Brain Activation During Working Memory Is Altered in Patients With Type 1 Diabetes During Hypoglycemia

OBJECTIVE To investigate the effects of acute hypoglycemia on working memory and brain function in patients with type 1 diabetes. RESEARCH DESIGN AND METHODS Using blood oxygen level–dependent (BOLD) functional magnetic resonance imaging during euglycemic (5.0 mmol/L) and hypoglycemic (2.8 mmol/L) hyperinsulinemic clamps, we compared brain activation response to a working-memory task (WMT) in type 1 diabetic subjects (n = 16) with that in age-matched nondiabetic control subjects (n = 16). Behavioral performance was assessed by percent correct responses. RESULTS During euglycemia, the WMT activated the bilateral frontal and parietal cortices, insula, thalamus, and cerebellum in both groups. During hypoglycemia, activation decreased in both groups but remained 80% larger in type 1 diabetic versus control subjects (P < 0.05). In type 1 diabetic subjects, higher HbA1c was associated with lower activation in the right parahippocampal gyrus and amygdala (R2 = 0.45, P < 0.002). Deactivation of the default-mode network (DMN) also was seen in both groups during euglycemia. However, during hypoglycemia, type 1 diabetic patients deactivated the DMN 70% less than control subjects (P < 0.05). Behavioral performance did not differ between glycemic conditions or groups. CONCLUSIONS BOLD activation was increased and deactivation was decreased in type 1 diabetic versus control subjects during hypoglycemia. This higher level of brain activation required by type 1 diabetic subjects to attain the same level of cognitive performance as control subjects suggests reduced cerebral efficiency in type 1 diabetes

Resting-State Brain Functional Connectivity Is Altered in Type 2 Diabetes

Type 2 diabetes mellitus (T2DM) is a risk factor for Alzheimer disease (AD). Populations at risk for AD show altered brain activity in the default mode network (DMN) before cognitive dysfunction. We evaluated this brain pattern in T2DM patients. We compared T2DM patients (n = 10, age = 56 ± 2.2 years, fasting plasma glucose [FPG] = 8.4 ± 1.3 mmol/L, HbA1c = 7.5 ± 0.54%) with nondiabetic age-matched control subjects (n = 11, age = 54 ± 1.8 years, FPG = 4.8 ± 0.2 mmol/L) using resting-state functional magnetic resonance imaging to evaluate functional connectivity strength among DMN regions. We also evaluated hippocampal volume, cognition, and insulin sensitivity by homeostasis model assessment of insulin resistance (HOMA-IR). Control subjects showed stronger correlations versus T2DM patients in the DMN between the seed (posterior cingulate) and bilateral middle temporal gyrus (β = 0.67 vs. 0.43), the right inferior and left medial frontal gyri (β = 0.75 vs. 0.54), and the left thalamus (β = 0.59 vs. 0.37), respectively, with no group differences in cognition or hippocampal size. In T2DM patients, HOMA-IR was inversely correlated with functional connectivity in the right inferior frontal gyrus and precuneus. T2DM patients showed reduced functional connectivity in the DMN compared with control subjects, which was associated with insulin resistance in selected brain regions, but there were no group effects of brain structure or cognition

Affine image registration of arterial spin labeling MRI using deep learning networks

Convolutional neural networks (CNN) have demonstrated good accuracy and speed in spatially registering high signal-to-noise ratio (SNR) structural magnetic resonance imaging (sMRI) images. However, some functional magnetic resonance imaging (fMRI) images, e.g., those acquired from arterial spin labeling (ASL) perfusion fMRI, are of intrinsically low SNR and therefore the quality of registering ASL images using CNN is not clear. In this work, we aimed to explore the feasibility of a CNN-based affine registration network (ARN) for registration of low-SNR three-dimensional ASL perfusion image time series and compare its performance with that from the state-of-the-art statistical parametric mapping (SPM) algorithm. The six affine parameters were learned from the ARN using both simulated motion and real acquisitions from ASL perfusion fMRI data and the registered images were generated by applying the transformation derived from the affine parameters. The speed and registration accuracy were compared between ARN and SPM. Several independent datasets, including meditation study (10 subjects × 2), bipolar disorder study (26 controls, 19 bipolar disorder subjects), and aging study (27 young subjects, 33 older subjects), were used to validate the generality of the trained ARN model. The ARN method achieves superior image affine registration accuracy (total translation/total rotation errors of ARN vs. SPM: 1.17 mm/1.23° vs. 6.09 mm/12.90° for simulated images and reduced MSE/L1/DSSIM/Total errors of 18.07% / 19.02% / 0.04% / 29.59% for real ASL test images) and 4.4 times (ARN vs. SPM: 0.50 s vs. 2.21 s) faster speed compared to SPM. The trained ARN can be generalized to align ASL perfusion image time series acquired with different scanners, and from different image resolutions, and from healthy or diseased populations. The results demonstrated that our ARN markedly outperforms the iteration-based SPM both for simulated motion and real acquisitions in terms of registration accuracy, speed, and generalization

Deletion of the background potassium channel TREK-1 results in a depression-resistant phenotype

Depression is a devastating illness with a lifetime prevalence of up to 20%. The neurotransmitter serotonin or 5-hydroxytryptamine (5-HT) is involved in the pathophysiology of depression and in the effects of antidepressant treatments. However, molecular alterations that underlie the pathology or treatment of depression are still poorly understood. The TREK-1 protein is a background K+ channel regulated by various neurotransmitters including 5-HT. In mice, the deletion of its gene (Kcnk2, also called TREK-1) led to animals with an increased efficacy of 5-HT neurotransmission and a resistance to depression in five different models and a substantially reduced elevation of corticosterone levels under stress. TREK-1–deficient (Kcnk2−/−) mice showed behavior similar to that of naive animals treated with classical antidepressants such as fluoxetine. Our results indicate that alterations in the functioning, regulation or both of the TREK-1 channel may alter mood, and that this particular K+ channel may be a potential target for new antidepressants