Intersubject Differences Research Articles

Group Integrative Dynamic Factor Models With Application to Multiple Subject Brain Connectivity.

This work introduces a novel framework for dynamic factor model-based group-level analysis of multiple subjects time-series data, called GRoup Integrative DYnamic factor (GRIDY) models. The framework identifies and characterizes intersubject similarities and differences between two predetermined groups by considering a combination of group spatial information and individual temporal dynamics. Furthermore, it enables the identification of intrasubject similarities and differences over time by employing different model configurations for each subject. Methodologically, the framework combines a novel principal angle-based rank selection algorithm and a noniterative integrative analysis framework. Inspired by simultaneous component analysis, this approach also reconstructs identifiable latent factor series with flexible covariance structures. The performance of the GRIDY models is evaluated through simulations conducted under various scenarios. An application is also presented to compare resting-state functional MRI data collected from multiple subjects in autism spectrum disorder and controlgroups.

A reproducible representation of healthy tibiofemoral kinematics during stair descent using REFRAME – Part II: Exploring optimisation criteria and inter-subject differences

Kinematic analysis is a central component of movement biomechanics, describing the relative motion of joint segments during different activities, in different subject cohorts, and at different timepoints. Establishing whether two sets of kinematic signals represent fundamentally similar or different underlying motion patterns is especially challenging, given 1) the lack of consensus around reference frame and joint axis definition, and 2) the substantial effect that minimal variations in frame position and orientation are known to have on signal magnitude and characteristics. As such, enormous variability in the reporting of tibiofemoral kinematics has resulted in joint movement patterns that remain controversially discussed. Previously, we demonstrated the ability of the REference FRame Alignment MEthod (REFRAME) to reorientate and reposition differently aligned local segment frames to achieve convergence in signals representing the same underlying motion, thereby offering a novel approach to consistently report joint motion. In this study, for the first time, we apply REFRAME to assess the rotational and translational in vivo tibiofemoral motion of ten healthy subjects during stair descent based on kinematic signals collected using a moving videofluoroscope. Kinematics were analysed before and after different REFRAME implementations, revealing generally neutral ab/adduction behaviour, accompanied by varying degrees of a sinusoidal int/external tibial rotation pattern over the activity cycle. Our data demonstrate that different selected implementations of REFRAME are able to highlight different characteristics of the motion patterns: Minimisation of the translational root-mean-square revealed proximodistal translation patterns with overall neutral progression, while anteroposterior translation showed seemingly different levels of correlation with flexion/extension in different subjects. On the other hand, REFRAME minimisation of translational variances exposed differences in the relative mean displacement between the femoral and tibial origins between subjects, highlighting differences in mean centre of rotation positions. This early application of REFRAME for providing an understanding of tibiofemoral kinematics demonstrates the potential of this novel approach to bring clarity to an otherwise complex representation of highly variable time-series signals, while highlighting the philosophical challenges of clinically interpretating kinematic signals in the first place.

Contribution of the plantar fascia and long plantar ligaments to the stability of the longitudinal arch of the foot

The contribution of the Plantar Fascia (PF) and Long Plantar Ligament (LPL), two ligaments extending from the hindfoot to the forefoot, to arch stability has been studied in the past using in vivo, in vitro, and in silico methodologies. In silico studies were based on one single model obtained from one single subject and did not account for the known inter-subject morphological and biomechanical variations. In the present study, we developed computational dynamic models of nine different legs obtained from nine different individuals to evaluate the role of the LPL and PF in arch support, accounting for biological differences between subjects. These models were validated by comparing the simulation results against experimental results from the corresponding cadaver legs. After validation, we simulated body weight conditions for each model by applying a vertical load to the tibia, starting from zero and increasing linearly to 720 N. Kinematic and dynamic parameters, including the variation of the medial arch angle and of the navicular height, as well as the passive forces developed by the LPL and PF, were used to evaluate the contribution of these ligaments to arch support under body weight. The results indicate that a total collapse of the medial longitudinal arch occurred only when both the LPL and PF were absent, but a stable arch was maintained when either one of these two ligament structures were present. The results varied significantly among the specific models, highlighting the importance of using multiple models to account for inter-subject morphological differences.

A radial basis deformable residual convolutional neural model embedded with local multi-modal feature knowledge and its application in cross-subject classification

In the spatial cognition and emotion recognition tasks based on electroencephalography (EEG), the signal information representation of the single modal is incomplete because of the significant inter-subject differences in the EEG signals, resulting in low generalization performance of classification models. To address this issue, we propose a radial basis deformable residual convolutional neural networks model embedded with local multi-modal feature knowledge (RBDRCNN-LMFK). The RBDRCNN leverages Euclidean distance alignment, deformable convolution, depth-separable convolution, and residual connection modules for effective EEG feature extraction. The embedded local feature knowledge algorithm enables the effective combination of multi-modal information. To further validate the effectiveness of the algorithm, we used the left-one-subject-out cross-validation algorithm on the virtual city walking (VCW), SJTU Emotion EEG Dataset IV (SEED IV), and Building block gesture recognition (BBGR) datasets for spatial cognition and emotion recognition tasks. The average accuracy of the VCW was 97.84 % using the kNN classifier, surpassing the Concate fusion method’s 96.62 %. The average accuracy for the SEED IV dataset was 87.56 %, higher than the Concate Fusion’s 69.97 %. On the BBGR dataset, the kNN classifier achieved an average accuracy of 87.80 %, compared to 85.31 % with the Concate fusion. The results show that the model enhances the recognition of biologically significant features in EEG signals by embedding local features and increases the correlation between different brain regions associated with the task. This work illustrates a promising direction of using deep learning models to discover effective task-related features from highly diverse EEG signals and enhance brain regions’ correlation through multi-modal knowledge embedding.

Improving source estimation of retinotopic MEG responses by combining data from multiple subjects

Abstract Magnetoencephalography (MEG) is a functional brain imaging modality, which measures the weak magnetic field arising from neuronal activity. The source amplitudes and locations are estimated from the sensor data by solving an ill-posed inverse problem. Commonly used solutions for these problems operate on data from individual subjects. Combining the measurements of multiple subjects has been suggested to increase the spatial resolution of MEG by leveraging the intersubject differences for increased information. In this article, we compare 3 multisubject analysis methods on a retinotopic mapping dataset recorded from 20 subjects. The compared methods are eLORETA with source-space averaging, minimum Wasserstein estimates (MWE), and MWE with source-space averaging. The results were quantified by the geodesic distances between early (60–100 ms) MEG peak activations and fMRI-based retinotopic target points in the primary visual cortex (V1). By increasing the subject count from 1 to 10, the median distances decreased by 6.6–9.4 mm (33–46%) compared with the single-subject median distances of around 20 mm. The observed peak activation locations with multisubject analysis also comply better with the established retinotopic maps of the primary visual cortex. Our results suggest that higher spatial accuracy can be achieved by pooling data from multiple subjects. The strength of MWE lies in individualized and sparse source estimates, but in our data, averaging eLORETA estimates across individuals in source space outperformed MWE in spatial accuracy.

Discernible interindividual patterns of global efficiency decline during theoretical brain surgery

The concept of functional localization within the brain and the associated risk of resecting these areas during removal of infiltrating tumors, such as diffuse gliomas, are well established in neurosurgery. Global efficiency (GE) is a graph theory concept that can be used to simulate connectome disruption following tumor resection. Structural connectivity graphs were created from diffusion tractography obtained from the brains of 80 healthy adults. These graphs were then used to simulate parcellation resection in every gross anatomical region of the cerebrum by identifying every possible combination of adjacent nodes in a graph and then measuring the drop in GE following nodal deletion. Progressive removal of brain parcellations led to patterns of GE decline that were reasonably predictable but had inter-subject differences. Additionally, as expected, there were deletion of some nodes that were worse than others. However, in each lobe examined in every subject, some deletion combinations were worse for GE than removing a greater number of nodes in a different region of the brain. Among certain patients, patterns of common nodes which exhibited worst GE upon removal were identified as “connectotypes”. Given some evidence in the literature linking GE to certain aspects of neuro-cognitive abilities, investigating these connectotypes could potentially mitigate the impact of brain surgery on cognition.

Strategies and safety simulations for ultrasonic cervical spinal cord neuromodulation

Objective. Focused ultrasound spinal cord neuromodulation has been demonstrated in small animals. However, most of the tested neuromodulatory exposures are similar in intensity and exposure duration to the reported small animal threshold for possible spinal cord damage. All efforts must be made to minimize the risk and assure the safety of potential human studies, while maximizing potential treatment efficacy. This requires an understanding of ultrasound propagation and heat deposition within the human spine. Approach. Combined acoustic and thermal modelling was used to assess the pressure and heat distributions produced by a 500 kHz source focused to the C5/C6 level via two approaches (a) the posterior acoustic window between vertebral posterior arches, and (b) the lateral intervertebral foramen from which the C6 spinal nerve exits. Pulse trains of fifty 0.1 s pulses (pulse repetition frequency: 0.33 Hz, free-field spatial peak pulse-averaged intensity: 10 W cm−2) were simulated for four subjects and for ±10 mm translational and ±10∘ rotational source positioning errors. Main results. Target pressures ranged between 20%–70% of free-field spatial peak pressures with the posterior approach, and 20%–100% with the lateral approach. When the posterior source was optimally positioned, peak spine heating values were below 1 ∘C, but source mispositioning resulted in bone heating up to 4 ∘C. Heating with the lateral approach did not exceed 2 ∘C within the mispositioning range. There were substantial inter-subject differences in target pressures and peak heating values. Target pressure varied three to four-fold between subjects, depending on approach, while peak heating varied approximately two-fold between subjects. This results in a nearly ten-fold range between subjects in the target pressure achieved per degree of maximum heating. Significance. This study highlights the utility of trans-spine ultrasound simulation software and need for precise source-anatomy positioning to assure the subject-specific safety and efficacy of focused ultrasound spinal cord therapies.

Diffusion tensor imaging and fiber tractography of the normal epididymis.

To evaluate the feasibility of diffusion tensor imaging (DTI) and fiber tractography (FT) of the normal epididymis and to determine normative apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values. Twenty-eight healthy volunteers underwent MRI of the scrotum, including DTI on a 3.0T system. For each anatomic part of the epididymis (head, body and tail) free-hand regions of interest were drawn and the mean ADC and FA were measured by two radiologists in consensus. Parametric statistical tests were used to determine intersubject differences in ADC and FA between the anatomic parts of each normal epididymis and between bilateral epididymides. Fiber tracts of the epididymis were reconstructed using the MR Diffusion tool. The mean ADC and FA of the normal epididymis was 1.31 × 10-3 mm2/s and 0.20, respectively. No differences in ADC (p = 0.736) and FA (p = 0.628) between the anatomic parts of each normal epididymis were found. Differences (p = 0.020) were observed in FA of the body between the right and the left epididymis. FT showed the fiber tracts of the normal epididymis. Main study's limitations include the following: small number of participants with narrow age range, absence of histologic confirmation and lack of quantitative assessment of the FT reconstructions. DTI and FT of the normal epididymis is feasible and allow the noninvasive assessment of the structural and geometric organization of the organ.

Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics

Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines’ suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline’s performance across criteria and datasets, to inform future best practices in functional connectomics.

Anat-SFSeg: Anatomically-guided superficial fiber segmentation with point-cloud deep learning

Diffusion magnetic resonance imaging (dMRI) tractography is a critical technique to map the brain's structural connectivity. Accurate segmentation of white matter, particularly the superficial white matter (SWM), is essential for neuroscience and clinical research. However, it is challenging to segment SWM due to the short adjacent gyri connection in a U-shaped pattern. In this work, we propose an Anatomically-guided Superficial Fiber Segmentation (Anat-SFSeg) framework to improve the performance on SWM segmentation. The framework consists of a unique fiber anatomical descriptor (named FiberAnatMap) and a deep learning network based on point-cloud data. The spatial coordinates of fibers represented as point clouds, as well as the anatomical features at both the individual and group levels, are fed into a neural network. The network is trained on Human Connectome Project (HCP) datasets and tested on the subjects with a range of cognitive impairment levels. One new metric named fiber anatomical region proportion (FARP), quantifies the ratio of fibers in the defined brain regions and enables the comparison with other methods. Another metric named anatomical region fiber count (ARFC), represents the average fiber number in each cluster for the assessment of inter-subject differences. The experimental results demonstrate that Anat-SFSeg achieves the highest accuracy on HCP datasets and exhibits great generalization on clinical datasets. Diffusion tensor metrics and ARFC show disorder severity associated alterations in patients with Alzheimer's disease (AD) and mild cognitive impairments (MCI). Correlations with cognitive grades show that these metrics are potential neuroimaging biomarkers for AD. Furthermore, Anat-SFSeg could be utilized to explore other neurodegenerative, neurodevelopmental or psychiatric disorders.

Predicting the in vivo developmental toxicity of fenarimol from in vitro toxicity data using PBTK modelling-facilitated reverse dosimetry approach

In vitro methods are widely used in modern toxicological testing; however, the data cannot be directly employed for risk assessment. In vivo toxicity of chemicals can be predicted from in vitro data using physiologically based toxicokinetic (PBTK) modelling-facilitated reverse dosimetry (PBTK-RD). In this study, a minimal-PBTK model was constructed to predict the in-vivo kinetic profile of fenarimol (FNL) in rats and humans. The model was verified by comparing the observed and predicted pharmacokinetics of FNL for rats (calibrator) and further applied to humans. Using the PBTK-RD approach, the reported in vitro developmental toxicity data for FNL was translated to in vivo dose-response data to predict the assay equivalent oral dose in rats and humans. The predicted assay equivalent rat oral dose (36.46 mg/kg) was comparable to the literature reported in vivo BMD10 value (22.8 mg/kg). The model was also employed to derive the chemical-specific adjustment factor (CSAF) for interspecies toxicokinetics variability of FNL. Further, Monte Carlo simulations were performed to predict the population variability in the plasma concentration of FNL and to derive CSAF for intersubject human kinetic differences. The comparison of CSAF values for interspecies and intersubject toxicokinetic variability with their respective default values revealed that the applied uncertainty factors were adequately protective.

Reduced mitochondrial respiratory capacity in patients with acute episodes of bipolar disorder: Could bipolar disorder be a state-dependent mitochondrial disease?

Bipolar disorder (BD) is a chronic and recurrent disease characterized by acute mood episodes and periods of euthymia. The available literature postulates that a biphasic dysregulation of mitochondrial bioenergetics might underpin the neurobiology of BD. However, most studies focused on inter-subject differences rather than intra-subject variations between different mood states. To test this hypothesis, in this preliminary proof-of-concept study, we measured in vivo mitochondrial respiration in patients with BD during a mood episode and investigated differences compared to healthy controls (HC) and to the same patients upon clinical remission. This longitudinal study recruited 20 patients with BD admitted to our acute psychiatric ward with a manic (n = 15) or depressive (n = 5) episode, and 10 matched HC. We assessed manic and depressive symptoms using standardized psychometric scales. Different mitochondrial oxygen consumption rates (OCRs: Routine, Leak, electron transport chain [ETC], Rox) were assessed during the acute episode (T0) and after clinical remission (T1) using high-resolution respirometry at 37°C by polarographic oxygen sensors in a two-chamber Oxygraph-2k system in one million of peripheral blood mononuclear cells (PMBC). Specific OCRs were expressed as mean ± SD in picomoles of oxygen per million cells. Significant results were adjusted for age, sex, and body mass index. The longitudinal analysis showed a significant increase in the maximal oxygen consumption capacity (ETC) in clinical remission (25.7 ± 16.7) compared to the acute episodes (19.1 ± 11.8, p = 0.025), and was observed separately for patients admitted with a manic episode (29.2 ± 18.9 in T1, 22.3 ± 11.9 in T0, p = 0.076), and at a trend-level for patients admitted with a depressive episode (15.4 ± 3.9 in T1 compared to 9.4 ± 3.2 in T0, p = 0.107). Compared to HC, significant differences were observed in ETC in patients with a bipolar mood episode (H = 11.7; p = 0.003). Individuals with bipolar depression showed lower ETC than those with a manic episode (t = -3.7, p = 0.001). Also, significant differences were observed in ETC rates between HC and bipolar depression (Z = 1.000, p = 0.005). Bioenergetic and mitochondrial dysregulation could be present in both manic and depressive phases in BD and, importantly, they may restore after clinical remission. These preliminary results suggest that mitochondrial respiratory capacity could be a biomarker of illness activity and clinical response in BD. Further studies with larger samples and similar approaches are needed to confirm these results and identify potential biomarkers in different phases of the disease.

Interictal epileptiform discharges contribute to word-finding difficulty in epilepsy through multiple cognitive mechanisms.

Cognitive impairment often impacts quality of life in epilepsy even if seizures are controlled. Word-finding difficulty is particularly prevalent and often attributed to etiological (static, baseline) circuit alterations. We sought to determine whether interictal discharges convey significant superimposed contributions to word-finding difficulty in patients, and if so, through which cognitive mechanism(s). Twenty-three patients undergoing intracranial monitoring for drug-resistant epilepsy participated in multiple tasks involving word production (auditory naming, short-term verbal free recall, repetition) to probe word-finding difficulty across different cognitive domains. We compared behavioral performance between trials with versus without interictal discharges across six major brain areas and adjusted for intersubject differences using mixed-effects models. We also evaluated for subjective word-finding difficulties through retrospective chart review. Subjective word-finding difficulty was reported by the majority (79%) of studied patients preoperatively. During intracranial recordings, interictal epileptiform discharges (IEDs) in the medial temporal lobe were associated with long-term lexicosemantic memory impairments as indexed by auditory naming (p = .009), in addition to their established impact on short-term verbal memory as indexed by free recall (p = .004). Interictal discharges involving the lateral temporal cortex and lateral frontal cortex were associated with delayed reaction time in the auditory naming task (p = .016 and p = .018), as well as phonological working memory impairments as indexed by repetition reaction time (p = .002). Effects of IEDs across anatomical regions were strongly dependent on their precise timing within the task. IEDs appear to act through multiple cognitive mechanisms to form a convergent basis for the debilitating clinical word-finding difficulty reported by patients with epilepsy. This was particularly notable for medial temporal spikes, which are quite common in adult focal epilepsy. In parallel with the treatment of seizures, the modulation of interictal discharges through emerging pharmacological means and neurostimulation approaches may be an opportunity to help address devastating memory and language impairments in epilepsy.

The Importance of Understanding Foot Posture Strategies during Stair Descent for Forensic Incident Investigations

Falls on stairs are commonly claimed to be caused by slipping on the tread nosing, but few studies have explored specific foot kinematics at initial contact during stair descent. Further understanding of such kinematics can aid forensic incident investigations. We investigated foot posture at initial contact with the tread surface of seven participants while descending stairs to determine: a) foot posture variability across participants, and b) the effects of foot posture with respect to the nosing on foot placement. We calculated foot posture as the angle at initial contact relative to the horizontal plane using two consistent landmark points on the subjects’ shoes. Our preliminary results demonstrated inter-subject and intra-subject significant differences in foot posture (P<0.05) for both legs. These results demonstrate that further exploring this at specific instances of the gait cycle during stair descent is crucial to better understand fall events during forensic investigations.

Towards PPG-based anger detection for emotion regulation

BackgroundAnger dyscontrol is a common issue after traumatic brain injury (TBI). With the growth of wearable physiological sensors, there is new potential to facilitate the rehabilitation of such anger in the context of daily life. This potential, however, depends on how well physiological markers can distinguish changing emotional states and for such markers to generalize to real-world settings. Our study explores how wearable photoplethysmography (PPG), one of the most widely available physiological sensors, could be used detect anger within a heterogeneous population.MethodsThis study collected the TRIEP (Toronto Rehabilitation Institute Emotion-Physiology) dataset, which comprised of 32 individuals (10 TBI), exposed to a variety of elicitation material (film, pictures, self-statements, personal recall), over two day sessions. This complex dataset allowed for exploration into how the emotion-PPG relationship varied over changes in individuals, endogenous/exogenous drivers of emotion, and day-to-day differences. A multi-stage analysis was conducted looking at: (1) times-series visual clustering, (2) discriminative time-interval features of anger, and (3) out-of-sample anger classification.ResultsCharacteristics of PPG are largely dominated by inter-subject (between individuals) differences first, then intra-subject (day-to-day) changes, before differentiation into emotion. Both TBI and non-TBI individuals showed evidence of linear separable features that could differentiate anger from non-anger classes within time-interval analysis. However, what is more challenging is that these separable features for anger have various degrees of stability across individuals and days.ConclusionThis work highlights how there are contextual, non-stationary challenges to the emotion-physiology relationship that must be accounted for before emotion regulation technology can perform in real-world scenarios. It also affirms the need for a larger breadth of emotional sampling when building classification models.

Estimating dynamic individual coactivation patterns based on densely sampled resting-state fMRI data and utilizing it for better subject identification.

As a complex dynamic system, the brain exhibits spatially organized recurring patterns of activity over time. Coactivation patterns (CAPs), which analyzes data from each single frame, have been utilized to detect transient brain activity states recently. However, previous CAP analyses have been conducted at the group level, which might neglect meaningful individual differences. Here, we estimated individual CAP states at both subject- and scan-level based on a densely sampled dataset: Midnight Scan Club. We used differential identifiability, which measures the gap between intra- and inter-subject similarity, to evaluate individual differences. We found individual CAPs at the subject-level achieved the best fingerprinting ability by maintaining high intra-subject similarity and enlarging inter-subject differences, and brain regions of association networks mainly contributed to the identifiability. On the other hand, scan-level CAP states were unstable across scans for the same participant. Expectedly, we found subject-specific CAPs became more reliable and discriminative with more data (i.e., longer duration). As the acquisition time of each participant is limited in practice, our results recommend a data collection strategy that collects more scans with appropriate duration (e.g., 12 ~ 15min/scan) to obtain more reliable subject-specific CAPs, when total acquisition time is fixed (e.g., 150min). In summary, this work has constructed reliable subject-specific CAP states with meaningful individual differences, and recommended an appropriate data collection strategy, which can guide subsequent investigations into individualized brain dynamics.

Signatures of disease progression in knee osteoarthritis: insights from an integrated multi-scale modeling approach, a proof of concept.

Introduction: Knee osteoarthritis (KOA) is characterized by articular cartilage degeneration. It has been widely accepted that the mechanical joint environment plays a significant role in the onset and progression of this disease. In silico models have been used to study the interplay between mechanical loading and cartilage degeneration, hereby relying mainly on two key mechanoregulatory factors indicative of collagen degradation and proteoglycans depletion. These factors are the strain in collagen fibril direction (SFD) and maximum shear strain (MSS) respectively. Methods: In this study, a multi-scale in silico modeling approach was used based on a synergy between musculoskeletal and finite element modeling to evaluate the SFD and MSS. These strains were evaluated during gait based on subject-specific gait analysis data collected at baseline (before a 2-year follow-up) for a healthy and progressive early-stage KOA subject with similar demographics. Results: The results show that both SFD and MSS factors allowed distinguishing between a healthy subject and a KOA subject, showing progression at 2 years follow-up, at the instance of peak contact force as well as during the stance phase of the gait cycle. At the peak of the stance phase, the SFD were found to be more elevated in the KOA patient with the median being 0.82% higher in the lateral and 0.4% higher in the medial compartment of the tibial cartilage compared to the healthy subject. Similarly, for the MSS, the median strains were found to be 3.6% higher in the lateral and 0.7% higher in the medial tibial compartment of the KOA patient compared to the healthy subject. Based on these intersubject SFD and MSS differences, we were additionally able to identify that the tibial compartment of the KOA subject at risk of progression. Conclusion/discussion: We confirmed the mechanoregulatory factors as potential biomarkers to discriminate patients at risk of disease progression. Future studies should evaluate the sensitivity of the mechanoregulatory factors calculated based on this multi-scale modeling workflow in larger patient and control cohorts.

The acquisition of Hindi split-ergativity and differential object marking by Dutch L1 speakers: systematicity and variation

ABSTRACT We investigated the acquisition of Hindi split ergativity (zero or -ne marking) and Hindi differential object marking (DOM; zero or -ko marking) by L1 speakers of Dutch. Both grammatical phenomena are conditioned by multiple syntactic and semantic features. On a descriptive level, the study aims to examine when and how Dutch-speaking learners acquire and apply the conditional features associated with -ne and -ko marking in Hindi as a foreign language (HFL). A specific learner corpus was created based on a picture description task that elicited semi-spontaneous oral production data from 15 Dutch-speaking learners of Hindi, from four cross-sectional stages of the Hindi course trajectory. We annotated the corpus data for multiple features associated with -ne and -ko marking. Using a mixed-effects logistic regression analysis, we found an increase in the use and accuracy of each case marker over the different years of study, but individual learner profile analyses revealed considerable intersubject differences in learner behavior. We argue that the developmental stages for the emergence of -ne and -ko marking are in line with predictions based on Processability Theory. We additionally include mastery level analysis to account for a combined perspective on language development. Our findings reveal that HFL learners reach higher mastery levels for split ergativity than for DOM, even though DOM (-ko marking) emerges before split ergativity (-ne marking). We conclude that developmental stages and between-learner variation are not mutually exclusive.

The Effects of Recline Angle and Restraint Geometry on Lap Belt-Pelvis Interaction for Above-Normal BMI Motor Vehicle Occupants.

The interaction of the three-point seat belt with the occupant, particularly the lap belt with the pelvis, is affected by a multitude of intrinsic and extrinsic factors, including the torso recline angle, lap belt angle, and occupant body mass index (BMI). While field data analyses have shown the strong safety benefit for seat belt use regardless of occupant size or crash direction, the term "submarining" historically has been used to describe a scenario in which the lap belt loads the abdominal soft tissue and organs, superior and posterior to the pelvic bone. While contemporary restraint systems work to effectively address the risk of submarining in occupants properly seated and properly belted, scenarios in which the lap belt may not properly engage the load-bearing pelvis remain. These scenarios, including a reclined torso angle or shallow lap belt angle, require further study. In this research study, eight non-injurious seated belt pull tests were conducted on two constrained whole-body cadavers of above-normal BMI (≥ 25kg/m2) with controlled variation of torso and lap belt-pelvis angles. Test factors affecting belt engagement with the pelvis were identified for each subject. Belt engagement was largely affected by the initial placement of the lap belt. The initial belt placement was affected by the torso angle which influenced the distribution of the abdominal soft tissue. The belt disengagement thresholds differed between subjects due to the inter-subject differences in soft tissue distribution, which affected the lap belt kinematics relative to the pelvis. In addition to improving the understanding of this particular submarining mechanism, this study provides a dataset for future validation of human body model soft tissue deformation response from lap belt loading.

Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition

EEG signals have been reported to be informative and reliable for emotion recognition in recent years. However, the inter-subject variability of emotion-related EEG signals still poses a great challenge for the practical applications of EEG-based emotion recognition. Inspired by recent neuroscience studies on inter-subject correlation, we proposed a Contrastive Learning method for Inter-Subject Alignment (CLISA) to tackle the cross-subject emotion recognition problem. Contrastive learning was employed to minimize the inter-subject differences by maximizing the similarity in EEG signal representations across subjects when they received the same emotional stimuli in contrast to different ones. Specifically, a convolutional neural network was applied to learn inter-subject aligned spatiotemporal representations from EEG time series in contrastive learning. The aligned representations were subsequently used to extract differential entropy features for emotion classification. CLISA achieved state-of-the-art cross-subject emotion recognition performance on our THU-EP dataset with 80 subjects and the publicly available SEED dataset with 15 subjects. It could generalize to unseen subjects or unseen emotional stimuli in testing. Furthermore, the spatiotemporal representations learned by CLISA could provide insights into the neural mechanisms of human emotion processing.