Extramedullary relapse of acute myeloid leukemia in the breast: A radiological case report.

This report presents a unique case of a 42-year-old female with a history of acute myeloid leukemia (AML) who exhibited an extramedullary relapse in the breast. Given the rarity of such presentations, this case underscores the importance of considering AML in the differential diagnosis of breast lesions, especially in patients with a pertinent medical history. Additionally, this case highlights the radiological and pathological challenges in distinguishing AML from other breast malignancies. The importance of timely diagnosis and the clinical implications of such a presentation are also discussed.

Angiolipoma an Uncommon Breast Mass in Men: A Case Series.

Angiolipomas are uncommon benign masses of the breast which are rarely described in the male breast. They do not have a typical mammographic appearance and can present with concerning features such as microcalcifications or irregular borders. Ultrasound is helpful in evaluating these masses most commonly appearing as oval, circumscribed, and hyperechoic. Clinical, radiological, and pathological information needs to be carefully evaluated as angiolipomas can be confused with malignant pathology. Three cases of angiolipomas of the male breast are reported in this study with mammographic, sonographic, and pathologic correlation.

Disappearing grouped breast calcifications: An ominous sign.

Spontaneous disappearing breast calcifications is a rarely-reported phenomenon and the relatively small number of studies that have been done mostly associated the resolution of benign-appearing breast calcifications with benign processes. We present a case of a postmenopausal woman who had spontaneously resolving grouped, coarse heterogenous calcifications in the setting a new soft tissue mass which was pathologically proven to be invasive ductal carcinoma. A handful of studies have shown the resolution of indeterminant calcifications to be associated with malignancy, and interestingly, all of these cases also demonstrated a new parenchymal abnormality which is akin to the present case. Overall, the majority of benign-appearing spontaneous resolving microcalcifications likely are related to benign processes; however, radiologists should be aware of the association of disappearing grouped, indeterminant calcifications with the resulting development of malignancy, especially in the presence of a nearby, newly-forming soft tissue mass.

Ectopic tooth buds and parotid aplasia are diagnostic features of partial facial duplication on pre- and postnatal MRI: Case report and literature review.

Craniofacial duplication is a rare congenital anomaly. A case of hemi-mandibular duplication with an accessory oral cavity is presented with along with first-time reported pre- and postnatal MRI, surgical approach and a literature review. MRI clearly depicts the ectopic tooth buds and parotid aplasia in this condition, features that are diagnostic of partial facial duplication. MRI is diagnostic for this condition and can be useful to avoid misdiagnosis of a facial mass.

Baseline gray- and white-matter volume predict successful weight loss in the elderly.

OBJECTIVE: The purpose of this study was to investigate whether structural brain phenotypes could be used to predict weight loss success following behavioral interventions in older adults with overweight or obesity and cardiometabolic dysfunction. METHODS: A support vector machine with a repeated random subsampling validation approach was used to classify participants into the upper and lower halves of the weight loss distribution following 18 months of a weight loss intervention. Predictions were based on baseline brain gray matter and white matter volume from 52 individuals who completed the intervention and a magnetic resonance imaging session. RESULTS: The support vector machine resulted in an average classification accuracy of 72.62% based on gray matter and white matter volume. A receiver operating characteristic analysis indicated that classification performance was robust based on an area under the curve of 0.82. CONCLUSIONS: Findings suggest that baseline brain structure was able to predict weight loss success following 18 months of treatment. The identification of brain structure as a predictor of successful weight loss was an innovative approach to identifying phenotypes for responsiveness to intensive lifestyle interventions. This phenotype could prove useful in future research focusing on the tailoring of treatment for weight loss.

Global integration of the hot-state brain network of appetite predicts short term weight loss in older adult.

Obesity is a public health crisis in North America. While lifestyle interventions for weight loss (WL) remain popular, the rate of success is highly variable. Clearly, self-regulation of eating behavior is a challenge and patterns of activity across the brain may be an important determinant of success. The current study prospectively examined whether integration across the Hot-State Brain Network of Appetite (HBN-A) predicts WL after 6-months of treatment in older adults. Our metric for network integration was global efficiency (GE). The present work is a sub-study (n = 56) of an ongoing randomized clinical trial involving WL. Imaging involved a baseline food-cue visualization functional MRI (fMRI) scan following an overnight fast. Using graph theory to build functional brain networks, we demonstrated that regions of the HBN-A (insula, anterior cingulate cortex (ACC), superior temporal pole (STP), amygdala and the parahippocampal gyrus) were highly integrated as evidenced by the results of a principal component analysis (PCA). After accounting for known correlates of WL (baseline weight, age, sex, and self-regulatory efficacy) and treatment condition, which together contributed 36.9% of the variance in WL, greater GE in the HBN-A was associated with an additional 19% of the variance. The ACC of the HBN-A was the primary driver of this effect, accounting for 14.5% of the variance in WL when entered in a stepwise regression following the covariates, p = 0.0001. The HBN-A is comprised of limbic regions important in the processing of emotions and visceral sensations and the ACC is key for translating such processing into behavioral consequences. The improved integration of these regions may enhance awareness of body and emotional states leading to more successful self-regulation and to greater WL. This is the first study among older adults to prospectively demonstrate that, following an overnight fast, GE of the HBN-A during a food visualization task is predictive of WL.

Meal replacement: calming the hot-state brain network of appetite.

There is a growing awareness in the field of neuroscience that the self-regulation of eating behavior is driven by complex networks within the brain. These networks may be vulnerable to "hot states" which people can move into and out of dynamically throughout the course of a day as a function of changes in affect or visceral cues. The goal of the current study was to identify and determine differences in the Hot-state Brain Network of Appetite (HBN-A) that exists after a brief period of food restraint followed either by the consumption of a meal replacement (MR) or water. Fourteen overweight/obese adults came to our laboratory on two different occasions. Both times they consumed a controlled breakfast meal and then were restricted from eating for 2.5 h prior to an MRI scan. On one visit, they consumed a meal replacement (MR) liquid meal after this period of food restriction; on the other visit they consumed an equal amount of water. After these manipulations, the participants underwent a resting fMRI scan. Our first study aim employed an exploratory, data-driven approach to identify hubs relevant to the HBN-A. Using data from the water condition, five regions were found to be the hubs or nodes of the HBN-A: insula, anterior cingulated cortex, the superior temporal pole, the amygdala, and the hippocampus. We then demonstrated that the consumption of a liquid MR dampened interconnectivity between the nodes of the HBN-A as compared to water. Importantly and consistent with these network data, the consumption of a MR beverage also lowered state cravings and hunger.

Corrigendum: Coping with brief periods of food restriction: mindfulness matters.

[This corrects the article on p. 13 in vol. 4, PMID: 22685430.].

Drive for consumption, craving, and connectivity in the visual cortex during the imagery of desired food.

There is considerable interest in understanding food cravings given the obesogenic environment of Western Society. In this paper we examine how the imagery of palatable foods affects cravings and functional connectivity in the visual cortex for people who differ on the power of food scale (PFS). Fourteen older, overweight/obese adults came to our laboratory on two different occasions. Both times they ate a controlled breakfast meal and then were restricted from eating for 2.5 h prior to scanning. On 1 day they consumed a BOOST(®) liquid meal after the period of food restriction, whereas on the other day they only consumed water (NO BOOST(®) condition). After these manipulations, they had an fMRI scan in which they were asked to image both neutral objects and their favorite snack foods; they also completed visual analog scales for craving, hunger, and the vividness of the imagery experiences. Irrespective of the BOOST(®) manipulation, we observed marked increases in food cravings when older, overweight/obese adults created images of favorite foods in their minds as opposed to creating an image of neutral objects; however, the increase in food craving following the imagery of desired food was more pronounced among those scoring high than low on the PFS. Furthermore, local efficiency within the visual cortex when imaging desired food was higher for those scoring high as compared to low on the PFS. The active imagery of desired foods seemed to have overpowered the BOOST(®) manipulation when evaluating connectivity in the visual cortex.

Defining nodes in complex brain networks.

Network science holds great promise for expanding our understanding of the human brain in health, disease, development, and aging. Network analyses are quickly becoming the method of choice for analyzing functional MRI data. However, many technical issues have yet to be confronted in order to optimize results. One particular issue that remains controversial in functional brain network analyses is the definition of a network node. In functional brain networks a node represents some predefined collection of brain tissue, and an edge measures the functional connectivity between pairs of nodes. The characteristics of a node, chosen by the researcher, vary considerably in the literature. This manuscript reviews the current state of the art based on published manuscripts and highlights the strengths and weaknesses of three main methods for defining nodes. Voxel-wise networks are constructed by assigning a node to each, equally sized brain area (voxel). The fMRI time-series recorded from each voxel is then used to create the functional network. Anatomical methods utilize atlases to define the nodes based on brain structure. The fMRI time-series from all voxels within the anatomical area are averaged and subsequently used to generate the network. Functional activation methods rely on data from traditional fMRI activation studies, often from databases, to identify network nodes. Such methods identify the peaks or centers of mass from activation maps to determine the location of the nodes. Small (~10-20 millimeter diameter) spheres located at the coordinates of the activation foci are then applied to the data being used in the network analysis. The fMRI time-series from all voxels in the sphere are then averaged, and the resultant time series is used to generate the network. We attempt to clarify the discussion and move the study of complex brain networks forward. While the "correct" method to be used remains an open, possibly unsolvable question that deserves extensive debate and research, we argue that the best method available at the current time is the voxel-wise method.

Coping with brief periods of food restriction: mindfulness matters.

The obesity epidemic had spawned considerable interest in understanding peoples' responses to palatable food cues that are plentiful in obesogenic environments. In this paper we examine how trait mindfulness of older, obese adults may moderate brain networks that arise from exposure to such cues. Nineteen older, obese adults came to our laboratory on two different occasions. Both times they ate a controlled breakfast meal and then were restricted from eating for 2.5 h. After this brief period of food restriction, they had an fMRI scan in which they were exposed to food cues and then underwent a 5 min recovery period to evaluate brain networks at rest. On one day they consumed a BOOST® liquid meal prior to scanning, whereas on the other day they only consumed water (NO BOOST® condition). We found that adults high in trait mindfulness were able to return to their default mode network (DMN), as indicated by greater global efficiency in the precuneus, during the post-exposure rest period. This effect was stronger for the BOOST® than NO BOOST® treatment condition. Older adults low in trait mindfulness did not exhibit this pattern in the DMN. In fact, the brain networks of those low on the MAAS suggests that they continued to be pre-occupied with the elaboration of food cues even after cue exposure had ended. Further work is needed to examine whether mindfulness-based therapies alter brain networks to food cues and whether these changes are related to eating behavior.

Pediatric head injury prediction: investigating the distance between the skull and the brain using medical imaging - biomed 2009.

Injuries caused by Motor Vehicle Crashes (MVCs) are the leading cause of death and head injury for children in the United States. Improved finite element models that incorporate the correct size and shape of the pediatric brain and skull with current injury prevention metrics would improve the prediction of such pediatric head injuries. As an individual ages the shape of his/her brain, skull and meninges change with age. An accurate model needs to incorporate the interactive nature of these changes throughout pediatric development. The focus of this study is to quantify how the thickness and volume of the Cerebral Spinal Fluid (CSF) layer change with age in the pediatric population. The results of this study and others will be incorporated in creating a pediatric finite element model at various ages. This model may then be used by the automotive industry or in the study and analysis of shaken baby syndrome (SBS). The data-set consisted of fifty-nine individuals ranging in age from newborn to twenty-one years of age. Data was collected on the thickness of the CSF layer on the MR scans by taking forty measurements at specified points on three slices for each individual. The volume of the CSF layer was also quantified by making a mask of the CSF layer on all individuals. Both the thickness measurements and the volume analyses were then normalized utilizing a centroid value for each individual. Both the CSF thickness measurements and the volume analyses show a general logistic decrease in the amount of CSF from infancy to twenty-one years of age. Large CSF variation between individuals of the same age was observed; thus, future studies should utilize a longitudinal cohort study to control for individual differences in CSF. Sexual dimorphism in the size and shape of the CSF layer should also be investigated.