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Biclustering is the task of simultaneously clustering the samples and features of a data set. In doing so, subsets of samples that exhibit similar behaviors across subsets of features can be identified. Motivated by a longitudinal diffusion tensor imaging study of sport-related concussion (SRC), we present the problem of biclustering multivariate longitudinal data in which subjects and features are grouped simultaneously based on longitudinal patterns rather than magnitude. We propose a penalized regression based method for solving this problem by exploiting the heterogeneity in the longitudinal patterns within subjects and features. We evaluate the performance of the proposed methods via a simulation study and apply them to the motivating dataset, revealing distinctive patterns of white-matter abnormalities within subgroups of SRC cases.

Background: Current protein biomarkers are only moderately predictive at identifying individuals with mild traumatic brain injury or concussion. Therefore, more accurate diagnostic markers are needed for sport-related concussion. Methods: This was a multicenter, prospective, case-control study of athletes who provided blood samples and were diagnosed with a concussion or were a matched non-concussed control within the National Collegiate Athletic Association–Department of Defense Concussion Assessment, Research, and Education Consortium conducted between 2015 and 2019. The blood was collected within 48 h of injury to identify protein abnormalities at the acute and subacute timepoints. Athletes with concussion were divided into 6 h post-injury (0–6 h post-injury) and after 6 h post-injury (7–48 h post-injury) groups. We applied a highly multiplexed proteomic technique that used a DNA aptamers assay to target 1305 proteins in plasma samples from athletes with and without sport-related concussion. Results: A total of 140 athletes with concussion (79.3% males; aged 18.71 ± 1.10 years, mean ± SD) and 21 non-concussed athletes (76.2% males; 19.14 ± 1.10 years) were included in this study. We identified 338 plasma proteins that significantly differed in abundance (319 upregulated and 19 downregulated) in concussed athletes compared to non-concussed athletes. The top 20 most differentially abundant proteins discriminated concussed athletes from non-concussed athletes with an area under the curve (AUC) of 0.954 (95% confidence interval: 0.922‒0.986). Specifically, after 6 h of injury, the individual AUC of plasma erythrocyte membrane protein band 4.1 (EPB41) and alpha-synuclein (SNCA) were 0.956 and 0.875, respectively. The combination of EPB41 and SNCA provided the best AUC (1.000), which suggests this combination of candidate plasma biomarkers is the best for diagnosing concussion in athletes after 6 h of injury. Conclusion: Our data suggest that proteomic profiling may provide novel diagnostic protein markers and that a combination of EPB41 and SNCA is the most predictive biomarker of concussion after 6 h of injury.

Biomechanical assessments of running typically take place inside motion capture laboratories. However, it is unclear whether data from these in-lab gait assessments are representative of gait during real-world running. This study sought to test how well real-world gait patterns are represented by in-lab gait data in two cohorts of runners equipped with consumer-grade wearable sensors measuring speed, step length, vertical oscillation, stance time, and leg stiffness. Cohort 1 (N = 49) completed an in-lab treadmill run plus five real-world runs of self-selected distances on self-selected courses. Cohort 2 (N = 19) completed a 2.4 km outdoor run on a known course plus five real-world runs of self-selected distances on self-selected courses. The degree to which in-lab gait reflected real-world gait was quantified using univariate overlap and multivariate depth overlap statistics, both for all real-world running and for real-world running on flat, straight segments only. When comparing in-lab and real-world data from the same subject, univariate overlap ranged from 65.7% (leg stiffness) to 95.2% (speed). When considering all gait metrics together, only 32.5% of real-world data were well-represented by in-lab data from the same subject. Pooling in-lab gait data across multiple subjects led to greater distributional overlap between in-lab and real-world data (depth overlap 89.3–90.3%) due to the broader variability in gait seen across (as opposed to within) subjects. Stratifying real-world running to only include flat, straight segments did not meaningfully increase the overlap between in-lab and real-world running (changes of

Summary: Functional connectomes (FCs) containing pairwise estimations of functional couplings between pairs of brain regions are commonly represented by correlation matrices. As symmetric positive definite matrices, FCs can be transformed via tangent space projections, resulting into tangent-FCs. Tangent-FCs have led to more accurate models predicting brain conditions or aging. Motivated by the fact that tangent-FCs seem to be better biomarkers than FCs, we hypothesized that tangent-FCs have also a higher fingerprint. We explored the effects of six factors: fMRI condition, scan length, parcellation granularity, reference matrix, main-diagonal regularization, and distance metric. Our results showed that identification rates are systematically higher when using tangent-FCs across the “fingerprint gradient” (here including test-retest, monozygotic and dizygotic twins). Highest identification rates were achieved when minimally (0.01) regularizing FCs while performing tangent space projection using Riemann reference matrix and using correlation distance to compare the resulting tangent-FCs. Such configuration was validated in a second dataset (resting-state).

ObjectiveThe aim of this study was to investigate phosphorylated tau (p-tau181) protein in plasma in a cohort of mild traumatic brain injury (mTBI) patients and a cohort of concussed athletes.MethodsThis pilot study comprised two independent cohorts. The first cohort—part of a Traumatic Head Injury Neuroimaging Classification (THINC) study—with a mean age of 46 years was composed of uninjured controls (UIC, n = 30) and mTBI patients (n = 288) recruited from the emergency department with clinical computed tomography (CT) and research magnetic resonance imaging (MRI) findings. The second cohort—with a mean age of 19 years—comprised 133 collegiate athletes with (n = 112) and without (n = 21) concussions. The participants enrolled in the second cohort were a part of a multicenter, prospective, case-control study conducted by the NCAA-DoD Concussion Assessment, Research and Education (CARE) Consortium at six CARE Advanced Research Core (ARC) sites between 2015 and 2019. Blood was collected within 48 h of injury for both cohorts. Plasma concentration (pg/ml) of p-tau181 was measured using the Single Molecule Array ultrasensitive assay.ResultsConcentrations of plasma p-tau181 in both cohorts were significantly elevated compared to controls within 48 h of injury, with the highest concentrations of p-tau181 within 18 h of injury, with an area under the curve (AUC) of 0.690–0.748, respectively, in distinguishing mTBI patients and concussed athletes from controls. Among the mTBI patients, the levels of plasma p-tau181 were significantly higher in patients with positive neuroimaging (either CT+/MRI+, n = 74 or CT−/MRI+, n = 89) compared to mTBI patients with negative neuroimaging (CT−/MRI−, n = 111) findings and UIC (P-values < 0.05).ConclusionThese findings indicate that plasma p-tau181 concentrations likely relate to brain injury, with the highest levels in patients with neuroimaging evidence of injury. Future research is needed to replicate and validate this protein assay's performance as a possible early diagnostic biomarker for mTBI/concussions.

Summary: Cannabis use has become popular among athletes, many of whom are exposed to repetitive subconcussive head impacts. We aimed to test whether chronic cannabis use would be neuroprotective or exacerbating against acute subconcussive head impacts. This trial included 43 adult soccer players (Cannabis group using cannabis at least once a week for the past 6 months, n = 24; non-cannabis control group, n = 19). Twenty soccer headings, induced by our controlled heading model, significantly impaired ocular-motor function, but the degrees of impairments were less in the cannabis group compared to controls. The control group significantly increased its serum S100B level after heading, whereas no change was observed in the cannabis group. There was no group difference in serum neurofilament light levels at any time point. Our data suggest that chronic cannabis use may be associated with an enhancement of oculomotor functional resiliency and suppression of the neuroinflammatory response following 20 soccer headings.

Studying the association of the brain's structure and function with neurocognitive outcomes requires a comprehensive analysis that combines different sources of information from a number of brain-imaging modalities. Recently developed regularization methods provide a novel approach using information about brain structure to improve the estimation of coefficients in the linear regression models. Our proposed method, which is a special case of the Tikhonov regularization, incorporates structural connectivity derived with Diffusion Weighted Imaging and cortical distance information in the penalty term. Corresponding to previously developed methods that inform the estimation of the regression coefficients, we incorporate additional information via a Laplacian matrix based on the proximity measure on the cortical surface. Our contribution consists of constructing a principled formulation of the penalty term and testing the performance of the proposed approach via extensive simulation studies and a brain-imaging application. The penalty term is constructed as a weighted combination of structural connectivity and proximity between cortical areas. Simulation studies mimic the real brain-imaging settings. We apply our approach to the study of data collected in the Human Connectome Project, where the cortical properties of the left hemisphere are found to be associated with vocabulary comprehension.

BackgroundPhysical activity (PA) has an impact on physical and mental health in neurotypical populations, and addressing these variables may improve the prevalent burden of anxiety in adults with autism spectrum disorder (ASD). Gamified mobile apps using behavior change techniques present a promising way of increasing PA and reducing sedentary time, thus reducing anxiety in adults with ASD. ObjectiveThis study aimed to compare the effectiveness of a gamified and behavior change technique–based mobile app, PuzzleWalk, versus a commercially available app, Google Fit, on increasing PA and reducing sedentary time as an adjunct anxiety treatment for this population. MethodsA total of 24 adults with ASD were assigned to either the PuzzleWalk or Google Fit group for 5 weeks using a covariate-adaptive randomization design. PA and anxiety were assessed over 7 days at 3 different data collection periods (ie, baseline, intervention start, and intervention end) using triaxial accelerometers and the Beck Anxiety Inventory. Group differences in outcome variables were assessed using repeated-measures analysis of covariance, adjusting for age, sex, and BMI. ResultsThe findings indicated that the PuzzleWalk group spent a significantly longer amount of time on app use compared with the Google Fit group (F2,38=5.07; P=.01; partial η2=0.21), whereas anxiety was unfavorably associated with increases in light PA and decreases in sedentary time after intervention (all P

ObjectiveTo investigate the plasma proteomic profiling in identifying biomarkers related to return to sport (RTS) following a sport-related concussion (SRC).MethodsThis multicenter, prospective, case-control study was part of a larger cohort study conducted by the NCAA-DoD Concussion Assessment, Research, and Education (CARE) Consortium, athletes (n = 140) with blood collected within 48 h of injury and reported day to asymptomatic were included in this study, divided into two groups: (1) recovery

AbstractModeling communication dynamics in the brain is a key challenge in network neuroscience. We present here a framework that combines two measurements for any system where different communication processes are taking place on top of a fixed structural topology: path processing score (PPS) estimates how much the brain signal has changed or has been transformed between any two brain regions (source and target); path broadcasting strength (PBS) estimates the propagation of the signal through edges adjacent to the path being assessed. We use PPS and PBS to explore communication dynamics in large-scale brain networks. We show that brain communication dynamics can be divided into three main “communication regimes” of information transfer: absent communication (no communication happening); relay communication (information is being transferred almost intact); and transducted communication (the information is being transformed). We use PBS to categorize brain regions based on the way they broadcast information. Subcortical regions are mainly direct broadcasters to multiple receivers; Temporal and frontal nodes mainly operate as broadcast relay brain stations; visual and somatomotor cortices act as multichannel transducted broadcasters. This work paves the way toward the field of brain network information theory by providing a principled methodology to explore communication dynamics in large-scale brain networks.

Abstract Introduction Diffusion magnetic resonance imaging may allow for microscopic characterization of white matter degeneration in early stages of Alzheimer's disease. Methods Multishell Diffusion magnetic resonance imaging data were acquired from 100 participants (40 cognitively normal, 38 with subjective cognitive decline, and 22 with mild cognitive impairment [MCI]). White matter microscopic degeneration in 27 major tracts of interest was assessed using diffusion tensor imaging (DTI), neurite orientation dispersion and density imaging, and q‐space imaging. Results Lower DTI fractional anisotropy and higher radial diffusivity were observed in the cingulum, thalamic radiation, and forceps major of participants with MCI. These tracts of interest also had the highest predictive power to discriminate groups. Diffusion metrics were associated with cognitive performance, particularly Rey Auditory Verbal Learning Test immediate recall, with the highest association observed in participants with MCI. Discussion While DTI was the most sensitive, neurite orientation dispersion and density imaging and q‐space imaging complementarily characterized reduced axonal density accompanied with dispersed and less restricted white matter microstructures.

Multiple sclerosis is a neurodegenerative and inflammatory disease, a hallmark of which is demyelinating lesions in the white matter. We hypothesized that alterations in white matter microstructures can be non-invasively characterized by advanced diffusion magnetic resonance imaging. Seven diffusion metrics were extracted from hybrid diffusion imaging acquisitions via classic diffusion tensor imaging, neurite orientation dispersion and density imaging, and q-space imaging. We investigated the sensitivity of the diffusion metrics in 36 sets of regions of interest in the brain white matter of six female patients (age 52.8 ± 4.3 years) with multiple sclerosis. Each region of interest set included a conventional T2-defined lesion, a matched perilesion area, and normal-appearing white matter. Six patients with multiple sclerosis (n = 5) or clinically isolated syndrome (n = 1) at a mild to moderate disability level were recruited. The patients exhibited microstructural alterations from normal-appearing white matter transitioning to perilesion areas and lesions, consistent with decreased tissue restriction, decreased axonal density, and increased classic diffusion tensor imaging diffusivity. The findings suggest that diffusion compartment modeling and q-space analysis appeared to be sensitive for detecting subtle microstructural alterations between perilesion areas and normal-appearing white matter.

Dynamical systems theory suggests that studying the complexity of biological signals could lead to a single gait metric that reliably predicts risk of running-related injury (RRI). The purposes of this pilot study were to examine center of mass (COM) acceleration complexity at baseline, prior to RRI, and the change between timepoints between collegiate runners who developed RRI during a competitive season and those who remained uninjured, and to determine if complexity at these timepoints was associated with increased odds of RRI. Twenty-two collegiate runners from the same cross-country team wore a waist-mounted triaxial accelerometer (100 Hz) during easy-intensity runs throughout the competitive season. RRIs requiring medical attention were reported via an online survey. Control entropy was used to estimate the complexity of the resultant COM acceleration recorded during each run. Associations between complexity and RRI were assessed using a frequency-matching strategy where uninjured participants were paired with injured participants using complexity from the most time-proximal run prior to RRI. Seven runners sustained an RRI. No significant differences were observed between injured and uninjured groups for baseline complexity (p = 0.364, d = 0.405), pre-injury complexity (p = 0.258, d = 0.581), or change from baseline to pre-injury (p = 0.101, d = 0.963). There were no statistically significant associations found between complexity and RRI risk. Although no significant associations were found, the median effect from the models indicated that an increase in baseline complexity, pre-injury complexity, and change in complexity from baseline each corresponded to an increased odds of sustaining an RRI [baseline: odds ratio (OR) = 1.560, 95% CI = 0.587–4.143, p = 0.372; pre-injury: OR = 1.926, 95% CI: 0.689–5.382, p = 0.211; change from baseline: OR = 1.119; 95% CI: 0.839–1.491, p = 0.445). Despite non-significance and wide confidence intervals that included both positive and negative associations, the point estimates for >98% of the 10,000 frequency-case–control-matched model fits indicated that matching strategy did not influence the directionality of the association estimates between complexity and RRI risk (i.e., odds ratio >1.0). This pilot study demonstrates initial feasibility that additional research may support COM acceleration complexity as a useful single-metric monitoring system for RRI risk during real-world training. Follow-up work should assess longitudinal associations between gait complexity and running-related injury in larger cohorts.

Progressive accumulation of tau neurofibrillary tangles in the brain is a defining pathologic feature of Alzheimer's disease (AD). Tau pathology exhibits a predictable spatiotemporal spreading pattern, but the underlying mechanisms of this spread are poorly understood. Although AD is conventionally considered a disease of the gray matter, it is also associated with pronounced and progressive deterioration of the white matter (WM). A link between abnormal tau and WM degeneration is suggested by findings from both animal and postmortem studies, but few studies demonstrated their interplay in vivo. Recent advances in diffusion magnetic resonance imaging and the availability of tau positron emission tomography (PET) have made it possible to evaluate the association of tau and WM degeneration (tau-WM) in vivo. In this study, we explored the spatial pattern of tau-WM associations across the whole brain to evaluate the hypothesis that tau deposition is associated with WM microstructural alterations not only in isolated tracts, but in continuous structural connections in a stereotypic pattern. Sixty-two participants, including 22 cognitively normal subjects, 22 individuals with subjective cognitive decline, and 18 with mild cognitive impairment were included in the study. WM characteristics were inferred by classic diffusion tensor imaging (DTI) and a complementary diffusion compartment model – neurite orientation dispersion and density imaging (NODDI) that provides a proxy for axonal density. A data-driven iterative searching (DDIS) approach, coupled with whole-brain graph theory analyses, was developed to continuously track tau-WM association patterns. Without applying prior knowledge of the tau spread, we observed a distinct spatial pattern that resembled the typical propagation of tau pathology in AD. Such association pattern was not observed between diffusion and amyloid-β PET signal. Tau-related WM degeneration is characterized by an increase in the mean diffusivity (with a dominant change in the radial direction) and a decrease in the intra-axonal volume fraction. These findings suggest that cortical tau deposition (as measured in tau PET) is associated with a lower axonal packing density and greater diffusion freedom. In conclusion, our in vivo findings using a data-driven method on cross-sectional data underline the important role of WM alterations in the AD pathological cascade with an association pattern similar to the postmortem Braak staging of AD. Future studies will focus on longitudinal analyses to provide in vivo evidence of tau pathology spreads along neuroanatomically connected brain areas.

Offspring of parents with substance use disorders (SUD) discount future rewards at a steeper rate on the monetary delay discounting task (DD) than typically developing youth. However, brain activation during DD has yet to be studied in drug naïve youth with a family history (FH) of SUD. Here, we investigate brain activation differences in high-risk youth during DD. We recruited substance naïve youth, aged 11–12, into three groups to compare brain activation during DD: (1) High-risk youth (n = 35) with a FH of SUD and externalizing psychiatric disorders, (2) psychiatric controls (n = 25) who had no FH of SUD, but with equivalent externalizing psychiatric disorders as high-risk youth, and (3) a healthy control group (n = 24) with no FH of SUD and minimal psychopathology. A whole-brain voxel wise analysis of the [Delay > Baseline], [Immediate > Baseline], and [Control > Baseline] contrasts identified functional regions of interest, from which extracted parameter estimates were tested for significant group differences. Relative to control youth, high-risk youth showed stronger activation in the left posterior insula and thalamus when making delayed choices, and stronger activation of the parahippocampal gyrus when making both delayed and control choices (ps

It has been well established that Functional Connectomes (FCs), as estimated from functional MRI (fMRI) data, have an individual fingerprint that can be used to identify an individual from a population (subject-identification). Although identification rate is high when using resting-state FCs, other tasks show moderate to low values. Furthermore, identification rate is task-dependent, and is low when distinct cognitive states, as captured by different fMRI tasks, are compared. Here we propose an embedding framework, GEFF (Graph Embedding for Functional Fingerprinting), based on group-level decomposition of FCs into eigenvectors. GEFF creates an eigenspace representation of a group of subjects using one or more task FCs (Learning Stage). In the Identification Stage, we compare new instances of FCs from the Learning subjects within this eigenspace (validation dataset). The validation dataset contains FCs either from the same tasks as the Learning dataset or from the remaining tasks that were not included in Learning. Assessment of validation FCs within the eigenspace results in significantly increased subject-identification rates for all fMRI tasks tested and potentially task-independent fingerprinting process. It is noteworthy that combining resting-state with one fMRI task for GEFF Learning Stage covers most of the cognitive space for subject identification. Thus, while designing an experiment, one could choose a task fMRI to ask a specific question and combine it with resting-state fMRI to extract maximum subject differentiability using GEFF. In addition to subject-identification, GEFF was also used for identification of cognitive states, i.e. to identify the task associated to a given FC, regardless of the subject being already in the Learning dataset or not (subject-independent task-identification). In addition, we also show that eigenvectors from the Learning Stage can be characterized as task- and subject-dominant, subject-dominant or neither, using two-way ANOVA of their corresponding loadings, providing a deeper insight into the extent of variance in functional connectivity across individuals and cognitive states.

Prior studies have reported long-term differences in brain structure (brain morphometry) as being associated with cumulative concussion and contact sport participation. There is emerging evidence to suggest that similar effects of prior concussion and contact sport participation on brain morphometry may be present in younger cohorts of active athletes. We investigated the relationship between prior concussion and primary sport participation with subcortical and cortical structures in active collegiate contact sport and non-contact sport athletes. Contact sport athletes (CS; N = 190) and matched non-contact sport athletes (NCS; N = 95) completed baseline clinical testing and participated in up to four serial neuroimaging sessions across a 6-months period. Subcortical and cortical structural metrics were derived using FreeSurfer. Linear mixed-effects (LME) models examined the effects of years of primary sport participation and prior concussion (0, 1+) on brain structure and baseline clinical variables. Athletes with prior concussion across both groups reported significantly more baseline concussion and psychological symptoms (all ps < 0.05). The relationship between years of primary sport participation and thalamic volume differed between CS and NCS (p = 0.015), driven by a significant inverse association between primary years of participation and thalamic volume in CS (p = 0.007). Additional analyses limited to CS alone showed that the relationship between years of primary sport participation and dorsal striatal volume was moderated by concussion history (p = 0.042). Finally, CS with prior concussion had larger hippocampal volumes than CS without prior concussion (p = 0.015). Years of contact sport exposure and prior concussion(s) are associated with differences in subcortical volumes in young-adult, active collegiate athletes, consistent with prior literature in retired, primarily symptomatic contact sport athletes. Longitudinal follow-up studies in these athletes are needed to determine clinical significance of current findings.

Human functional brain connectivity is usually measured either at “rest” or during cognitive tasks, ignoring life’s moments of mental transition. We propose a different approach to understanding brain network transitions. We applied a novel independent component analysis of functional connectivity during motor inhibition (stop signal task) and during the continuous transition to an immediately ensuing rest. A functional network reconfiguration process emerged that: (i) was most prominent in those without familial alcoholism risk, (ii) encompassed brain areas engaged by the task, yet (iii) appeared only transiently after task cessation. The pattern was not present in a pre-task rest scan or in the remaining minutes of post-task rest. Finally, this transient network reconfiguration related to a key behavioral trait of addiction risk: reward delay discounting. These novel findings illustrate how dynamic brain functional reconfiguration during normally unstudied periods of cognitive transition might reflect addiction vulnerability, and potentially other forms of brain dysfunction.

ObjectivesWe extend the method of Significant Zero Crossings of Derivatives (SiZer) to address within-subject correlations of repeatedly collected longitudinal biomarker data and the computational aspects of the methodology when analyzing massive biomarker databases. SiZer is a powerful visualization tool for exploring structures in curves by mapping areas where the first derivative is increasing, decreasing or does not change (plateau) thus exploring changes and normalization of biomarkers in the presence of therapy.MethodsWe propose a penalized spline SiZer (PS-SiZer) which can be expressed as a linear mixed model of the longitudinal biomarker process to account for irregularly collected data and within-subject correlations. Through simulations we show how sensitive PS-SiZer is in detecting existing features in longitudinal data versus existing versions of SiZer. In a real-world data analysis PS-SiZer maps are used to map areas where the first derivative of weight change after antiretroviral therapy (ART) start is significantly increasing, decreasing or does not change, thus exploring the durability of weight increase after the start of therapy. We use weight data repeatedly collected from persons living with HIV initiating ART in five regions in the International Epidemiologic Databases to Evaluate AIDS (IeDEA) worldwide collaboration and compare the durability of weight gain between ART regimens containing and not containing the drug stavudine (d4T), which has been associated with shorter durability of weight gain.ResultsThrough simulations we show that the PS-SiZer is more accurate in detecting relevant features in longitudinal data than existing SiZer variants such as the local linear smoother (LL) SiZer and the SiZer with smoothing splines (SS-SiZer). In the illustration we include data from 185,010 persons living with HIV who started ART with a d4T (53.1%) versus non-d4T (46.9%) containing regimen. The largest difference in durability of weight gain identified by the SiZer maps was observed in Southern Africa where weight gain in patients treated with d4T-containing regimens lasted 59.9 weeks compared to 133.8 weeks for those with non-d4T-containing regimens. In the other regions, persons receiving d4T-containing regimens experienced weight gains lasting 38-62 weeks versus 55-93 weeks in those receiving non-d4T-based regimens.DiscussionPS-SiZer, a SiZer variant, can handle irregularly collected longitudinal data and within-subject correlations and is sensitive in detecting even subtle features in biomarker curves.

A heightened hedonic response to sweet tastes has been associated with increased alcohol preference and alcohol consumption in both humans and animals. The principal goal of this study was to examine blood oxygenation level dependent (BOLD) activation to high- and low-concentration sweet solutions in subjects who are either positive (FHP) or negative (FHN) for a family history of alcoholism. Seventy-four non-treatment seeking, community-recruited, healthy volunteers (22.8±1.6 SD years; 43% men) rated a range of sucrose concentrations in a taste test and underwent functional magnetic resonance imaging (fMRI) during oral delivery of water, 0.83M, and 0.10M sucrose. Sucrose compared to water produced robust activation in primary gustatory cortex, ventral insula, amygdala, and ventral striatum. FHP subjects displayed greater bilateral amygdala activation than FHN subjects in the low sucrose concentration (0.10M). In secondary analyses, the right amygdala response to the 0.10M sucrose was greatest in FHP women. When accounting for group differences in drinks per week, the family history groups remained significantly different in their right amygdala response to 0.10M sucrose. Our findings suggest that the brain response to oral sucrose differs with a family history of alcoholism, and that this response to a mildly reinforcing primary reward might be an endophenotypic marker of alcoholism risk. Keywords: Alcohol, fMRI, Sweet, Taste, Gustatory

Dynamic functional connectivity (dFC) estimates time-dependent associations between pairs of brain region time series as typically acquired during functional MRI. dFC changes are most commonly quantified by pairwise correlation coefficients between the time series within a sliding window. Here, we applied a recently developed bootstrap-based technique (Kudela et al., 2017) to robustly estimate subject-level dFC and its confidence intervals in a task-based fMRI study (24 subjects who tasted their most frequently consumed beer and Gatorade as an appetitive control). We then combined information across subjects and scans utilizing semiparametric mixed models to obtain a group-level dFC estimate for each pair of brain regions, flavor, and the difference between flavors. The proposed approach relies on the estimated group-level dFC accounting for complex correlation structures of the fMRI data, multiple repeated observations per subject, experimental design, and subject-specific variability. It also provides condition-specific dFC and confidence intervals for the whole brain at the group level. As a summary dFC metric, we used the proportion of time when the estimated associations were either significantly positive or negative. For both flavors, our fully-data driven approach yielded regional associations that reflected known, biologically meaningful brain organization as shown in prior work, as well as closely resembled resting state networks (RSNs). Specifically, beer flavor-potentiated associations were detected between several reward-related regions, including the right ventral striatum (VST), lateral orbitofrontal cortex, and ventral anterior insular cortex (vAIC). The enhancement of right VST-vAIC association by a taste of beer independently validated the main activation-based finding (Oberlin et al., 2016). Most notably, our novel dFC methodology uncovered numerous associations undetected by the traditional static FC analysis. The data-driven, novel dFC methodology presented here can be used for a wide range of task-based fMRI designs to estimate the dFC at multiple levels—group-, individual-, and task-specific, utilizing a combination of well-established statistical methods.

Physical activity (PA) is associated with greater fatigability in older adults; little is known about magnitude, shape, timing and variability of the entire 24-h rest–activity rhythm (RAR) associated with fatigability. We identified which features of the 24-h RAR pattern were independently and jointly associated with greater perceived physical fatigability (Pittsburgh Fatigability Scale, PFS, 0–50) in older adults (n = 181, 71.3 ± 6.7 years). RARs were characterized using anti-logistic extended cosine models and 4-h intervals of PA means and standard deviations across days. A K-means clustering algorithm approach identified four profiles of RAR features: “Less Active/Robust”, “Earlier Risers”, “More Active/Robust” and “Later RAR”. Quantile regression tested associations of each RAR feature/profile on median PFS adjusted for age, sex, race, body mass index and depression symptomatology. Later rise times (up mesor; β = 1.38, p = 0.01) and timing of midpoint of activity (acrophase; β = 1.29, p = 0.01) were associated with higher PFS scores. Lower PA between 4 a.m. and 8 a.m. was associated with higher PFS scores (β = −4.50, p = 0.03). “Less Active/Robust” (β = 6.14, p = 0.01) and “Later RAR” (β = 3.53, p = 0.01) patterns were associated with higher PFS scores compared to “Earlier Risers”. Greater physical fatigability in older adults was associated with dampened, more variable, and later RARs. This work can guide development of interventions aimed at modifying RARs to reduce fatigability in older adults.

This study examined the concordance between accelerometry-measured and self-reported physical activity (PA) and sedentary time in adults with autism. Twenty-four participants wore an ActiGraph GT3X + accelerometer for seven consecutive days and completed the International Physical Activity Questionnaire-Short Form (IPAQ-SF) on the last day of their study participation. Bland-Altman plots assessed the magnitude of agreement between the two measures. Nearly 80% of the participants accumulated the recommended = 150 min of moderate to vigorous PA (MVPA)/week, but were also sedentary for over nine hours/day according to accelerometry data. Findings showed that adults with autism tended to overreport MVPA (b = 1.606, p < 0.01) and underreport sedentary time (b = 1.161, p = 0.03) via the IPAQ-SF, as compared to objective measurements.

Various methods exist to measure physical activity. Subjective methods, such as diaries and surveys, are relatively inexpensive ways of measuring one’s physical activity; however, they are prone to measurement error and bias due to self-reporting. Wearable accelerometers offer a non-invasive and objective measure of one’s physical activity and are now widely used in observational studies. Accelerometers record high frequency data and each produce an unlabeled time series at the sub-second level. An important activity to identify from the data collected is walking, since it is often the only form of activity for certain populations. Currently, most methods use an activity summary which ignores the nuances of walking data. We propose methodology to model specific continuous responses with a functional linear model utilizing spectra obtained from the local fast Fourier transform (FFT) of walking as a predictor. Utilizing prior knowledge of the mechanics of walking, we incorporate this as additional information for the structure of our transformed walking spectra. The methods were applied to the in-the-laboratory data obtained from the Developmental Epidemiologic Cohort Study (DECOS).

Reducing variability of quantitative suspension array assays is key for multi-center and large sero-epidemiological studies. To maximize precision and robustness of an in-house IgG multiplex assay, we analyzed the effect of several conditions on variability to find the best combination. The following assay conditions were studied through a fractional factorial design: antigen-bead coupling (stock vs. several), sample predilution (stock vs. daily), temperature of incubation of sample with antigen-bead (22°C vs. 37°C), plate washing (manual vs. automatic) and operator expertise (expert vs. apprentice). IgG levels against seven P. falciparum antigens with heterogeneous immunogenicities were measured in test samples, in a positive control and in blanks. We assessed the variability and MFI quantification range associated to each combination of conditions, and their interactions, and evaluated the minimum number of samples and blank replicates to achieve good replicability. Results showed that antigen immunogenicity and sample seroreactivity defined the optimal dilution to assess the effect of assay conditions on variability. We found that a unique antigen-bead coupling, samples prediluted daily, incubation at 22°C, and automatic washing, had lower variability. However, variability increased when performing several couplings and incubating at 22°C vs. 37°C. In addition, no effect of temperature was seen with a unique coupling. The expertise of the operator had no effect on assay variability but reduced the MFI quantification range. Finally, differences between sample replicates were minimal, and two blanks were sufficient to capture assay variability, as suggested by the constant Intraclass Correlation Coefficient of three and two blanks. To conclude, a single coupling was the variable that most consistently reduced assay variability, being clearly advisable. In addition, we suggest having more sample dilutions instead of replicates to increase the likelihood of sample MFIs falling in the linear part of the antigen-specific curve, thus increasing precision.

Comprehensive assessment of cellular responses to the RTS,S/AS01E vaccine is needed to understand potential correlates and ultimately mechanisms of protection against malaria disease. Cellular responses recognizing the RTS,S/AS01E-containing circumsporozoite protein (CSP) and Hepatitis B surface antigen (HBsAg) were assessed before and 1 month after primary vaccination by intracellular cytokine staining and 16-color flow cytometry in 105 RTS,S/AS01-vaccinated and 74 rabies-vaccinated participants (controls) in a pediatric phase III trial in Africa. RTS,S/AS01E-vaccinated children had significantly higher frequencies of CSP- and HBsAg-specific CD4+ T cells producing IL-2, TNF-α, and CD40L and HBsAg-specific CD4+ T producing IFN-γ and IL-17 than baseline and the control group. Vaccine-induced responses were identified in both central and effector memory (EM) compartments. EM CD4+ T cells expressing IL-4 and IL-21 were detected recognizing both vaccine antigens. Consistently higher response rates to both antigens in RTS,S/AS01E-vaccinated than comparator-vaccinated children were observed. RTS,S/AS01E induced polyfunctional CSP- and HBsAg-specific CD4+ T cells, with a greater degree of polyfunctionality in HBsAg responses. In conclusion, RTS,S/AS01E vaccine induces T cells of higher functional heterogeneity and polyfunctionality than previously characterized. Responses detected in memory CD4+ T cell compartments may provide correlates of RTS,S/AS01-induced immunity and duration of protection in future correlates of immunity studies.

Multiplex bead-based immunoassays are used to measure concentrations of several analytes simultaneously. These assays include control standard curves (SC) to reduce between-plate variability and normalize quantitation of analytes of biological samples. Suboptimal calibration might result in large random error and decreased number of samples with analyte concentrations within the limits of quantification. Suboptimal calibration may be a consequence of poor fitness of the functions used for the SC, the treatment of the background noise and the method used to estimate the limits of quantification. Currently assessment of fitness of curves is largely dependent on operator and that may add additional error. Moreover, there is no software to automate data managing and quality control. In this article we present a R package, drLumi, with functions for managing data, calibrating assays and performing quality control. To optimize the assay the package implements: i) three dose-response functions, ii) four approaches for treating background noise and iii) three methods for estimating limits of quantifications. Other implemented functions are focused on the quality control of the fitted standard curve: detection of outliers, estimation of the confidence or prediction interval, and estimation of summary statistics. With demonstration purpose, we apply the software to 30 cytokines, chemokines and growth factors measured in a multiplex bead-based immunoassay in a study aiming to measure correlates of risk or protection from malaria of the RTS,S malaria vaccine nested in the Phase 3 randomized controlled trial of this vaccine.

Wearable accelerometers have recently become a standalone tool for the objective assessment of physical activity (PA). In free-living studies, accelerometers are placed by protocol on a pre-defined body location (e.g., non-dominant wrist). However, the protocol is not always followed, e.g., the sensor can be moved between wrists or reattached in a different orientation. Such protocol violations often result in PA miscalculation. We propose an approach, PLOE (“Placement, Location and Orientation Evaluation method”), to determine the sensor position using statistical features from the raw accelerometer measurements. We compare the estimated position with the study protocol and identify discrepancies. We apply PLOE to the measurements collected from 45 older adults who wore ActiGraph GT3X+ accelerometers on the left and right wrist for seven days. We found that 15.6% of participants who wore accelerometers violated the protocol for one or more days. The sensors were worn on the wrong hand during 6.9% of the days of simultaneous wearing of devices. During the periods of discrepancies, the daily PA was miscalculated by more than 20%. Our findings show that correct placement of the device has a significant effect on the PA estimates. These results demonstrate a need for the evaluation of sensor position.