Reconstruction of lingual sulcus in a severely atrophic mandible using a modified approach as a pre‐prosthetic surgery: Case series

Shallow lingual vestibule and lack of keratinized attached mucosa are considered risk factors for the long‐term success of dental implants. This article describes a modified surgical approach accompanied by a free gingival graft to correct the shallow lingual/buccal vestibule and to increase the keratinized tissue around dental implants.     

Interaction of opioid with insulin/IGFs signaling in Alzheimer's disease

Journal of Molecular Neuroscience - Alzheimer's disease is associated with biochemical and histopathological changes characterized by molecular abnormalities. Due to the lack of effective...     

Effects of ketamine and midazolam on resting state connectivity and comparison with ENIGMA connectivity deficit patterns in schizophrenia

Subanesthetic administration of ketamine is a pharmacological model to elicit positive and negative symptoms of psychosis in healthy volunteers. We used resting‐state pharmacological functional MRI (rsPhfMRI) to identify cerebral networks affected by ketamine and compared them to the functional connectivity (FC) in schizophrenia. Ketamine can produce sedation and we contrasted its effects with the effects of the anxiolytic drug midazolam. Thirty healthy male volunteers (age = 19–37 years) underwent a randomized, three‐way, cross‐over study consisting of three imaging sessions, with 48 hr between sessions. A session consisted of a control period followed by infusion of placebo or ketamine or midazolam. The ENIGMA rsfMRI pipeline was used to derive two long‐distance (seed‐based and dual‐regression) and one local (regional homogeneity, ReHo) FC measures. Ketamine induced significant reductions in the connectivity of the salience network (Cohen's d: 1.13 ± 0.28, p = 4.0 × 10^?3), auditory network (d: 0.67 ± 0.26, p = .04) and default mode network (DMN, d: 0.63 ± 0.26, p = .05). Midazolam significantly reduced connectivity in the DMN (d: 0.77 ± 0.27, p = .03). The effect sizes for ketamine for resting networks showed a positive correlation (r = .59, p = .07) with the effect sizes for schizophrenia‐related deficits derived from ENIGMA's study of 261 patients and 327 controls. Effect sizes for midazolam were not correlated with the schizophrenia pattern (r = ?.17, p = .65). The subtraction of ketamine and midazolam patterns showed a significant positive correlation with the pattern of schizophrenia deficits (r = .68, p = .03). RsPhfMRI reliably detected the shared and divergent pharmacological actions of ketamine and midazolam on cerebral networks. The pattern of disconnectivity produced by ketamine was positively correlated with the pattern of connectivity deficits observed in schizophrenia, suggesting a brain functional basis for previously poorly understood effects of the drug.     

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.     

Subcortical shape alterations in major depressive disorder: Findings from the ENIGMA major depressive disorder working group

Alterations in regional subcortical brain volumes have been investigated as part of the efforts of an international consortium, ENIGMA, to identify reliable neural correlates of major depressive disorder (MDD). Given that subcortical structures are comprised of distinct subfields, we sought to build significantly from prior work by precisely mapping localized MDD-related differences in subcortical regions using shape analysis. In this meta-analysis of subcortical shape from the ENIGMA-MDD working group, we compared 1,781 patients with MDD and 2,953 healthy controls (CTL) on individual measures of shape metrics (thickness and surface area) on the surface of seven bilateral subcortical structures: nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Harmonized data processing and statistical analyses were conducted locally at each site, and findings were aggregated by meta-analysis. Relative to CTL, patients with adolescent-onset MDD (≤ 21 years) had lower thickness and surface area of the subiculum, cornu ammonis (CA) 1 of the hippocampus and basolateral amygdala (Cohen's d = ?0.164 to ?0.180). Relative to first-episode MDD, recurrent MDD patients had lower thickness and surface area in the CA1 of the hippocampus and the basolateral amygdala (Cohen's d = ?0.173 to ?0.184). Our results suggest that previously reported MDD-associated volumetric differences may be localized to specific subfields of these structures that have been shown to be sensitive to the effects of stress, with important implications for mapping treatments to patients based on specific neural targets and key clinical features.     

The additive impact of cardio‐metabolic disorders and psychiatric illnesses on accelerated brain aging

Severe mental illnesses (SMI) including major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia spectrum disorder (SSD) elevate accelerated brain aging risks. Cardio‐metabolic disorders (CMD) are common comorbidities in SMI and negatively impact brain health. We validated a linear quantile regression index (QRI) approach against the machine learning “BrainAge” index in an independent SSD cohort (N = 206). We tested the direct and additive effects of SMI and CMD effects on accelerated brain aging in the N = 1,618 (604 M/1,014 F, average age = 63.53 ± 7.38) subjects with SMI and N = 11,849 (5,719 M/6,130 F; 64.42 ± 7.38) controls from the UK Biobank. Subjects were subdivided based on diagnostic status: SMI+/CMD+ (N = 665), SMI+/CMD− (N = 964), SMI−/CMD+ (N = 3,765), SMI−/CMD− (N = 8,083). SMI (F = 40.47, p = 2.06 × 10⁻¹⁰) and CMD (F = 24.69, p = 6.82 × 10⁻⁷) significantly, independently impacted whole‐brain QRI in SMI+. SSD had the largest effect (Cohen’s d = 1.42) then BD (d = 0.55), and MDD (d = 0.15). Hypertension had a significant effect on SMI+ (d = 0.19) and SMI− (d = 0.14). SMI effects were direct, independent of MD, and remained significant after correcting for effects of antipsychotic medications. Whole‐brain QRI was significantly (p < 10⁻¹⁶) associated with the volume of white matter hyperintensities (WMH). However, WMH did not show significant association with SMI and was driven by CMD, chiefly hypertension (p < 10⁻¹⁶). We used a simple and robust index, QRI, the demonstrate additive effect of SMI and CMD on accelerated brain aging. We showed a greater effect of psychiatric illnesses on QRI compared to cardio‐metabolic illness. Our findings suggest that subjects with SMI should be among the targets for interventions to protect against age‐related cognitive decline.     

Public Health Implications of Recommendations to Individualize Glycemic Targets in Adults With Diabetes

OBJECTIVE

To estimate how many U.S. adults with diabetes would be eligible for individualized A1C targets based on 1) the 2012 American Diabetes Association (ADA) guideline and 2) a published approach for individualized target ranges.

RESEARCH DESIGN AND METHODS

We studied adults with diabetes ≥20 years of age from the National Health and Nutrition Examination Survey 2007–2008 (n = 757). We assigned A1C targets based on duration, age, diabetes-related complications, and comorbid conditions according to 1) the ADA guideline and 2) a strategy by Ismail-Beigi focused on setting target ranges. We estimated the number and proportion of adults with each A1C target and compared individualized targets to measured levels.

RESULTS

Using ADA guideline recommendations, 31% (95% CI 27–34%) of the U.S. adult diabetes population would have recommended A1C targets of <7.0%, and 69% (95% CI 66–73%) would have A1C targets less stringent than <7.0%. Using the Ismail-Beigi strategy, 56% (51–61%) would have an A1C target of ≤7.0%, and 44% (39–49%) would have A1C targets less stringent than <7.0%. If a universal A1C <7.0% target were applied, 47% (41–54%) of adults with diabetes would have inadequate glycemic control; this proportion declined to 30% (26–36%) with the ADA guideline and 31% (27–36%) with the Ismail-Beigi strategy.

CONCLUSIONS

Using individualized glycemic targets, about half of U.S. adults with diabetes would have recommended A1C targets of ≥7.0% but one-third would still be considered inadequately controlled. Diabetes research and performance measurement goals will need to be revised in order to encourage the individualization of glycemic targets.For nearly a decade, diabetes care guidelines from the American Diabetes Association (ADA) have recommended that the goal of glycemic control should be to lower the A1C to <7.0% for adults living with diabetes (1). This recommendation currently motivates diabetes public health programs and diabetes care translational research. All of these efforts have the overall intention of shifting the national distribution of A1C levels downward in order to improve diabetes outcomes and may lead to overtreatment of A1C levels in certain diabetes populations.Although the standard A1C target of <7.0% is probably the best-known feature of the ADA guidelines, the ADA guidelines also recommend that A1C targets should be based on individual clinical circumstances. Similar recommendations for individualized targets have been supported by the Veterans Health Administration-Department of Defense (VA-DoD), American Geriatric Society, American College of Physicians (ACP), and American Association of Clinical Endocrinologists (AACE) (2–5). Recommendations to individualize targets are based on major type 2 diabetes trials that found different levels of benefit, and even harm, from lower A1C levels depending on diabetes population characteristics (e.g., duration of diabetes, age, and comorbidity) (6–10). According to the ADA, lower A1C targets are recommended for patients with a short duration of diabetes, long life expectancy, and no significant cardiovascular disease (1). Conversely, higher A1C targets are recommended for patients with longstanding diabetes, advanced age, limited life expectancy, a history of macrovascular or advanced microvascular complications, extensive comorbidities, or a high risk for severe hypoglycemia (1–5). Although guidelines have identified these special populations, recommendations on how to set individualized A1C targets have been open to interpretation.Recently, a formal strategy for individualizing targets was published by Ismail-Beigi et al. (11). Similar to diabetes care guidelines, this strategy was based on expert interpretation of outcomes from prominent diabetes trials, including the U.K. Prospective Diabetes Study (UKPDS), Action to Control Cardiovascular Risk in Diabetes (ACCORD), Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified-Release Control Evaluation (ADVANCE), and Veterans Affairs Diabetes Trial (VADT) (6–10). The Ismail-Beigi strategy used the same clinical characteristics proposed in previous guidelines from the VA-DoD, American Geriatric Society, and ACP (e.g., age, duration of diabetes, history of macrovascular and microvascular complications, comorbidity, and psychosocioeconomic context). Based on their strategy, only adults 20–44 years of age with no history of diabetes-related complications would be recommended an A1C target of ≤6.5%, and several populations are recommended individualized A1C targets above the conventional ADA threshold of <7.0%, including adults 45–65 years of age with established macrovascular or advanced microvascular complications, adults >65 years of age with longstanding diabetes or established macrovascular or advanced microvascular complications, and all adults with advanced age. Additionally, because the Ismail-Beigi strategy suggested ranges of glycemic targets (i.e., ∼7, 7.0–8.0, or ∼8.0%), there exists the potential that some patients who could safely tolerate lower glycemic targets may be undertreated in order to stay within range.These recent calls for greater individualization of A1C targets raise fundamental public health questions. The degree to which the individualization of diabetes care is regarded as important depends on how many U.S. adults with diabetes may be candidates for A1C targets more or less stringent than the conventional target of <7.0%. Previous assessments of diabetes care quality have used population-level A1C thresholds to judge the quality of care (12–14); however, the diabetes care quality may differ from previous reports using these newer standards of individualization (15). In order to understand the potential impact of the individualization of glycemic targets on diabetes care quality, we characterized the U.S. adult diabetes population by clinical variables that have been proposed as reasons to individualize A1C targets. We then operationalized the ADA and Ismail-Beigi strategies for individualization to estimate 1) the distribution of the U.S. adult diabetes population across each individualized A1C target and 2) the size of the population who have measured A1C levels that are at or below their recommended individualized A1C target.     

Accurate cortical tissue classification on MRI by modeling cortical folding patterns

Accurate tissue classification is a crucial prerequisite to MRI morphometry. Automated methods based on intensity histograms constructed from the entire volume are challenged by regional intensity variations due to local radiofrequency artifacts as well as disparities in tissue composition, laminar architecture and folding patterns. Current work proposes a novel anatomy‐driven method in which parcels conforming cortical folding were regionally extracted from the brain. Each parcel is subsequently classified using nonparametric mean shift clustering. Evaluation was carried out on manually labeled images from two datasets acquired at 3.0 Tesla (n = 15) and 1.5 Tesla (n = 20). In both datasets, we observed high tissue classification accuracy of the proposed method (Dice index >97.6% at 3.0 Tesla, and >89.2% at 1.5 Tesla). Moreover, our method consistently outperformed state‐of‐the‐art classification routines available in SPM8 and FSL‐FAST, as well as a recently proposed local classifier that partitions the brain into cubes. Contour‐based analyses localized more accurate white matter–gray matter (GM) interface classification of the proposed framework compared to the other algorithms, particularly in central and occipital cortices that generally display bright GM due to their highly degree of myelination. Excellent accuracy was maintained, even in the absence of correction for intensity inhomogeneity. The presented anatomy‐driven local classification algorithm may significantly improve cortical boundary definition, with possible benefits for morphometric inference and biomarker discovery. Hum Brain Mapp 36:3563–3574, 2015. © 2015 Wiley Periodicals, Inc.     

Sleep architecture and obstructive sleep apnea in obese children with and without metabolic syndrome: a case control study

Purpose

Obesity and biochemical parameters of metabolic disorders are both closely related to obstructive sleep apnea (OSA). The aim of this study was to compare sleep architecture and OSA in obese children with and without metabolic syndrome.

Methods

Forty-two children with metabolic syndrome were selected as case group and 38 children without metabolic syndrome were matched for age, sex, and BMI as control group. The standardized Persian version of bedtime problems, excessive daytime sleepiness, awakenings during the night, regularity and duration of sleep, snoring (BEARS) and Children’s Sleep Habits Questionnaires were completed, and polysomnography (PSG) was performed for all study subjects. Scoring was performed using the manual of American Academy of Sleep Medicine for children. Data were analyzed using chi-square test, T test, Mann–Whitney U test, and logistic regression analysis.

Results

Non-rapid eye movement (NREM) sleep and N1 stage in the case group were significantly longer than the control group, while REM sleep was significantly shorter. Waking after sleep onset (WASO) was significantly different between two groups. Severe OSA was more frequent in the control group. Multivariate logistic regression analysis showed that severe OSA (OR 21.478, 95 % CI 2.160–213.600; P = 0.009) and REM sleep (OR 0.856, 95 % CI 0.737–0.994; P = 0.041) had independent association with metabolic syndrome.

Conclusions

Obese children with metabolic syndrome had increased WASO, N1 sleep stage, and severe OSA. But the results regarding sleep architecture are most likely a direct result of OSA severity. More longitudinal studies are needed to confirm the association of metabolic syndrome and OSA.