Application of deep learning for automatic segmentation of brain tumors on magnetic resonance imaging: a heuristic approach in the clinical scenario.

PURPOSE: Accurate brain tumor segmentation on magnetic resonance imaging (MRI) has wide-ranging applications such as radiosurgery planning. Advances in artificial intelligence, especially deep learning (DL), allow development of automatic segmentation that overcome the labor-intensive and operator-dependent manual segmentation. We aimed to evaluate the accuracy of the top-performing DL model from the 2018 Brain Tumor Segmentation (BraTS) challenge, the impact of missing MRI sequences, and whether a model trained on gliomas can accurately segment other brain tumor types. METHODS: We trained the model using Medical Decathlon dataset, applied it to the BraTS 2019 glioma dataset, and developed additional models using individual and multimodal MRI sequences. The Dice score was calculated to assess the model's accuracy compared to ground truth labels by neuroradiologists on BraTS dataset. The model was then applied to a local dataset of 105 brain tumors, performance of which was qualitatively evaluated. RESULTS: The DL model using pre- and post-gadolinium contrast T1 and T2 FLAIR sequences performed best, with a Dice score 0.878 for whole tumor, 0.732 tumor core, and 0.699 active tumor. Lack of T1 or T2 sequences did not significantly degrade performance, but FLAIR and T1C were important contributors. All segmentations performed by the model in the local dataset, including non-glioma cases, were considered accurate by a pool of specialists. CONCLUSION: The DL model could use available MRI sequences to optimize glioma segmentation and adopt transfer learning to segment non-glioma tumors, thereby serving as a useful tool to improve treatment planning and personalized surveillance of patients.

Therapeutic Efficacy of Rhemercise: A Novel Mindfulness Technique in Patients With Opioid Use Disorder.

Objective: Rhemercise is a novel mindfulness technique used to prevent relapse in opioid use disorder (OUD). Rhemercise is a quantifiable and intentional slow-breathing technique that could increase subjective well-being, which helps to prevent relapse in OUD by reducing craving, negative affect, and visceral reactivity. The objective of this study was to assess the efficacy of rhemercise as an adjunctive therapy in patients with OUD undergoing detoxification.Methods: This was a hospital-based, open-label, prospective, and exploratory study conducted between June 2018 and June 2019 that included 126 male inpatients admitted for detoxification of OUD. Patients with OUD diagnosed according to ICD-10 criteria who were aged 18-65 years were included in the study. Patients with other psychiatric disorders were excluded. Participants were divided into 2 groups: group A (n = 63) comprised patients receiving treatment as usual + rhemercise, and group B (n = 63) received treatment as usual only. Assessment tools included the Clinical Opiate Withdrawal Scale, Brief Pain Inventory, and Subjective Well-Being Inventory.Results: Various domains of the Subjective Well-Being Inventory (general well-being-positive affect [P = .02], confidence in coping [P = .007], inadequate mental mastery [P = .002]) improved significantly among OUD patients who received rhemercise treatment compared to treatment as usual.Conclusion: Rhemercise promoted general well-being and positive affect and decreased the opioid withdrawal symptoms, thereby potentially reducing the overall risk for relapse. Future studies are warranted with rhemercise to validate these promising findings.

A connectivity model of the anatomic substrates underlying Gerstmann syndrome.

The Gerstmann syndrome is a constellation of neurological deficits that include agraphia, acalculia, left-right discrimination and finger agnosia. Despite a growing interest in this clinical phenomenon, there remains controversy regarding the specific neuroanatomic substrates involved. Advancements in data-driven, computational modelling provides an opportunity to create a unified cortical model with greater anatomic precision based on underlying structural and functional connectivity across complex cognitive domains. A literature search was conducted for healthy task-based functional MRI and PET studies for the four cognitive domains underlying Gerstmann's tetrad using the electronic databases PubMed, Medline, and BrainMap Sleuth (2.4). Coordinate-based, meta-analytic software was utilized to gather relevant regions of interest from included studies to create an activation likelihood estimation (ALE) map for each cognitive domain. Machine-learning was used to match activated regions of the ALE to the corresponding parcel from the cortical parcellation scheme previously published under the Human Connectome Project (HCP). Diffusion spectrum imaging-based tractography was performed to determine the structural connectivity between relevant parcels in each domain on 51 healthy subjects from the HCP database. Ultimately 102 functional MRI studies met our inclusion criteria. A frontoparietal network was found to be involved in the four cognitive domains: calculation, writing, finger gnosis, and left-right orientation. There were three parcels in the left hemisphere, where the ALE of at least three cognitive domains were found to be overlapping, specifically the anterior intraparietal area, area 7 postcentral (7PC) and the medial intraparietal sulcus. These parcels surround the anteromedial portion of the intraparietal sulcus. Area 7PC was found to be involved in all four domains. These regions were extensively connected in the intraparietal sulcus, as well as with a number of surrounding large-scale brain networks involved in higher-order functions. We present a tractographic model of the four neural networks involved in the functions which are impaired in Gerstmann syndrome. We identified a 'Gerstmann Core' of extensively connected functional regions where at least three of the four networks overlap. These results provide clinically actionable and precise anatomic information which may help guide clinical translation in this region, such as during resective brain surgery in or near the intraparietal sulcus, and provides an empiric basis for future study.

Galantamine-Memantine Combination and Kynurenine Pathway Enzyme Inhibitors in the Treatment of Neuropsychiatric Disorders.

The kynurenine pathway (KP) is a major route for L-tryptophan (L-TRP) metabolism, yielding a variety of bioactive compounds including kynurenic acid (KYNA), 3-hydroxykynurenine (3-HK), quinolinic acid (QUIN), and picolinic acid (PIC). These tryptophan catabolites are involved in the pathogenesis of many neuropsychiatric disorders, particularly when the KP becomes dysregulated. Accordingly, the enzymes that regulate the KP such as indoleamine 2,3-dioxygenase (IDO)/tryptophan 2,3-dioxygenase, kynurenine aminotransferases (KATs), and kynurenine 3-monooxygenase (KMO) represent potential drug targets as enzymatic inhibition can favorably rebalance KP metabolite concentrations. In addition, the galantamine-memantine combination, through its modulatory effects at the alpha7 nicotinic acetylcholine receptors and N-methyl-D-aspartate receptors, may counteract the effects of KYNA. The aim of this review is to highlight the effectiveness of IDO-1, KAT II, and KMO inhibitors, as well as the galantamine-memantine combination in the modulation of different KP metabolites. KAT II inhibitors are capable of decreasing the KYNA levels in the rat brain by a maximum of 80%. KMO inhibitors effectively reduce the central nervous system (CNS) levels of 3-HK, while markedly boosting the brain concentration of KYNA. Emerging data suggest that the galantamine-memantine combination also lowers L-TRP, kynurenine, KYNA, and PIC levels in humans. Presently, there are only 2 pathophysiological mechanisms (cholinergic and glutamatergic) that are FDA approved for the treatment of cognitive dysfunction for which purpose the galantamine-memantine combination has been designed for clinical use against Alzheimer's disease. The alpha7 nicotinic-NMDA hypothesis targeted by the galantamine-memantine combination has been implicated in the pathophysiology of various CNS diseases. Similarly, KYNA is well capable of modulating the neuropathophysiology of these disorders. This is known as the KYNA-centric hypothesis, which may be implicated in the management of certain neuropsychiatric conditions. In line with this hypothesis, KYNA may be considered as the "conductor of the orchestra" for the major pathophysiological mechanisms underlying CNS disorders. Therefore, there is great opportunity to further explore and compare the biological effects of these therapeutic modalities in animal models with a special focus on their effects on KP metabolites in the CNS and with the ultimate goal of progressing to clinical trials for many neuropsychiatric diseases.

A Cortical Parcellation Based Analysis of Ventral Premotor Area Connectivity.

Introduction. The ventral premotor area (VPM) plays a crucial role in executing various aspects of motor control. These include hand reaching, joint coordination, and direction of movement in space. While many studies discuss the VPM and its relationship to the rest of the motor network, there is minimal literature examining the connectivity of the VPM outside of the motor network. Using region-based fMRI studies, we built a neuroanatomical model to account for these extra-motor connections.Methods. Thirty region-based fMRI studies were used to generate an activation likelihood estimation (ALE) using BrainMap software. Cortical parcellations overlapping the ALE were used to construct a preliminary model of the VPM connections outside the motor network. Diffusion spectrum imaging (DSI)-based fiber tractography was performed to determine the connectivity between cortical parcellations in both hemispheres, and a laterality index (LI) was calculated with resultant tract volumes. The resulting connections were described using the cortical parcellation scheme developed by the Human Connectome Project (HCP).Results. Four cortical regions were found to comprise the VPM. These four regions included 6v, 4, 3b, and 3a. Across mapped brains, these areas showed consistent interconnections between each other. Additionally, ipsilateral connections to the primary motor cortex, supplementary motor area, and dorsal premotor cortex were demonstrated. Inter-hemispheric asymmetries were identified, especially with areas 1, 55b, and MI connecting to the ipsilateral VPM regions.Conclusion. We describe a preliminary cortical model for the underlying connectivity of the ventral premotor area. Future studies should further characterize the neuroanatomic underpinnings of this network for neurosurgical applications.

Parcellation-based modeling of the supplementary motor area.

INTRODUCTION: The supplementary motor area (SMA) plays an important role in the initiation and coordination of internally and externally cued movements. Such movements include reaching, grasping, speaking, and bilateral hand coordination. While many studies discuss the SMA and its relationship to other parts of the motor network, there is minimal literature examining the connectivity of the SMA outside of the motor network. Using region-based fMRI studies, we built a neuroanatomical model to account for these extra-motor connections. METHODS: Thirty region-based fMRI studies were used to generate an activation likelihood estimation (ALE) using BrainMap software. Cortical parcellations overlapping the ALE were used to construct a preliminary model of the SMA connections outside the motor network. DSI-based fiber tractography was performed to determine the connectivity between cortical parcellations. The resulting connections were described using the cortical parcellation scheme developed by the Human Connectome Project (HCP). RESULTS: Four left hemisphere regions were found to comprise the SMA. These included areas SFL, SCEF, 6ma, and 6mp. Across mapped brains, these areas showed consistent interconnections between each other. Additionally, ipsilateral connections to the primary motor cortex, left inferior and middle frontal gyri, the anterior cingulate gyrus, and insula were demonstrated. Connections to the contralateral SMA, anterior cingulate, lateral premotor, and inferior frontal cortices were also identified. CONCLUSIONS: We describe a preliminary cortical model for the underlying structural connectivity of the supplementary motor area outside the motor network. Future studies should further characterize the neuroanatomic underpinnings of this network for the purposes of medical application.

Parcellation-based anatomic model of the semantic network.

INTRODUCTION: The semantic network is an important mediator of language, enabling both speech production and the comprehension of multimodal stimuli. A major challenge in the field of neurosurgery is preventing semantic deficits. Multiple cortical areas have been linked to semantic processing, though knowledge of network connectivity has lacked anatomic specificity. Using attentional task-based fMRI studies, we built a neuroanatomical model of this network. METHODS: One hundred and fifty-five task-based fMRI studies related to categorization of visual words and objects, and auditory words and stories were used to generate an activation likelihood estimation (ALE). Cortical parcellations overlapping the ALE were used to construct a preliminary model of the semantic network based on the cortical parcellation scheme previously published under the Human Connectome Project. Deterministic fiber tractography was performed on 25 randomly chosen subjects from the Human Connectome Project, to determine the connectivity of the cortical parcellations comprising the network. RESULTS: The ALE analysis demonstrated fourteen left hemisphere cortical regions to be a part of the semantic network: 44, 45, 55b, IFJa, 8C, p32pr, SFL, SCEF, 8BM, STSdp, STSvp, TE1p, PHT, and PBelt. These regions showed consistent interconnections between parcellations. Notably, the anterior temporal pole, a region often implicated in semantic function, was absent from our model. CONCLUSIONS: We describe a preliminary cortical model for the underlying structural connectivity of the semantic network. Future studies will further characterize the neurotractographic details of the semantic network in the context of medical application.

Parcellation-based anatomic modeling of the default mode network.

BACKGROUND: The default mode network (DMN) is an important mediator of passive states of mind. Multiple cortical areas, such as the anterior cingulate cortex, posterior cingulate cortex, and lateral parietal lobe, have been linked in this processing, though knowledge of network connectivity had limited tractographic specificity. METHODS: Using resting-state fMRI studies related to the DMN, we generated an activation likelihood estimation (ALE). We built a tractographical model of this network based on the cortical parcellation scheme previously published under the Human Connectome Project. DSI-based fiber tractography was performed to determine the structural connections between cortical parcellations comprising the network. RESULTS: Seventeen cortical regions were found to be part of the DMN: 10r, 31a, 31pd, 31pv, a24, d23ab, IP1, p32, POS1, POS2, RSC, PFm, PGi, PGs, s32, TPOJ3, and v23ab. These regions showed consistent interconnections between adjacent parcellations, and the cingulum was found to connect the anterior and posterior cingulate clusters within the network. CONCLUSIONS: We present a preliminary anatomic model of the default mode network. Further studies may refine this model with the ultimate goal of clinical application.

Anatomy and White Matter Connections of the Middle Frontal Gyrus.

BACKGROUND: The middle frontal gyrus (MFG) is involved in attention, working memory, and language-related processing. A detailed understanding of the subcortical white matter tracts connected within the MFG can facilitate improved navigation of white matter lesions in and around this gyrus and explain the postoperative morbidity after surgery. We aimed to characterize the fiber tracts within the MFG according to their connection to neuroanatomic structures through the use of diffusion spectrum imaging-based fiber tractography and validate the findings by gross anatomic dissection for qualitative visual agreement. METHODS: Tractography analysis was completed using diffusion imaging data from 10 healthy, adult subjects enrolled in the Human Connectome Project. We assessed the MFG as a whole component according to its fiber connectivity with other neural regions. Mapping was completed on all tracts within both hemispheres, with the resultant tract volumes used to calculate a lateralization index. A modified Klingler technique was used on 10 postmortem dissections to demonstrate the location and orientation of the major tracts. RESULTS: Two major connections of the MFG were identified: the superior longitudinal fasciculus, which connects the MFG to parts of the inferior parietal lobule, posterior temporal lobe, and lateral occipital cortex; and the inferior fronto-occipital fasciculus, which connected the MFG to the lingual gyrus and cuneus. Intra- and intergyral short association, U-shaped fibers were also identified. CONCLUSIONS: Subcortical white matter pathways integrated within the MFG include the superior longitudinal fasciculus and inferior fronto-occipital fasciculus. The MFG is implicated in a variety of tasks involving attention and memory, making it an important cortical region. The postoperative neurologic outcomes related to surgery in and around the MFG could be clarified in the context of the anatomy of the fiber bundles highlighted in the present study.

mHealth Apps for Enhanced Management of Spinal Surgery Patients: A Review.

mHealth (mobile health) refers to mobile technologies that aid medical and public health practices. As of February 2019, 81% of Americans own a smartphone, and mHealth applications (apps) have become increasingly common with more than 400,000 mHealth applications currently available. Advancements in mobile technology now allow us to provide personalized up-to-date information, track personal health data, remind and engage patients, and communicate in a cost-effective way. There are new opportunities for healthcare providers to integrate mHealth into clinical practice. We discuss the current scientific evidence, and research into mHealth technology.

Parcellation-based modeling of the dorsal premotor area.

BACKGROUND: The dorsal premotor area (DPM) plays an important role in hand coordination and muscle recruitment for lifting activities. Lesions in the area have demonstrated that the DPM is critical in the integration of movements that require combinations of reaching, grasping, and lifting. While many have looked at its functional connectivity, few studies have shown the full anatomical connectivity of DPM including its connections beyond the motor network. Using region-based fMRI studies, we built a neuroanatomical model to account for these extra-motor connections. OBJECTIVE: In this study, we performed meta-analysis and tractography with the goal of creating a map of the dorsal premotor network using the Human Connectome Project parcellation scheme nomenclature (i.e. the Glasser Atlas). While there are other possible ways to map this, we feel that it is critical that neuroimaging begin to move towards all of its data expressed in a single nomenclature which can be compared across studies, and a potential framework that we can build upon in future studies. METHODS: Thirty region-based fMRI studies were used to generate an activation likelihood estimation (ALE) using BrainMap software (Research Imaging Institute of Texas Health Science Center San Antonio). Cortical parcellations overlapping the ALE were used to construct a preliminary model of the Dorsal Premotor Area. Diffusion spectrum imaging (DSI) based tractography was performed to determine the connectivity between cortical parcellations and connections throughout cortex. The resulting connectivities were described using the cortical parcellation scheme developed by the Human Connectome Project (HCP). RESULTS: Three left hemisphere regions were found to comprise the Dorsal Premotor Area. These included areas 6a, 6d. and 6v, Across mapped brains, these areas showed consistent interconnections between each other. Additionally, ipsilateral connections to the premotor cortex, sensorimotor cortex, superior and inferior parietal lobule, middle and inferior frontal gyrus, and insula were demonstrated. Connections to the contralateral supplementary motor area and premotor cortex were also identified. CONCLUSIONS: We describe a preliminary cortical model for the underlying structural connectivity of the Dorsal Premotor Area. Future studies should further characterize the neuroanatomic underpinnings of this network.

A parcellation-based model of the auditory network.

INTRODUCTION: The auditory network plays an important role in interaction with the environment. Multiple cortical areas, such as the inferior frontal gyrus, superior temporal gyrus and adjacent insula have been implicated in this processing. However, understanding of this network's connectivity has been devoid of tractography specificity. METHODS: Using attention task-based functional magnetic resonance imaging (MRI) studies, an activation likelihood estimation (ALE) of the auditory network was generated. Regions of interest corresponding to the cortical parcellation scheme previously published under the Human Connectome Project were co-registered onto the ALE in the Montreal Neurological Institute coordinate space, and visually assessed for inclusion in the network. Diffusion spectrum MRI-based fiber tractography was performed to determine the structural connections between cortical parcellations comprising the network. RESULTS: Fifteen cortical regions were found to be part of the auditory network: areas 44 and 8C, auditory area 1, 4, and 5, frontal operculum area 4, the lateral belt, medial belt and parabelt, parietal area F centromedian, perisylvian language area, retroinsular cortex, supplementary and cingulate eye field and the temporoparietal junction area 1. These regions showed consistent interconnections between adjacent parcellations. The frontal aslant tract was found to connect areas within the frontal lobe, while the arcuate fasciculus was found to connect the frontal and temporal lobe, and subcortical U-fibers were found to connect parcellations within the temporal area. Further studies may refine this model with the ultimate goal of clinical application.

Anatomy and White Matter Connections of the Inferior Temporal Gyrus.

BACKGROUND: The inferior temporal gyrus (ITG) is known to be involved in high-cognitive functions, including visual and language comprehensions and emotion regulation. A detailed understanding of the nature of association fibers could significantly improve postoperative morbidity related to declining capacity. Through diffusion spectrum imaging-based fiber tracking, we have characterized these connections on the basis of their relationships to other cortical areas. METHODS: Diffusion spectrum images from 10 healthy adults of the Human Connectome Project were randomly selected and used for tractography analysis. We evaluated the ITG as a whole based on connectivity with other regions. All ITG tracts were mapped in both hemispheres, and a lateralization index was calculated with resultant tract volumes. RESULTS: We identified 5 major connections of the ITG: U-fiber, inferior longitudinal fasciculus, vertical occipital fasciculus, arcuate fasciculus, and uncinate fasciculus. There was no fiber lateralization detected. CONCLUSIONS: This study highlights the principal white-matter pathways of the ITG and demonstrates key underlying connections. We present a summary of the relevant clinical anatomy for this region of the cerebrum as part of a larger effort to understand it in its entirety.