DBSegment: Fast and robust segmentation of deep brain structures considering domain generalization.

Segmenting deep brain structures from magnetic resonance images is important for patient diagnosis, surgical planning, and research. Most current state-of-the-art solutions follow a segmentation-by-registration approach, where subject magnetic resonance imaging (MRIs) are mapped to a template with well-defined segmentations. However, registration-based pipelines are time-consuming, thus, limiting their clinical use. This paper uses deep learning to provide a one-step, robust, and efficient deep brain segmentation solution directly in the native space. The method consists of a preprocessing step to conform all MRI images to the same orientation, followed by a convolutional neural network using the nnU-Net framework. We use a total of 14 datasets from both research and clinical collections. Of these, seven were used for training and validation and seven were retained for testing. We trained the network to segment 30 deep brain structures, as well as a brain mask, using labels generated from a registration-based approach. We evaluated the generalizability of the network by performing a leave-one-dataset-out cross-validation, and independent testing on unseen datasets. Furthermore, we assessed cross-domain transportability by evaluating the results separately on different domains. We achieved an average dice score similarity of 0.89 ± 0.04 on the test datasets when compared to the registration-based gold standard. On our test system, the computation time decreased from 43 min for a reference registration-based pipeline to 1.3 min. Our proposed method is fast, robust, and generalizes with high reliability. It can be extended to the segmentation of other brain structures. It is publicly available on GitHub, and as a pip package for convenient usage.

StimVision v2: Examples and Applications in Subthalamic Deep Brain Stimulation for Parkinson's Disease.

OBJECTIVE: Subthalamic deep brain stimulation (DBS) is an established therapy for Parkinson's disease. Connectomic DBS modeling is a burgeoning subfield of research aimed at characterizing the axonal connections activated by DBS. This article describes our approach and methods for evolving the StimVision software platform to meet the technical demands of connectomic DBS modeling in the subthalamic region. MATERIALS AND METHODS: StimVision v2 was developed with Visualization Toolkit (VTK) libraries and integrates four major components: 1) medical image visualization, 2) axonal pathway visualization, 3) electrode positioning, and 4) stimulation calculation. RESULTS: StimVision v2 implemented two key technological advances for connectomic DBS analyses in the subthalamic region. First was the application of anatomical axonal pathway models to patient-specific DBS models. Second was the application of a novel driving-force method to estimate the response of those axonal pathways to DBS. Example simulations with directional DBS electrodes and clinically defined therapeutic DBS settings are presented to demonstrate the general outputs of StimVision v2 models. CONCLUSIONS: StimVision v2 provides the opportunity to evaluate patient-specific axonal pathway activation from subthalamic DBS using anatomically detailed pathway models and electrically detailed electric field distributions with interactive adjustment of the DBS electrode position and stimulation parameter settings.

Comparison of Anatomical Pathway Models with Tractography Estimates of the Pallidothalamic, Cerebellothalamic, and Corticospinal Tracts.

Introduction: Models of structural connectivity in the human brain are typically simulated using tractographic approaches. However, the nonlinear fitting of anatomical pathway atlases to de novo subject brains represents a simpler alternative that is hypothesized to provide more anatomically realistic results. Therefore, the goal of this study was to perform a side-by-side comparison of the streamline estimates generated by either pathway atlas fits or tractographic reconstructions in the same subjects. Methods: Our analyses focused on reconstruction of the corticospinal tract (CST), cerebellothalamic (CBT), and pallidothalamic (PT) pathways using example datasets from the Human Connectome Project (HCP). We used MRtrix3 to explore whole brain, as well as manual seed-to-target, tractography approaches. In parallel, we performed nonlinear fits of an axonal pathway atlas to each HCP dataset using Advanced Normalization Tools (ANTs). Results: The different methods produced notably different estimates for each pathway in each subject. The fitted atlas pathways were highly stereotyped and exhibited low variability in their streamline trajectories. Manual tractography resulted in pathway estimates that generally corresponded with the fitted atlas pathways, but with a higher degree of variability in the individual streamlines. Pathway reconstructions derived from whole-brain tractography exhibited the highest degree of variability and struggled to create anatomically realistic representations for either the CBT or PT pathways. Conclusion: The speed, simplicity, reproducibility, and realism of anatomical pathway model fits makes them an appealing option for some forms of structural connectivity modeling in the human brain. Impact statement Axonal pathway modeling is an important component of deep brain stimulation (DBS) research studies that seek to identify the brain connections that are directly activated by stimulation. The corticospinal tract, cerebellothalamic (CBT), and pallidothalamic (PT) pathways are specifically relevant to the study of subthalamic DBS for the treatment of Parkinson's disease. Our results suggest that anatomical pathway model fits of the CBT and PT pathways to de novo subject brains represent a more anatomically realistic option than tractographic approaches when studying subthalamic DBS.

Evolving characterization of the human hyperdirect pathway.

The hyperdirect pathway (HDP) represents the main glutamatergic input to the subthalamic nucleus (STN), through which the motor and prefrontal cerebral cortex can modulate basal ganglia activity. Further, direct activation of the motor HDP is thought to be an important component of therapeutic deep brain stimulation (DBS), mediating the disruption of pathological oscillations. Alternatively, unintended recruitment of the prefrontal HDP may partly explain some cognitive side effects of DBS therapy. Previous work describing the HDP has focused on non-human primate (NHP) histological pathway tracings, diffusion-weighted MRI analysis of human white matter, and electrophysiology studies involving paired cortical recordings with DBS. However, none of these approaches alone yields a complete understanding of the complexities of the HDP. As such, we propose that generative modeling methods hold promise to bridge anatomy and physiology results, from both NHPs and humans, into a more detailed representation of the human HDP. Nonetheless, numerous features of the HDP remain to be experimentally described before model-based methods can simulate corticosubthalamic activity with a high degree of scientific detail. Therefore, the goals of this review are to examine the experimental evidence for HDP projections from across the primate neocortex and discuss new data which are required to improve the utility of anatomical and biophysical models of the human corticosubthalamic system.

Latency-shift of intra-operative visual evoked potential predicts reversible homonymous hemianopia after intra-ventricular meningioma surgery.

OBJECTIVES: Intraoperative visual evoked potentials (VEPs) are used to monitor the function of optic radiation during neurosurgery with the P100 amplitude decrement as a predictor of post-operative visual deficit. However, there is currently no evidence of early VEP changes indicating reversible visual field affection. METHODS: In this case report, we used VEPs during surgery for a benign meningioma located in the atrium of the right lateral ventricle. The tumor was accessed through a transcortical approach via a two-centimeter corticotomy in the lateral aspect of the superior parietal lobule. We performed flash VEPs and simultaneous recordings of electroretinography alongside with multimodal intraoperative monitoring. RESULTS: We observed a significant and sustained unilateral latency shift of the P100 component of VEPs, while amplitudes temporarily dropped to 80% of baseline but recovered entirely at the end of surgery. After the operation, the patient had a left-sided lower-quadrant anopia, which recovered completely during the following three months. Diagnostic VEP with pattern reversal monocular full field stimulation at one month postoperatively showed normal latencies bilaterally. CONCLUSION: Our case indicates that the VEP (P100) latency may be a new and valuable indicator (in addition to VEP amplitude) of the visual pathways. SIGNIFICANCE: Monitoring VEPs may be useful to detect an imminent injury and a potentially reversible functional deficit.

Holographic Reconstruction of Axonal Pathways in the Human Brain.

Three-dimensional documentation of the axonal pathways connecting gray matter components of the human brain has wide-ranging scientific and clinical applications. Recent attempts to map human structural connectomes have concentrated on using tractography results derived from diffusion-weighted imaging data, but tractography is an indirect method with numerous limitations. Advances in holographic visualization platforms provide a new medium to integrate anatomical data, as well as a novel working environment for collaborative interaction between neuroanatomists and brain-imaging scientists. Therefore, we developed the first holographic interface for building axonal pathways, populated it with human histological and structural MRI data, and assembled world expert neuroanatomists to interactively define axonal trajectories of the cortical, basal ganglia, and cerebellar systems. This blending of advanced visualization hardware, software development, and neuroanatomy data enabled the translation of decades of amassed knowledge into a human axonal pathway atlas that can be applied to educational, scientific, or clinical investigations.

Post-operative deep brain stimulation assessment: Automatic data integration and report generation.

BACKGROUND: The gold standard for post-operative deep brain stimulation (DBS) parameter tuning is a monopolar review of all stimulation contacts, a strategy being challenged by recent developments of more complex electrode leads. OBJECTIVE: Providing a method to guide clinicians on DBS assessment and parameter tuning by automatically integrating patient individual data. METHODS: We present a fully automatic method for visualization of individual deep brain structures in relation to a DBS lead by combining precise electrode recovery from post-operative imaging with individual estimates of deep brain morphology utilizing a 7T-MRI deep brain atlas. RESULTS: The method was evaluated on 20 STN DBS cases. It demonstrated robust automatic creation of 3D-enabled PDF reports visualizing electrode to brain structure relations and proved valuable in detecting miss placed electrodes. DISCUSSION: Automatic DBS assessment is feasible and can conveniently provide clinicians with relevant information on DBS contact positions in relation to important anatomical structures.

Using automated electrode localization to guide stimulation management in DBS.

Deep Brain Stimulation requires extensive postoperative testing of stimulation parameters to achieve optimal outcomes. Testing is typically not guided by neuroanatomical information on electrode contact locations. To address this, we present an automated reconstruction of electrode locations relative to the treatment target, the subthalamic nucleus, comparing different targeting methods: atlas-, manual-, or tractography-based subthalamic nucleus segmentation. We found that most electrode contacts chosen to deliver stimulation were closest or second closest to the atlas-based subthalamic nucleus target. We suggest that information on each electrode contact's location, which can be obtained using atlas-based methods, might guide clinicians during postoperative stimulation testing.

Worsening of Verbal Fluency After Deep Brain Stimulation in Parkinson's Disease: A Focused Review.

Worsening of verbal fluency after treatment with deep brain stimulation in Parkinson's disease patients is one of the most often reported cognitive adverse effect. The underlying mechanisms of this decline are not well understood. The present focused review assesses the evidence for the reliability of the often-reported decline of verbal fluency, as well as the evidence for the suggested mechanisms including disease progression, reduced medication levels, electrode positions, and stimulation effect vs. surgical effects. Finally, we highlight the need for more systematic investigations of the large degree of heterogeneity in the prevalence of verbal fluency worsening after DBS, as well as provide suggestions for future research.

Probabilistic versus deterministic tractography for delineation of the cortico-subthalamic hyperdirect pathway in patients with Parkinson disease selected for deep brain stimulation.

OBJECTIVE Diffusion-weighted MRI (DWI) and tractography allows noninvasive mapping of the structural connections of the brain, and may provide important information for neurosurgical planning. The hyperdirect pathway, connecting the subthalamic nucleus (STN) with the motor cortex, is assumed to play a key role in mediating the effects of deep brain stimulation (DBS), which is an effective but poorly understood treatment for Parkinson disease. This study aimed to apply recent methodological advances in DWI acquisition and analysis to the delineation of the hyperdirect pathway in patients with Parkinson disease selected for surgery. METHODS High spatial and angular resolution DWI data were acquired preoperatively from 5 patients with Parkinson disease undergoing DBS. The authors compared the delineated hyperdirect pathways and associated STN target maps generated by 2 different tractography methods: a tensor-based deterministic method, typically available in clinical settings, and an advanced probabilistic method based on constrained spherical deconvolution. In addition, 10 high-resolution data sets with the same scanning parameters were acquired from a healthy control participant to assess the robustness of the tractography results. RESULTS Both tractography approaches identified connections between the ipsilateral motor cortex and the STN. However, the 2 methods provided substantially different target regions in the STN, with the target center of gravity differing by > 1.4 mm on average. The probabilistic method (based on constrained spherical deconvolution) plausibly reconstructed a continuous set of connections from the motor cortex, terminating in the dorsolateral region of the STN. In contrast, the tensor-based method reconstructed a comparatively sparser and more variable subset of connections. Furthermore, across the control scans, the probabilistic method identified considerably more consistent targeting regions within the STN compared with the deterministic tensor-based method, which demonstrated a 1.9-2.4 times higher variation. CONCLUSIONS These data provide a strong impetus for the use of a robust probabilistic tractography framework based on constrained spherical deconvolution, or similar advanced DWI models, in clinical settings. The inherent limitations and demonstrated inaccuracy of the tensor-based method leave it questionable for use in high-precision stereotactic DBS surgery. The authors have also described a straightforward method for importing tractography-derived information into any clinical neuronavigation system, based on the generation of track-density images.

Duration of motherhood has incremental effects on mothers' neural processing of infant vocal cues: a neuroimaging study of women.

The transition to motherhood, and the resultant experience of caregiving, may change the way women respond to affective, infant signals in their environments. Nonhuman animal studies have robustly demonstrated that mothers process both infant and other salient signals differently from nonmothers. Here, we investigated how women with and without young infants respond to vocalisations from infants and adults (both crying and neutral). We examined mothers with infants ranging in age (1-14 months) to examine the effects of duration of maternal experience. Using functional magnetic resonance imaging, we found that mothers showed greater activity than nonmothers to vocalisations from adults or infants in a range of cortical regions implicated in the processing of affective auditory cues. This main effect of maternal status suggests a general difference in vocalisation processing across infant and adult sounds. We found that a longer duration of motherhood, and therefore more experience with an infant, was associated with greater infant-specific activity in key parental brain regions, including the orbitofrontal cortex and amygdala. We suggest that these incremental differences in neural activity in the maternal brain reflect the building of parental capacity over time. This is consistent with conceptualizations of caregiving as a dynamic, learning process in humans.