Understanding the regulation of islet cell mass has important implications for the discovery of regenerative therapies for diabetes. The liver plays a central role in metabolism and the regulation of ...endocrine cell number, but liver-derived factors that regulate α-cell and β-cell mass remain unidentified. We propose a nutrient-sensing circuit between liver and pancreas in which glucagon-dependent control of hepatic amino acid metabolism regulates α-cell mass. We found that glucagon receptor inhibition reduced hepatic amino acid catabolism, increased serum amino acids, and induced α-cell proliferation in an mTOR-dependent manner. In addition, mTOR inhibition blocked amino-acid-dependent α-cell replication ex vivo and enabled conversion of α-cells into β-like cells in vivo. Serum amino acids and α-cell proliferation were increased in neonatal mice but fell throughout postnatal development in a glucagon-dependent manner. These data reveal that amino acids act as sensors of glucagon signaling and can function as growth factors that increase α-cell proliferation.
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•Glucagon regulates hepatic amino acid catabolism and serum amino acid levels•mTOR activity is required for α-cell proliferation after glucagon receptor inhibition•Amino acids promote α-cell proliferation ex vivo in an mTOR-dependent manner•mTOR determines α-cell fate
Here, Solloway et al. propose a nutrient-sensing circuit between liver and pancreas in which glucagon-dependent clearance of amino acids is coupled to α-cell mass. Acting as sensors, amino acids relay the degree of hepatic glucagon signaling to islets and promote mTOR-dependent α-cell proliferation.
Purpose
Ultrahigh field (UHF) resting state functional magnetic resonance imaging (rsfMRI) has become increasingly available for clinical and basic research, bringing improvements in resolution and ...contrast over standard high field imaging. Despite these improvements, UHF connectivity studies present several challenges, including increased sensitivity to physiological confounds and a vastly increased data burden. We present a direct quantitative assessment of test–retest reliability of functional connectivity in several standard functional networks between subjects scanned at 3T and 7T.
Methods
Five healthy subjects were scanned over four sessions each in a scan‐rescan design at both 3T and 7T field strengths. Resting state fMRI data were segmented into four major intrinsic connectivity networks, and seed‐based peak correlations within and between these networks examined. The reliability of these correlations was assessed using intra‐class correlation coefficients (ICC).
Results
Across all data, over 4000 peak correlations were extracted for assessment. The reliability over all intrinsic networks was greater at 7T than 3T (median ICC 0.40 vs. 0.33, p ≤ 0.0014), with each network individually showing improvement. Inter‐network reliability was stronger than intra‐network reliability, but intra‐network reliability showed the greatest improvement between field strengths.
Conclusion
We demonstrate significantly increased reliability of resting state connectivity at UHF strengths over conventional field strengths using a novel hybrid seed‐based analysis. This result adds to the growing body of work supporting the migration of functional imaging studies to UHFs.
MV-NIS is an engineered measles virus that is selectively destructive to myeloma plasma cells and can be monitored by noninvasive radioiodine imaging of NIS gene expression. Two measles-seronegative ...patients with relapsing drug-refractory myeloma and multiple glucose-avid plasmacytomas were treated by intravenous infusion of 10(11) TCID50 (50% tissue culture infectious dose) infectious units of MV-NIS. Both patients responded to therapy with M protein reduction and resolution of bone marrow plasmacytosis. Further, one patient experienced durable complete remission at all disease sites. Tumor targeting was clearly documented by NIS-mediated radioiodine uptake in virus-infected plasmacytomas. Toxicities resolved within the first week after therapy. Oncolytic viruses offer a promising new modality for the targeted infection and destruction of disseminated cancer.
The data burden for resting-state fMRI analysis rises with increasing resolutions available at ultrahigh fields. Therefore, a fundamental preprocessing step in brain network analysis is to reduce the ...data, usually by performing some kind of data parcellation. Most functional parcellations based on rsfMRI connectivity are synthesized from the dense connectome. In contrast, most network analyses begin by reducing each parcel to a single exemplar time series. This disconnect between parcel formation and usage assumes that parcel exemplars adequately represent their member voxels, which is not always the case for commonly used parcellations.
We propose to parcellate the brain based on parcel cohesion, a measure of similarity between a parcel’s exemplar and its member voxels. A spatially constrained agglomerative hierarchical framework is used to synthesize parcels based on a minimum cohesion threshold, rather than a predetermined number of parcels.
Cohesive parcellation generally results in more parcels than existing approaches. The number of parcels scales with the amount of smoothing in preprocessing, yet retains adequate information to extract common intrinsic functional networks.
Cohesive parcellation performs better than several widely used anatomical, functional, and data-driven parcellations on the basis of parcel cohesion and comparably using several traditional measures of cluster validity.
Cohesive parcellation ensures that the way parcels are synthesized directly corresponds to the way they are used in subsequent analyses. The resulting parcels are straightforward to interpret and optimal for downstream analysis.
•Parcel cohesion is defined as the functional similarity between a parcel’s exemplar and its members.•A novel parcellation based on optimizing parcel cohesion is presented.•Cohesive parcellation ensures that parcel formation matches parcel usage in downstream analyses.•Cohesive parcellation compares favorably to several widely used parcellations.
•We find that the cardiac hemodynamic phase function is time shifted locally.•We find that the respiratory hemodynamic phase function has single form across the brain.•We propose automatic ...physiologic signal detection without the external physiologic signal measures and its correction method in resting state-fMRI data.•We compare the efficacy of the proposed method to RETROICOR.
Hemodynamic cardiac and respiratory-cycle fluctuations are a source of unwanted non-neuronal signal components, often called physiologic noise, in resting state (rs-) fMRI studies. Here, we use image-based retrospective correction of physiological motion (RETROICOR) with externally measured physiologic signals to investigate cardiac and respiratory hemodynamic phase functions reflected in rs-fMRI data. We find that the cardiac phase function is time shifted locally, while the respiratory phase function is described as single, fixed phase form across the brain. In light of these findings, we propose an update to Physiologic EStimation by Temporal ICA (PESTICA), our publically available software package that estimates physiologic signals when external physiologic measures are not available. This update incorporates: 1) auto-selection of slicewise physiologic regressors and generation of physiologic fixed phase regressors with total slices/TR sampling rate, 2) Fourier series expansion of the cardiac fixed phase regressor to account for time delayed cardiac noise 3) removal of cardiac and respiratory noise in imaging data. We compare the efficacy of the updated method to RETROICOR.
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Abstract This study for the first time investigated resting state corticolimbic connectivity abnormalities in unmedicated bipolar disorder (BD) and compared them with findings in healthy controls and ...unipolar major depressive disorder (MDD) patient groups. Resting state correlations of low frequency BOLD fluctuations (LFBF) in echoplanar functional magnetic resonance (fMRI) data were acquired from a priori defined regions of interests (ROIs) in the pregenual anterior cingulate cortex (pgACC), dorsomedial thalamus (DMTHAL), pallidostriatum (PST) and amygdala (AMYG), to investigate corticolimbic functional connectivity in unmedicated BD patients in comparison to healthy subjects and MDD patients. Data were acquired from 11 unmedicated BD patients six manic (BDM) and five depressed (BDD), and compared with data available from 15 unmedicated MDD and 15 healthy subjects. BD patients had significantly decreased pgACC connectivity to the left and right DMTHAL, similar to findings seen in MDD. Additionally, BD patients had decreased pgACC connectivity with the left and right AMYG as well as the left PST. An exploratory analysis revealed that both BDD and BDM patients had decreased connectivity between the pgACC and DMTHAL. The results of the study indicate a common finding of decreased corticolimbic functional connectivity in different types of mood disorders.
Head motion in functional MRI and resting-state MRI is a major problem. Existing methods do not robustly reflect the true level of motion artifact for in vivo fMRI data. The primary issue is that ...current methods assume that motion is synchronized to the volume acquisition and thus ignore intra-volume motion. This manuscript covers three sections in the use of gold-standard motion-corrupted data to pursue an intra-volume motion correction. First, we present a way to get motion corrupted data with accurately known motion at the slice acquisition level. This technique simulates important data acquisition-related motion artifacts while acquiring real BOLD MRI data. It is based on a novel motion-injection pulse sequence that introduces known motion independently for every slice: Simulated Prospective Acquisition CorrEction (SimPACE). Secondly, with data acquired using SimPACE, we evaluate several motion correction and characterization techniques, including several commonly used BOLD signal- and motion parameter-based metrics. Finally, we introduce and evaluate a novel, slice-based motion correction technique. Our novel method, SLice-Oriented MOtion COrrection (SLOMOCO) performs better than the volumetric methods and, moreover, accurately detects the motion of independent slices, in this case equivalent to the known injected motion. We demonstrate that SLOMOCO can model and correct for nearly all effects of motion in BOLD data. Also, none of the commonly used motion metrics was observed to robustly identify motion corrupted events, especially in the most realistic scenario of sudden head movement. For some popular metrics, performance was poor even when using the ideal known slice motion instead of volumetric parameters. This has negative implications for methods relying on these metrics, such as recently proposed motion correction methods such as data censoring and global signal regression.
•We present a pulse sequence to acquire BOLD data with known motion corruption.•BOLD data with induced motion is acquired in cadavers and live subjects at rest.•We shows contemporary motion measures are insensitive to intravolume motion.•We present a retrospective algorithm to obtain full slicewise motion estimates.•SLOMOCO is the first motion correction suitable for realistic head motion.
We present GERMLINE, a robust algorithm for identifying segmental sharing indicative of recent common ancestry between pairs of individuals. Unlike methods with comparable objectives, GERMLINE scales ...linearly with the number of samples, enabling analysis of whole-genome data in large cohorts. Our approach is based on a dictionary of haplotypes that is used to efficiently discover short exact matches between individuals. We then expand these matches using dynamic programming to identify long, nearly identical segmental sharing that is indicative of relatedness. We use GERMLINE to comprehensively survey hidden relatedness both in the HapMap as well as in a densely typed island population of 3000 individuals. We verify that GERMLINE is in concordance with other methods when they can process the data, and also facilitates analysis of larger scale studies. We bolster these results by demonstrating novel applications of precise analysis of hidden relatedness for (1) identification and resolution of phasing errors and (2) exposing polymorphic deletions that are otherwise challenging to detect. This finding is supported by concordance of detected deletions with other evidence from independent databases and statistical analyses of fluorescence intensity not used by GERMLINE.
Functional imaging studies indicate that imbalances in cortico-limbic activity and connectivity may underlie the pathophysiology of MDD. In this study, using functional Magnetic Resonance Imaging ...(fMRI), we investigated differences in cortico-limbic activity and connectivity between depressed patients and healthy controls.
Fifteen unmedicated unipolar depressed patients and 15 matched healthy subjects underwent fMRI during which they first completed a conventional block-design activation experiment in which they were exposed to negative and neutral pictures. Next, low frequency blood oxygenation dependent (BOLD) related fluctuations (LFBF) data were acquired at rest and during steady-state exposure to neutral, positive and negative pictures. LFBF correlations were calculated between anterior cingulate cortex (ACC) and limbic regions – amygdala (AMYG), pallidostriatum (PST) and medial thalamus (MTHAL) and used as a measure of cortico-limbic connectivity.
Depressed patients had increased activation of cortical and limbic regions. At rest and during exposure to neutral, positive, and negative pictures cortico-limbic LFBF correlations were decreased in depressed patients compared to healthy subjects.
The finding of increased activation of limbic regions and decreased LFBF correlations between ACC and limbic regions is consistent with the hypothesis that decreased cortical regulation of limbic activation in response to negative stimuli may be present in depression.
To determine whether circulating levels of the inflammatory markers C-reactive protein (CRP), interleukin (IL)-6, and tumor necrosis factor (TNF)-alpha are associated with cognitive ability and ...estimated lifetime cognitive decline in an elderly population with type 2 diabetes.
A cross-sectional study of 1,066 men and women aged 60-75 years with type 2 diabetes and living in Lothian, Scotland (the Edinburgh Type 2 Diabetes Study), was performed. Seven cognitive tests were used to measure abilities in memory, nonverbal reasoning, information processing speed, executive function, and mental flexibility. The results were used to derive a general intelligence factor (g). A vocabulary-based test was administered as an estimate of peak prior cognitive ability. Results on the cognitive tests were assessed for statistical association with inflammatory markers measured in a venous blood sample at the time of cognitive testing.
Higher IL-6 and TNF-alpha levels were associated with poorer age- and sex-adjusted scores on the majority of the individual cognitive tests. They were also associated with g using standardized regression coefficients -0.074 to -0.173 (P < 0.05). After adjusting for vocabulary, education level, cardiovascular dysfunction, duration of diabetes, and glycemic control, IL-6 remained associated with three of the cognitive tests and with g.
In this representative population of people with type 2 diabetes, elevated circulating levels of inflammatory markers were associated with poorer cognitive ability. IL-6 levels were also associated with estimated lifetime cognitive decline.
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