Independent Groups Of Subjects Research Articles

Intersubject correlations in reward and mentalizing brain circuits separately predict persuasiveness of two types of ISIS video propaganda

The Islamist group ISIS has been particularly successful at recruiting Westerners as terrorists. A hypothesized explanation is their simultaneous use of two types of propaganda: Heroic narratives, emphasizing individual glory, alongside Social narratives, which emphasize oppression against Islamic communities. In the current study, functional MRI was used to measure brain responses to short ISIS propaganda videos distributed online. Participants were shown 4 Heroic and 4 Social videos categorized as such by another independent group of subjects. Persuasiveness was measured using post-scan predictions of recruitment effectiveness. Inter-subject correlation (ISC) was used to measure commonality of brain activity time courses across individuals. ISCs in ventral striatum predicted rated persuasiveness for Heroic videos, while ISCs in mentalizing and default networks, especially in dmPFC, predicted rated persuasiveness for Social videos. This work builds on past findings that engagement of the reward circuit and of mentalizing brain regions predicts preferences and persuasion. The observed dissociation as a function of stimulus type is novel, as is the finding that intersubject synchrony in ventral striatum predicts rated persuasiveness. These exploratory results identify possible neural mechanisms by which political extremists successfully recruit prospective members and specifically support the hypothesized distinction between Heroic and Social narratives for ISIS propaganda.

Estimation of Absolute and Relative Body Fat Content Using Noninvasive Surrogates: Can DXA Be Bypassed?

Dual-energy x-ray absorptiometry (DXA) scanning is used for objective determination of body composition, but instrumentation is expensive and not generally available in customary clinical practice. Anthropometric surrogates are often substituted as anticipated correlates of absolute and relative body fat content in the clinical management of obesity and its associated medical risks. DXA and anthropometric data from a cohort of 9230 randomly selected American subjects, available through the ongoing National Health and Nutrition Examination Survey, was used to evaluate combinations of surrogates (age, height, total weight, waist circumference) as predictors of DXA-determined absolute and relative body fat content. Multiple regression analysis yielded linear combinations of the 4 surrogates that were closely predictive of DXA-determined absolute fat content (R2 =0.93 and 0.96 for male and female subjects). Accuracy of the new algorithm was improved over customary surrogate-based predictors such as body mass index. However prediction of relative body fat was less robust (R2 less than0.75), probably due to the nonlinear relation between degree of obesity (based on body mass index) and relative body fat. The paradigm was validated using an independent cohort from the National Health and Nutrition Examination Survey, as well as two independent external subject groups. The described regression-based algorithm is likely to be a sufficiently accurate predictor of absolute body fat (but not relative body fat) to substitute for DXA scanning in many clinical situations. Further work is needed to assess algorithm validity for subgroups of individuals with "atypical" body construction.

Epigenetic Dysregulation of the Homeobox A5 (HOXA5) Gene Associates with Subcutaneous Adipocyte Hypertrophy in Human Obesity.

Along with insulin resistance and increased risk of type 2 diabetes (T2D), lean first-degree relatives of T2D subjects (FDR) feature impaired adipogenesis in subcutaneous adipose tissue (SAT) and subcutaneous adipocyte hypertrophy well before diabetes onset. The molecular mechanisms linking these events have only partially been clarified. In the present report, we show that silencing of the transcription factor Homeobox A5 (HOXA5) in human preadipocytes impaired differentiation in mature adipose cells in vitro. The reduced adipogenesis was accompanied by inappropriate WNT-signaling activation. Importantly, in preadipocytes from FDR individuals, HOXA5 expression was attenuated, with hypermethylation of the HOXA5 promoter region found responsible for its downregulation, as revealed by luciferase assay. Both HOXA5 gene expression and DNA methylation were significantly correlated with SAT adipose cell hypertrophy in FDR, whose increased adipocyte size marks impaired adipogenesis. In preadipocytes from FDR, the low HOXA5 expression negatively correlated with enhanced transcription of the WNT signaling downstream genes NFATC1 and WNT2B. In silico evidence indicated that NFATC1 and WNT2B were directly controlled by HOXA5. The HOXA5 promoter region also was hypermethylated in peripheral blood leukocytes from these same FDR individuals, which was further revealed in peripheral blood leukocytes from an independent group of obese subjects. Thus, HOXA5 controlled adipogenesis in humans by suppressing WNT signaling. Altered DNA methylation of the HOXA5 promoter contributed to restricted adipogenesis in the SAT of lean subjects who were FDR of type 2 diabetics and in obese individuals.

Validation of population pharmacokinetic models: a comparison of internal and external validation approaches for hydrochlorothiazide

1. Model evaluation is an important issue in population analyses. Our aim was to perform and illustrate metrics and techniques for internal and external evaluation with an application to population pharmacokinetics of hydrochlorothiazide (HCTZ). 2. A nonlinear mixed effects model was used to study the pharmacokinetics of HCTZ. In addition, different types of internal assessment tools and external metrics were used for model evaluation. External evaluation was performed using an alternative dataset that included data from an independent group of subjects. For comparison, a previously published population pharmacokinetic model for HCTZ was applied to the same data. 3. A two-compartment model with first-order oral absorption using a constant time delay between administration and absorption and first-order elimination best described HCTZ pharmacokinetics. Age had a statistically significant effect on HCTZ clearance. The final model performed adequately in the internal and external assessment tests. The final model showed better predictive performance than the other previously published HCTZ model. 4. Finally, a robust population pharmacokinetic model for HCTZ in adults was constructed and validated internally and externally. Incorporating analytical assessment of nonlinear pharmacokinetics into the modelling may be a promising approach to improve the predictive power of the model.

Physiological indicators and subjective restorativeness with audio-visual interactions in urban soundscapes

The present study aimed to identify the trends of changes in physiological indicators and subjective restorativeness in response to audio-visual interactions in the environment. Four scenarios types were presented using four different modalities (video-sound, image-sound, sound-only, and video-only; each modality was evaluated by independent groups of subjects). The physiological responses and subjective restoration of subjects were measured to assess the interactions between the audio-visual modalities. These data were also analysed to determine the physiological and subjective differences between dynamic and static visual presentations. We found that with visual modalities, the heart rate (HR), heart rate variability (HRV) calculated using the standard deviation of the NN intervals (SDNNHRV), high-frequency band in the HRV power spectrum (HFHRV), alpha reactivity on electroencephalography, and skin temperature (ST) decreased; however, the beta reactivity on EEG and skin conductance level (SCL) increased. With auditory modalities, the SDNNHRV, HF-HRV, ST, and respiration depth decreased; however, the respiration rate and SCL increased. Use of static images and sound to reproduce the natural environment evoked more physiological comfort and subjective restorativeness. These findings could provide physiological insights for the theory of the restorative environment.

Circulating lipid and lipoprotein profiles and their correlation to cardiac function and cardiovascular outcomes in patients with acute myocardial infarction

Recent studies showed that lipoproteins represent major risk factors, both positive and negative, for atherosclerotic cardiovascular disease. The aim of the present study was to describe the relationship between plasma...

Cord blood DNA methylome in newborns later diagnosed with autism spectrum disorder reflects early dysregulation of neurodevelopmental and X-linked genes

BackgroundAutism spectrum disorder (ASD) is a neurodevelopmental disorder with complex heritability and higher prevalence in males. The neonatal epigenome has the potential to reflect past interactions between genetic and environmental factors during early development and influence future health outcomes.MethodsWe performed whole-genome bisulfite sequencing of 152 umbilical cord blood samples from the MARBLES and EARLI high-familial risk prospective cohorts to identify an epigenomic signature of ASD at birth. Samples were split into discovery and replication sets and stratified by sex, and their DNA methylation profiles were tested for differentially methylated regions (DMRs) between ASD and typically developing control cord blood samples. DMRs were mapped to genes and assessed for enrichment in gene function, tissue expression, chromosome location, and overlap with prior ASD studies. DMR coordinates were tested for enrichment in chromatin states and transcription factor binding motifs. Results were compared between discovery and replication sets and between males and females.ResultsWe identified DMRs stratified by sex that discriminated ASD from control cord blood samples in discovery and replication sets. At a region level, 7 DMRs in males and 31 DMRs in females replicated across two independent groups of subjects, while 537 DMR genes in males and 1762 DMR genes in females replicated by gene association. These DMR genes were significantly enriched for brain and embryonic expression, X chromosome location, and identification in prior epigenetic studies of ASD in post-mortem brain. In males and females, autosomal ASD DMRs were significantly enriched for promoter and bivalent chromatin states across most cell types, while sex differences were observed for X-linked ASD DMRs. Lastly, these DMRs identified in cord blood were significantly enriched for binding sites of methyl-sensitive transcription factors relevant to fetal brain development.ConclusionsAt birth, prior to the diagnosis of ASD, a distinct DNA methylation signature was detected in cord blood over regulatory regions and genes relevant to early fetal neurodevelopment. Differential cord methylation in ASD supports the developmental and sex-biased etiology of ASD and provides novel insights for early diagnosis and therapy.

Comparative Study of the Lower Limb Muscle Tone under the Conditions of Five-day Support Unloading Coupled with Different Regimens of Electromyostimulation

The method of electromyostimulation is diverse in its manifestation and is widespread in medicine and sports. One of the approaches to assessing the effectiveness of this method is a muscle tone study, because it is known that chronic electrical stimulation directly affects the characteristics of the muscle, changing its strength, endurance, fiber mass, and oxidative capabilities. In our study, we compared the effects of daily low- (25 Hz) and high-frequency (50 Hz) electromyostimulation with the control group under the conditions of five-day Dry Immersion in three independent groups of subjects. There was a tendency for the muscle tone in the mm. soleus, gastrocnemius, and tibialis anterior to decrease by the end of immersion in the control group. Low-frequency stimulation was more effective in preserving the muscle tone of the soleus muscle, while high-frequency stimulation was more effective for the gastrocnemius muscle. Neither low-frequency nor high-frequency stimulation prevented a decrease in the lower leg tibialis anterior muscle tone, although immediately after completion of immersion, its tone was significantly higher than the baseline values compared to the control group. The use of daily sessions of electromyostimulation during five-day support unloading selectively prevents a decrease in the tone of the lower leg muscles. Low-frequency electromyostimulation is most preferable for muscles with the predominant content of slow fibers, while high-frequency stimulation, for fast fibers.

Corticolimbic brain anomalies are associated with cognitive subtypes in psychosis: A longitudinal study.

Earlier studies examining structural brain abnormalities associated with cognitively derived subgroups were mainly cross-sectional in design and had mixed findings. Thus, we obtained cross-sectional and longitudinal data to characterize the extent and trajectory of brain structure abnormalities underlying distinct cognitive subtypes ("preserved," "deteriorated," and "compromised") seen in psychotic spectrum disorders. Data from 364 subjects (225 patients with psychotic conditions and 139 healthy controls) were first used to determine the relationship of cognitive subtypes with cross-sectional measures of subcortical volume and cortical thickness. To probe neurodevelopmental abnormalities, brain structure laterality was examined. To examine whether neuroprogressive abnormalities persist, longitudinal brain structural changes over 5 years were examined within a subset of 101 subjects. Subsequent discriminant analysis using the identified brain measures was performed on an independent subject group. Cross-sectional comparisons showed that cortical thinning and limbic volume reductions were most widespread in "deteriorated" cognitive subtype. Laterality comparisons showed more rightward amygdala lateralization in "compromised" than "preserved" subtype. Longitudinal comparisons revealed progressive hippocampal shrinkage in "deteriorated" compared with healthy controls and "preserved" subtype, which correlated with worse negative symptoms, cognitive and psychosocial functioning. Post-hoc discrimination analysis on an independent group of 52 subjects using the identified brain structures found an overall accuracy of 71% for classification of cognitive subtypes. These findings point toward distinct extent and trajectory of corticolimbic abnormalities associated with cognitive subtypes in psychosis, which can allow further understanding of the biological course of cognitive functioning over illness course and with treatment.

Predicting individual decision-making responses based on single-trial EEG

Decision-making plays an essential role in the interpersonal interactions and cognitive processing of individuals. There has been increasing interest in being able to predict an individual’s decision-making response (i.e., acceptance or rejection). We proposed an electroencephalogram (EEG)-based computational intelligence framework to predict individual responses. Specifically, the discriminative spatial network pattern (DSNP), a supervised learning approach, was applied to single-trial EEG data to extract the DSNP feature from the single-trial brain network. A linear discriminate analysis (LDA) trained on the DSNP features was then used to predict the individual response trial-by-trial. To verify the performance of the proposed DSNP, we recruited two independent subject groups, and recorded the EEGs using two types of EEG systems. The performances of the trial-by-trial predictors achieved an accuracy of 0.88 ± 0.09 for the first dataset, and 0.90 ± 0.10 for the second dataset. These trial-by-trial prediction performances suggested that individual responses could be predicted trial-by-trial by using the specific pattern of single-trial EEG networks, and our proposed method has the potential to establish the biologically inspired artificial intelligence decision system.

Identification of a Simple and Novel Cut-Point Based Cerebrospinal Fluid and MRI Signature for Predicting Alzheimer's Disease Progression that Reinforces the 2018 NIA-AA Research Framework.

The 2018 NIA-AA research framework proposes a classification system with Amyloid-β deposition, pathologic Tau, and Neurodegeneration (ATN) for diagnosis and staging of Alzheimer's disease (AD). Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database can be utilized to identify diagnostic signatures for predicting AD progression, and to determine the utility of this NIA-AA research framework. Profiles of 320 peptides from baseline cerebrospinal fluid (CSF) samples of 287 normal, mild cognitive impairment (MCI), and AD subjects followed over a 3-10-year period were measured via multiple reaction monitoring mass spectrometry. CSF Aβ42, total-Tau (tTau), phosphorylated-Tau (pTau-181), and hippocampal volume were also measured. From these candidate markers, optimal signatures with decision thresholds to separate AD and normal subjects were first identified via unbiased regression and tree-based algorithms. The best performing signature determined via cross-validation was then tested in an independent group of MCI subjects to predict future progression. This multivariate analysis yielded a simple diagnostic signature comprising CSF pTau-181 to Aβ42 ratio, MRI hippocampal volume, and low CSF levels of a novel PTPRN peptide, with a decision threshold on each marker. When applied to a separate MCI group at baseline, subjects meeting these signature criteria experience 4.3-fold faster progression to AD compared to a 2.2-fold faster progression using only conventional markers. This novel 4-marker signature represents an advance over the current diagnostics based on widely used markers, and is easier to use in practice than recently published complex signatures. This signature also reinforces the ATN construct from the 2018 NIA-AA research framework.

Dynamic 11C-PiB PET Shows Cerebrospinal Fluid Flow Alterations in Alzheimer Disease and Multiple Sclerosis.

Cerebrospinal fluid (CSF) plays an important role in solute clearance and maintenance of brain homeostasis. 11C-Pittsburgh compound B (PiB) PET was recently proposed as a tool for detection of CSF clearance alterations in Alzheimer disease. The current study investigates the magnitude of 11C-PiB PET signal in the lateral ventricles of an independent group of Alzheimer and mild cognitive impairment subjects. We have also evaluated multiple sclerosis as a model of disease with CSF clearance alterations without amyloid-β tissue accumulation. Methods: A set of 11 Alzheimer and 12 mild cognitive impairment subjects and a set of 20 multiple sclerosis subjects with matched controls underwent MRI and dynamic 11C-PiB PET. Lateral ventricle regions of interest were generated manually from MRI data. PET data were analyzed using cerebellum or a supervised reference region for the Alzheimer and multiple sclerosis data sets, respectively. The magnitude of 11C-PiB signal in the lateral ventricles was calculated as area under the curve from 35 to 80 min and SUV ratio (SUVR) from 50 to 70 min. Compartmental modeling analysis was performed on a separate data set containing 11 Alzheimer and matched control subjects; this analysis included an arterial input function, to further understand the kinetics of the lateral ventricular 11C-PiB signal. Results: ANOVA revealed significant group differences in lateral ventricular SUVR across the Alzheimer, mild cognitive impairment, and healthy control groups (P = 0.004). Pairwise comparisons revealed significantly lower lateral ventricular SUVR in Alzheimer subjects than in healthy controls (P < 0.001) or mild cognitive impairment subjects (P = 0.029). Lateral ventricular SUVR was significantly lower in multiple sclerosis subjects than in healthy controls (P = 0.008). Compartmental modeling analysis revealed significantly lower uptake rates of 11C-PiB signal from blood (P = 0.005) and brain tissue (P = 0.004) to the lateral ventricles and significantly lower 11C-PiB signal clearance out of the lateral ventricles (P = 0.002) in Alzheimer subjects than in healthy controls. Conclusion: These results indicate that dynamic 11C-PiB PET can be used to observe pathologic changes in CSF dynamics. We have replicated previous work demonstrating CSF clearance deficits in Alzheimer disease associated with amyloid-β deposits and have extended the observations to include ventricular CSF clearance deficits in mild cognitive impairment and multiple sclerosis.

A longitudinal model for tau aggregation in Alzheimer's disease based on structural connectivity.

Tau tangles are a pathological hallmark of Alzheimer?s disease (AD) with strong correlations existing between tau aggregation and cognitive decline. Studies in mouse models have shown that the characteristic patterns of tau spatial spread associated with AD progression are determined by neural connectivity rather than physical proximity between different brain regions. We present here a network diffusion model for tau aggregation based on longitudinal tau measures from positron emission tomography (PET) and structural connectivity graphs from diffusion tensor imaging (DTI). White matter fiber bundles reconstructed via tractography from the DTI data were used to compute normalized graph Laplacians which served as graph diffusion kernels for tau spread. By linearizing this model and using sparse source localization, we were able to identify distinct patterns of propagative and generative buildup of tau at a population level. A gradient descent approach was used to solve the sparsity-constrained optimization problem. Model fitting was performed on subjects from the Harvard Aging Brain Study cohort. The fitted model parameters include a scalar factor controlling the network-based tau spread and a network-independent seed vector representing seeding in different regions-of-interest. This parametric model was validated on an independent group of subjects from the same cohort. We were able to predict with reasonably high accuracy the tau buildup at a future time-point. The network diffusion model, therefore, successfully identifies two distinct mechanisms for tau buildup in the aging brain and offers a macroscopic perspective on tau spread.

Микродинамика межличностного восприятия: роль времени и этноса воспринимаемого человека

The article presents the results of an empirical study of the microdynamics of person perception. Stimulus images of persons from four ethnic groups — Tuvans, Russians, Kabardians and Komi, were presented to three independent groups of subjects to assess specific individual psychological characteristics, with different exposure times — 200, 1000 and 3000 ms. The subjects were Tuvans living in Kyzyl, the Republic of Tuva. For each of the evaluated qualities, the adequacy of the interpersonal assessment was calculated and its relations with such variable studies as the ethnicity and exposure time of the stimulus image were determined. The results obtained indicate a complex, non-linear relationship between the adequacy of interpersonal assessment and exposure time of the stimulus image, as well as the important role of the ethnic group of communicants in the structure of person perception.

MicroRNA profiles in urine by next-generation sequencing can stratify bladder cancer subtypes.

Bladder cancer (BC) is the most frequent malignancy of the urinary tract with a high incidence in men and smokers. Currently, there are no non-invasive markers useful for BC diagnosis and subtypes classification that could overcome invasive procedures such as cystoscopy. Dysregulated miRNA profiles have been associated with numerous cancers, including BC. Cell-free miRNAs are abundantly present in a variety of biofluids including urine and make them promising candidates in cancer biomarker discovery.In the present study, the identification of miRNA fingerprints associated with different BC status was performed by next-generation sequencing on urine samples from 66 BC and 48 controls. Three signatures based on dysregulated miRNAs have been identified by regression models, assessing the power to discriminate different BC subtypes. Altered miRNAs according to invasiveness and grade were validated by qPCR on 112 cases and 65 controls (among which 46 cases and 16 controls were an independent group of subjects while the rest were replica samples).The area under the curve (AUC) computed including three miRNAs (miR-30a-5p, let-7c-5p and miR-486-5p) altered in all BC subtypes showed a significantly increased accuracy in the discrimination of cases and controls (AUC model = 0.70; p-value = 0.01).In conclusions, the non-invasive detection in urine of a selected number of miRNAs altered in different BC subtypes could lead to an accurate early diagnosis of cancer and stratification of patients.

T146. AROUSAL AFFECTS DIFFERENTIALLY FIRST-EPISODE PSYCHOSIS PATIENTS AND CONTROL SUBJECTS’ DEFAULT MODE NETWORK FUNCTIONING DURING MOVIE VIEWING

BackgroundFunctional alterations of the default mode network (DMN) are frequently reported in psychotic disorders, but the functional role of these alterations remains poorly known. In addition to previous studies that have applied different types of tasks or recorded resting-state neuroimaging data, there has recently been more interest in the use of movie stimuli in studying brain functioning in patient populations, because this could provide a more naturalistic account of brain functioning in real life-like situations.MethodsSeventy-one first-episode psychosis (FEP) patients (mean age = 26.0 yrs, 47 (66%) males) and 57 controls (mean age = 26.86 yrs, 24 (42%) males) from the Helsinki Early Psychosis Study watched scenes from the movie Alice in Wonderland (Tim Burton, 2010) during 3 T fMRI-BOLD imaging. We used intersubject correlation (ISC) analysis, in which the correlation between voxel-wise BOLD time series in every within-group pair of subjects is calculated. In this study, time-windowed ISC was calculated with a 10-TR (time of repetition, 1.8 s) window with 1-TR steps over the fMRI time series. In each ISC window, a two-sample t test was performed to obtain a t-statistic time series of differences between the groups. An independent group of control subjects (n = 17, 10 males, mean age 26.5 yrs) rated how emotionally arousing the currently seen events of the stimulus are, producing a time-varying rating used as a regressor. General linear model was used to identify brain regions where the t-statistic time series covaries with the arousal rating. To make the interpretation of results less ambiguous, the arousal rating was divided into high and low arousal regressor by z scoring the rating and taking only the positive and negative values, respectively. Nonparametric clusterwise permutation test was used for statistical inference (cluster-defining threshold of p = 0.05, familywise error corrected threshold of p = 0.05, number of permutations = 5000). Furthermore, by using an experience-sampling setup during the same brain-scanning session, a partially overlapping sample of participants reported how emotionally aroused they were feeling during scanning.ResultsThe results show significant correlation between the t-statistic time series and low arousal regressor, especially in the DMN including the anterior and posterior cingulate cortex, medial prefrontal cortex, precuneus, and bilateral lateral temporoparietal regions. Closer inspection reveals that during moments of low arousal in the movie stimulus, the ISC of healthy controls goes up but the ISC of patients does not. In the experience-sampling portion of the study, the patients reported more arousal than the control subjects.DiscussionIntersubject correlation in the DMN depended differentially on arousal in FEP patients and control subjects. More specifically, during moments when the stimulus was rated less emotionally arousing, control subjects’ DMN functioning synchronized more while the patients’ did not. In connection with the difference in reported arousal during the same imaging session, our findings provide preliminary evidence for a contribution of arousal on the functional alterations of the DMN and suggest that this may be related to higher baseline arousal in the patients. Higher arousal and the related distortion of high order integrative functioning that characterizes DMN could contribute to the pathogenesis of psychosis.

Clustering of Whole-Brain White Matter Short Association Bundles Using HARDI Data.

Human brain connectivity is extremely complex and variable across subjects. While long association and projection bundles are stable and have been deeply studied, short association bundles present higher intersubject variability, and few studies have been carried out to adequately describe the structure, shape, and reproducibility of these bundles. However, their analysis is crucial to understand brain function and better characterize the human connectome. In this study, we propose an automatic method to identify reproducible short association bundles of the superficial white matter, based on intersubject hierarchical clustering. The method is applied to the whole brain and finds representative clusters of similar fibers belonging to a group of subjects, according to a distance metric between fibers. We experimented with both affine and non-linear registrations and, due to better reproducibility, chose the results obtained from non-linear registration. Once the clusters are calculated, our method performs automatic labeling of the most stable connections based on individual cortical parcellations. We compare results between two independent groups of subjects from a HARDI database to generate reproducible connections for the creation of an atlas. To perform a better validation of the results, we used a bagging strategy that uses pairs of groups of 27 subjects from a database of 74 subjects. The result is an atlas with 44 bundles in the left hemisphere and 49 in the right hemisphere, of which 33 bundles are found in both hemispheres. Finally, we use the atlas to automatically segment 78 new subjects from a different HARDI database and to analyze stability and lateralization results.

Prior automatic posture and activity identification improves physical activity energy expenditure prediction from hip-worn triaxial accelerometry.

Accelerometry is increasingly used to quantify physical activity (PA) and related energy expenditure (EE). Linear regression models designed to derive PAEE from accelerometry-counts have shown their limits, mostly due to the lack of consideration of the nature of activities performed. Here we tested whether a model coupling an automatic activity/posture recognition (AAR) algorithm with an activity-specific count-based model, developed in 61 subjects in laboratory conditions, improved PAEE and total EE (TEE) predictions from a hip-worn triaxial-accelerometer (ActigraphGT3X+) in free-living conditions. Data from two independent subject groups of varying body mass index and age were considered: 20 subjects engaged in a 3-h urban-circuit, with activity-by-activity reference PAEE from combined heart-rate and accelerometry monitoring (Actiheart); and 56 subjects involved in a 14-day trial, with PAEE and TEE measured using the doubly-labeled water method. PAEE was estimated from accelerometry using the activity-specific model coupled to the AAR algorithm (AAR model), a simple linear model (SLM), and equations provided by the companion-software of used activity-devices (Freedson and Actiheart models). AAR-model predictions were in closer agreement with selected references than those from other count-based models, both for PAEE during the urban-circuit (RMSE = 6.19 vs 7.90 for SLM and 9.62 kJ/min for Freedson) and for EE over the 14-day trial, reaching Actiheart performances in the latter (PAEE: RMSE = 0.93 vs. 1.53 for SLM, 1.43 for Freedson, 0.91 MJ/day for Actiheart; TEE: RMSE = 1.05 vs. 1.57 for SLM, 1.70 for Freedson, 0.95 MJ/day for Actiheart). Overall, the AAR model resulted in a 43% increase of daily PAEE variance explained by accelerometry predictions. NEW & NOTEWORTHY Although triaxial accelerometry is widely used in free-living conditions to assess the impact of physical activity energy expenditure (PAEE) on health, its precision and accuracy are often debated. Here we developed and validated an activity-specific model which, coupled with an automatic activity-recognition algorithm, improved the variance explained by the predictions from accelerometry counts by 43% of daily PAEE compared with models relying on a simple relationship between accelerometry counts and EE.

An automated method for identifying an independent component analysis-based language-related resting-state network in brain tumor subjects for surgical planning

As a noninvasive and “task-free” technique, resting-state functional magnetic resonance imaging (rs-fMRI) has been gradually applied to pre-surgical functional mapping. Independent component analysis (ICA)-based mapping has shown advantage, as no a priori information is required. We developed an automated method for identifying language network in brain tumor subjects using ICA on rs-fMRI. In addition to standard processing strategies, we applied a discriminability-index-based component identification algorithm to identify language networks in three different groups. The results from the training group were validated in an independent group of healthy human subjects. For the testing group, ICA and seed-based correlation were separately computed and the detected language networks were assessed by intra-operative stimulation mapping to verify reliability of application in the clinical setting. Individualized language network mapping could be automatically achieved for all subjects from the two healthy groups except one (19/20, success rate = 95.0%). In the testing group (brain tumor patients), the sensitivity of the language mapping result was 60.9%, which increased to 87.0% (superior to that of conventional seed-based correlation [47.8%]) after extending to a radius of 1 cm. We established an automatic and practical component identification method for rs-fMRI-based pre-surgical mapping and successfully applied it to brain tumor patients.

Metabolomic biomarkers of pancreatic cancer: a meta-analysis study.

Pancreatic cancer (PC) is an aggressive disease with high mortality rates, however, there is no blood test for early detection and diagnosis of this disease. Several research groups have reported on metabolomics based clinical investigations to identify biomarkers of PC, however there is a lack of a centralized metabolite biomarker repository that can be used for meta-analysis and biomarker validation. Furthermore, since the incidence of PC is associated with metabolic syndrome and Type 2 diabetes mellitus (T2DM), there is a need to uncouple these common metabolic dysregulations that may otherwise diminish the clinical utility of metabolomic biosignatures. Here, we attempted to externally replicate proposed metabolite biomarkers of PC reported by several other groups in an independent group of PC subjects. Our study design included a T2DM cohort that was used as a non-cancer control and a separate cohort diagnosed with colorectal cancer (CRC), as a cancer disease control to eliminate possible generic biomarkers of cancer. We used targeted mass spectrometry for quantitation of literature-curated metabolite markers and identified a biomarker panel that discriminates between normal controls (NC) and PC patients with high accuracy. Further evaluation of our model with CRC, however, showed a drop in specificity for the PC biomarker panel. Taken together, our study underscores the need for a more robust study design for cancer biomarker studies so as to maximize the translational value and clinical implementation.