Intra-subject Similarity Research Articles

Inter- and intra-subject similarity in network functional connectivity across a full narrative movie.

Naturalistic paradigms, such as watching movies during functional magnetic resonance imaging, are thought to prompt the emotional and cognitive processes typically elicited in real life situations. Therefore, naturalistic viewing (NV) holds great potential for studying individual differences. Previous studies have primarily focused on using shorter movie clips, geared toward eliciting specific and often isolated emotions, while the potential behind using full narratives depicted in commercial movies as a proxy for real-life experiences has barely been explored. Here, we offer preliminary evidence that a full narrative movie (FNM), that is, a movie covering a complete narrative arc, can capture complex socio-affective dynamics and their links to individual differences. Using the studyforrest dataset, we investigated inter- and intra-subject similarity in network functional connectivity (NFC) of 14 meta-analytically defined networks across a full narrative, audio-visual movie split into eight consecutive movie segments. We characterized the movie segments by valence and arousal portrayed within the sequences, before utilizing a linear mixed model to analyze which factors explain inter- and intra-subject similarity. Our results show that the model best explaining inter-subject similarity comprised network, movie segment, valence and a movie segment by valence interaction. Intra-subject similarity was influenced significantly by the same factors and an additional three-way interaction between movie segment, valence and arousal. Overall, inter- and intra-subject similarity in NFC were sensitive to the ongoing narrative and emotions in the movie. We conclude that FNMs offer complex content and dynamics that might be particularly valuable for studying individual differences. Further characterization of movie features, such as the overarching narratives, that enhance individual differences is needed for advancing the potential of NV research.

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.

PPG biometric recognition with singular value decomposition and local mean decomposition

Although some methods of feature extraction for photoplethysmography (PPG) biometric recognition have been extensively studied, effectiveness of local features, time cost of feature extraction, and robust identification for small-scale data remain challenging. To address these issues, we proposed a feature-extraction method of PPG biometrics combining singular value decomposition with local mean decomposition and time-domain parameters. First, we used the singular-value-decomposition method to de-noise the original PPG data. Second, we extracted the local-mean-decomposition-based and time-domain features, which are fused into a concatenated feature. Finally, we combined the concatenated feature with four classifiers for classification and decision-making. Extensive experiments on the three datasets have shown that the waveform of the PPG signal de-noised by singular value decomposition was smoother and more regular, the concatenated feature had strong inter-subject distinguishability and intra-subject similarity, and the concatenated feature combined with a random-forest classifier was the best and could achieve 99.40%, 99.88%, and 99.56% recognition rates on the respective datasets. The method is competitive with several state-of-the-art methods.

A deep learning-based multisite neuroimage harmonization framework established with a traveling-subject dataset

The accumulation of multisite large-sample MRI datasets collected during large brain research projects in the last decade has provided critical resources for understanding the neurobiological mechanisms underlying cognitive functions and brain disorders. However, the significant site effects observed in imaging data and their derived structural and functional features have prevented the derivation of consistent findings across multiple studies. The development of harmonization methods that can effectively eliminate complex site effects while maintaining biological characteristics in neuroimaging data has become a vital and urgent requirement for multisite imaging studies. Here, we propose a deep learning-based framework to harmonize imaging data obtained from pairs of sites, in which site factors and brain features can be disentangled and encoded. We trained the proposed framework with a publicly available traveling subject dataset from the Strategic Research Program for Brain Sciences (SRPBS) and harmonized the gray matter volume maps derived from eight source sites to a target site. The proposed framework significantly eliminated intersite differences in gray matter volumes. The embedded encoders successfully captured both the abstract textures of site factors and the concrete brain features. Moreover, the proposed framework exhibited outstanding performance relative to conventional statistical harmonization methods in terms of site effect removal, data distribution homogenization, and intrasubject similarity improvement. Finally, the proposed harmonization network provided fixable expandability, through which new sites could be linked to the target site via indirect schema without retraining the whole model. Together, the proposed method offers a powerful and interpretable deep learning-based harmonization framework for multisite neuroimaging data that can enhance reliability and reproducibility in multisite studies regarding brain development and brain disorders.

Unrevealing Reliable Cortical Parcellation of Individual Brains Using Resting-State Functional Magnetic Resonance Imaging and Masked Graph Convolutions.

Brain parcellation helps to understand the structural and functional organization of the cerebral cortex. Resting-state functional magnetic resonance imaging (fMRI) and connectivity analysis provide useful information to delineate individual brain parcels in vivo. We proposed an individualized cortical parcellation based on graph neural networks (GNN) to learn the reliable functional characteristics of each brain parcel on a large fMRI dataset and to infer the areal probability of each vertex on unseen subjects. A subject-specific confidence mask was implemented in the GNN model to account for the tradeoff between the topographic alignment across subjects and functional homogeneity of brain parcels on individual brains. The individualized brain parcellation achieved better functional homogeneity at rest and during cognitive tasks compared with the group-registered atlas (p-values < 0.05). In addition, highly reliable and replicable parcellation maps were generated on multiple sessions of the same subject (intrasubject similarity = 0.89), while notable variations in the topographic organization were captured across subjects (intersubject similarity = 0.81). Moreover, the intersubject variability of brain parcellation indicated large variations in the association cortices while keeping a stable parcellation on the primary cortex. Such topographic variability was strongly associated with the functional connectivity variability, significantly predicted cognitive behaviors, and generally followed the myelination, cytoarchitecture, and functional organization of the human brain. This study provides new avenues to the precise individualized mapping of the cortical areas through deep learning and shows high potentials in the personalized localization diagnosis and treatment of neurological disorders.

On the intra-subject similarity of hand vein patterns in biometric recognition

In these years, biometric recognition based on hand vein patterns is receiving an always increasing attention from both industry and academia, thanks to the advantages it offers with respect to conventional approaches, such as those relying on fingerprint, iris, or face. Nevertheless, there are still several properties of vein traits that need to be investigated and well understood. In this paper, we here analyze the level of similarity, evaluated in terms of recognition rate of a biometric system, of vein patterns in the fingers, palms, and dorsa of a person’s left and right hands. In other words, we analyze whether a subject, enrolled using vein patterns, either finger-vein, palm-vein, dorsal-vein, from one hand, can be recognized using the corresponding patterns from the other hand. Our investigation is conducted using deep-learning-based feature extraction approaches, three different vein modalities, and four different databases. The obtained experimental results show that corresponding fingers, palms, and dorsal regions from different hands of the same subject show more resemblance with respect to the traits from the same hand of different persons. Furthermore, our findings point out that similarities among vein patterns in corresponding fingers could be used for recognition purposes, while this still cannot be applied to palm and dorsum vein patterns.

Tissue Probability Based Registration of Diffusion-Weighted Magnetic Resonance Imaging.

Current registration methods for diffusion-MRI (dMRI) data mostly focus on white matter (WM) areas. Recently, dMRI has been employed for the characterization of gray matter (GM) microstructure, emphasizing the need for registration methods that consider all tissue types. To develop a dMRI registration method based on GM, WM, and cerebrospinal fluid (CSF) tissue probability maps (TPMs). Retrospective longitudinal study. Thirty-two healthy participants were scanned twice (legacy data), divided into a training-set (n=16) and a test-set (n=16), and 35 randomly-selected participants from the Human Connectome Project. 3.0T, diffusion-weighted spin-echo echo-planar sequence; T1-weighted spoiled gradient-recalled echo (SPGR) sequence. A joint segmentation-registration approach was implemented: Diffusion tensor imaging (DTI) maps were classified into TPMs using machine-learning approaches. The resulting GM, WM, and CSF probability maps were employed as features for image alignment. Validation was performed on the test dataset and the HCP dataset. Registration performance was compared with current mainstream registration tools. Classifiers used for segmentation were evaluated using leave-one-out cross-validation and scored using Dice-index. Registration success was evaluated by voxel-wise variance, normalized cross-correlation of registered DTI maps, intra- and inter-subject similarity of the registered TPMs, and region-based intra-subject similarity using an anatomical atlas. One-way ANOVAs were performed to compare between our method and other registration tools. The proposed method outperformed mainstream registration tools as indicated by lower voxel-wise variance of registered DTI maps (SD decrease of 10%) and higher similarity between registered TPMs within and across participants, for all tissue types (Dice increase of 0.1-0.2; P < 0.05). A joint segmentation-registration approach based on diffusion-driven TPMs provides a more accurate registration of dMRI data, outperforming other registration tools. Our method offers a "translation" of diffusion data into structural information in the form of TPMs, allowing to directly align diffusion and structural images. 1 Technical Efficacy Stage: 1.

Cross-Subject Assistance: Inter- and Intra-Subject Maximal Correlation for Enhancing the Performance of SSVEP-Based BCIs.

The current state-of-the-art methods significantly improve the detection performance of the steady-state visual evoked potentials (SSVEPs) by using the individual calibration data. However, the time-consuming calibration sessions limit the number of training trials and may give rise to visual fatigue, which weakens the effectiveness of the individual training data. For addressing this issue, this study proposes a novel inter- and intra-subject maximal correlation (IISMC) method to enhance the robustness of SSVEP recognition via employing the inter- and intra-subject similarity and variability. Through efficient transfer learning, similar experience under the same task is shared across subjects. IISMC extracts subject-specific information and similar task-related information from oneself and other subjects performing the same task by maximizing the inter- and intra-subject correlation. Multiple weak classifiers are built from several existing subjects and then integrated to construct the strong classifiers by the average weighting. Finally, a powerful fusion predictor is obtained for target recognition. The proposed framework is validated on a benchmark data set of 35 subjects, and the experimental results demonstrate that IISMC obtains better performance than the state of the art task-related component analysis (TRCA). The proposed method has great potential for developing high-speed BCIs.

Analyzing the effects of free water modeling by deep learning on diffusion MRI structural connectivity estimates in glioma patients.

Diffusion-weighted MRI makes it possible to quantify subvoxel brain microstructure and to reconstruct white matter fiber trajectories with which structural connectomes can be created. However, at the border between cerebrospinal fluid and white matter, or in the presence of edema, the obtained MRI signal originates from both the cerebrospinal fluid as well as from the white matter partial volume. Diffusion tractography can be strongly influenced by these free water partial volume effects. Thus, including a free water model can improve diffusion tractography in glioma patients. Here, we analyze how including a free water model influences structural connectivity estimates in healthy subjects as well as in brain tumor patients. During a clinical study, we acquired diffusion MRI data of 35 glioma patients and 28 age- and sex-matched controls, on which we applied an open-source deep learning based free water model. We performed deterministic as well as probabilistic tractography before and after free water modeling, and utilized the tractograms to create structural connectomes. Finally, we performed a quantitative analysis of the connectivity matrices. In our experiments, the number of tracked diffusion streamlines increased by 13% for high grade glioma patients, 9.25% for low grade glioma, and 7.65% for healthy controls. Intra-subject similarity of hemispheres increased significantly for the patient as well as for the control group, with larger effects observed in the patient group. Furthermore, inter-subject differences in connectivity between brain tumor patients and healthy subjects were reduced when including free water modeling. Our results indicate that free water modeling increases the similarity of connectivity matrices in brain tumor patients, while the observed effects are less pronounced in healthy subjects. As the similarity between brain tumor patients and healthy controls also increased, connectivity changes in brain tumor patients may have been overestimated in studies that did not perform free water modeling.

The effect of intramammary pirlimycin hydrochloride on the fecal microbiome of early-lactation heifers

The purpose of this study was to determine the effect of intramammary pirlimycin on the fecal microbiome of dairy cattle. Primiparous heifers were enrolled and assigned to a treatment or control group at a ratio of 2:1. In part 1 of the study, treated heifers (T1) were given intramammary pirlimycin into one infected quarter once daily for 2 d at 24-h intervals, according to the label instructions. Control heifers received no treatment. In part 2 of the study, treated heifers (T2) were given intramammary pirlimycin into one infected quarter once daily for 8 d at 24-h intervals, according to the label instructions. All enrolled heifers (T1, T2, and control) had quarter-level milk samples aseptically collected for bacterial culture and fecal samples collected for 16S rRNA gene sequencing on d 0, 2, 7, 14, 21, and 28. Milk samples were plated on Columbia blood agar and incubated at 37°C for 24 h. Bacteria were identified using MALDI-TOF mass spectrometry. The DNA was extracted from feces using PowerFecal kits (Qiagen, Venlo, the Netherlands). The 16S rRNA gene amplicon library construction and sequencing was performed at the University of Missouri DNA Core facility. Testing for differences in fecal community composition was performed via one-way permutational multivariate ANOVA of Bray-Curtis and Jaccard similarities using Past 3.13 (https://folk.uio.no/ohammer/past/). Mean total count of operational taxonomic units and Chao1, Shannon, and Simpson α-diversity indices were determined and compared via t-test or Wilcoxon rank sum test. A treatment-dependent effect was present in the observed and predicted richness of feces from cows in the T1 group at d 2 posttreatment. Additionally, intramammary pirlimycin induced a significant change in the composition of the fecal microbiota by d 2 in the treated groups. Based on calculated intra-subject similarities, intramammary pirlimycin was associated with a significant acute change in the fecal microbiota of dairy heifers and that chance reversed when the antimicrobial exposure was brief, but sustained following longer exposure. Overall, intramammary pirlimycin administration affected the fecal microbiome of lactating dairy heifers. Further work is necessary to determine the effect of these changes on the heifer and the dairy environment as well as if treatment is influencing antimicrobial resistance among enteric and environmental bacteria.

Coordinated activities of trunk and upper extremity muscles during walker-assisted paraplegic gait: A synergy study

Individuals with spinal cord injury (SCI) at lower thoracic levels might walk independently with the aid of mechanical orthoses and walker by using their unimpaired trunk and upper extremity muscles (TUEM). The required motor skills and the associated subtasks of the paraplegic locomotion, however, have not been well understood yet. The purpose of this study was to investigate the coordination of the TUEM activities throughout the paraplegic gait cycle using synergy analysis. For eight paraplegic individuals (30.6 ± 11.6 years; SCI level: T12), the kinematics data and the surface electromyography (EMG) signals of TUEM were recorded during 15 gait cycles. Non-negative matrix factorization method was used to extract muscle synergies from the EMG results, which were time normalized in association with four stages of each gait step. For each subject, the number, structures and activation profiles of the muscle synergies across different gait cycles were highly similar, resulting in three subject-specific templates of synergy structure and activation profile with mean intra-subject similarity scores of 0.94 ± 0.02 (p < 0.037) and 0.90 ± 0.06 (p < 0.001), respectively. Hierarchical clustering of the subject-specific templates revealed five representative templates for all subjects, among which, three that were in common for at least six subjects were considered as the characteristic synergies of paraplegic locomotion. The first and second characteristic synergies (inter-subject similarity scores >0.91, p < 0.037) composed mainly of the pectoralis major, latissimus dorsi, longissimus, iliocostalis quadratus lumborum, and the triceps, posterior deltoid and lower trapezius muscles, respectively, and were activated during the ipsilateral and the contralateral leg raising and swing stages. The third characteristic synergy, (inter-subject similarity scores >0.95, p < 0.02), contained mainly the abdominal and lumbopelvic muscles and was activated during the balance adjustment and walker propulsion stages of the gait cycle. These results provide more insight into the motor skills and the associated subtasks of the paraplegic locomotion.

Whole Brain Functional Connectivity Pattern Homogeneity Mapping.

Mounting studies have demonstrated that brain functions are determined by its external functional connectivity patterns. However, how to characterize the voxel-wise similarity of whole brain functional connectivity pattern is still largely unknown. In this study, we introduced a new method called functional connectivity homogeneity (FcHo) to delineate the voxel-wise similarity of whole brain functional connectivity patterns. FcHo was defined by measuring the whole brain functional connectivity patterns similarity of a given voxel with its nearest 26 neighbors using Kendall’s coefficient concordance (KCC). The robustness of this method was tested in four independent datasets selected from a large repository of MRI. Furthermore, FcHo mapping results were further validated using the nearest 18 and six neighbors and intra-subject reproducibility with each subject scanned two times. We also compared FcHo distribution patterns with local regional homogeneity (ReHo) to identify the similarity and differences of the two methods. Finally, FcHo method was used to identify the differences of whole brain functional connectivity patterns between professional Chinese chess players and novices to test its application. FcHo mapping consistently revealed that the high FcHo was mainly distributed in association cortex including parietal lobe, frontal lobe, occipital lobe and default mode network (DMN) related areas, whereas the low FcHo was mainly found in unimodal cortex including primary visual cortex, sensorimotor cortex, paracentral lobule and supplementary motor area. These results were further supported by analyses of the nearest 18 and six neighbors and intra-subject similarity. Moreover, FcHo showed both similar and different whole brain distribution patterns compared to ReHo. Finally, we demonstrated that FcHo can effectively identify the whole brain functional connectivity pattern differences between professional Chinese chess players and novices. Our findings indicated that FcHo is a reliable method to delineate the whole brain functional connectivity pattern similarity and may provide a new way to study the functional organization and to reveal neuropathological basis for brain disorders.

Inter- and Intrasubject Similarity of Muscle Synergies During Bench Press With Slow and Fast Velocity.

We investigated the effect of low and high bar velocity on inter- and intrasubject similarity of muscle synergies during bench press. A total of 13 trained male subjects underwent two exercise conditions: a slow- and a fast-velocity bench press. Surface electromyography was recorded from 13 muscles, and muscle synergies were extracted using a nonnegative matrix factorization algorithm. The intrasubject similarity across conditions and intersubject similarity within conditions were computed for muscle synergy vectors and activation coefficients. Two muscle synergies were sufficient to describe the dataset variability. For the second synergy activation coefficient, the intersubject similarity within the fast-velocity condition was greater than the intrasubject similarity of the activation coefficient across the conditions. An opposite pattern was observed for the first muscle synergy vector. We concluded that the activation coefficients are robust within conditions, indicating a robust temporal pattern of muscular activity across individuals, but the muscle synergy vector seemed to be individually assigned.

Assessment of Upper Limb Motor Dysfunction for Children with Cerebral Palsy Based on Muscle Synergy Analysis

Muscle synergies are considered to be building blocks underlying motor behaviors. The goal of this study is to explore an objective and effective method to assess the upper limb motor dysfunction of cerebral palsy (CP) children from the aspect of muscle synergy analysis. Fourteen CP children and 10 typically developed (TD) children were recruited to perform three similar upper limb motion tasks related to the movements of elbow and shoulder joints, and surface electromyographic (sEMG) signals were recorded from 10 upper arm and shoulder muscles involved in the defined tasks. Non-negative matrix factorization algorithm was used to extract muscle synergies and the corresponding activation patterns during three similar tasks. For each subject in TD group, four muscle synergies were extracted in each task. Whereas, fewer mature synergies were recruited in CP group, and many abnormal synergy structures specific to CP group appeared. In view of neuromuscular control strategy differences, three synergy-related parameters were proposed and synergy structure similarity coefficient was found to have high ability in depicting the inter-subject similarity within task and the intra-subject similarity between tasks. Seven upper limb assessment (UPA) metrics, which were defined as the combinations of synergy structure similarity coefficients of three tasks, were proposed to assess the upper limb motor function of CP children. The experimental results demonstrated that these UPA metrics were able to assess upper limb motor function comprehensively and effectively. The proposed assessment method can serve as a promising approach to quantify the abnormality of muscle synergies, thus offering potential to derive a physiologically based quantitative index for assessing upper limb motor function in CP clinical diagnosis and rehabilitation.

Shoulder kinematics and spatial pattern of trapezius electromyographic activity in real and virtual environments.

The design of an industrial workstation tends to include ergonomic assessment steps based on a digital mock-up and a virtual reality setup. Lack of interaction and system fidelity is often reported as a main issue in such virtual reality applications. This limitation is a crucial issue as thorough ergonomic analysis is required for an investigation of the biomechanics. In the current study, we investigated the biomechanical responses of the shoulder joint in a simulated assembly task for comparison with the biomechanical responses in virtual environments. Sixteen male healthy novice subjects performed the task on three different platforms: real (RE), virtual (VE), and virtual environment with force feedback (VEF) with low and high precision demands. The subjects repeated the task 12 times (i.e., 12 cycles). High density electromyography from the upper trapezius and rotation angles of the shoulder joint were recorded and split into the cycles. The angular trajectories and velocity profiles of the shoulder joint angles over a cycle were computed in 3D. The inter-subject similarity in terms of normalized mutual information on kinematics and electromyography was investigated. Compared with RE the task in VE and VEF was characterized by lower kinematic maxima. The inter-subject similarity in RE compared with intra-subject similarity across the platforms was lower in terms of movement trajectories and greater in terms of trapezius muscle activation. The precision demand resulted in lower inter- and intra-subject similarity across platforms. The proposed approach identifies biomechanical differences in the shoulder joint in both VE and VEF compared with the RE platform, but these differences are less marked in VE mostly due to technical limitations of co-localizing the force feedback system in the VEF platform.

Cortical short-term fatigue effects assessed via rhythmic brain–muscle coherence

This study is aimed at assessing the short-term effects of muscular fatigue on the sensorimotor areas organization in the left and right hemispheres. Magnetoencephalographic (MEG) and electromyographic (EMG) activities were simultaneously recorded during the execution of a non-fatiguing motor task, performed before and after a task known to induce muscle fatigue (Fatigue). Coherence between cerebral and muscular rhythms as well as cerebral and muscular rhythms spectral densities were estimated during this non-fatiguing task and at rest. The MEG-EMG coherence in the beta band (13-32 Hz) was higher after than before Fatigue. The background activity reduction during contraction with respect to rest (i.e. the cerebral reactivity) was less evident after than before Fatigue in the gamma (33-45 Hz) and beta bands. When differentiating subjects on the base of Fatigue endurance times, while a huge inter-subject variability was found, an evident intra-subject similarity was observed for left and right arms, suggesting that resistance to fatigue is more an individual ability than a motor skill differentiated for the dominant and non-dominant side. In conclusion, signs of a more selective neural recruitment, more coupled with muscular activity, appeared as short-term effects of muscular fatigue in primary sensorimotor cortical areas. Evidence suggested that the reduction of cortical recruitment and the increased cortico-muscular coupling are distinct mechanisms.

Detection of Visual Dysfunction in Optic Atrophy by Functional Magnetic Resonance Imaging During Monocular Visual Stimulation

To evaluate functional magnetic resonance imaging as an objective method for detecting visual dysfunction in various ophthalmologic disorders involving the optic nerve and the chiasm. We performed functional magnetic resonance imaging during monocular visual stimulation on seven patients with visual field loss caused by lesions of the optic nerve and the chiasm and on three normal control subjects with no visual field loss. We correlated static threshold perimetry in the seven patients with the results of functional magnetic resonance imaging. In the three normal control subjects, we found good intrasubject similarity in areas of bilateral occipital lobe activation between monocular stimulation of the right and left eyes. In the patients with unilateral optic neuropathy, including glaucoma, stimulation of the affected eye induced no activation of the primary visual cortex in the portion corresponding to the central visual field defects and reduced activity of the associated visual cortex. In the patients with chiasmal compression, monocular stimulation resulted in a marked asymmetry of activation in the primary visual cortex, which corresponded to the visual field abnormality. Functional magnetic resonance imaging appears to be useful in confirming the clinical diagnosis of optic atrophy because it can objectively disclose visual field loss, even a small defect such as central scotoma.

A note on the intrasubject similarity of subjective probabilities obtained by estimates and by bets

A note on the intrasubject similarity of subjective probabilities obtained by estimates and by bets