Imaging diagnosis: magnetic resonance imaging of diffuse leptomeningeal oligodendrogliomatosis in a dog with "dural tail sign"

A case of diffuse leptomeningeal oligodendrogliomatosis affecting the brain and spinal cord of a dog is presented. A 7.5-year old, male neutered Staffordshire bull terrier presented for evaluation of a chronic history of tetraparesis and seizures, with a multifocal neuroanatomical localization was determined. Extra-axial intradural lesions with an atypical presentation of a dural tail sign were seen on MRI. Histologically, the lesions were consistent with leptomeningeal oligodendrogliomatosis. To the authors’ knowledge, a dural tail sign has not previously been reported as an MRI characteristic of diffuse leptomeningeal oligodendrogliomatosis in dogs

Core language brain network for fMRI-language task used in clinical applications

Functional magnetic resonance imaging (fMRI) is widely used in clinical applications to highlight brain areas involved in specific cognitive processes. Brain impairments, such as tumors, suppress the fMRI activation of the anatomical areas they invade and, thus, brain-damaged functional networks present missing links/areas of activation. The identification of the missing circuitry components is of crucial importance to estimate the damage extent. The study of functional networks associated to clinical tasks but performed by healthy individuals becomes, therefore, of paramount concern. These `healthy' networks can, indeed, be used as control networks for clinical studies. In this work we investigate the functional architecture of 20 healthy individuals performing a language task designed for clinical purposes. We unveil a common architecture persistent across all subjects under study, which involves Broca's area, Wernicke's area, the Premotor area, and the pre-Supplementary motor area. We study the connectivity weight of this circuitry by using the k-core centrality measure and we find that three of these areas belong to the most robust structure of the functional language network for the specific task under study. Our results provide useful insight for clinical applications on primarily important functional connections which, thus, should be preserved through brain surgery.Comment: 14 pages, 7 figure

Clinical Applications of fMRI

The clinical scenario in which fMRI is requested by a referring physician is usually after detection of a lesion in the brain of a patient for whom surgical intervention is being contemplated. This unit discusses the suitable parameters of a functional MRI system and also presents the for imaging language function.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145198/1/cpmia0601.pd

Finding influential nodes for integration in brain networks using optimal percolation theory

Global integration of information in the brain results from complex interactions of segregated brain networks. Identifying the most influential neuronal populations that efficiently bind these networks is a fundamental problem of systems neuroscience. Here we apply optimal percolation theory and pharmacogenetic interventions in-vivo to predict and subsequently target nodes that are essential for global integration of a memory network in rodents. The theory predicts that integration in the memory network is mediated by a set of low-degree nodes located in the nucleus accumbens. This result is confirmed with pharmacogenetic inactivation of the nucleus accumbens, which eliminates the formation of the memory network, while inactivations of other brain areas leave the network intact. Thus, optimal percolation theory predicts essential nodes in brain networks. This could be used to identify targets of interventions to modulate brain function.Comment: 20 pages, 6 figures, Supplementary Inf

Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach

Lung cancer can be classified into two main categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), which are different in treatment strategy and survival probability. The lung CT images of SCLC and NSCLC are similar such that their subtle differences are hardly visually discernible by the human eye through conventional imaging evaluation. We hypothesize that SCLC/NSCLC differentiation could be achieved via computerized image feature analysis and classification in feature space, as termed a radiomic model. The purpose of this study was to use CT radiomics to differentiate SCLC from NSCLC adenocarcinoma. Patients with primary lung cancer, either SCLC or NSCLC adenocarcinoma, were retrospectively identified. The post-diagnosis pre-treatment lung CT images were used to segment the lung cancers. Radiomic features were extracted from histogram-based statistics, textural analysis of tumor images and their wavelet transforms. A minimal-redundancy-maximal-relevance method was used for feature selection. The predictive model was constructed with a multilayer artificial neural network. The performance of the SCLC/NSCLC adenocarcinoma classifier was evaluated by the area under the receiver operating characteristic curve (AUC). Our study cohort consisted of 69 primary lung cancer patients with SCLC (n = 35; age mean ± SD = 66.91± 9.75 years), and NSCLC adenocarcinoma (n = 34; age mean ± SD = 58.55 ± 11.94 years). The SCLC group had more male patients and smokers than the NSCLC group (P \u3c 0.05). Our SCLC/NSCLC classifier achieved an overall performance of AUC of 0.93 (95% confidence interval = [0.85, 0.97]), sensitivity = 0.85, and specificity = 0.85). Adding clinical data such as smoking history could improve the performance slightly. The top ranking radiomic features were mostly textural features. Our results showed that CT radiomics could quantitatively represent tumor heterogeneity and therefore could be used to differentiate primary lung cancer subtypes with satisfying results. CT image processing with the wavelet transformation technique enhanced the radiomic features for SCLC/NSCLC classification. Our pilot study should motivate further investigation of radiomics as a non-invasive approach for early diagnosis and treatment of lung cancer

Monolingual and bilingual language networks in healthy subjects using functional MRI and graph theory

Bilingualism requires control of multiple language systems, and may lead to architectural differences in language networks obtained from clinical fMRI tasks. Emerging connectivity metrics such as k-core may capture these differences, highlighting crucial network components based on resiliency. We investigated the influence of bilingualism on clinical fMRI language tasks and characterized bilingual networks using connectivity metrics to provide a patient care benchmark. Sixteen right-handed subjects (mean age 42-years; nine males) without neurological history were included: eight native English-speaking monolinguals and eight native Spanish-speaking (L1) bilinguals with acquired English (L2). All subjects underwent fMRI with gold-standard clinical language tasks. Starting from active clusters on fMRI, we inferred the persistent functional network across subjects and ran centrality measures to characterize differences. Our results demonstrated a persistent network “core” consisting of Broca’s area, the pre-supplementary motor area, and the premotor area. K-core analysis showed that Wernicke’s area was engaged by the “core” with weaker connection in L2 than L1

Finding influential nodes for integration in brain networks using optimal percolation theory

Global integration of information in the brain results from complex interactions of segregated brain networks. Identifying the most influential neuronal populations that efficiently bind these networks is a fundamental problem of systems neuroscience. Here, we apply optimal percolation theory and pharmacogenetic interventions in vivo to predict and subsequently target nodes that are essential for global integration of a memory network in rodents. The theory predicts that integration in the memory network is mediated by a set of low-degree nodes located in the nucleus accumbens. This result is confirmed with pharmacogenetic inactivation of the nucleus accumbens, which eliminates the formation of the memory network, while inactivations of other brain areas leave the network intact. Thus, optimal percolation theory predicts essential nodes in brain networks. This could be used to identify targets of interventions to modulate brain function

Gray matter density reduction associated with adjuvant chemotherapy in older women with breast cancer

PURPOSE: The purpose of this study was to evaluate longitudinal changes in brain gray matter density (GMD) before and after adjuvant chemotherapy in older women with breast cancer. METHODS: We recruited 16 women aged ≥ 60 years with stage I-III breast cancers receiving adjuvant chemotherapy (CT) and 15 age- and sex-matched healthy controls (HC). The CT group underwent brain MRI and the NIH Toolbox for Cognition testing prior to adjuvant chemotherapy (time point 1, TP1) and within 1 month after chemotherapy (time point 2, TP2). The HC group underwent the same assessments at matched intervals. GMD was evaluated with the voxel-based morphometry. RESULTS: The mean age was 67 years in the CT group and 68.5 years in the HC group. There was significant GMD reduction within the chemotherapy group from TP1 to TP2. Compared to the HC group, the CT group displayed statistically significantly greater GMD reductions from TP1 to TP2 in the brain regions involving the left anterior cingulate gyrus, right insula, and left middle temporal gyrus (pFWE(family-wise error)-corrected < 0.05). The baseline GMD in left insula was positively correlated with the baseline list-sorting working memory score in the HC group (pFWE-corrected < 0.05). No correlation was observed for the changes in GMD with the changes in cognitive testing scores from TP1 to TP2 (pFWE-corrected < 0.05). CONCLUSIONS: Our findings indicate that GMD reductions were associated with adjuvant chemotherapy in older women with breast cancer. Future studies are needed to understand the clinical significance of the neuroimaging findings. This study is registered on ClinicalTrials.gov (NCT01992432)

Elevated cerebrospinal fluid pressure in patients with Alzheimer's disease

BACKGROUND: Abnormalities in cerebrospinal fluid (CSF) production and turnover, seen in normal pressure hydrocephalus (NPH) and in Alzheimer's disease (AD), may be an important cause of amyloid retention in the brain and may relate the two diseases. There is a high incidence of AD pathology in patients being shunted for NPH, the AD-NPH syndrome. We now report elevated CSF pressure (CSFP), consistent with very early hydrocephalus, in a subset of AD patients enrolled in a clinical trial of chronic low-flow CSF drainage. Our objective was to determine the frequency of elevated CSFP in subjects meeting National Institutes of Neurological and Communicative Diseases and Stroke – Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) criteria for AD, excluding those with signs of concomitant NPH. METHODS: AD subjects by NINCDS-ADRDA criteria (n = 222), were screened by history, neurological examination, and radiographic imaging to exclude those with clinical or radiographic signs of NPH. As part of this exclusion process, opening CSFP was measured supine under general anesthesia during device implantation surgery at a controlled pCO(2 )of 40 Torr (40 mmHg). RESULTS: Of the 222 AD subjects 181 had pressure measurements recorded. Seven subjects (3.9%) enrolled in the study had CSFP of 220 mmH(2)0 or greater, mean 249 ± 20 mmH(2)0 which was significantly higher than 103 ± 47 mmH(2)O for the AD-only group. AD-NPH patients were significantly younger and significantly less demented on the Mattis Dementia Rating Scale (MDRS). CONCLUSION: Of the AD subjects who were carefully screened to exclude those with clinical NPH, 4% had elevated CSFP. These subjects were presumed to have the AD-NPH syndrome and were withdrawn from the remainder of the study