Explore the vast range of titles available.

Functional connectomics is one of the most rapidly expanding areas of neuroimaging research. Yet, concerns remain regarding the use of resting-state fMRI (R-fMRI) to characterize inter-individual variation in the functional connectome. In particular, recent findings that “micro” head movements can introduce artifactual inter-individual and group-related differences in R-fMRI metrics have raised concerns. Here, we first build on prior demonstrations of regional variation in the magnitude of framewise displacements associated with a given head movement, by providing a comprehensive voxel-based examination of the impact of motion on the BOLD signal (i.e., motion–BOLD relationships). Positive motion–BOLD relationships were detected in primary and supplementary motor areas, particularly in low motion datasets. Negative motion–BOLD relationships were most prominent in prefrontal regions, and expanded throughout the brain in high motion datasets (e.g., children). Scrubbing of volumes with FD>0.2 effectively removed negative but not positive correlations; these findings suggest that positive relationships may reflect neural origins of motion while negative relationships are likely to originate from motion artifact. We also examined the ability of motion correction strategies to eliminate artifactual differences related to motion among individuals and between groups for a broad array of voxel-wise R-fMRI metrics. Residual relationships between motion and the examined R-fMRI metrics remained for all correction approaches, underscoring the need to covary motion effects at the group-level. Notably, global signal regression reduced relationships between motion and inter-individual differences in correlation-based R-fMRI metrics; Z-standardization (mean-centering and variance normalization) of subject-level maps for R-fMRI metrics prior to group-level analyses demonstrated similar advantages. Finally, our test–retest (TRT) analyses revealed significant motion effects on TRT reliability for R-fMRI metrics. Generally, motion compromised reliability of R-fMRI metrics, with the exception of those based on frequency characteristics — particularly, amplitude of low frequency fluctuations (ALFF). The implications of our findings for decision-making regarding the assessment and correction of motion are discussed, as are insights into potential differences among volume-based metrics of motion. •Positive but not negative motion-BOLD relationships appear to be neural in origin.•Motion should always be accounted for in group-level analyses.•Global signal regression and Z-standardization mitigate motion effects.•Motion compromises test-retest reliability, and correction strategies improve.

As researchers increase their efforts to characterize variations in the functional connectome across studies and individuals, concerns about the many sources of nuisance variation present and their impact on resting state fMRI (R-fMRI) measures continue to grow. Although substantial within-site variation can exist, efforts to aggregate data across multiple sites such as the 1000 Functional Connectomes Project (FCP) and International Neuroimaging Data-sharing Initiative (INDI) datasets amplify these concerns. The present work draws upon standardization approaches commonly used in the microarray gene expression literature, and to a lesser extent recent imaging studies, and compares them with respect to their impact on relationships between common R-fMRI measures and nuisance variables (e.g., imaging site, motion), as well as phenotypic variables of interest (age, sex). Standardization approaches differed with regard to whether they were applied post-hoc vs. during pre-processing, and at the individual vs. group level; additionally they varied in whether they addressed additive effects vs. additive+multiplicative effects, and were parametric vs. non-parametric. While all standardization approaches were effective at reducing undesirable relationships with nuisance variables, post-hoc approaches were generally more effective than global signal regression (GSR). Across approaches, correction for additive effects (global mean) appeared to be more important than for multiplicative effects (global SD) for all R-fMRI measures, with the exception of amplitude of low frequency fluctuations (ALFF). Group-level post-hoc standardizations for mean-centering and variance-standardization were found to be advantageous in their ability to avoid the introduction of artifactual relationships with standardization parameters; though results between individual and group-level post-hoc approaches were highly similar overall. While post-hoc standardization procedures drastically increased test–retest (TRT) reliability for ALFF, modest reductions were observed for other measures after post-hoc standardizations—a phenomena likely attributable to the separation of voxel-wise from global differences among subjects (global mean and SD demonstrated moderate TRT reliability for these measures). Finally, the present work calls into question previous observations of increased anatomical specificity for GSR over mean centering, and draws attention to the near equivalence of global and gray matter signal regression. •Global mean and SD for R-fMRI measures showed strong site, motion and age effects.•Post-hoc standardizations were more effective in reducing nuisance effects than GSR.•Correction for additive effects is more important than for multiplicative effects.•Group-level standardizations are advantageous to individual-level standardizations.

Functional connectivity analyses of resting-state fMRI data are rapidly emerging as highly efficient and powerful tools for in vivo mapping of functional networks in the brain, referred to as intrinsic connectivity networks (ICNs). Despite a burgeoning literature, researchers continue to struggle with the challenge of defining computationally efficient and reliable approaches for identifying and characterizing ICNs. Independent component analysis (ICA) has emerged as a powerful tool for exploring ICNs in both healthy and clinical populations. In particular, temporal concatenation group ICA (TC-GICA) coupled with a back-reconstruction step produces participant-level resting state functional connectivity maps for each group-level component. The present work systematically evaluated the test–retest reliability of TC-GICA derived RSFC measures over the short-term (<45 min) and long-term (5–16 months). Additionally, to investigate the degree to which the components revealed by TC-GICA are detectable via single-session ICA, we investigated the reproducibility of TC-GICA findings. First, we found moderate-to-high short- and long-term test–retest reliability for ICNs derived by combining TC-GICA and dual regression. Exceptions to this finding were limited to physiological- and imaging-related artifacts. Second, our reproducibility analyses revealed notable limitations for template matching procedures to accurately detect TC-GICA based components at the individual scan level. Third, we found that TC-GICA component's reliability and reproducibility ranks are highly consistent. In summary, TC-GICA combined with dual regression is an effective and reliable approach to exploratory analyses of resting state fMRI data.

Characterizing the brain connectome using neuroimaging data and measures derived from graph theory emerged as a new approach that has been applied to brain maturation, cognitive function and neuropsychiatric disorders. For a broad application of this method especially for clinical populations and longitudinal studies, the reliability of this approach and its robustness to confounding factors need to be explored. Here we investigated test–retest reliability of graph metrics of functional networks derived from functional magnetic resonance imaging (fMRI) recorded in 33 healthy subjects during rest. We constructed undirected networks based on the Anatomic-Automatic-Labeling (AAL) atlas template and calculated several commonly used measures from the field of graph theory, focusing on the influence of different strategies for confound correction. For each subject, method and session we computed the following graph metrics: clustering coefficient, characteristic path length, local and global efficiency, assortativity, modularity, hierarchy and the small-worldness scalar. Reliability of each graph metric was assessed using the intraclass correlation coefficient (ICC). Overall ICCs ranged from low to high (0 to 0.763) depending on the method and metric. Methodologically, the use of a broader frequency band (0.008–0.15Hz) yielded highest reliability indices (mean ICC=0.484), followed by the use of global regression (mean ICC=0.399). In general, the second order metrics (small-worldness, hierarchy, assortativity) studied here, tended to be more robust than first order metrics. In conclusion, our study provides methodological recommendations which allow the computation of sufficiently robust markers of network organization using graph metrics derived from fMRI data at rest. ► Test–retest reliability of graph metrics derived from resting-state fMRI. ► Different preprocessing and confound correction methods are examined. ► Moderate overall reliability, but differed between methods. ► Using a broad filter frequency range (0.008–0.15Hz) yielded best results.

The Tower of London - Freiburg version (TOL-F) was developed in three parallel-test versions (A, B, and C) that only differ in their physical appearance by interchanged ball colors, but not in their cognitive demands. We addressed the question whether the test-retest reliability of an identical problem set differs from the parallel test-retest reliability of a structurally identical problem set with a marginally different physical appearance. Reliabilities were assessed in two samples of young adults over a 1-week interval: In the parallel test-retest sample ( = 93; 49 female), half of the participants accomplished version A at the first session and version B at the second session, while the other half started with version B in the first session and continued with A in the second session. In the identical test-retest sample ( = 86; 48 female), half of the participants performed on version A in both the first and the second session, while the other half went through the same procedure with version B. For overall planning accuracy, intraclass correlation coefficients for absolute agreement were = .501 for the parallel test-retest and = .605 for the identical test-retest sample, with Pearson correlations of = .559 and = .708 respectively. Greatest lower bound estimates of reliability were adequate to high in the two samples (ranging between .765 and .854) confirming previous studies. Although the TOL-F revealed only moderate intraclass correlations for absolute agreement, it showed some of the highest psychometric indices compared to repeated assessments with other TOL tests.

As large language models (LLMs) continue to gain popularity due to their human-like traits and the intimacy they offer to users, their societal impact inevitably expands. This leads to the rising necessity for comprehensive studies to fully understand LLMs and reveal their potential opportunities, drawbacks and overall societal impact. With that in mind, this research conducted an extensive investigation into seven LLMs, aiming to assess the temporal stability and inter-rater agreement on their responses on personality instruments in two time points. In addition, LLMs’ personality profile was analysed and compared with human normative data. The findings revealed varying levels of inter-rater agreement in the LLMs’ responses over a short time, with some LLMs showing higher agreement (e.g. Llama3 and GPT-4o) compared with others (e.g. GPT-4 and Gemini). Furthermore, agreement depended on used instruments as well as on domain or trait. This implies the variable robustness in LLMs’ ability to reliably simulate stable personality characteristics. In the case of scales which showed at least fair agreement, LLMs displayed mostly a socially desirable profile in both agentic and communal domains, as well as a prosocial personality profile reflected in higher agreeableness and conscientiousness and lower Machiavellianism. Exhibiting temporal stability and coherent responses on personality traits is crucial for AI systems due to their societal impact and AI safety concerns.

Background Kinematic analysis of the 3D reach-to-grasp drinking task is recommended in stroke rehabilitation research. The number of trials required to reach performance stability, as an important aspect of reliability, has not been investigated for this task. Thus, the aims of this study were to determine the number of trials needed for the drinking task to reach within-session performance stability and to investigate trends in performance over a set of trials in non-disabled people and in a sample of individuals with chronic stroke. In addition, the between-sessions test–retest reliability in persons with stroke was established. Methods The drinking task was performed at least 10 times, following a standardized protocol, in 44 non-disabled and 8 post-stroke individuals. A marker-based motion capture system registered arm and trunk movements during 5 pre-defined phases of the drinking task. Intra class correlation statistics were used to determine the number of trials needed to reach performance stability as well as to establish test–retest reliability. Systematic within-session trends over multiple trials were analyzed with a paired t-test. Results For most of the kinematic variables 2 to 3 trials were needed to reach good performance stability in both investigated groups. More trials were needed for movement times in reaching and returning phase, movement smoothness, time to peak velocity and inter-joint-coordination. A small but significant trend of improvement in movement time over multiple trials was demonstrated in the non-disabled group, but not in the stroke group. A mean of 3 trials was sufficient to reach good to excellent test–retest reliability for most of the kinematic variables in the stroke sample. Conclusions This is the first study that determines the number of trials needed for good performance stability (non-disabled and stroke) and test–retest reliability (stroke) for temporal, endpoint and angular metrics of the drinking task. For most kinematic variables, 3–5 trials are sufficient to reach good reliability. This knowledge can be used to guide future kinematic studies.

The maximal Lyapunov exponent (MLE) has been used to quantify the dynamic stability of human locomotion. The method for estimating MLE requires selecting a proper time series of kinematic variables and reconstructing phase space using proper time delay. The data length also affects the reliability of the measured MLE. However, there has been no criterion for the choice of the time series, time delay or data length. Here, we quantified the effect of these factors on the test–retest reliability of MLE estimations. We recruited 15 young and healthy adults and let them walk on a treadmill three times. We calculated MLE employing various lengths of time series of 18 frequently used kinematic variables and two typical choices of time delay: fixed delay and delay selected by average mutual information algorithm. Then, we measured the intraclass correlation coefficient (ICC) of the measured MLE under each condition. Our results show that the choice of time delay does not affect reliability. Five among the 18 kinematic variables enabled excellent reliability with ICC above 0.9 within 450 strides and also enabled ICC above 0.75 even with 60 or less strides. These findings can contribute to establishing the criteria for measuring the dynamic stability of human walking.

Stability of oscillatory signatures across magnetoencephalography (MEG) measurements is an important prerequisite for basic and clinical research that has been insufficiently addressed. Here, we evaluated the test–retest reliability of auditory steady-state responses (ASSRs) over two MEG sessions. The study required participants (N=13) to detect the rare occurrence of pure tones interspersed within a stream of 5Hz or 40Hz amplitude-modulated (AM) tones. Intraclass correlations (ICC; Shrout and Fleiss, 1979) were derived to assess stability of spectral power changes and the inter-trial phase coherence (ITPC) of task-elicited neural responses. ASSRs source activity was estimated using eLORETA beamforming from bilateral auditory cortex. ASSRs to 40Hz AM stimuli evoked stronger power modulation and phase-locking than 5Hz stimulation. Overall, spectral power and ITPC values at both sensor- and source-level showed robust ICC values. Notably, ITPC measures yielded higher ICCs (~0.86–0.96) between sessions compared to the assessment of spectral power change (~0.61–0.82). Our data indicate that spectral modulations and phase consistency of ASSRs in MEG data are highly reproducible, providing support for MEG-measured oscillatory parameters in basic and clinical research. •Spectral and phase-locking measures of ASSR to 5Hz and 40Hz AM tones are reliable.•Relative to spectral measures of ASSR, intraclass correlations derived from ITPC are more robust.•Sensor- and source-derived ITPC measures of ASSR show comparably high reliability.

Two studies examined the psychometric properties of the Theory of Mind Inventory (ToMI). In Study One, 135 caregivers completed the ToMI for children (ages 3 through 17) with autism spectrum disorder (ASD). Findings revealed excellent test–retest reliability and internal consistency. Principle Components Analysis revealed three subscales related to the complexity of ToM understanding. In Study Two, data were collected for 124 typically developing children (2 through 12 years). Findings again revealed excellent test–retest and internal consistency. The ToMI distinguished groups by age (younger vs. older children) and developmental status (typically developing vs. ASD), and predicted child performance on a ToM task battery. Utility of the ToMI, study limitations and directions for future research are discussed.