Beyond eloquence and onto centrality: a new paradigm in planning supratentorial neurosurgery.

PURPOSE: Minimizing post-operational neurological deficits as a result of brain surgery has been one of the most pertinent endeavours of neurosurgical research. Studies have utilised fMRIs, EEGs and MEGs in order to delineate and establish eloquent areas, however, these methods have not been utilized by the wider neurosurgical community due to a lack of clinical endpoints. We sought to ascertain if there is a correlation between graph theory metrics and the neurosurgical notion of eloquent brain regions. We also wanted to establish which graph theory based nodal centrality measure performs the best in predicting eloquent areas. METHODS: We obtained diffusion neuroimaging data from the Human Connectome Project (HCP) and applied a parcellation scheme to it. This enabled us to construct a weighted adjacency matrix which we then analysed. Our analysis looked at the correlation between PageRank centrality and eloquent areas. We then compared PageRank centrality to eigenvector centrality and degree centrality to see what the best measure of empirical neurosurgical eloquence was. RESULTS: Areas that are considered neurosurgically eloquent tended to be predicted by high PageRank centrality. By using summary scores for the three nodal centrality measures we found that PageRank centrality best correlated to empirical neurosurgical eloquence. CONCLUSION: The notion of eloquent areas is important to neurosurgery and graph theory provides a mathematical framework to predict these areas. PageRank centrality is able to consistently find areas that we consider eloquent. It is able to do so better than eigenvector and degree central measures.

Comparison of Abbreviated Breast MRI vs Digital Breast Tomosynthesis for Breast Cancer Detection Among Women With Dense Breasts Undergoing Screening.

Importance: Improved screening methods for women with dense breasts are needed because of their increased risk of breast cancer and of failed early diagnosis by screening mammography. Objective: To compare the screening performance of abbreviated breast magnetic resonance imaging (MRI) and digital breast tomosynthesis (DBT) in women with dense breasts. Design, Setting, and Participants: Cross-sectional study with longitudinal follow-up at 48 academic, community hospital, and private practice sites in the United States and Germany, conducted between December 2016 and November 2017 among average-risk women aged 40 to 75 years with heterogeneously dense or extremely dense breasts undergoing routine screening. Follow-up ascertainment of cancer diagnoses was complete through September 12, 2019. Exposures: All women underwent screening by both DBT and abbreviated breast MRI, performed in randomized order and read independently to avoid interpretation bias. Main Outcomes and Measures: The primary end point was the invasive cancer detection rate. Secondary outcomes included sensitivity, specificity, additional imaging recommendation rate, and positive predictive value (PPV) of biopsy, using invasive cancer and ductal carcinoma in situ (DCIS) to define a positive reference standard. All outcomes are reported at the participant level. Pathology of core or surgical biopsy was the reference standard for cancer detection rate and PPV; interval cancers reported until the next annual screen were included in the reference standard for sensitivity and specificity. Results: Among 1516 enrolled women, 1444 (median age, 54 [range, 40-75] years) completed both examinations and were included in the analysis. The reference standard was positive for invasive cancer with or without DCIS in 17 women and for DCIS alone in another 6. No interval cancers were observed during follow-up. Abbreviated breast MRI detected all 17 women with invasive cancer and 5 of 6 women with DCIS. Digital breast tomosynthesis detected 7 of 17 women with invasive cancer and 2 of 6 women with DCIS. The invasive cancer detection rate was 11.8 (95% CI, 7.4-18.8) per 1000 women for abbreviated breast MRI vs 4.8 (95% CI, 2.4-10.0) per 1000 women for DBT, a difference of 7 (95% CI, 2.2-11.6) per 1000 women (exact McNemar P = .002). For detection of invasive cancer and DCIS, sensitivity was 95.7% (95% CI, 79.0%-99.2%) with abbreviated breast MRI vs 39.1% (95% CI, 22.2%-59.2%) with DBT (P = .001) and specificity was 86.7% (95% CI, 84.8%-88.4%) vs 97.4% (95% CI, 96.5%-98.1%), respectively (P < .001). The additional imaging recommendation rate was 7.5% (95% CI, 6.2%-9.0%) with abbreviated breast MRI vs 10.1% (95% CI, 8.7%-11.8%) with DBT (P = .02) and the PPV was 19.6% (95% CI, 13.2%-28.2%) vs 31.0% (95% CI, 17.0%-49.7%), respectively (P = .15). Conclusions and Relevance: Among women with dense breasts undergoing screening, abbreviated breast MRI, compared with DBT, was associated with a significantly higher rate of invasive breast cancer detection. Further research is needed to better understand the relationship between screening methods and clinical outcome. Trial Registration: ClinicalTrials.gov Identifier: NCT02933489.

Physical and psychosocial factors associated with depression among adults with type 2 diabetes mellitus at a Federally Qualified Healthcare Center.

PURPOSE: The purpose of this study is to examine the prevalence of depression and physical and psychosocial factors associated with depression among adults with Type 2 Diabetes Mellitus (T2DM). METHODS: The sample included 421 patients with T2DM at a Federally Qualified Healthcare Center in a southern state. The Patient Health Questionnaire (PHQ-9) was used to measure the severity of depression. RESULTS: The multiple logistic regression analyses revealed that the likelihood of depression increased as the level of pain increased and as the level of ambulation difficulties increased. The likelihood of depression increased as the number of traumatic events increased and as the number of SES-related stressors increased. Expectedly, the likelihood of depression decreased as levels of self-esteem increased. CONCLUSIONS: The findings support that health care providers developing care plans for individuals with diabetes need to include assessments and interventions that address both the physical and psychosocial needs of patients.

Neurochemical supplementation in patients with depressed levels of participation after brain tumor surgery: Rationale and preliminary results.

A unique challenge in some brain tumor patients is the fact that tumors arising in certain areas of the brain involve the neural structures of consciousness or alertness, limiting the patient's ability to participate in rehabilitation following surgery. A critical question is whether neurostimulant therapy can help patients participate in rehabilitation efforts. We performed a retrospective review of all patients undergoing brain tumor surgery by the senior author from 2012 to 2018. We limited this study to patients with tumors occupying critical structures related to consciousness, alertness, and motor initiation. A combination of methylphenidate and levodopa/carbidopa was used to monitor the progress of patients through neurorehabilitation efforts. We identified 101 patients who experienced an inability to participate in rehabilitation (ITPR) in the post-operative period. Of these, 86 patients (85%) were treated with methylphenidate and levodopa/carbidopa. Cases of ITPR were related to dysfunction of the brainstem (12/86 cases, 14%), thalamus (17/86 cases, 20%), hypothalamus (14/86 cases, 16%), basal ganglia (13/86 cases, 15%), and medial frontal lobe (30/86 cases, 35%). Of the 86 individuals treated, 47/86 patients (55%) showed early improvement in their ability to participate with rehabilitation. At three month follow-up, 58/86 patients (67%) had returned to living independently or were at least interactive and cooperative during follow-up examination. This feasibility report suggests that combined therapy with methylphenidate and levodopa/carbidopa may help patients participate in neurorehabilitation efforts in the immediate post-operative period following brain tumor surgery. Randomized, controlled clinical trials are needed to explore this concept more thoroughly.

Measuring graphical strength within the connectome: A neuroanatomic, parcellation-based study.

INTRODUCTION: Graph theory is a promising mathematical tool to study the connectome. However, little research has been undertaken to correlate graph metrics to functional properties of the brain. In this study, we report a unique association between the strength of cortical regions and their function. METHODS: Eight structural graphs were constructed within DSI Studio using publicly available imaging data derived from the Human Connectome Project. Whole-brain fiber tractography was performed to quantify the strength of each cortical region comprising our atlas. RESULTS: Rank-order analysis revealed 27 distinct areas with high average strength, several of which are associated with eloquent cortical functions. Area 4 localizes to the primary motor cortex and is important for fine motor control. Areas 2, 3a and 3b localize to the primary sensory cortex and are involved in primary sensory processing. Areas V1-V4 in the occipital pole are involved in primary visual processing. Several language areas, including area 44, were also found to have high average strength. CONCLUSIONS: Regions of average high strength tend to localize to eloquent areas of the brain, such as the primary sensorimotor cortex, primary visual cortex, and Broca's area. Future studies will examine the dynamic effects of neurologic disease on this metric.

Bayesian classifier with simplified learning phase for detecting microcalcifications in digital mammograms.

Detection of clustered microcalcifications (MCs) in mammograms represents a significant step towards successful detection of breast cancer since their existence is one of the early signs of cancer. In this paper, a new framework that integrates Bayesian classifier and a pattern synthesizing scheme for detecting microcalcification clusters is proposed. This proposed work extracts textural, spectral, and statistical features of each input mammogram and generates models of real MCs to be used as training samples through a simplified learning phase of the Bayesian classifier. Followed by an estimation of the classifier's decision function parameters, a mammogram is segmented into the identified targets (MCs) against background (healthy tissue). The proposed algorithm has been tested using 23 mammograms from the mini-MIAS database. Experimental results achieved MCs detection with average true positive (sensitivity) and false positive (specificity) of 91.3% and 98.6%, respectively. Results also indicate that the modeling of the real MCs plays a significant role in the performance of the classifier and thus should be given further investigation.

Unsupervised Detection of Suspicious Tissue Using Data Modeling and PCA.

Breast cancer is a major cause of death and morbidity among women all over the world, and it is a fact that early detection is a key in improving outcomes. Therefore development of algorithms that aids radiologists in identifying changes in breast tissue early on is essential. In this work an algorithm that investigates the use of principal components analysis (PCA) is developed to identify suspicious regions on mammograms. The algorithm employs linear structure and curvelinear modeling prior to PCA implementations. Evaluation of the algorithm is based on the percentage of correct classification, false positive (FP) and false negative (FN) in all experimental work using real data. Over 90% accuracy in block classification is achieved using mammograms from MIAS database.