Breast‐Specific Gamma Imaging Influences Surgical Management in Patients with Breast Cancer

Breast‐specific gamma imaging (BSGI) is a physiologic breast imaging modality that provides more sensitive detection of breast lesions than mammography or ultrasound, and appears to have greater specificity than breast MRI. The purpose of this study was to evaluate how often BSGI changed surgical management in patients with breast cancer. Charts were reviewed from 218 consecutive eligible patients who had preoperative evaluation with BSGI or MRI before surgery for breast cancer from January 2008 to May 2010. Patients who were initially considered eligible for breast‐conserving therapy (BCT) were evaluated to determine how many ultimately had mastectomies. Patients who underwent mastectomy because of personal choice or ineligibility for BCT were excluded. Management was changed to mastectomy in 11.9% of those who had BSGI and 28.9% of those who had MRI. Review of pathology demonstrated that all patients who underwent mastectomies were not candidates for breast conservation. 15.4% of patients who underwent BCT based on BSGI findings required a single re‐excision due to positive surgical margins. 14.4% required mastectomy. In the MRI group, 18.8% required a single re‐excision, and 6.3% required mastectomy. Evaluation with BSGI changed management to mastectomy in a substantial proportion of patients believed to be eligible for BCT following standard imaging. BSGI is effective in evaluation of extent of disease in patients with breast cancer, and is comparable to MRI in terms of its influence on surgical management.     

Metabolic network failures in Alzheimer's disease: A biochemical road map

Introduction

The Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance.

ENIGMA-DTI: Translating reproducible white matter deficits into personalized vulnerability metrics in cross-diagnostic psychiatric research

The ENIGMA-DTI (diffusion tensor imaging) workgroup supports analyses that examine the effects of psychiatric, neurological, and developmental disorders on the white matter pathways of the human brain, as well as the effects of normal variation and its genetic associations. The seven ENIGMA disorder-oriented working groups used the ENIGMA-DTI workflow to derive patterns of deficits using coherent and coordinated analyses that model the disease effects across cohorts worldwide. This yielded the largest studies detailing patterns of white matter deficits in schizophrenia spectrum disorder (SSD), bipolar disorder (BD), major depressive disorder (MDD), obsessive–compulsive disorder (OCD), posttraumatic stress disorder (PTSD), traumatic brain injury (TBI), and 22q11 deletion syndrome. These deficit patterns are informative of the underlying neurobiology and reproducible in independent cohorts. We reviewed these findings, demonstrated their reproducibility in independent cohorts, and compared the deficit patterns across illnesses. We discussed translating ENIGMA-defined deficit patterns on the level of individual subjects using a metric called the regional vulnerability index (RVI), a correlation of an individual's brain metrics with the expected pattern for a disorder. We discussed the similarity in white matter deficit patterns among SSD, BD, MDD, and OCD and provided a rationale for using this index in cross-diagnostic neuropsychiatric research. We also discussed the difference in deficit patterns between idiopathic schizophrenia and 22q11 deletion syndrome, which is used as a developmental and genetic model of schizophrenia. Together, these findings highlight the importance of collaborative large-scale research to provide robust and reproducible effects that offer insights into individual vulnerability and cross-diagnosis features.     

Large‐scale brain network abnormalities in Huntington's disease revealed by structural covariance

Huntington's disease (HD) is a progressive neurodegenerative disorder that can be diagnosed with certainty decades before symptom onset. Studies using structural MRI have identified grey matter (GM) loss predominantly in the striatum, but also involving various cortical areas. So far, voxel‐based morphometric studies have examined each brain region in isolation and are thus unable to assess the changes in the interrelation of brain regions. Here, we examined the structural covariance in GM volumes in pre‐specified motor, working memory, cognitive flexibility, and social‐affective networks in 99 patients with manifest HD (mHD), 106 presymptomatic gene mutation carriers (pre‐HD), and 108 healthy controls (HC). After correction for global differences in brain volume, we found that increased GM volume in one region was associated with increased GM volume in another. When statistically comparing the groups, no differences between HC and pre‐HD were observed, but increased positive correlations were evident for mHD, relative to pre‐HD and HC. These findings could be explained by a HD‐related neuronal loss heterogeneously affecting the examined network at the pre‐HD stage, which starts to dominate structural covariance globally at the manifest stage. Follow‐up analyses identified structural connections between frontoparietal motor regions to be linearly modified by disease burden score (DBS). Moderator effects of disease load burden became significant at a DBS level typically associated with the onset of unequivocal HD motor signs. Together with existing findings from functional connectivity analyses, our data indicates a critical role of these frontoparietal regions for the onset of HD motor signs. Hum Brain Mapp 37:67–80, 2016. © 2015 Wiley Periodicals, Inc.