, collected neuroimaging data are often inevitably contaminated by noise, which may lead to errors in clustering and clinical interpretation. Additionally, most methods ignore the importance of feature grouping towards optimal clustering. In this paper, we exploit the underlying heterogeneous clusters of features to serve as weak supervision for improved clustering of subjects, which is achieved by simultaneously clustering subjects and features via nonnegative matrix tri-factorization. In order to suppress noise, we further introduce adaptive regularization based on coefficient distribution modeling. Particularly, unlike conventional sparsity regularization techniques that assume zero mean of the coefficients, we form the distributions using the data of interest so that they could better fit the non-negative coefficients. In this manner, the proposed approach is expected to be more effective and robust against noise. We compared the proposed method with standard techniques and recently published methods demonstrating superior clustering performance on synthetic data with known ground truth labels. Furthermore, when applying our proposed technique to magnetic resonance imaging (MRI) data from a cohort of patients with Parkinson’s disease, we identified two stable and highly reproducible patient clusters characterized by frontal and posterior cortical/medial temporal atrophy patterns, respectively, which also showed corresponding differences in cognitive characteristics.

ImportanceEnlarged perivascular spaces (ePVSs) have been associated with cerebral small-vessel disease (cSVD). Although their etiology may differ based on brain location, study of ePVSs has been limited to specific brain regions; therefore, their risk factors and significance remain uncertain.ObjectiveToperform a whole-brain investigation of ePVSs in a large community-based cohort.Design, Setting, and ParticipantsThis cross-sectional study analyzed data from the Atrial Fibrillation substudy of the population-based Multi-Ethnic Study of Atherosclerosis. Demographic, vascular risk, and cardiovascular disease data were collected from September 2016 to May 2018. Brain magnetic resonance imaging was performed from March 2018 to July 2019. The reported analysis was conducted between August and October 2022. A total of 1026 participants with available brain magnetic resonance imaging data and complete information on demographic characteristics and vascular risk factors were included.Main Outcomes and MeasuresEnlarged perivascular spaces were quantified using a fully automated deep learning algorithm. Quantified ePVS volumes were grouped into 6 anatomic locations: basal ganglia, thalamus, brainstem, frontoparietal, insular, and temporal regions, and were normalized for the respective regional volumes. The association of normalized regional ePVS volumes with demographic characteristics, vascular risk factors, neuroimaging indices, and prevalent cardiovascular disease was explored using generalized linear models.ResultsIn the 1026 participants, mean (SD) age was 72 (8) years; 541 (53%) of the participants were women. Basal ganglia ePVS volume was positively associated with age (β = 3.59 × 10−3; 95% CI, 2.80 × 10−3 to 4.39 × 10−3), systolic blood pressure (β = 8.35 × 10−4; 95% CI, 5.19 × 10−4 to 1.15 × 10−3), use of antihypertensives (β = 3.29 × 10−2; 95% CI, 1.92 × 10−2 to 4.67 × 10−2), and negatively associated with Black race (β = −3.34 × 10−2; 95% CI, −5.08 × 10−2 to −1.59 × 10−2). Thalamic ePVS volume was positively associated with age (β = 5.57 × 10−4; 95% CI, 2.19 × 10−4 to 8.95 × 10−4) and use of antihypertensives (β = 1.19 × 10−2; 95% CI, 6.02 × 10−3 to 1.77 × 10−2). Insular region ePVS volume was positively associated with age (β = 1.18 × 10−3; 95% CI, 7.98 × 10−4 to 1.55 × 10−3). Brainstem ePVS volume was smaller in Black than in White participants (β = −5.34 × 10−3; 95% CI, −8.26 × 10−3 to −2.41 × 10−3). Frontoparietal ePVS volume was positively associated with systolic blood pressure (β = 1.14 × 10−4; 95% CI, 3.38 × 10−5 to 1.95 × 10−4) and negatively associated with age (β = −3.38 × 10−4; 95% CI, −5.40 × 10−4 to −1.36 × 10−4). Temporal region ePVS volume was negatively associated with age (β = −1.61 × 10−2; 95% CI, −2.14 × 10−2 to −1.09 × 10−2), as well as Chinese American (β = −2.35 × 10−1; 95% CI, −3.83 × 10−1 to −8.74 × 10−2) and Hispanic ethnicities (β = −1.73 × 10−1; 95% CI, −2.96 × 10−1 to −4.99 × 10−2).Conclusions and RelevanceIn this cross-sectional study of ePVSs in the whole brain, increased ePVS burden in the basal ganglia and thalamus was a surrogate marker for underlying cSVD, highlighting the clinical importance of ePVSs in these locations.

ImportanceLittle is known about the associations of strict blood pressure (BP) control with microstructural changes in small vessel disease markers.ObjectiveTo investigate the regional associations of intensive vs standard BP control with small vessel disease biomarkers, such as white matter lesions (WMLs), fractional anisotropy (FA), mean diffusivity (MD), and cerebral blood flow (CBF).Design, Setting, and ParticipantsThe Systolic Blood Pressure Intervention Trial (SPRINT) is a multicenter randomized clinical trial that compared intensive systolic BP (SBP) control (SBP target InterventionsAt baseline, 355 participants received intensive SBP treatment and 315 participants received standard SBP treatment.Main Outcomes and MeasuresThe main outcomes were regional changes in WMLs, FA, MD (in white matter regions of interest), and CBF (in gray matter regions of interest).ResultsAt baseline, 355 participants (mean [SD] age, 67.7 [8.0] years; 200 men [56.3%]) received intensive BP treatment and 315 participants (mean [SD] age, 67.0 [8.4] years; 199 men [63.2%]) received standard BP treatment. Intensive treatment was associated with smaller mean increases in WML volume compared with standard treatment (644.5 mm3 vs 1258.1 mm3). The smaller mean increases were observed specifically in the deep white matter regions of the left anterior corona radiata (intensive treatment, 30.3 mm3 [95% CI, 16.0-44.5 mm3]; standard treatment, 80.5 mm3 [95% CI, 53.8-107.2 mm3]), left tapetum (intensive treatment, 11.8 mm3 [95% CI, 4.4-19.2 mm3]; standard treatment, 27.2 mm3 [95% CI, 19.4-35.0 mm3]), left superior fronto-occipital fasciculus (intensive treatment, 3.2 mm3 [95% CI, 0.7-5.8 mm3]; standard treatment, 9.4 mm3 [95% CI, 5.5-13.4 mm3]), left posterior corona radiata (intensive treatment, 26.0 mm3 [95% CI, 12.9-39.1 mm3]; standard treatment, 52.3 mm3 [95% CI, 34.8-69.8 mm3]), left splenium of the corpus callosum (intensive treatment, 45.4 mm3 [95% CI, 25.1-65.7 mm3]; standard treatment, 83.0 mm3 [95% CI, 58.7-107.2 mm3]), left posterior thalamic radiation (intensive treatment, 53.0 mm3 [95% CI, 29.8-76.2 mm3]; standard treatment, 106.9 mm3 [95% CI, 73.4-140.3 mm3]), and right posterior thalamic radiation (intensive treatment, 49.5 mm3 [95% CI, 24.3-74.7 mm3]; standard treatment, 102.6 mm3 [95% CI, 71.0-134.2 mm3]).Conclusions and RelevanceThis study suggests that intensive BP treatment, compared with standard treatment, was associated with a slower increase of WMLs, improved diffusion tensor imaging, and FA and CBF changes in several brain regions that represent vulnerable areas that may benefit from more strict BP control.Trial RegistrationClinicalTrials.gov Identifier: NCT01206062

Background and purpose Quantitative susceptibility mapping (QSM) has been shown to be valuable in direct targeting for subthalamic nucleus (STN) DBS, given its higher quality of contrast between the STN border and adjacent anatomical structures. The objective is to demonstrate the feasibility of using 1.5 T QSM for direct targeting in STN DBS planning. Material and methods Eleven patients underwent MRI acquisitions using a 1.5 T scanner, including multi-echo gradient echo sequences for generating QSM images. 22 STN targets were planned with direct targeting method using QSM images by one stereotactic neurosurgeon and indirect targeting method using standard protocol by a second stereotactic neurosurgeon. The two physicians were blinded to each other's results. Results The mean coordinates for the STN using direct targeting relative to the mid-commissural point (MCP) was 11.41 ± 2.43 mm lateral, 2.48 ± 0.53 mm posterior and 4.45 ± 0.95 mm inferior. The mean coordinates for the STN using indirect targeting was 11.79 ± 2.51 mm lateral, 2.55 ± 0.54 mm posterior, and 4.84 ± 1.03 mm inferior. The mean (±SEM) radial error between the direct and indirect target was 0.67 ± 0.14 mm. In cases where DBS electrodes were implanted, the radial difference between the indirect and actual target (1.19 ± 0.30 mm) was statistically equivalent to the radial difference between the direct and actual target (1.0 ± 0.27 mm). Conclusions Direct targeting of the STN for DBS implantation using 1.5 T QSM was found to be statistically equivalent to standard protocol surgery planning. This may offer a simpler, more intuitive alternative for DBS surgery planning at centers with 1.5 T MRIs.

Neuroimaging biomarkers that distinguish between changes due to typical brain ageing and Alzheimer’s disease are valuable for determining how much each contributes to cognitive decline. Supervised machine learning models can derive multivariate patterns of brain change related to the two processes, including the Spatial Patterns of Atrophy for Recognition of Alzheimer’s Disease (SPARE-AD) and of Brain Aging (SPARE-BA) scores investigated herein. However, the substantial overlap between brain regions affected in the two processes confounds measuring them independently. We present a methodology, and associated results, towards disentangling the two. T1-weighted MRI scans of 4054 participants (48–95 years) with Alzheimer’s disease, mild cognitive impairment (MCI), or cognitively normal (CN) diagnoses from the Imaging-based coordinate SysTem for AGIng and NeurodeGenerative diseases (iSTAGING) consortium were analysed. Multiple sets of SPARE scores were investigated, in order to probe imaging signatures of certain clinically or molecularly defined sub-cohorts. First, a subset of clinical Alzheimer’s disease patients (n = 718) and age- and sex-matched CN adults (n = 718) were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using CN individuals) and SPARE-AD1 (classification of CN versus Alzheimer’s disease) models. Second, analogous groups were selected based on clinical and molecular markers to train SPARE-BA2 and SPARE-AD2 models: amyloid-positive Alzheimer’s disease continuum group (n = 718; consisting of amyloid-positive Alzheimer’s disease, amyloid-positive MCI, amyloid- and tau-positive CN individuals) and amyloid-negative CN group (n = 718). Finally, the combined group of the Alzheimer’s disease continuum and amyloid-negative CN individuals was used to train SPARE-BA3 model, with the intention to estimate brain age regardless of Alzheimer’s disease-related brain changes. The disentangled SPARE models, SPARE-AD2 and SPARE-BA3, derived brain patterns that were more specific to the two types of brain changes. The correlation between the SPARE-BA Gap (SPARE-BA minus chronological age) and SPARE-AD was significantly reduced after the decoupling (r = 0.56–0.06). The correlation of disentangled SPARE-AD was non-inferior to amyloid- and tau-related measurements and to the number of APOE ε4 alleles but was lower to Alzheimer’s disease-related psychometric test scores, suggesting the contribution of advanced brain ageing to the latter. The disentangled SPARE-BA was consistently less correlated with Alzheimer’s disease-related clinical, molecular and genetic variables. By employing conservative molecular diagnoses and introducing Alzheimer’s disease continuum cases to the SPARE-BA model training, we achieved more dissociable neuroanatomical biomarkers of typical brain ageing and Alzheimer’s disease.

Introduction: Atrial fibrillation (AF) increases stroke risk and is associated with accelerated cognitive decline. Information is mixed about the relationship of AF and other supraventricular (SV) arrhythmias with brain changes on magnetic resonance imaging (MRI). Hypothesis: Prevalent AF and SV ectopy are associated with reduced gray matter (GM) volume and subclinical vascular brain injury. Methods: In the Multi-Ethnic Study of Atherosclerosis (MESA), participants completed Zio Patch monitoring and brain MRI. Prevalent AF was estimated from ambulatory monitoring, hospital surveillance, Medicare claims, and MESA electrocardiogram (ECG). For secondary analyses, AF was categorized into continuous - AF during 100% of monitoring - and intermittent - prevalent AF with Results: In MESA, 967 participants without a history of stroke or transient ischemic attack (TIA) completed >24 hours of ECG monitoring (median, 14.0 days) and completed brain MRI a median 17 months later. Mean (standard deviation) age was 72 (8) years; 53% were women. A total of 116 participants had prevalent AF, of whom 20 had continuous AF. Compared with no AF, prevalent AF was associated with lower WM FA (-0.22 standard deviations, 95% CI: -0.39, -0.04) and higher prevalence of CMBs (prevalence ratio: 1.46, 95% CI: 1.18, 1.80). In secondary analyses, compared with no AF, continuous but not intermittent AF was associated with lower GM volume (-18.6 mL, 95% CI: -31.4, -5.8). Rates of premature atrial contractions and runs of SV tachycardia were not significantly associated with brain MRI measures. Conclusions: In a multi-ethnic sample free of a history of stroke or TIA, prevalent AF was associated with lower WM FA, a measure of WM injury, and presence of CMBs. These results highlight the importance of AF prevention for brain health. Further analysis of subclinical infarcts is needed in this sample, and study in other settings is needed to confirm whether greater AF burden is associated with reduced GM volume.

Key Points Question Is obstructive sleep apnea (OSA) associated with brain white matter hyperintensities (WMHs)? Findings In this cohort study of 529 participants of the Study of Health in Pomerania-Trend baseline, a statistically significant association was found between increased OSA and increased brain WMHs. Meaning The associations found in this study between OSA and brain WMHs may indicate a novel, potentially treatable white matter disease pathomechanism., Importance Underlying pathomechanisms of brain white matter hyperintensities (WMHs), commonly observed in older individuals and significantly associated with Alzheimer disease and brain aging, have not yet been fully elucidated. One potential contributing factor to WMH burden is chronic obstructive sleep apnea (OSA), a disorder highly prevalent in the general population with readily available treatment options. Objective To investigate potential associations between OSA and WMH burden. Design, Setting, and Participants Analyses were conducted in 529 study participants of the Study of Health in Pomerania–Trend baseline (SHIP-Trend-0) study with complete WMH, OSA, and important clinical data available. SHIP-Trend-0 is a general population–based, cross-sectional, observational study to facilitate the investigation of a large spectrum of common risk factors, subclinical disorders, and clinical diseases and their relationships among each other with patient recruitment from Western Pomerania, Germany, starting on September 1, 2008, with data collected until December 31, 2012. Data analysis was performed from February 1, 2019, to January 31, 2021. Exposures The apnea-hypopnea index (AHI) and oxygen desaturation index (ODI) were assessed during a single-night, laboratory-based polysomnography measurement. Main Outcomes and Measures The primary outcome was WMH data automatically segmented from 1.5-T magnetic resonance images. Results Of 529 study participants (mean [SD] age, 52.15 [13.58] years; 282 female [53%]), a total of 209 (40%) or 102 (19%) individuals were diagnosed with OSA according to AHI or ODI criteria (mean [SD] AHI, 7.98 [12.55] events per hour; mean [SD] ODI, 3.75 [8.43] events per hour). Both AHI (β = 0.024; 95% CI, 0.011-0.037; P, This cohort study assesses whether the increased burden of obstructive sleep apnea is associated with white matter hyperintensity load and investigates the influence of additional metabolic, vascular, and lifestyle white matter hyperintensity risk factors on this association.

BackgroundWith growing numbers of patients receiving deep brain stimulation (DBS), radiologists are encountering these neuromodulation devices at an increasing rate. Current MRI safety guidelines, however, limit MRI access in these patients.PurposeTo describe an MRI (1.5 T and 3 T) experience and safety profile in a large cohort of participants with active DBS systems and characterize the hardware-related artifacts on images from functional MRI.Materials and MethodsIn this prospective study, study participants receiving active DBS underwent 1.5- or 3-T MRI (T1-weighted imaging and gradient-recalled echo [GRE]-echo-planar imaging [EPI]) between June 2017 and October 2018. Short- and long-term adverse events were tracked. The authors quantified DBS hardware-related artifacts on images from GRE-EPI (functional MRI) at the cranial coil wire and electrode contacts. Segmented artifacts were then transformed into standard space to define the brain areas affected by signal loss. Two-sample t tests were used to assess the difference in artifact size between 1.5- and 3-T MRI.ResultsA total of 102 participants (mean age ± standard deviation, 60 years ± 11; 65 men) were evaluated. No MRI-related short- and long-term adverse events or acute changes were observed. DBS artifacts were most prominent near the electrode contacts and over the frontoparietal cortical area where the redundancy of the extension wire is placed subcutaneously. The mean electrode contact artifact diameter was 9.3 mm ± 1.6, and 1.9% ± 0.8 of the brain was obscured by the coil artifact. The coil artifacts were larger at 3 T than at 1.5 T, obscuring 2.1% ± 0.7 and 1.4% ± 0.7 of intracranial volume, respectively (P < .001). The superficial frontoparietal cortex and deep structures neighboring the electrode contacts were most commonly obscured.ConclusionWith a priori local safety testing, patients receiving deep brain stimulation may safely undergo 1.5- and 3-T MRI. Deep brain stimulation hardware-related artifacts only affect a small proportion of the brain.© RSNA, 2019Online supplemental material is available for this article.See also the editorial by Martin in this issue.

BACKGROUND: Chronic pain occurs in 83% of Parkinson disease (PD) patients and deep brain stimulation (DBS) has shown to result in pain relief in a subset of patients, though the mechanism is unclear. OBJECTIVE: To compare functional magnetic resonance imaging (MRI) data in PD patients with chronic pain without DBS, those whose pain was relieved (PR) with DBS and those whose pain was not relieved (PNR) with DBS. METHODS: Functional MRI (fMRI) with blood oxygen level-dependent activation data was obtained in 15 patients in control, PR, and PNR patients. fMRI was obtained in the presence and absence of a mechanical stimuli with DBS ON and DBS OFF. Voxel-wise analysis using pain OFF data was used to determine which regions were altered during pain ON periods. RESULTS: At the time of MRI, pain was scored a 5.4 ± 1.2 out of 10 in the control, 4.25 ± 1.18 in PNR, and 0.8 ± 0.67 in PR cohorts. Group analysis of control and PNR groups showed primary somatosensory (SI) deactivation, whereas PR patients showed thalamic deactivation and SI activation. DBS resulted in more decreased activity in PR than PNR (P

We propose a segmentation methodology for brainstem cranial nerves using statistical shape model (SSM)-based deformable 3D contours from T2 MR images. We create shape models for ten pairs of cranial nerves. High-resolution T2 MR images are segmented for nerve centerline using a 1-Simplex discrete deformable 3D contour model. These segmented centerlines comprise training datasets for the shape model. Point correspondence for the training dataset is performed using an entropy-based energy minimization framework applied to particles located on the centerline curve. The shape information is incorporated into the 1-Simplex model by introducing a shape-based internal force, making the deformation stable against low resolution and image artifacts. The proposed method is validated through extensive experiments using both synthetic and patient MRI data. The robustness and stability of the proposed method are experimented using synthetic datasets. SSMs are constructed independently for ten pairs (CNIII–CNXII) of brainstem cranial nerves using ten non-pathological image datasets of the brainstem. The constructed ten SSMs are assessed in terms of compactness, specificity and generality. In order to quantify the error distances between segmented results and ground truths, two metrics are used: mean absolute shape distance (MASD) and Hausdorff distance (HD). MASD error using the proposed shape model is 0.19 ± 0.13 (mean ± std. deviation) mm and HD is 0.21 mm which are sub-voxel accuracy given the input image resolution. This paper described a probabilistic digital atlas of the ten brainstem-attached cranial nerve pairs by incorporating a statistical shape model with the 1-Simplex deformable contour. The integration of shape information as a priori knowledge results in robust and accurate centerline segmentations from even low-resolution MRI data, which is essential in neurosurgical planning and simulations for accurate and robust 3D patient-specific models of critical tissues including cranial nerves.

Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the two types of lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia. This manual annotation was then used to train a deep convolution neural network (CNN). Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI. We tested different combinations of the three modalities to determine the most informative data sources for the detection tasks. In the detection of CMBs using single class and multiclass models, we achieved an average sensitivity and precision of between 0.84–0.88 and 0.40–0.59, respectively. The same framework detected non-hemorrhage iron deposits with an average sensitivity and precision of about 0.75–0.81 and 0.62–0.75, respectively. Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data (particularly QSM) can improve the detection of CMB and non-hemorrhage iron deposits with sensitivity and precision that is compatible with use in large-scale research studies.

Introduction Relationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects). Methods Three brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD. Results WMHs were associated with advanced brain aging, AD-like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an ≈10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aβ) positivity after age 65. Brain aging, AD-like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD. Discussion A Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium.

BACKGROUND Deep brain stimulation (DBS) is a well-accepted treatment of Parkinson's disease (PD). Motor phenotypes include tremor-dominant (TD), akinesia-rigidity (AR), and postural instability gait disorder (PIGD). The mechanism of action in how DBS modulates motor symptom relief remains unknown. OBJECTIVE Blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) was used to determine whether the functional activity varies in response to DBS depending on PD phenotypes. MATERIALS AND METHODS Subjects underwent an fMRI scan with DBS cycling ON and OFF. The effects of DBS cycling on BOLD activation in each phenotype were documented through voxel-wise analysis. For each region of interest, ANOVAs were performed using T-values and covariate analyses were conducted. Further, a correlation analysis was performed comparing stimulation settings to T-values. Lastly, T-values of subjects with motor improvement were compared to those who worsened. RESULTS As a group, BOLD activation with DBS-ON resulted in activation in the motor thalamus (p

Background The majority of Parkinson's disease patients with deep brain stimulation (DBS) use a monopolar configuration, which presents challenges for EEG and MRI studies. The literature reports algorithms to convert monopolar to bipolar settings. Purpose/hypothesis To assess brain responses of Parkinson's disease patients implanted with DBS during fMRI studies using their clinical and presumed equivalent settings using a published conversion recipe. Study type Prospective. Subjects Thirteen DBS patients. Field strength/sequence 1.5T and 3T, fMRI using gradient echo-planar imaging. Assessment Patients underwent 30/30sec ON/OFF DBS fMRI scans using monopolar and bipolar settings. To convert to a bipolar setting, the negative contact used for the monopolar configuration remained constant and the adjacent dorsal contact was rendered positive, while increasing the voltage by 30%. fMRI activation/deactivation maps and motor Unified Parkinson's Disease Rating Scale (UPDRS-III) scores were compared for patients in both configurations. Statistical tests T-tests were used to compare UPDRS scores and volumes of tissue activated (VTA) diameters in monopolar and bipolar configurations. Results The patterns of fMRI activation in the monopolar and bipolar configurations were generally different. The thalamus, pallidum, and visual cortices exhibited higher activation using the patient's clinical settings than the presumed equivalent settings. VTA diameters were lower (7 mm vs. 6.3 mm, P = 0.047) and UPDRS scores were generally higher in the bipolar (33.2 ± 16) than in the monopolar configuration (28.3 ± 17.4), without reaching statistical significance (P > 0.05). Data conclusion Monopolar and bipolar configurations result in different patterns of brain activation while using a previously published monopolar-bipolar conversion algorithm. Clinical benefits may be achieved with varying patterns of brain responses. Blind conversion from one to the other should be avoided for purposes of understanding the mechanisms of DBS. Level of evidence 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018.

The Dual Contouring algorithm (DC) is a grid-based process used to generate surface meshes from volumetric data. However, DC is unable to guarantee 2-manifold and watertight meshes due to the fact that it produces only one vertex for each grid cube. We present a modified Dual Contouring algorithm that is capable of overcoming this limitation. The proposed method decomposes an ambiguous grid cube into a set of tetrahedral cells and uses novel polygon generation rules that produce 2-manifold and watertight surface meshes with good-quality triangles. These meshes, being watertight and 2-manifold, are geometrically correct, and therefore can be used to initialize tetrahedral meshes. The 2-manifold DC method has been extended into the multi-material domain. Due to its multi-material nature, multi-material surface meshes will contain non-manifold elements along material interfaces or shared boundaries. The proposed multi-material DC algorithm can (1) generate multi-material surface meshes where each material sub-mesh is a 2-manifold and watertight mesh, (2) preserve the non-manifold elements along the material interfaces, and (3) ensure that the material interface or shared boundary between materials is consistent. The proposed method is used to generate multi-material surface meshes of deep brain anatomical structures from a digital atlas of the basal ganglia and thalamus. Although deep brain anatomical structures can be labeled as functionally separate, they are in fact continuous tracts of soft tissue in close proximity to each other. The multi-material meshes generated by the proposed DC algorithm can accurately represent the closely-packed deep brain structures as a single mesh consisting of multiple material sub-meshes. Each sub-mesh represents a distinct functional structure of the brain. Printed and/or digital atlases are important tools for medical research and surgical intervention. While these atlases can provide guidance in identifying anatomical structures, they do not take into account the wide variations in the shape and size of anatomical structures that occur from patient to patient. Accurate, patient-specific representations are especially important for surgical interventions like deep brain stimulation, where even small inaccuracies can result in dangerous complications. The last part of this research effort extends the discrete deformable 2-simplex mesh into the multi-material domain where geometry-based internal forces and image-based external forces are used in the deformation process. This multi-material deformable framework is used to segment anatomical structures of the deep brain region from Magnetic Resonance (MR) data.

Printed and/or digital atlases are important tools for medical research and surgical intervention. While these atlases can provide guidance in identifying anatomical structures, they do not take into account the wide variations in the shape and size of anatomical structures that can occur from patient to patient. Accurate, patient-specific representations are especially important for surgical interventions like deep brain stimulation, where even small inaccuracies can result in dangerous complications. This research effort extends the discrete deformable 2-simplex mesh into the multi-material domain where geometry-based internal forces and image-based external forces are used in the deformation process. Multi-material 2-simplex meshes having shared boundaries are initialized from multi-material triangular surface meshes. A multi-material deformable framework is presented and used to segment anatomical structures of the deep brain region such as the subthalamic nucleus.

This paper presents a cranial nerve segmentation technique that combines a 3D deformable contour and a 3D contour Statistical Shape Model (SSM). A set of training data for the construction of the 3D contour shape model is produced using a 1-simplex based discrete deformable contour model where the centerline identification proceeds by optimizing internal and external forces. Point-correspondence for the training dataset is performed using an entropy-based energy minimization of particles on the centerline curve. The resulting average shape is used as a prior knowledge, which is incorporated into the 1-simplex as a reference shape model, making the approach stable against low resolution and image artifacts during segmentation using MRI data. Shape variability is shown using the first 3 modes of variation. The segmentation result is validated quantitatively, with ground truth provided by an expert.

This paper presents a methodology for the digital formatting of a printed atlas of the brainstem and the delineation of cranial nerves from this digital atlas. It also describes on-going work on the 3D resampling and refinement of the 2D functional regions and nerve contours. In MRI-based anatomical modeling for neurosurgery planning and simulation, the complexity of the functional anatomy entails a digital atlas approach, rather than less descriptive voxel or surface-based approaches. However, there is an insufficiency of descriptive digital atlases, in particular of the brainstem. Our approach proceeds from a series of numbered, contour-based sketches coinciding with slices of the brainstem featuring both closed and open contours. The closed contours coincide with functionally relevant regions, whereby our objective is to fill in each corresponding label, which is analogous to painting numbered regions in a paint-by-numbers kit. Any open contour typically coincides with a cranial nerve. This 2D phase is needed in order to produce densely labeled regions that can be stacked to produce 3D regions, as well as identifying the embedded paths and outer attachment points of cranial nerves. Cranial nerves are modeled using an explicit contour based technique called 1-Simplex. The relevance of cranial nerves modeling of this project is two-fold: i) this atlas will fill a void left by the brain segmentation communities, as no suitable digital atlas of the brainstem exists, and ii) this atlas is necessary to make explicit the attachment points of major nerves (except I and II) having a cranial origin. Keywords: digital atlas, contour models, surface models

The Dual Contouring algorithm (DC) is a grid-based process used to generate surface meshes from volumetric data. The advantage of DC is that it can reproduce sharp features by inserting vertices anywhere inside the grid cube, as opposed to the Marching Cubes (MC) algorithm that can insert vertices only on the grid edges. However, DC is unable to guarantee 2-manifold and watertight meshes due to the fact that it produces only one vertex for each grid cube. We present a modified Dual Contouring algorithm that is capable of overcoming this limitation. Our method decomposes an ambiguous grid cube into a maximum of twelve tetrahedral cells; we introduce novel polygon generation rules that produce 2-manifold and watertight surface meshes. We have applied our proposed method on realistic data, and a comparison of the results of our proposed method with results from traditional DC shows the effectiveness of our method.