Multi-Scale Glycemic Variability: A Link to Gray Matter Atrophy and Cognitive Decline in Type 2 Diabetes

Objective: Type 2 diabetes mellitus (DM) accelerates brain aging and cognitive decline. Complex interactions between hyperglycemia, glycemic variability and brain aging remain unresolved. This study investigated the relationship between glycemic variability at multiple time scales, brain volumes and cognition in type 2 DM. Research Design and Methods Forty-three older adults with and 26 without type 2 DM completed 72-hour continuous glucose monitoring, cognitive tests and anatomical MRI. We described a new analysis of continuous glucose monitoring, termed Multi-Scale glycemic variability (Multi-Scale GV), to examine glycemic variability at multiple time scales. Specifically, Ensemble Empirical Mode Decomposition was used to identify five unique ultradian glycemic variability cycles (GVC1–5) that modulate serum glucose with periods ranging from 0.5–12 hrs. Results: Type 2 DM subjects demonstrated greater variability in GVC3–5 (period 2.0–12 hrs) than controls (P<0.0001), during the day as well as during the night. Multi-Scale GV was related to conventional markers of glycemic variability (e.g. standard deviation and mean glycemic excursions), but demonstrated greater sensitivity and specificity to conventional markers, and was associated with worse long-term glycemic control (e.g. fasting glucose and HbA1c). Across all subjects, those with greater glycemic variability within higher frequency cycles (GVC1–3; 0.5–2.0 hrs) had less gray matter within the limbic system and temporo-parietal lobes (e.g. cingulum, insular, hippocampus), and exhibited worse cognitive performance. Specifically within those with type 2 DM, greater glycemic variability in GVC2–3 was associated with worse learning and memory scores. Greater variability in GVC5 was associated with longer DM duration and more depression. These relationships were independent of HbA1c and hypoglycemic episodes. Conclusions: Type 2 DM is associated with dysregulation of glycemic variability over multiple scales of time. These time-scale-dependent glycemic fluctuations might contribute to brain atrophy and cognitive outcomes within this vulnerable population

Machine learning based data monitoring system for chicken poultry

Livestock is one of the production sectors that can produce many resources for human needs such as meat, egg, milk, leather, wool, and fur. Farmer must ensure that all farm animals are in good condition to achieve optimum level of production. The welfare of livestock can be determined by observing and analyzing the animal's health and behaviour. The livestock that has a symptom of being sick leads to a low level of production compared to good animal welfare. In the case of chicken poultry, a low nutrient diet and inconsistent daylight may lead to decreased of laid eggs. As a result, the poultry are unable to produce the desired amount of egg to end consumer which trigger an issue in food security. The objective of this study is to evaluate chicken production based on its effectiveness by implementing machine learning. The level of chicken production is determined by using fuzzy logic as the machine learning platform based on the collected data. After the data were evaluated by fuzzy logic, the result of the system will indicate whether the chicken will produce a low, normal, or high level of production. By using this system, farm owners are able to predict whether the chickens on their farms are able to produce the desired quantity of chicken

The Value of Success: Acquiring Gains, Avoiding Losses, and Simply Being Successful

A large network of spatially contiguous, yet anatomically distinct regions in medial frontal cortex is involved in reward processing. Although it is clear these regions play a role in critical aspects of reward-related learning and decision-making, the individual contributions of each component remains unclear. We explored dissociations in reward processing throughout several key regions in the reward system and aimed to clarify the nature of previously observed outcome-related activity in a portion of anterior medial orbitofrontal cortex (mOFC). Specifically, we tested whether activity in anterior mOFC was related to processing successful actions, such that this region would respond similarly to rewards with and without tangible benefits, or whether this region instead encoded only quantifiable outcome values (e.g., money). Participants performed a task where they encountered monetary gains and losses (and non-gains and non-losses) during fMRI scanning. Critically, in addition to the outcomes with monetary consequences, the task included trials that provided outcomes without tangible benefits (participants were simply told that they were correct or incorrect). We found that anterior mOFC responded to all successful outcomes regardless of whether they carried tangible benefits (monetary gains and non-losses) or not (controls). These results support the hypothesis that anterior mOFC processes rewards in terms of a common currency and is capable of providing reward-based signals for everything we value, whether it be primary or secondary rewards or simply a successful experience without objectively quantifiable benefits

Effect of Propranolol on Functional Connectivity in Autism Spectrum Disorder—A Pilot Study

A decrease in interaction between brain regions is observed in individuals with autism spectrum disorder (ASD), which is believed to be related to restricted neural network access in ASD. Propranolol, a beta-adrenergic antagonist, has revealed benefit during performance of tasks involving flexibility of access to networks, a benefit also seen in ASD. Our goal was to determine the effect of propranolol on functional connectivity in ASD during a verbal decision making task as compared to nadolol, thereby accounting for the potential spurious fMRI effects due to peripheral hemodynamic effects of propranolol. Ten ASD subjects underwent fMRI scans after administration of placebo, propranolol or nadolol, while performing a phonological decision making task. Comparison of functional connectivity between pre-defined ROI-pairs revealed a significant increase with propranolol compared to nadolol, suggesting a potential imaging marker for the cognitive effects of propranolol in ASD

Treatment of multiple system atrophy using intravenous immunoglobulin

<p>Abstract</p> <p>Background</p> <p>Multiple system atrophy (MSA) is a progressive neurodegenerative disorder of unknown etiology, manifesting as combination of parkinsonism, cerebellar syndrome and dysautonomia. Disease-modifying therapies are unavailable. Activation of microglia and production of toxic cytokines suggest a role of neuroinflammation in MSA pathogenesis. This pilot clinical trial evaluated safety and tolerability of intravenous immunoglobulin (IVIG) in MSA.</p> <p>Methods</p> <p>This was a single-arm interventional, single-center, open-label pilot study. Interventions included monthly infusions of the IVIG preparation Privigen®, dose 0.4 gram/kg, for 6 months. Primary outcome measures evaluated safety and secondary outcome measures evaluated preliminary efficacy of IVIG. Unified MSA Rating Scale (UMSARS) was measured monthly. Quantitative brain imaging using 3T MRI was performed before and after treatment.</p> <p>Results</p> <p>Nine subjects were enrolled, and seven (2 women and 5 men, age range 55–64 years) completed the protocol. There were no serious adverse events. Systolic blood pressure increased during IVIG infusions (p<0.05). Two participants dropped out from the study because of a non-threatening skin rash. The UMSARS-I (activities of daily living) and USMARS-II (motor functions) improved significantly post-treatment. UMSARS-I improved in all subjects (pre-treatment 23.9 ± 6.0 vs. post-treatment 19.0±5.9 (p=0.01). UMSARS-II improved in 5 subjects, was unchanged in 1 and worsened in 1 (pre-treatment 26.1±7.5 vs. post-treatment 23.3±7.3 (p=0.025). The MR imaging results were not different comparing pre- to post-treatment.</p> <p>Conclusions</p> <p>Treatment with IVIG appears to be safe, feasible and well tolerated and may improve functionality in MSA. A larger, placebo-controlled study is needed.</p

Outcome-related neural activations during the reward task in fMRI.

<p>A main effect of outcome was observed in anterior mOFC, such that this region responded to all successful outcomes regardless of stimulus type (A). A conjunction of successful gains, losses, and controls showed common success-related activity in a specific portion of anterior mOFC (B). The y-axis reflects parameter estimates (beta weights), and black bars represent standard errors.</p

An interaction of stimulus and outcome was observed in two regions.

<p>Pregenual ACC specifically differentiated the reception of monetary gains (A), whereas NAcc differentiated only monetary successes, gains and non-losses, from failures (B). The y-axis reflects parameter estimates (beta weights), and black bars represent standard errors.</p

Summary of neural activations observed for each F-test performed.

<p>All activations meet the criteria of p<.001 and a cluster size of at least 18 contiguous voxels.</p

Example trial of the reward task.

<p>Stimuli were presented randomly for 1000ms. During this time, participants made a response by pressing a button corresponding to each stimulus type. A legend of the response options always appeared at the bottom of the screen. This was followed by 3000ms of feedback, which showed the outcome for each trial. Trials were separated by a fixation cross of variable length.</p

Pilot study results of emotion ratings during the reward task.

<p>Participants gave ratings of each of 4 emotions on a 1–5 scale for the last outcome they had experienced. Ratings occurred randomly every 4–8 trials and were presented in a random order for each type of emotion. Ratings of positive emotions joy/excitement and calm/relief by stimulus type and outcome (A). Ratings of negative emotions agitation/frustration and dejection/disappointment by stimulus type and outcome (B). Black bars represent standard errors.</p