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Structural covariance network disruption has been considered an important pathophysiological indicator for schizophrenia. Here, we introduced a novel individualized structural covariance network measure, referred to as a texture similarity network (TSN), and hypothesized that the TSN could reliably reveal unique intersubject heterogeneity and complex dysconnectivity patterns in schizophrenia. The TSN was constructed by measuring the covariance of 180 three-dimensional voxelwise gray-level co-occurrence matrix feature maps between brain areas in each participant. We first tested the validity and reproducibility of the TSN in characterizing the intersubject variability in 2 longitudinal test-retest healthy cohorts. The TSN was further applied to elucidate intersubject variability and dysconnectivity patterns in 10 schizophrenia case-control datasets (609 schizophrenia cases vs. 579 controls) as well as in a first-episode depression dataset (69 patients with depression vs. 69 control participants). The test-retest analysis demonstrated higher TSN intersubject than intrasubject variability. Moreover, the TSN reliably revealed higher intersubject variability in both chronic and first-episode schizophrenia, but not in depression. The TSN also reproducibly detected coexistent increased and decreased TSN strength in widespread brain areas, increased global small-worldness, and the coexistence of both structural hyposynchronization in the central networks and hypersynchronization in peripheral networks in patients with schizophrenia but not in patients with depression. Finally, aberrant intersubject variability and covariance strength patterns revealed by the TSN showed a missing or weak correlation with other individualized structural covariance network measures, functional connectivity, and regional volume changes. These findings support the reliability of a TSN in revealing unique structural heterogeneity and complex dysconnectivity in patients with schizophrenia. [ABSTRACT FROM AUTHOR]

Background and Purpose: Conclusions from prior literature regarding the impact of sex, age, and height on spinal cord (SC) MRI morphometrics are conflicting, while the effect of body weight on SC morphometrics has been found to be nonsignificant. The purpose of this case‐control study is to assess the associations between cervical SC MRI morphometric parameters and age, sex, height, and weight to establish their potential role as confounding variables in a clinical study of people with multiple sclerosis (MS) compared to a cohort of healthy volunteers. Methods: Sixty‐nine healthy volunteers and 31 people with MS underwent cervical SC MRI at 3 Tesla field strength. Images were centered at the C3/C4 intervertebral disc and processed using Spinal Cord Toolbox v.4.0.2. Mixed‐effects linear regression models were used to evaluate the effects of biological variables and disease status on morphometric parameters. Results: Sex, age, and height had significant effects on cord and gray matter (GM) cross‐sectional area (CSA) as well as the GM:cord CSA ratio. There were no significant effects of body weight on morphometric parameters. The effect of MS disease duration on cord CSA in the C4 level was significant when controlling for all other variables. Conclusions: Studies of disease‐related changes in SC morphometry should control for sex, age, and height to account for physiological variation. [ABSTRACT FROM AUTHOR]

Major depressive disorder (MDD) is a clinically heterogeneous disorder. Its mechanism is still unknown. Although the altered intersubject variability in functional connectivity (IVFC) within gray‐matter has been reported in MDD, the alterations to IVFC within white‐matter (WM‐IVFC) remain unknown. Based on the resting‐state functional MRI data of discovery (145 MDD patients and 119 healthy controls [HCs]) and validation cohorts (54 MDD patients, and 78 HCs), we compared the WM‐IVFC between the two groups. We further assessed the meta‐analytic cognitive functions related to the alterations. The discriminant WM‐IVFC values were used to classify MDD patients and predict clinical symptoms in patients. In combination with the Allen Human Brain Atlas, transcriptome‐neuroimaging association analyses were further conducted to investigate gene expression profiles associated with WM‐IVFC alterations in MDD, followed by a set of gene functional characteristic analyses. We found extensive WM‐IVFC alterations in MDD compared to HCs, which were associated with multiple behavioral domains, including sensorimotor processes and higher‐order functions. The discriminant WM‐IVFC could not only effectively distinguish MDD patients from HCs with an area under curve ranging from 0.889 to 0.901 across three classifiers, but significantly predict depression severity (r = 0.575, p = 0.002) and suicide risk (r = 0.384, p = 0.040) in patients. Furthermore, the variability‐related genes were enriched for synapse, neuronal system, and ion channel, and predominantly expressed in excitatory and inhibitory neurons. Our results obtained good reproducibility in the validation cohort. These findings revealed intersubject functional variability changes of brain WM in MDD and its linkage with gene expression profiles, providing potential implications for understanding the high clinical heterogeneity of MDD. [ABSTRACT FROM AUTHOR]

Introduction: Group information-guided independent component analysis (GIGICA) and independent vector analysis (IVA) are two methods that improve estimation of subject-specific independent components in neuroimaging studies. These methods have shown better performance than traditional group independent component analysis (GICA) with respect to intersubject variability (ISV). Methods: In this study, we compared the patterns of community structure, spatial variance, and prediction performance of GIG-ICA and IVA-GL, respectively. The dataset was obtained from the publicly available Autism Brain Imaging Data Exchange (ABIDE) database, comprising 75 healthy controls (HC) and 102 Autism Spectrum Disorder (ASD) participants. The greedy rule was used to match components from IVA-GL and GIG-ICA in order to compare the similarities between the two methods. Results: Robust correspondence was observed between the two methods the following networks: cerebellum network (CRN; |r| = 0.7813), default mode network (DMN; |r| = 0.7263), self-reference network (SRN; |r| = 0.7818), ventral attention network (VAN; |r| = 0.7574), and visual network (VSN; |r| = 0.7503). Additionally, the Sensorimotor Network demonstrated the highest similarity between IVA-GL and GIGICA (SOM: |r| = 0.8125). Our findings revealed a significant difference in the number of modules identified by the two methods (HC: p < 0.001; ASD: p < 0.001). GIG-ICA identified significant differences in FNC between HC and ASD compared to IVA-GL. However, in correlation analysis, IVA-GL identified a statistically negative correlation between FNC of ASD and the social total subscore of the classic Autism Diagnostic Observation Schedule (ADOS: pi = -0.26, p = 0.0489). Moreover, both methods demonstrated similar prediction performances on age within specific networks, as indicated by GIG-ICA-CRN (R2 = 0.91, RMSE = 3.05) and IVA-VAN (R2 = 0.87, RMSE = 3.21). Conclusion: In summary, IVA-GL demonstrated lower modularity, suggesting greater sensitivity in estimating networks with higher intersubject variability. The improved age prediction of cerebellar-attention networks underscores their importance in the developmental progression of ASD. Overall, IVA-GL may be appropriate for investigating disorders with greater variability, while GIG-ICA identifies functional networks with distinct modularity patterns. [ABSTRACT FROM AUTHOR]

Background and purposeNeurological and neurodegenerative diseases can affect the spinal cord (SC) of pediatric patients. Magnetic resonance imaging (MRI) allows for in vivo quantification of SC atrophy via cross-sectional area (CSA). The study of CSA values in the general population is important to disentangle disease-related changes from intersubject variability. This study aimed at providing normative values for cervical CSA in children, extending our previous work performed with adults.MethodsSeventy-eight children (age 7-17 years) were selected from a Developmental Dyslexia study. All subjects underwent a 3T brain MRI session and any incidental findings were reported on the scans. A sagittal 1 mm3 3-dimensional T1 -weighted brain acquisition extended to the upper cervical cord was used to measure CSA at C2-C3, as well as spinal canal area and skull volume (V-scale). These three metrics were linearly fitted as a function of age to extract trends and percentage annual changes. Sex differences of CSA were assessed using least squares regression analyses, adjusting for age. We tested normalization strategies proven to be effective in reducing the intersubject variability of adults' CSA.ResultsCSA changed as a function of age at a faster rate when compared with skull volume (CSA: 1.82% increase, V-scale: .60% reduction). Sex had a statistically significant effect on CSA. Normalization methods based on canal area and skull volume reduced the CSA intersubject variability up to 16.84%.ConclusionsWe present CSA normative values in a large cohort of children, reporting on sources of intersubject variability and how to reduce them applying normalization methods previously developed.

Background and purposeThe quantification of spinal cord (SC) atrophy by MRI has assumed an important role in assessment of neuroinflammatory/neurodegenerative diseases and traumatic SC injury. Recent technical advances make possible the quantification of gray matter (GM) and white matter tissues in clinical settings. However, the goal of a reliable diagnostic, prognostic or predictive marker is still elusive, in part due to large intersubject variability of SC areas. Here, we investigated the sources of this variability and explored effective strategies to reduce it.MethodsOne hundred twenty-nine healthy subjects (mean age: 41.0 ± 15.9) underwent MRI on a Siemens 3T Skyra scanner. Two-dimensional PSIR at the C2-C3 vertebral level and a sagittal 1 mm3 3D T1-weighted brain acquisition extended to the upper cervical cord were acquired. Total cross-sectional area and GM area were measured at C2-C3, as well as measures of the vertebra, spinal canal and the skull. Correlations between the different metrics were explored using Pearson product-moment coefficients. The most promising metrics were used to normalize cord areas using multiple regression analyses.ResultsThe most effective normalization metrics were the V-scale (from SienaX) and the product of the C2-C3 spinal canal diameters. Normalization methods based on these metrics reduced the intersubject variability of cord areas of up to 17.74%. The measured cord areas had a statistically significant sex difference, while the effect of age was moderate.ConclusionsThe present work explored in a large cohort of healthy subjects the source of intersubject variability of SC areas and proposes effective normalization methods for its reduction.

Objectives. This study evaluated the effects of wearing the Laevo v2.56 exoskeleton (Laevo, The Netherlands) on physiological parameters related to working load and metabolic cost (MC) during a lifting task, explored the variability in exoskeleton performance among users and determined whether perceived discomfort negatively correlates with a reduction in MC. Methods. Twenty participants completed a 4-min repetitive lifting task with/without the exoskeleton. Respiratory gases, heart rate, blood lactate and ratings of perceived exertion and experienced discomfort were collected, and MC was calculated. Results. Wearing the exoskeleton significantly reduced MC and oxygen uptake during the lifting task by 4.8 and 3.8%, respectively. Workload reduction occurred in 65% of the participants. Conclusion. The Laevo v2.56 exoskeleton reduced MC and workload in a repetitive lifting task in a subject-dependent manner. Future studies should focus on identifying factors that could cause performance variability such as user–robot interaction forces. [ABSTRACT FROM AUTHOR]

Motor imagery brain–computer interfaces (MI–BCIs) that use electroencephalogram (EEG) signals are essential for motor rehabilitation. One of the most important problems in MI–BCIs is intersubject variability, as brain signals can vary significantly among individuals due to differences in cognitive processing and mental states. To address this issue, some studies have proposed intrasubject learning models that train and evaluate within specific individuals, while others have suggested intersubject learning models that aim to generalize across subjects. However, insufficient data are available for intrasubject learning, so they cannot use a deep learning model, and still, the problem of intersubject variability is unsolved in intersubject learning. To tackle these challenges, we developed a new framework based on deep metric learning called the Personal grouping method for Reducing InterSubject variability using deep Metric learning (PRISM). PRISM aims to reduce intersubject variability by calculating the similarity between subjects based on their EEG signals and generating clusters. Based on these clusters, the model can train by reducing intersubject variability while maintaining the advantage of intersubject learning. Our thorough evaluation indicates that PRISM is poised to redefine the standards in MI–BCIs, significantly alleviating the challenges of intersubject variability. We compared the performance of PRISM and the existing learning paradigm, and the performance of PRISM was similar to that of the intrasubject learning model (p > 0.4), which is the best existing model, and it performed better than the intersubject learning model (**p < 0.001). Our results indicate that the PRISM framework outperforms intersubject learning and closely matches intrasubject learning models. This suggests that PRISM combines the specificity of intrasubject learning with intersubject model generalizability, thereby enhancing performance. [ABSTRACT FROM AUTHOR]

Background/hypothesis: There is increasing awareness of interindividual variability in brain function, with potentially major implications for repetitive transcranial magnetic stimulation (rTMS) efficacy. We perform a secondary analysis using data from a double-blind randomized controlled 4-week trial of 20 Hz active versus sham rTMS to dorsolateral prefrontal cortex (DLPFC) during a working memory task in participants with schizophrenia. We hypothesized that rTMS would change local functional activity and variability in the active group compared with sham., Study Design: 83 participants were randomized in the original trial, and offered neuroimaging pre- and post-treatment. Of those who successfully completed both scans (n = 57), rigorous quality control left n = 42 (active/sham: n = 19/23), who were included in this analysis. Working memory-evoked activity during an N-Back (3-Back vs 1-Back) task was contrasted. Changes in local brain activity were examined from an 8 mm ROI around the rTMS coordinates. Individual variability was examined as the mean correlational distance (MCD) in brain activity pattern from each participant to others within the same group., Results: We observed an increase in task-evoked left DLPFC activity in the active group compared with sham (F1,36 = 5.83, False Discovery Rate (FDR))-corrected P = .04). Although whole-brain activation patterns were similar in both groups, active rTMS reduced the MCD in activation pattern compared with sham (F1,36 = 32.57, P < .0001). Reduction in MCD was associated with improvements in attention performance (F1,16 = 14.82, P = .0014, uncorrected)., Conclusions: Active rTMS to DLPFC reduces individual variability of brain function in people with schizophrenia. Given that individual variability is typically higher in schizophrenia patients compared with controls, such reduction may "normalize" brain function during higher-order cognitive processing., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

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. [ABSTRACT FROM AUTHOR]

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.

Visual stimulus-induced gamma oscillations in electroencephalogram (EEG) recordings have been recently shown to be compromised in subjects with preclinical Alzheimer's Disease (AD), suggesting that gamma could be an inexpensive biomarker for AD diagnosis provided its characteristics remain consistent across multiple recordings. Previous magnetoencephalography studies in young subjects have reported consistent gamma power over recordings separated by a few weeks to months. Here, we assessed the consistency of stimulus-induced slow (20–35 Hz) and fast gamma (36–66 Hz) oscillations in subjects (n  = 40) (age: 50–88 years) in EEG recordings separated by a year, and tested the consistency in the magnitude of gamma power, its temporal evolution and spectral profile. Gamma had distinct spectral/temporal characteristics across subjects, which remained consistent across recordings (average intraclass correlation of ~0.7). Alpha (8–12 Hz) and steady-state-visually evoked-potentials were also reliable. We further tested how EEG features can be used to identify 2 recordings as belonging to the same versus different subjects and found high classifier performance (AUC of ~0.89), with temporal evolution of slow gamma and spectral profile being most informative. These results suggest that EEG gamma oscillations are reliable across sessions separated over long durations and can also be a potential tool for subject identification. [ABSTRACT FROM AUTHOR]

It has been revealed that intersubject variability (ISV) in intrinsic functional connectivity (FC) is associated with a wide variety of cognitive and behavioral performances. However, the underlying organizational principle of ISV in FC and its related gene transcriptional profiles remain unclear. Using resting-state fMRI data from the Human Connectome Project (299 adult participants) and microarray gene expression data from the Allen Human Brain Atlas, we conducted a transcription-neuroimaging association study to investigate the spatial configurations of ISV in intrinsic FC and their associations with spatial gene transcriptional profiles. We found that the multimodal association cortices showed the greatest ISV in FC, while the unimodal cortices and subcortical areas showed the least ISV. Importantly, partial least squares regression analysis revealed that the transcriptional profiles of genes associated with human accelerated regions (HARs) could explain 31.29% of the variation in the spatial distribution of ISV in FC. The top-related genes in the transcriptional profiles were enriched for the development of the central nervous system, neurogenesis and the cellular components of synapse. Moreover, we observed that the effect of gene expression profile on the heterogeneous distribution of ISV in FC was significantly mediated by the cerebral blood flow configuration. These findings highlighted the spatial arrangement of ISV in FC and their coupling with variations in transcriptional profiles and cerebral blood flow supply.

Objective: This study explored the bioequivalence of a proposed biosimilar HOT-3010 vs. its reference product (adalimumab) among healthy Chinese male subjects. The study also investigated the tolerance, immunogenicity, and pharmacokinetics (PK).Methods: A randomized, double-blind, two-arm, parallel study was performed to examine the bioequivalence of HOT-3010 (40 mg) with that of adalimumab (Humira®, AbbVie) as a reference drug. The study subjects were followed up for 71 days.Results: PK properties exhibited by HOT-3010 (N = 66) and adalimumab (N = 68) groups were similar. The 90% confidence intervals of the ratios for Cmax, AUC0-t, and AUC0∞ were observed to be in the range 80–125% on comparing the two groups. For anti-drug antibodies (ADA), the number of subjects found to be positive in the HOT-3010 group and adalimumab group were 29 (43.94%) and 32 (47.06%), whereas 27 (40.91%) and 27 (39.71%) subjects were found to be positive for NAb, respectively. Treatment-related treatment-emergent adverse events (TEAEs) were recorded in 32 subjects each in both the groups, respectively.Conclusion: The PK characteristics and immunogenicity exhibited by HOT-3010 were similar to that of the reference product, adalimumab. The safety profiles were similar in both the treatment groups with mild-moderate adverse effects.

Prenatal drug exposure (PDE) is known to affect fetal brain development with documented long‐term consequences. Most studies of PDE effects on the brain are based on animal models. In this study, based on a large sample of 133 human neonates and leveraging a novel linear mixed‐effect model designed for intersubject variability analyses, we studied the effects of six prenatally exposed drugs (i.e., nicotine, alcohol, selective serotonin reuptake inhibitor, marijuana, cocaine, and opioids) on neonatal whole‐brain functional organization and compared them with five other critical nondrug variables (i.e., gestational age at birth/scan, sex, birth weight, and maternal depression). The behavioral implications were also examined. Magnitude‐wise, through summing across individual drug effects, our results highlighted ~5% of whole‐brain functional connections (FCs) affected by PDE, which was highly comparable with the combined effects of the five nond rug variables. Spatially, the detected PDE effects featured drug‐specific patterns with a common bias in higher‐order brain regions/networks. Regarding brain–behavioral relationships, the detected connections showing significant drug effects also demonstrated significant correlations with 3‐month behavioral outcomes. Further mediation analyses supported a mediation role of the detected brain FCs between PDE status and cognitive/language outcomes. Our findings of widespread, and spatially biased PDE effect patterns coupled with significant behavioral implications may hopefully stimulate more human‐based studies into effects of PDE on long‐term developmental outcomes. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate the end-point force trajectories of the fibularis longus (FIB), lateral gastrocnemius (LG), and medial gastrocnemius (MG) muscles. Most information about individual muscle function has come from studies that use models based on electromyographic (EMG) recordings. In this study (N = 20 subjects) we used electrical stimulation (20 Hz) to elicit activity in individual muscles, recorded the end-point forces at the foot, and verified the selectivity of stimulation by using magnetic resonance imaging. Unexpectedly, no significant differences were found between LG and MG force directions. Stimulation of LG and MG resulted in downward and medial or lateral forces depending on the subject. We found FIB end-point forces to be significantly different from those of LG and MG. In all subjects, stimulation of FIB resulted in downward and lateral forces. Based on our results, we suggest that there are multiple factors determining when and whether LG or MG will produce a medial or lateral force and FIB consistently plays a significant role in eversion/ abduction and plantar flexion. We suggest that the intersubject variability we found is not simply an artifact of experimental or technical error but is functionally relevant and should be addressed in future studies and models. [ABSTRACT FROM AUTHOR]

Investigations of between‐person variability are enjoying a recent resurgence in functional magnetic resonance imaging (fMRI) research. Several recent studies have found persistent between‐person differences in blood‐oxygenated‐level dependent (BOLD) activation patterns and resting‐state functional connectivity. Conflicting findings have been reported regarding the extent to which (a) between‐person or (b) within‐person cognitive state differences explain differences in BOLD activation patterns. These discrepancies may arise due to statistical analysis choices, parcellation resolution, and limited sampling of task‐fMRI datasets. We attempt to address these issues in a large‐scale analysis of several task‐fMRI paradigms. Using a novel application of multivariate distance matrix regression, we examine between‐person and task‐condition variability estimates across varying levels of "resolution", from a coarse region‐of‐interest level to the vertex‐level, and across different distance metrics. These analyses revealed that under most circumstances, differences in task conditions explained a greater amount of variance in activation map differences than between‐person differences. However, this finding was reversed when comparing activation maps at a "high‐resolution" vertex level. More generally, we observed that when moving from "low" to "high" resolutions, the variance explained by between‐person differences increased while variance explained by task conditions decreased. We further analyzed the relationships among subject‐level activation maps across all task‐conditions using an unsupervised clustering approach and identified a superordinate task structure. This structure went beyond conventional task labels and highlighted those experimental manipulations across task conditions that produce contrasting versus similar whole‐brain activation patterns. Overall, these analyses suggest that the question of the subject‐ versus task‐effects on BOLD activation patterns is nontrivial, and depends on the comparison "resolution," choice of distance metric, and the coding of task‐conditions. [ABSTRACT FROM AUTHOR]

Neural rhythms or oscillations are ubiquitous in neuroimaging data. These spectral responses have been linked to several cognitive processes; including working memory, attention, perceptual binding and neuronal coordination. In this paper, we show how Bayesian methods can be used to finesse the ill-posed problem of reconstructing—and explaining—oscillatory responses. We offer an overview of recent developments in this field, focusing on (i) the use of MEG data and Empirical Bayes to build hierarchical models for group analyses—and the identification of important sources of inter-subject variability and (ii) the construction of novel dynamic causal models of intralaminar recordings to explain layer-specific activity. We hope to show that electrophysiological measurements contain much more spatial information than is often thought: on the one hand, the dynamic causal modelling of non-invasive (low spatial resolution) electrophysiology can afford sub-millimetre (hyper-acute) resolution that is limited only by the (spatial) complexity of the underlying (dynamic causal) forward model. On the other hand, invasive microelectrode recordings (that penetrate different cortical layers) can reveal laminar-specific responses and elucidate hierarchical message passing and information processing within and between cortical regions at a macroscopic scale. In short, the careful and biophysically grounded modelling of sparse data enables one to characterise the neuronal architectures generating oscillations in a remarkable detail. [ABSTRACT FROM AUTHOR]

Purpose: For ultrahigh field (UHF) MRI, the expected local specific absorption rate (SAR) distribution is usually calculated by numerical simulations using a limited number of generic body models and adding a safety margin to take into account intersubject variability. Assessment of this variability with a large model database would be desirable. In this study, a procedure to create such a database with accurate subject‐specific models is presented. Using 23 models, intersubject variability is investigated for prostate imaging at 7T with an 8‐channel fractionated dipole antenna array with 16 receive loops. Method: From Dixon images of a volunteer acquired at 1.5T with a mockup array in place, an accurate dielectric model is built. Following this procedure, 23 subject‐specific models for local SAR assessment at 7T were created enabling an extensive analysis of the intersubject B1+ and peak local SAR variability. Results: For the investigated setup, the maximum possible peak local SAR ranges from 2.6 to 4.6 W/kg for 8 × 1 W input power. The expected peak local SAR values represent a Gaussian distribution (μ/σ=2.29/0.29 W/kg) with realistic prostate‐shimmed phase settings and a gamma distribution Γ(24,0.09) with multidimensional radiofrequency pulses. Prostate‐shimmed phase settings are similar for all models. Using 1 generic phase setting, average B1+ reduction is 7%. Using only 1 model, the required safety margin for intersubject variability is 1.6 to 1.8. Conclusion: The presented procedure allows for the creation of a customized model database. The results provide valuable insights into B1+ and local SAR variability. Recommended power thresholds per channel are 3.1 W with phase shimming on prostate or 2.6 W for multidimensional pulses. [ABSTRACT FROM AUTHOR]

Introduction: Group information-guided independent component analysis (GIG-ICA) and independent vector analysis (IVA) are two methods that improve estimation of subject-specific independent components in neuroimaging studies. These methods have shown better performance than traditional group independent component analysis (GICA) with respect to intersubject variability (ISV)., Methods: In this study, we compared the patterns of community structure, spatial variance, and prediction performance of GIG-ICA and IVA-GL, respectively. The dataset was obtained from the publicly available Autism Brain Imaging Data Exchange (ABIDE) database, comprising 75 healthy controls (HC) and 102 Autism Spectrum Disorder (ASD) participants. The greedy rule was used to match components from IVA-GL and GIG-ICA in order to compare the similarities between the two methods., Results: Robust correspondence was observed between the two methods the following networks: cerebellum network (CRN; | r | = 0.7813), default mode network (DMN; | r | = 0.7263), self-reference network (SRN; | r | = 0.7818), ventral attention network (VAN; | r | = 0.7574), and visual network (VSN; | r | = 0.7503). Additionally, the Sensorimotor Network demonstrated the highest similarity between IVA-GL and GIG-ICA (SOM: | r | = 0.8125). Our findings revealed a significant difference in the number of modules identified by the two methods (HC: p < 0.001; ASD: p < 0.001). GIG-ICA identified significant differences in FNC between HC and ASD compared to IVA-GL. However, in correlation analysis, IVA-GL identified a statistically negative correlation between FNC of ASD and the social total subscore of the classic Autism Diagnostic Observation Schedule (ADOS: pi = -0.26, p  = 0.0489). Moreover, both methods demonstrated similar prediction performances on age within specific networks, as indicated by GIG-ICA-CRN ( R 2  = 0.91, RMSE = 3.05) and IVA-VAN ( R 2  = 0.87, RMSE = 3.21)., Conclusion: In summary, IVA-GL demonstrated lower modularity, suggesting greater sensitivity in estimating networks with higher intersubject variability. The improved age prediction of cerebellar-attention networks underscores their importance in the developmental progression of ASD. Overall, IVA-GL may be appropriate for investigating disorders with greater variability, while GIG-ICA identifies functional networks with distinct modularity patterns., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Jing, Klugah-Brown, Xia, Sheng and Biswal.)

Electroencephalography (EEG) captures electrophysiological signatures of cortical events from the scalp with high-dimensional electrode montages. Usually, excessive sources produce outliers and potentially affect the actual event related sources. Besides, EEG manifests inherent inter-subject variability of the brain dynamics, at the resting state and/or under the performance of task(s), caused probably due to the instantaneous fluctuation of psychophysiological states. A wavelet coherence (WC) analysis for optimally selecting associative inter-subject channels is proposed here and is being used to boost performances of motor imagery (MI)-based inter-subject brain computer interface (BCI). The underlying hypothesis is that optimally associative inter-subject channels can reduce the effects of outliers and, thus, eliminate dissimilar cortical patterns. The proposed approach has been tested on the dataset IVa from BCI competition III, including EEG data acquired from five healthy subjects who were given visual cues to perform 280 trials of MI for the right hand and right foot. Experimental results have shown increased classification accuracy (81.79%) using the WC-based selected 16 channels compared to the one (56.79%) achieved using all the available 118 channels. The associative channels lie mostly around the sensorimotor regions of the brain, reinforced by the previous literature, describing spatial brain dynamics during sensorimotor oscillations. Apparently, the proposed approach paves the way for optimised EEG channel selection that could boost further the efficiency and real-time performance of BCI systems.

Purpose Specific absorption rate (SAR) calculations in parallel transmission are commonly performed by using electromagnetic simulations on generic models. In this study, we propose a probabilistic analysis to study the safety factor employed to account for SAR intersubject variability versus risk relationship in head imaging at 7T. Methods Thirty-three finite-element electromagnetic simulations were conducted to sample the four-dimensional parameter space consisting of the head length, head breadth, and shifts in Z and Y random variables. Based on the SAR matrices for each configuration, a multivariate second-order polynomial of the SAR versus the different parameters was reconstructed for different types of radiofrequency pulses. A Monte Carlo calculation was then performed to compute the probability of occurrence of a given SAR value. Results By testing a large number of radiofrequency excitation pulses, the SAR calculated for the average model amplified by a safety margin of 1.5 was found to return a probability of less than 1% to be exceeded across the adult Caucasian population given the investigated parameters. Conclusion The proposed method to study SAR intersubject variability uses a reasonable number of electromagnetic simulations. Look-ahead SAR safety margins can be deduced based on risk/benefit ratio assessments. Magn Reson Med 78:1217-1223, 2017. © 2016 International Society for Magnetic Resonance in Medicine. [ABSTRACT FROM AUTHOR]

Human chronotypes (differences in preference for early or late rising each day) have been extensively studied in recent years, but no attempt has been made to compare human chronotypes with the chronotypes of other animal species. We evaluated behavioral chronotypes in 16 mammalian species along a body size gradient of five orders of magnitude (from mice to cattle). Individuals of all species were studied under a 12 L:12D photoperiod in a thermoneutral environment with food and water available at all times. Rhythms of locomotor activity were analyzed for onset time, acrophase, and robustness. Neither of these rhythmic parameters was significantly related to body size, but onset time and acrophase varied considerably from species to species, thus characterizing diurnal and nocturnal species. Chronotype spreads ranged from less than an hour in sheep to almost 24 h in cats, thus extending both below and above the human chronotype spread of 6 h. The variability of chronotype (as quantified by the standard deviation of group means) was much larger between species than within species and also larger between individuals of a species than within individuals on consecutive days. These results help situate the matter of human chronotypes within the broader context of variability in the phase angle of entrainment of circadian rhythms in animals. [ABSTRACT FROM AUTHOR]

The optimal control scheme for powered prostheses can be determined using simulation experiments, for which an accurate model of prosthesis control is essential. This paper focuses on electromyographic (EMG) control signal characteristics across two different control schemes. We constructed a functional EMG model comprising three EMG signal characteristics-standard deviation, kurtosis, and median power frequency-using data collected under realistic conditions for prosthesis control (closed-loop, dynamic, anisometric contractions). We examined how the model changed when subjects used zero-order or first-order control. Control order had a statistically significant effect on EMG characteristics, but the effect size was small and generally did not exceed inter-subject variability. Therefore, we suggest that this functional EMG model remains valid across different control schemes. [ABSTRACT FROM AUTHOR]

Infancy is a period featuring a high level of intersubject variability but the brain basis for such variability and the potential genetic/ environmental contributions remain largely unexplored. The assessment of the brain's functional connectivity during infancy by the resting state functional magnetic resonance imaging (rsfMRI) technique (Biswal et al., 1995) provides a unique means to probe the brain basis of intersubject variability during infancy. In this study, an unusually large typically developing human infant sample including 58 singletons, 132 dizygotic twins, and 98 monozygotic twins with rsfMRI scans during the first 2 years of life was recruited to delineate the spatial and temporal developmental patterns of both the intersubject variability of and genetic effects on the brain's functional connectivity. Through systematic voxelwise functional connectivity analyses, our results revealed that the intersubject variability at birth features lower variability in primary functional areas but higher values in association areas. Although the relative pattern remains largely consistent, the magnitude of intersubject variability undergoes an interesting U-shaped growth during the first 2 years of life. Overall, the intersubject variability patterns during infancy show both adult-like and infant-specific characteristics (Mueller et al., 2013). On the other hand, age-dependent genetic effects were observed showing significant but bidirectional relationships with intersubject variability. The temporal and spatial patterns of the intersubject variability of and genetic contributions to the brain's functional connectivity documented in this study shed light on the largely uncharted functional development of the brain during infancy. [ABSTRACT FROM AUTHOR]

Motion smoothness is critical in transmitting implicit information of body action, such as aesthetic qualities in dance performances. We expected that the perception of motion smoothness would be characterized by great intersubject variability deriving from differences in personal backgrounds and attitudes toward expressive body actions. We used functional magnetic resonance imaging and a humanoid robot to investigate the effects of the motion smoothness of expressive body actions and the intersubject variability due to personal attitudes on perceptions during dance observation. The effect of motion smoothness was analyzed by both conventional subtraction analysis and functional connectivity analyses that detect cortical networks reflecting intersubject variability. The results showed that the cortical networks of motion- and body-sensitive visual areas showed increases in activity in areas corresponding with motion smoothness, but the intersubject variability of personal attitudes toward art did not influence these active areas. In contrast, activation of cortical networks, including the parieto-frontal network, has large intersubject variability, and this variability is associated with personal attitudes about the consciousness of art. Thus, our results suggest that activity in the cortical network involved in understanding action is influenced by personal attitudes about the consciousness of art during observations of expressive body actions. [ABSTRACT FROM AUTHOR]

It has been revealed that intersubject variability (ISV) in intrinsic functional connectivity (FC) is associated with a wide variety of cognitive and behavioral performances. However, the underlying organizational principle of ISV in FC and its related gene transcriptional profiles remain unclear. Using resting-state fMRI data from the Human Connectome Project (299 adult participants) and microarray gene expression data from the Allen Human Brain Atlas, we conducted a transcription-neuroimaging association study to investigate the spatial configurations of ISV in intrinsic FC and their associations with spatial gene transcriptional profiles. We found that the multimodal association cortices showed the greatest ISV in FC, while the unimodal cortices and subcortical areas showed the least ISV. Importantly, partial least squares regression analysis revealed that the transcriptional profiles of genes associated with human accelerated regions (HARs) could explain 31.29% of the variation in the spatial distribution of ISV in FC. The top-related genes in the transcriptional profiles were enriched for the development of the central nervous system, neurogenesis and the cellular components of synapse. Moreover, we observed that the effect of gene expression profile on the heterogeneous distribution of ISV in FC was significantly mediated by the cerebral blood flow configuration. These findings highlighted the spatial arrangement of ISV in FC and their coupling with variations in transcriptional profiles and cerebral blood flow supply. [ABSTRACT FROM AUTHOR]

Recently, a new cytoarchitectonic map of the human inferior parietal lobule (IPL) has been proposed, with the IPL consisting of seven cytoarchitectonically distinct areas (Caspers et al. in Neuroimage 33(2):430–448, ). The aim of the present study was to investigate the different aspects of variability of these IPL areas. As one aspect of variability, we analysed the topographical relationship between the localisation of the borders of the areas and macroanatomical landmarks. Although five areas occupy the surface supramarginal gyrus and two the angular gyrus, their borders cannot be reliably detected by means of macroanatomy. To account for variability in size and extent of the areas in stereotaxic space, cytoarchitectonic probabilistic maps have been calculated for each IPL area. Hemisphere- and gender-related differences have been investigated on basis of volumes of cytoarchitectonic areas. For one of them, area PFcm, a significant gender difference in volume was found with males having larger volumes than females; this difference exceeds that of gender differences in total brain volume. The different aspects of variability and volumetric asymmetry may underlie some of the well-known functional asymmetries in the IPL, observed, for example during fMRI experiments analysing spatial attention or motor attention, and planning. The cytoarchitectonic probabilistic maps of the seven IPL areas provide a robust anatomical reference and open new perspectives for further structure–function investigations of the human IPL. [ABSTRACT FROM AUTHOR]

In this paper, we present a statistical parts-based model (PBM) of appearance, applied to the problem of modeling intersubject anatomical variability in magnetic resonance (MR) brain images. In contrast to global image models such as the active appearance model (AAM), the PBM consists of a collection of localized image regions, referred to as parts, whose appearance, geometry and occurrence frequency are quantified statistically. The parts-based approach explicitly addresses the case where one-to-one correspondence does not exist between all subjects in a population due to anatomical differences, as model parts are not required to appear in all subjects. The model is constructed through a fully automatic machine learning algorithm, identifying image patterns that appear with statistical regularity in a large collection of subject images. Parts are represented by generic scale-invariant features, and the model can, therefore, be applied to a wide variety of image domains. Experimentation based on 2-D MR slices shows that a PBM learned from a set of 102 subjects can be robustly fit to 50 new subjects with accuracy comparable to 3 human raters. Additionally, it is shown that unlike global models such as the AAM, PBM fitting is stable in the presence of unexpected, local perturbation. [ABSTRACT FROM AUTHOR]

The regional absorption of lobucavir (LBV), an experimental antiviral agent, and ganciclovir (DHPG) was investigated in rabbit intestine using an in situ single-pass perfusion technique. Duodenal, jejunal, and colonic segments in anesthetized rabbits were perfused with drug solutions in a hypotonic buffer at 0.2 mL/min. Effluent perfusate samples for drug analysis were collected every 10 min for 180 min. To account for water absorption during perfusion, an intestinal absorption model was developed to estimate the absorptive clearance (PeA): PeA = Qave × ln((Qin × Cin)/(Qout × Cout)), where Qave is a logarithmic average of the inflow (Qin) and outflow perfusion rate (Qout); Cin and Cout are drug inflow and outflow concentrations. The PeA of LBV in the duodenum and jejunum was 2.1 ± 0.77 and 1.7 ± 0.46 µL/min/cm (n = 3), respectively, 4.8- and 3.0-fold higher than that of DHPG in the same animals. However, LBV PeA decreased significantly in the colon (0.47 ± 0.11 µL/min/cm) and was similar to that of DHPG which exhibited no regional differences in absorption. The interplay between PeA and solubility was studied using a compartmental absorption and transit model, and simulations were performed to investigate dose-limited absorption and the sources of variability in absorption where two compounds differ significantly. The dose range where absorption started to decrease was predicted using the model, with LBV exhibiting the phenomenon at a lower dose than DHPG (450 vs. 750 mg). Furthermore, the intersubject variability in human absorption of both compounds was reproduced when the variability in both PeA and the small intestinal transit time was considered in the model. The variability in the ascending colonic transit time also contributed to the intersubject variability observed for DHPG. The results demonstrate value of integrating in situ studies and modeling in predicting these absorption characteristics. © 2006 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 95:2276–2292, 2006 [ABSTRACT FROM AUTHOR]