A decade of test-retest reliability of functional connectivity: A systematic review and meta-analysis

Pre-acquired Functional Connectivity Predicts Choice Inconsistency

Economic choice theories usually assume that humans maximize utility in their choices. However, studies have shown that humans make inconsistent choices, leading to suboptimal behavior, even without context-dependent manipulations. Previous studies showed that activation in value and motor networks are associated with inconsistent choices at the moment of choice. Here, we investigated if the neural predispositions, measured before a choice task, can predict choice inconsistency in a later risky choice task. Using functional connectivity (FC) measures from resting-state functional magnetic resonance imaging (rsfMRI), derived before any choice was made, we aimed to predict subjects' inconsistency levels in a later-performed choice task. We hypothesized that rsfMRI FC measures extracted from value and motor brain areas would predict inconsistency. Forty subjects (21 females) completed a rsfMRI scan before performing a risky choice task. We compared models that were trained on FC that included only hypothesized value and motor regions with models trained on whole-brain FC. We found that both model types significantly predicted inconsistency levels. Moreover, even the whole-brain models relied mostly on FC between value and motor areas. For external validation, we used a neural network pretrained on FC matrices of 37,000 subjects and fine-tuned it on our data and again showed significant predictions. Together, this shows that the tendency for choice inconsistency is predicted by predispositions of the nervous system and that synchrony between the motor and value networks plays a crucial role in this tendency.

Effects of repetitive transcranial magnetic stimulation on individual variability of resting-state functional connectivity in major depressive disorder

BACKGROUND

Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for major depressive disorder (MDD), but substantial heterogeneity in outcomes remains. We examined a potential mechanism of action of rTMS to normalize individual variability in resting-state functional connectivity (rs-fc) before and after a course of treatment.

METHODS

Variability in rs-fc was examined in healthy controls (baseline) and individuals with MDD (baseline and after 4-6 weeks of rTMS). Seed-based connectivity was calculated to 4 regions associated with MDD: left dorsolateral prefrontal cortex (DLPFC), right subgenual anterior cingulate cortex (sgACC), bilateral insula, and bilateral precuneus. Individual variability was quantified for each region by calculating the mean correlational distance of connectivity maps relative to the healthy controls; a higher variability score indicated a more atypical/idiosyncratic connectivity pattern.

RESULTS

We included data from 66 healthy controls and 252 individuals with MDD in our analyses. Patients with MDD did not show significant differences in baseline variability of rs-fc compared with controls. Treatment with rTMS increased rs-fc variability from the right sgACC and precuneus, but the increased variability was not associated with clinical outcomes. Interestingly, higher baseline variability of the right sgACC was significantly associated with less clinical improvement (p = 0.037, uncorrected; did not survive false discovery rate correction).Limitations: The linear model was constructed separately for each region of interest.

CONCLUSION

This was, to our knowledge, the first study to examine individual variability of rs-fc related to rTMS in individuals with MDD. In contrast to our hypotheses, we found that rTMS increased the individual variability of rs-fc. Our results suggest that individual variability of the right sgACC and bilateral precuneus connectivity may be a potential mechanism of rTMS.

Structural-functional brain network coupling predicts human cognitive ability

Individual differences in general cognitive ability (GCA) have a biological basis within the structure and function of the human brain. Network neuroscience investigations revealed neural correlates of GCA in structural as well as in functional brain networks. However, whether the relationship between structural and functional networks, the structural-functional brain network coupling (SC-FC coupling), is related to individual differences in GCA remains an open question. We used data from 1030 adults of the Human Connectome Project, derived structural connectivity from diffusion weighted imaging, functional connectivity from resting-state fMRI, and assessed GCA as a latent g-factor from 12 cognitive tasks. Two similarity measures and six communication measures were used to model possible functional interactions arising from structural brain networks. SC-FC coupling was estimated as the degree to which these measures align with the actual functional connectivity, providing insights into different neural communication strategies. At the whole-brain level, higher GCA was associated with higher SC-FC coupling, but only when considering path transitivity as neural communication strategy. Taking region-specific variations in the SC-FC coupling strategy into account and differentiating between positive and negative associations with GCA, allows for prediction of individual cognitive ability scores in a cross-validated prediction framework (correlation between predicted and observed scores: r = 0.25, p < .001). The same model also predicts GCA scores in a completely independent sample (N = 567, r = 0.19, p < .001). Our results propose structural-functional brain network coupling as a neurobiological correlate of GCA and suggest brain region-specific coupling strategies as neural basis of efficient information processing predictive of cognitive ability.

Longitudinal regression of covariance matrix outcomes

In this study, a longitudinal regression model for covariance matrix outcomes is introduced. The proposal considers a multilevel generalized linear model for regressing covariance matrices on (time-varying) predictors. This model simultaneously identifies covariate-associated components from covariance matrices, estimates regression coefficients, and captures the within-subject variation in the covariance matrices. Optimal estimators are proposed for both low-dimensional and high-dimensional cases by maximizing the (approximated) hierarchical-likelihood function. These estimators are proved to be asymptotically consistent, where the proposed covariance matrix estimator is the most efficient under the low-dimensional case and achieves the uniformly minimum quadratic loss among all linear combinations of the identity matrix and the sample covariance matrix under the high-dimensional case. Through extensive simulation studies, the proposed approach achieves good performance in identifying the covariate-related components and estimating the model parameters. Applying to a longitudinal resting-state functional magnetic resonance imaging data set from the Alzheimer's Disease (AD) Neuroimaging Initiative, the proposed approach identifies brain networks that demonstrate the difference between males and females at different disease stages. The findings are in line with existing knowledge of AD and the method improves the statistical power over the analysis of cross-sectional data.

Effect of scanning duration and sample size on reliability in resting state fMRI dynamic causal modeling analysis

Despite its widespread use, resting-state functional magnetic resonance imaging (rsfMRI) has been criticized for low test-retest reliability. To improve reliability, researchers have recommended using extended scanning durations, increased sample size, and advanced brain connectivity techniques. However, longer scanning runs and larger sample sizes may come with practical challenges and burdens, especially in rare populations. Here we tested if an advanced brain connectivity technique, dynamic causal modeling (DCM), can improve reliability of fMRI effective connectivity (EC) metrics to acceptable levels without extremely long run durations or extremely large samples. Specifically, we employed DCM for EC analysis on rsfMRI data from the Human Connectome Project. To avoid bias, we assessed four distinct DCMs and gradually increased sample sizes in a randomized manner across ten permutations. We employed pseudo true positive and pseudo false positive rates to assess the efficacy of shorter run durations (3.6, 7.2, 10.8, 14.4 min) in replicating the outcomes of the longest scanning duration (28.8 min) when the sample size was fixed at the largest (n = 160 subjects). Similarly, we assessed the efficacy of smaller sample sizes (n = 10, 20, …, 150 subjects) in replicating the outcomes of the largest sample (n = 160 subjects) when the scanning duration was fixed at the longest (28.8 min). Our results revealed that the pseudo false positive rate was below 0.05 for all the analyses. After the scanning duration reached 10.8 min, which yielded a pseudo true positive rate of 92%, further extensions in run time showed no improvements in pseudo true positive rate. Expanding the sample size led to enhanced pseudo true positive rate outcomes, with a plateau at n = 70 subjects for the targeted top one-half of the largest ECs in the reference sample, regardless of whether the longest run duration (28.8 min) or the viable run duration (10.8 min) was employed. Encouragingly, smaller sample sizes exhibited pseudo true positive rates of approximately 80% for n = 20, and 90% for n = 40 subjects. These data suggest that advanced DCM analysis may be a viable option to attain reliable metrics of EC when larger sample sizes or run times are not feasible.

Connectome caricatures: removing large-amplitude co-activation patterns in resting-state fMRI emphasizes individual differences

High-amplitude co-activation patterns are sparsely present during resting-state fMRI but drive functional connectivity1-5. Further, they resemble task activation patterns and are well-studied3,5-10. However, little research has characterized the remaining majority of the resting-state signal. In this work, we introduced caricaturing-a method to project resting-state data to a subspace orthogonal to a manifold of co-activation patterns estimated from the task fMRI data. Projecting to this subspace removes linear combinations of these co-activation patterns from the resting-state data to create Caricatured connectomes. We used rich task data from the Human Connectome Project (HCP)11 and the UCLA Consortium for Neuropsychiatric Phenomics12 to construct a manifold of task co-activation patterns. Caricatured connectomes were created by projecting resting-state data from the HCP and the Yale Test-Retest13 datasets away from this manifold. Like caricatures, these connectomes emphasized individual differences by reducing between-individual similarity and increasing individual identification14. They also improved predictive modeling of brain-phenotype associations. As caricaturing removes group-relevant task variance, it is an initial attempt to remove task-like co-activations from rest. Therefore, our results suggest that there is a useful signal beyond the dominating co-activations that drive resting-state functional connectivity, which may better characterize the brain's intrinsic functional architecture.

Network Representation of fMRI Data Using Visibility Graphs: The Impact of Motion and Test-Retest Reliability

Visibility graphs provide a novel approach for analysing time-series data. Graph theoretical analysis of visibility graphs can provide new features for data mining applications in fMRI. However, visibility graphs features have not been used widely in the field of neuroscience. This is likely due to a lack of understanding of their robustness in the presence of noise (e.g., motion) and their test-retest reliability. In this study, we investigated visibility graph properties of fMRI data in the human connectome project (N = 1010) and tested their sensitivity to motion and test-retest reliability. We also characterised the strength of connectivity obtained using degree synchrony of visibility graphs. We found that strong correlation (r > 0.5) between visibility graph properties, such as the number of communities and average degrees, and motion in the fMRI data. The test-retest reliability (Intraclass correlation coefficient (ICC)) of graph theoretical features was high for the average degrees (0.74, 95% CI = [0.73, 0.75]), and moderate for clustering coefficient (0.43, 95% CI = [0.41, 0.44]) and average path length (0.41, 95% CI = [0.38, 0.44]). Functional connectivity between brain regions was measured by correlating the visibility graph degrees. However, the strength of correlation was found to be moderate to low (r < 0.35). These findings suggest that even small movement in fMRI data can strongly influence robustness and reliability of visibility graph features, thus, requiring robust motion correction strategies prior to data analysis. Further studies are necessary for better understanding of the potential application of visibility graph features in fMRI.

State-transition dynamics of resting-state functional magnetic resonance imaging data: model comparison and test-to-retest analysis

Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior. While microstate analysis has been widely employed to analyze EEG data, its use remains less prevalent in functional magnetic resonance imaging (fMRI) data, largely due to the slower timescale of such data. In the present study, we extend various data clustering methods used in EEG microstate analysis to resting-state fMRI data from healthy humans to extract their state-transition dynamics. We show that the quality of clustering is on par with that for various microstate analyses of EEG data. We then develop a method for examining test-retest reliability of the discrete-state transition dynamics between fMRI sessions and show that the within-participant test-retest reliability is higher than between-participant test-retest reliability for different indices of state-transition dynamics, different networks, and different data sets. This result suggests that state-transition dynamics analysis of fMRI data could discriminate between different individuals and is a promising tool for performing fingerprinting analysis of individuals.

Enhancing cognitive control with transcranial magnetic stimulation in subject-specific frontoparietal networks

BACKGROUND

Cognitive control processes, including those involving frontoparietal networks, are highly variable between individuals, posing challenges to basic and clinical sciences. While distinct frontoparietal networks have been associated with specific cognitive control functions such as switching, inhibition, and working memory updating functions, there have been few basic tests of the role of these networks at the individual level.

METHODS

To examine the role of cognitive control at the individual level, we conducted a within-subject excitatory transcranial magnetic stimulation (TMS) study in 19 healthy individuals that targeted intrinsic ("resting") frontoparietal networks. Person-specific intrinsic networks were identified with resting state functional magnetic resonance imaging scans to determine TMS targets. The participants performed three cognitive control tasks: an adapted Navon figure-ground task (requiring set switching), n-back (working memory), and Stroop color-word (inhibition).

OBJECTIVE

Hypothesis: We predicted that stimulating a network associated with externally oriented control [the "FPCN-B" (fronto-parietal control network)] would improve performance on the set switching and working memory task relative to a network associated with attention (the Dorsal Attention Network, DAN) and cranial vertex in a full within-subjects crossover design.

RESULTS

We found that set switching performance was enhanced by FPCN-B stimulation along with some evidence of enhancement in the higher-demand n-back conditions.

CONCLUSION

Higher task demands or proactive control might be a distinguishing role of the FPCN-B, and personalized intrinsic network targeting is feasible in TMS designs.

How do tasks impact the reliability of fMRI functional connectivity?

While there is growing interest in the use of functional magnetic resonance imaging-functional connectivity (fMRI-FC) for biomarker research, low measurement reliability of conventional acquisitions may limit applications. Factors known to impact FC reliability include scan length, head motion, signal properties, such as temporal signal-to-noise ratio (tSNR), and the acquisition state or task. As tasks impact signal in a region-wise fashion, they likely impact FC reliability differently across the brain, making task an important decision in study design. Here, we use the densely sampled Midnight Scan Club (MSC) dataset, comprising 5 h of rest and 6 h of task fMRI data in 10 healthy adults, to investigate regional effects of tasks on FC reliability. We further considered how BOLD signal properties contributing to tSNR, that is, temporal mean signal (tMean) and temporal standard deviation (tSD), vary across the brain, associate with FC reliability, and are modulated by tasks. We found that, relative to rest, tasks enhanced FC reliability and increased tSD for specific task-engaged regions. However, FC signal variability and reliability is broadly dampened during tasks outside task-engaged regions. From our analyses, we observed signal variability was the strongest driver of FC reliability. Overall, our findings suggest that the choice of task can have an important impact on reliability and should be considered in relation to maximizing reliability in networks of interest as part of study design.

Personalized connectivity-based network targeting model of transcranial magnetic stimulation for treatment of psychiatric disorders: computational feasibility and reproducibility

Repetitive transcranial magnetic stimulation (rTMS) holds promise for treating psychiatric disorders; however, the variability in treatment efficacy among individuals underscores the need for further improvement. Growing evidence has shown that TMS induces a broad network modulatory effect, and its effectiveness may rely on accurate modulation of the pathological network specific to each disorder. Therefore, determining the optimal TMS coil setting that will engage the functional pathway delivering the stimulation is crucial. Compared to group-averaged functional connectivity (FC), individual FC provides specific information about a person's brain functional architecture, offering the potential for more accurate network targeting for personalized TMS. However, the low signal-to-noise ratio (SNR) of FC poses a challenge when utilizing individual resting-state FC. To overcome this challenge, the proposed solutions include increasing the scan duration and employing the cluster method to enhance the stability of FC. This study aimed to evaluate the stability of a personalized FC-based network targeting model in individuals with major depressive disorder or schizophrenia with auditory verbal hallucinations. Using resting-state functional magnetic resonance imaging data from the Human Connectome Project, we assessed the model's stability. We employed longer scan durations and cluster methodologies to improve the precision in identifying optimal individual sites. Our findings demonstrate that a scan duration of 28 minutes and the utilization of the cluster method achieved stable identification of individual sites, as evidenced by the intraindividual distance falling below the ~1cm spatial resolution of TMS. The current model provides a feasible approach to obtaining stable personalized TMS targets from the scalp, offering a more accurate method of TMS targeting in clinical applications.

Longitudinal prognosis of Parkinson's outcomes using causal connectivity

Despite the prevalence of Parkinson's disease (PD), there are no clinically-accepted neuroimaging biomarkers to predict the trajectory of motor or cognitive decline or differentiate Parkinson's disease from atypical progressive parkinsonian diseases. Since abnormal connectivity in the motor circuit and basal ganglia have been previously shown as early markers of neurodegeneration, we hypothesize that patterns of interregional connectivity could be useful to form patient-specific predictive models of disease state and of PD progression. We use fMRI data from subjects with Multiple System Atrophy (MSA), Progressive Supranuclear Palsy (PSP), idiopathic PD, and healthy controls to construct predictive models for motor and cognitive decline and differentiate between the four subgroups. Further, we identify the specific connections most informative for progression and diagnosis. When predicting the one-year progression in the MDS-UPDRS-III1* and Montreal Cognitive assessment (MoCA), we achieve new state-of-the-art mean absolute error performance. Additionally, the balanced accuracy we achieve in the diagnosis of PD, MSA, PSP, versus healthy controls surpasses that attained in most clinics, underscoring the relevance of the brain connectivity features. Our models reveal the connectivity between deep nuclei, motor regions, and the thalamus as the most important for prediction. Collectively these results demonstrate the potential of fMRI connectivity as a prognostic biomarker for PD and increase our understanding of this disease.

Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions

Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.

Spinal fMRI demonstrates segmental organisation of functionally connected networks in the cervical spinal cord: A test-retest reliability study

Resting functional magnetic resonance imaging (fMRI) studies have identified intrinsic spinal cord activity, which forms organised motor (ventral) and sensory (dorsal) resting-state networks. However, to facilitate the use of spinal fMRI in, for example, clinical studies, it is crucial to first assess the reliability of the method, particularly given the unique anatomical, physiological, and methodological challenges associated with acquiring the data. Here, we characterise functional connectivity relationships in the cervical cord and assess their between-session test-retest reliability in 23 young healthy volunteers. Resting-state networks were estimated in two ways (1) by estimating seed-to-voxel connectivity maps and (2) by calculating seed-to-seed correlations. Seed regions corresponded to the four grey matter horns (ventral/dorsal and left/right) of C5-C8 segmental levels. Test-retest reliability was assessed using the intraclass correlation coefficient. Spatial overlap of clusters derived from seed-to-voxel analysis between sessions was examined using Dice coefficients. Following seed-to-voxel analysis, we observed distinct unilateral dorsal and ventral organisation of cervical spinal resting-state networks that was largely confined in the rostro-caudal extent to each spinal segmental level, with more sparse connections observed between segments. Additionally, strongest correlations were observed between within-segment ipsilateral dorsal-ventral connections, followed by within-segment dorso-dorsal and ventro-ventral connections. Test-retest reliability of these networks was mixed. Reliability was poor when assessed on a voxelwise level, with more promising indications of reliability when examining the average signal within clusters. Reliability of correlation strength between seeds was highly variable, with the highest reliability achieved in ipsilateral dorsal-ventral and dorso-dorsal/ventro-ventral connectivity. However, the spatial overlap of networks between sessions was excellent. We demonstrate that while test-retest reliability of cervical spinal resting-state networks is mixed, their spatial extent is similar across sessions, suggesting that these networks are characterised by a consistent spatial representation over time.

A multi-sample evaluation of the measurement structure and function of the modified monetary incentive delay task in adolescents

Interpreting the neural response elicited during task functional magnetic resonance imaging (fMRI) remains a challenge in neurodevelopmental research. The monetary incentive delay (MID) task is an fMRI reward processing task that is extensively used in the literature. However, modern psychometric tools have not been used to evaluate measurement properties of the MID task fMRI data. The current study uses data for a similar task design across three adolescent samples (N = 346 [Agemean 12.0; 44 % Female]; N = 97 [19.3; 58 %]; N = 112 [20.2; 38 %]) to evaluate multiple measurement properties of fMRI responses on the MID task. Confirmatory factor analysis (CFA) is used to evaluate an a priori theoretical model for the task and its measurement invariance across three samples. Exploratory factor analysis (EFA) is used to identify the data-driven measurement structure across the samples. CFA results suggest that the a priori model is a poor representation of these MID task fMRI data. Across the samples, the data-driven EFA models consistently identify a six-to-seven factor structure with run and bilateral brain region factors. This factor structure is moderately-to-highly congruent across the samples. Altogether, these findings demonstrate a need to evaluate theoretical frameworks for popular fMRI task designs to improve our understanding and interpretation of brain-behavior associations.

Psychometric Properties of the Turkish CardioToxicity Management Self-Efficacy Scale for Nurses

OBJECTIVES

This study was conducted to evaluate the validity and reliability of the CardioToxicity Management Self-Efficacy Scale (NSS-CTC) in Turkey.

METHOD

This methodological and descriptive study was undertaken with 204 oncology nurses. Information was gathered using a descriptive questionnaire and the NSS-CTC instrument. In the validity analysis of the scale, explanatory factor analysis and confirmatory factor analysis were used. In the reliability analysis, Cronbach α coefficient and Pearson correlation analysis were used to examine item-total score correlations, and Student t-test was used for test-retest analysis.

RESULTS

The scale, characterized by a two-dimensional structure confirmed through factor analysis, exhibited an explained variance rate of 60.44%. The factor loadings exceeded the threshold of 0.30, and all fitness indices surpassed the criterion of 0.90. Furthermore, the root-mean-square error of approximation (RMSEA) fell below 0.080 and demonstrated statistical significance. The scale demonstrated strong internal consistency, as indicated by the overall Cronbach α coefficient of 0.930, with its subdimensions exhibiting similarly high reliability, reflected in Cronbach α values of 0.871 and 0.912, respectively.

CONCLUSION

The NSS-CTS is a valid and reliable tool specifically developed for evaluating nurses' self-efficacy in the context of oncology wards, particularly in managing cardiotoxicity resulting from cancer treatments.

IMPLICATIONS FOR NURSING PRACTICE

This newly developed scale holds significant promise in gauging nurses' confidence levels when confronted with the intricacies of cardiotoxicity management. It responds to the growing imperative for nurses to continually enhance their knowledge and skills to effectively address the evolving challenges associated with cardiotoxicity in cancer care.

Lateralized Functional Connectivity of the Sensorimotor Cortex and its Variations During Complex Visuomotor Tasks

Previous studies have shown that the left hemisphere dominates motor function, often observed through homotopic activation measurements. Using a functional connectivity approach, this study investigated the lateralization of the sensorimotor cortex during handwriting and drawing, two complex visuomotor tasks with varying contextual demands. We found that both left- and right-lateralized connectivity in the primary motor cortex (M1), dorsal premotor cortex (PMd), somatosensory cortex, and visual regions were evident in adults (males and females), primarily in an interhemispheric integrative fashion. Critically, these lateralization tendencies remained highly invariant across task contexts, representing a task-invariant neural architecture for encoding fundamental motor programs consistently implemented in different task contexts. Additionally, the PMd exhibited a slight variation in lateralization degree between task contexts, reflecting the ability of the high-order motor system to adapt to varying task demands. However, connectivity-based lateralization of the sensorimotor cortex was not detected in 10-year-old children (males and females), suggesting that the maturation of connectivity-based lateralization requires prolonged development. In summary, this study demonstrates both task-invariant and task-sensitive connectivity lateralization in sensorimotor cortices that support the resilience and adaptability of skilled visuomotor performance. These findings align with the hierarchical organization of the motor system and underscore the significance of the functional connectivity-based approach in studying functional lateralization.

Measures of resting-state brain network segregation and integration vary in relation to data quantity: implications for within and between subject comparisons of functional brain network organization

Measures of functional brain network segregation and integration vary with an individual's age, cognitive ability, and health status. Based on these relationships, these measures are frequently examined to study and quantify large-scale patterns of network organization in both basic and applied research settings. However, there is limited information on the stability and reliability of the network measures as applied to functional time-series; these measurement properties are critical to understand if the measures are to be used for individualized characterization of brain networks. We examine measurement reliability using several human datasets (Midnight Scan Club and Human Connectome Project [both Young Adult and Aging]). These datasets include participants with multiple scanning sessions, and collectively include individuals spanning a broad age range of the adult lifespan. The measurement and reliability of measures of resting-state network segregation and integration vary in relation to data quantity for a given participant's scan session; notably, both properties asymptote when estimated using adequate amounts of clean data. We demonstrate how this source of variability can systematically bias interpretation of differences and changes in brain network organization if appropriate safeguards are not included. These observations have important implications for cross-sectional, longitudinal, and interventional comparisons of functional brain network organization.

Brain Functional Connectivity and Anatomical Features as Predictors of Cognitive Behavioral Therapy Outcome for Anxiety in Youths

Background

Because pediatric anxiety disorders precede the onset of many other problems, successful prediction of response to the first-line treatment, cognitive-behavioral therapy (CBT), could have major impact. However, existing clinical models are weakly predictive. The current study evaluates whether structural and resting-state functional magnetic resonance imaging can predict post-CBT anxiety symptoms.

Methods

Two datasets were studied: (A) one consisted of n=54 subjects with an anxiety diagnosis, who received 12 weeks of CBT, and (B) one consisted of n=15 subjects treated for 8 weeks. Connectome Predictive Modeling (CPM) was used to predict treatment response, as assessed with the PARS; additionally we investigated models using anatomical features, instead of functional connectivity. The main analysis included network edges positively correlated with treatment outcome, and age, sex, and baseline anxiety severity as predictors. Results from alternative models and analyses also are presented. Model assessments utilized 1000 bootstraps, resulting in a 95% CI for R2, r and mean absolute error (MAE).

Outcomes

The main model showed a mean absolute error of approximately 3.5 (95%CI: [3.1-3.8]) points a R2 of 0.08 [-0.14 - 0.26] and r of 0.38 [0.24 - 0.511]. When testing this model in the left-out sample (B) the results were similar, with a MAE of 3.4 [2.8 - 4.7], R2-0.65 [-2.29 - 0.16] and r of 0.4 [0.24 - 0.54]. The anatomical metrics showed a similar pattern, where models rendered overall low R2.

Interpretation

The analysis showed that models based on earlier promising results failed to predict clinical outcomes. Despite the small sample size, the current study does not support extensive use of CPM to predict outcome in pediatric anxiety.

Performance reserves in brain-imaging-based phenotype prediction

This study examines the impact of sample size on predicting cognitive and mental health phenotypes from brain imaging via machine learning. Our analysis shows a 3- to 9-fold improvement in prediction performance when sample size increases from 1,000 to 1 M participants. However, despite this increase, the data suggest that prediction accuracy remains worryingly low and far from fully exploiting the predictive potential of brain imaging data. Additionally, we find that integrating multiple imaging modalities boosts prediction accuracy, often equivalent to doubling the sample size. Interestingly, the most informative imaging modality often varied with increasing sample size, emphasizing the need to consider multiple modalities. Despite significant performance reserves for phenotype prediction, achieving substantial improvements may necessitate prohibitively large sample sizes, thus casting doubt on the practical or clinical utility of machine learning in some areas of neuroimaging.

Discovering individual fingerprints in resting-state functional connectivity using deep neural networks

Non-negligible idiosyncrasy due to interindividual differences is an ongoing issue in resting-state functional MRI (rfMRI) analysis. We show that a deep neural network (DNN) can be employed for individual identification by learning important features from the time-varying functional connectivity (FC) of rfMRI in the Human Connectome Project. We employed the trained DNN to identify individuals from an independent dataset acquired at our institution. The results revealed that the DNN could successfully identify 300 individuals with an error rate of 2.9% using 15 s time-window and 870 individuals with an error rate of 6.7%. A trained DNN with nonlinear hidden layers led to the proposal of the "fingerprint of FC" (fpFC) as representative edges of individual FC. The fpFCs for individuals exhibited commonly important and individual-specific edges across time-window lengths (from 5 min to 15 s). Furthermore, the utility of our model for another group of subjects was validated, supporting the feasibility of our technique in the context of transfer learning. In conclusion, our study offers an insight into the discovery of the intrinsic mode of the human brain using whole-brain resting-state FC and DNNs.

CCH attack frequency reduction after psilocybin correlates with hypothalamic functional connectivity

OBJECTIVE

To evaluate the feasibility and prophylactic effect of psilocybin as well as its effects on hypothalamic functional connectivity (FC) in patients with chronic cluster headache (CCH).

BACKGROUND

CCH is an excruciating and difficult-to-treat disorder with incompletely understood pathophysiology, although hypothalamic dysfunction has been implicated. Psilocybin may have beneficial prophylactic effects, but clinical evidence is limited.

METHODS

In this small open-label clinical trial, 10 patients with CCH were included and maintained headache diaries for 10 weeks. Patients received three doses of peroral psilocybin (0.14 mg/kg) on the first day of weeks five, six, and seven. The first 4 weeks served as baseline and the last 4 weeks as follow-up. Hypothalamic FC was determined using functional magnetic resonance imaging the day before the first psilocybin dose and 1 week after the last dose.

RESULTS

The treatment was well tolerated. Attack frequency was reduced by mean (standard deviation) 31% (31) from baseline to follow-up (pFWER  = 0.008). One patient experienced 21 weeks of complete remission. Changes in hypothalamic-diencephalic FC correlated negatively with a percent change in attack frequency (pFWER  = 0.03, R = -0.81), implicating this neural pathway in treatment response.

CONCLUSION

Our results indicate that psilocybin may have prophylactic potential and implicates the hypothalamus in possible treatment response. Further clinical studies are warranted.

Neural connectivity moderators and mechanisms of ketamine treatment among treatment-resistant depressed patients: a randomized controlled trial

BACKGROUND

Intravenous (IV) ketamine has emerged as a rapid and effective treatment for TRD. However, the specific neural mechanisms of ketamine's effects in humans remains unclear. Although neuroplasticity is implicated as a mechanism of action in animal models, relatively few randomized controlled trials (RCTs) in TRD patients have examined ketamine's impact on functional connectivity, a posited functional marker of neuroplasticity-particularly in the context of a mood-induction paradigm (termed miFC).

METHODS

152 adults with TRD (63% female; 37% male) were randomly allocated to receive a single infusion of ketamine or saline in a 2:1 ratio. We examined changes in connectivity (from baseline to 24-h post-infusion) that differed by treatment, and whether clinical treatment response at 24-h post-infusion was uniquely related (among patients allocated to ketamine relative to saline) to (1) pre-treatment connectivity and (2) changes in connectivity. We examined both miFC and rsFC, using prefrontal cortex and limbic seed regions. We also conducted a multiverse analysis to examine findings most robust against analytic decisions.

FINDINGS

Across both miFC and rsFC, ketamine was associated with greater in prefrontal/limbic connectivity compared to saline, and lower baseline connectivity of limbic and prefrontal regions predicted greater treatment response in patients receiving ketamine. Greater connectivity increases in participants receiving ketamine was uniquely related to greater treatment response. In addition, certain findings were identified as being reproducible against different analytic decisions in multiverse analyses.

INTERPRETATION

Our findings identify specific neural connectivity patterns impacted by ketamine and were uniquely related to outcomes following ketamine (relative to saline). These findings generally support prominent neuroplasticity models of ketamine's therapeutic efficacy. These findings lay new groundwork for understanding how to enhance and optimize ketamine treatments and develop novel rapid-acting treatments for depression.

FUNDING

This research was supported by NIH grant R01MH113857 and by the Clinical and Translational Sciences Institute at the University of Pittsburgh (UL1-TR-001857).

Longitudinal resting-state network connectivity changes in electroconvulsive therapy patients compared to healthy controls

OBJECTIVE

Electroconvulsive therapy (ECT) is effective for major depressive episodes. Understanding of underlying mechanisms has been increased by examining changes of brain connectivity but studies often do not correct for test-retest variability in healthy controls (HC). In this study, we investigated changes in resting-state networks after ECT in a multicenter study.

METHODS

Functional resting-state magnetic resonance imaging data, acquired before start and within one week after ECT, from 90 depressed patients were analyzed, as well as longitudinal data of 24 HC. Group-information guided independent component analysis (GIG-ICA) was used to spatially restrict decomposition to twelve canonical resting-state networks. Selected networks of interest were the default mode network (DMN), salience network (SN), and left and right frontoparietal network (LFPN, and RFPN). Whole-brain voxel-wise analyses were used to assess group differences at baseline, group by time interactions, and correlations with treatment effectiveness. In addition, between-network connectivity and within-network strengths were computed.

RESULTS

Within-network strength of the DMN was lower at baseline in ECT patients which increased after ECT compared to HC, after which no differences were detected. At baseline, ECT patients showed lower whole-brain voxel-wise DMN connectivity in the precuneus. Increase of within-network strength of the LFPN was correlated with treatment effectiveness. We did not find whole-brain voxel-wise or between-network changes.

CONCLUSION

DMN within-network connectivity normalized after ECT. Within-network increase of the LFPN in ECT patients was correlated with higher treatment effectiveness. In contrast to earlier studies, we found no whole-brain voxel-wise changes, which highlights the necessity to account for test-retest effects.

Test-Retest Reliability of Functional Connectivity in Adolescents With Depression

BACKGROUND

The test-retest reliability of functional magnetic resonance imaging is critical to identifying reproducible biomarkers for psychiatric illness. Recent work has shown how reliability limits the observable effect size of brain-behavior associations, hindering detection of these effects. However, while a fast-growing literature has explored both univariate and multivariate reliability in healthy individuals, relatively few studies have explored reliability in populations with psychiatric illnesses or how this interacts with age.

METHODS

Here, we investigated functional connectivity reliability over the course of 1 year in a longitudinal cohort of 88 adolescents (age at baseline = 15.63 ± 1.29 years; 64 female) with major depressive disorder (MDD) and without MDD (healthy volunteers [HVs]). We compared a univariate metric, intraclass correlation coefficient, and 2 multivariate metrics, fingerprinting and discriminability.

RESULTS

Adolescents with MDD had marginally higher mean intraclass correlation coefficient (μMDD = 0.34, 95% CI, 0.12-0.54; μHV = 0.27, 95% CI, 0.05-0.52), but both groups had poor average intraclass correlation coefficients (<0.4). Fingerprinting index was greater than chance and did not differ between groups (fingerprinting indexMDD = 0.75; fingerprinting indexHV = 0.91; Poisson tests p < .001). Discriminability indicated high multivariate reliability in both groups (discriminabilityMDD = 0.80; discriminabilityHV = 0.82; permutation tests p < .01). Neither univariate nor multivariate reliability was associated with symptom severity or edge-level effect size of group differences.

CONCLUSIONS

Overall, we found little evidence for a relationship between depression and reliability of functional connectivity during adolescence. These findings suggest that biomarker identification in depression is not limited due to reliability compared with healthy samples and support the shift toward multivariate analysis for improved power and reliability.

CARE4Kids Study: Endophenotypes of Persistent Post-Concussive Symptoms in Adolescents: Study Rationale and Protocol

Treatment of youth concussion during the acute phase continues to evolve, and this has led to the emergence of guidelines to direct care. While symptoms after concussion typically resolve in 14-28 days, a portion (∼20%) of adolescents endorse persistent post-concussive symptoms (PPCS) beyond normal resolution. This report outlines a study implemented in response to the National Institute of Neurological Diseases and Stroke call for the development and initial clinical validation of objective biological measures to predict risk of PPCS in adolescents. We describe our plans for recruitment of a Development cohort of 11- to 17-year-old youth with concussion, and collection of autonomic, neurocognitive, biofluid, and imaging biomarkers. The most promising of these measures will then be validated in a separate Validation cohort of youth with concussion, and a final, clinically useful algorithm will be developed and disseminated. Upon completion of this study, we will have generated a battery of measures predictive of high risk for PPCS, which will allow for identification and testing of interventions to prevent PPCS in the most high-risk youth.

Neuroimaging to Facilitate Clinical Trials in Huntington's Disease: Current Opinion from the EHDN Imaging Working Group

 Neuroimaging is increasingly being included in clinical trials of Huntington's disease (HD) for a wide range of purposes from participant selection and safety monitoring, through to demonstration of disease modification. Selection of the appropriate modality and associated analysis tools requires careful consideration. On behalf of the EHDN Imaging Working Group, we present current opinion on the utility and future prospects for inclusion of neuroimaging in HD trials. Covering the key imaging modalities of structural-, functional- and diffusion- MRI, perfusion imaging, positron emission tomography, magnetic resonance spectroscopy, and magnetoencephalography, we address how neuroimaging can be used in HD trials to: 1) Aid patient selection, enrichment, stratification, and safety monitoring; 2) Demonstrate biodistribution, target engagement, and pharmacodynamics; 3) Provide evidence for disease modification; and 4) Understand brain re-organization following therapy. We also present the challenges of translating research methodology into clinical trial settings, including equipment requirements and cost, standardization of acquisition and analysis, patient burden and invasiveness, and interpretation of results. We conclude, that with appropriate consideration of modality, study design and analysis, imaging has huge potential to facilitate effective clinical trials in HD.

Shared functional connectome fingerprints following ritualistic ayahuasca intake

The knowledge that brain functional connectomes are unique and reliable has enabled behaviourally relevant inferences at a subject level. However, whether such "fingerprints" persist under altered states of consciousness is unknown. Ayahuasca is a potent serotonergic psychedelic which produces a widespread dysregulation of functional connectivity. Used communally in religious ceremonies, its shared use may highlight relevant novel interactions between mental state and functional connectome (FC) idiosyncrasy. Using 7T fMRI, we assessed resting-state static and dynamic FCs for 21 Santo Daime members after collective ayahuasca intake in an acute, within-subject study. Here, connectome fingerprinting revealed FCs showed reduced idiosyncrasy, accompanied by a spatiotemporal reallocation of keypoint edges. Importantly, we show that interindividual differences in higher-order FC motifs are relevant to experiential phenotypes, given that they can predict perceptual drug effects. Collectively, our findings offer an example of how individualised connectivity markers can be used to trace a subject's FC across altered states of consciousness.

Test-retest reliability and predictive utility of a macroscale principal functional connectivity gradient

Mapping individual differences in brain function has been hampered by poor reliability as well as limited interpretability. Leveraging patterns of brain-wide functional connectivity (FC) offers some promise in this endeavor. In particular, a macroscale principal FC gradient that recapitulates a hierarchical organization spanning molecular, cellular, and circuit level features along a sensory-to-association cortical axis has emerged as both a parsimonious and interpretable measure of individual differences in behavior. However, the measurement reliabilities of this FC gradient have not been fully evaluated. Here, we assess the reliabilities of both global and regional principal FC gradient measures using test-retest data from the young adult Human Connectome Project (HCP-YA) and the Dunedin Study. Analyses revealed that the reliabilities of principal FC gradient measures were (1) consistently higher than those for traditional edge-wise FC measures, (2) higher for FC measures derived from general FC (GFC) in comparison with resting-state FC, and (3) higher for longer scan lengths. We additionally examined the relative utility of these principal FC gradient measures in predicting cognition and aging in both datasets as well as the HCP-aging dataset. These analyses revealed that regional FC gradient measures and global gradient range were significantly associated with aging in all three datasets, and moderately associated with cognition in the HCP-YA and Dunedin Study datasets, reflecting contractions and expansions of the cortical hierarchy, respectively. Collectively, these results demonstrate that measures of the principal FC gradient, especially derived using GFC, effectively capture a reliable feature of the human brain subject to interpretable and biologically meaningful individual variation, offering some advantages over traditional edge-wise FC measures in the search for brain-behavior associations.

Novel machine learning approaches for improving the reproducibility and reliability of functional and effective connectivity from functional MRI

Objective.New measures of human brain connectivity are needed to address gaps in the existing measures and facilitate the study of brain function, cognitive capacity, and identify early markers of human disease. Traditional approaches to measure functional connectivity (FC) between pairs of brain regions in functional MRI, such as correlation and partial correlation, fail to capture nonlinear aspects in the regional associations. We propose a new machine learning based measure of FC (ML.FC) which efficiently captures linear and nonlinear aspects.Approach.To capture directed information flow between brain regions, effective connectivity (EC) metrics, including dynamic causal modeling and structural equation modeling have been used. However, these methods are impractical to compute across the many regions of the whole brain. Therefore, we propose two new EC measures. The first, a machine learning based measure of effective connectivity (ML.EC), measures nonlinear aspects across the entire brain. The second, Structurally Projected Granger Causality (SP.GC) adapts Granger Causal connectivity to efficiently characterize and regularize the whole brain EC connectome to respect underlying biological structural connectivity. The proposed measures are compared to traditional measures in terms ofreproducibilityand theability to predict individual traitsin order to demonstrate these measures' internal validity. We use four repeat scans of the same individuals from the Human Connectome Project and measure the ability of the measures to predict individual subject physiologic and cognitive traits.Main results.The proposed new FC measure ofML.FCattains high reproducibility (mean intra-subjectR2of 0.44), while the proposed EC measure ofSP.GCattains the highest predictive power (meanR2across prediction tasks of 0.66).Significance.The proposed methods are highly suitable for achieving high reproducibility and predictiveness and demonstrate their strong potential for future neuroimaging studies.

Oxytocin enhances the triangular association among behavior, resting-state, and task-state functional connectivity

Considerable advances in the role of oxytocin (OT) effect on behavior and the brain network have been made, but the effect of OT on the association between inter-individual differences in functional connectivity (FC) and behavior is elusive. Here, by using a face-perception task and multiple connectome-based predictive models, we aimed to (1) determine whether OT could enhance the association among behavioral performance, resting-state FC (rsFC), and task-state FC (tsFC) and (2) if so, explore the role of OT in enhancing this triangular association. We found that in the OT group, the prediction performance of using rsFC or tsFC to predict task behavior was higher than that of the PL group. Additionally, the correlation coefficient between rsFC and tsFC was substantially higher in the OT group than in the PL group. The strength of these associations could be partly explained by OT altering the brain's FCs related to social cognition and face perception in both the resting and task states, mainly in brain regions such as the limbic system, prefrontal cortex, temporal poles, and temporoparietal junction. Taken together, these results provide novel evidence and a corresponding mechanism for how neuropeptides cause increased associations among inter-individual differences across different levels (e.g., behavior and large-scale brain networks in both resting and task-state), and may inspire future research on the role of neuropeptides in the cross levels association of both clinical and nonclinical use.

Network and State Specificity in Connectivity-Based Predictions of Individual Behavior

Predicting individual behavior from brain functional connectivity (FC) patterns can contribute to our understanding of human brain functioning. This may apply in particular if predictions are based on features derived from circumscribed, a priori defined functional networks, which improves interpretability. Furthermore, some evidence suggests that task-based FC data may yield more successful predictions of behavior than resting-state FC data. Here, we comprehensively examined to what extent the correspondence of functional network priors and task states with behavioral target domains influences the predictability of individual performance in cognitive, social, and affective tasks. To this end, we used data from the Human Connectome Project for large-scale out-of-sample predictions of individual abilities in working memory (WM), theory-of-mind cognition (SOCIAL), and emotion processing (EMO) from FC of corresponding and non-corresponding states (WM/SOCIAL/EMO/resting-state) and networks (WM/SOCIAL/EMO/whole-brain connectome). Using root mean squared error and coefficient of determination to evaluate model fit revealed that predictive performance was rather poor overall. Predictions from whole-brain FC were slightly better than those from FC in task-specific networks, and a slight benefit of predictions based on FC from task versus resting state was observed for performance in the WM domain. Beyond that, we did not find any significant effects of a correspondence of network, task state, and performance domains. Together, these results suggest that multivariate FC patterns during both task and resting states contain rather little information on individual performance levels, calling for a reconsideration of how the brain mediates individual differences in mental abilities.

Electroencephalogram measured functional connectivity for delirium detection: a systematic review

Objective

Delirium is an acute alteration of consciousness marked by confusion, inattention, and changes in cognition. Some speculate that delirium may be a disorder of functional connectivity, but the requirement to lay still may limit measurement with existing functional imaging modalities in this population. Electroencephalography (EEG) may allow for a more feasible approach to the study of potential connectivity disturbances in delirium. We conducted a systematic review to investigate whether there are EEG-measurable differences in brain functional connectivity in the resting state associated with delirium.

Methods

Medline, PubMed, PsychInfo, Embase and CINAHL were searched for relevant articles containing original data studying EEG functional connectivity measures in delirium.

Results

The search yielded 1,516 records. Following strict inclusion criteria, four studies were included in the review. The studies used a variety of EEG measures including phase lag index, coherence, entropy, shortest path length, minimum spanning tree, and network clustering coefficients to study functional connectivity between scalp electrodes. Across connectivity measures, delirium was associated with decreased brain functional connectivity. All four studies found decreased alpha band connectivity for patients with delirium. None of the studies directly compared the different motor subtypes of delirium.

Significance

This systematic review provides converging evidence for disturbances in oscillatory-based functional connectivity in delirium.

Anatomically compliant modes of variations: New tools for brain connectivity

Anatomical complexity and data dimensionality present major issues when analysing brain connectivity data. The functional and anatomical aspects of the connections taking place in the brain are in fact equally relevant and strongly intertwined. However, due to theoretical challenges and computational issues, their relationship is often overlooked in neuroscience and clinical research. In this work, we propose to tackle this problem through Smooth Functional Principal Component Analysis, which enables to perform dimensional reduction and exploration of the variability in functional connectivity maps, complying with the formidably complicated anatomy of the grey matter volume. In particular, we analyse a population that includes controls and subjects affected by schizophrenia, starting from fMRI data acquired at rest and during a task-switching paradigm. For both sessions, we first identify the common modes of variation in the entire population. We hence explore whether the subjects' expressions along these common modes of variation differ between controls and pathological subjects. In each session, we find principal components that are significantly differently expressed in the healthy vs pathological subjects (with p-values < 0.001), highlighting clearly interpretable differences in the connectivity in the two subpopulations. For instance, the second and third principal components for the rest session capture the imbalance between the Default Mode and Executive Networks characterizing schizophrenia patients.

A systematic review of neuroimaging approaches to mapping language in individuals

Although researchers often rely on group-level fMRI results to draw conclusions about the neurobiology of language, doing so without accounting for the complexities of individual brains may reduce the validity of our findings. Furthermore, understanding brain organization in individuals is critically important for both basic science and clinical translation. To assess the state of single-subject language localization in the functional neuroimaging literature, we carried out a systematic review of studies published through April 2020. Out of 977 papers identified through our search, 121 met our inclusion criteria for reporting single-subject fMRI results (fMRI studies of language in adults that report task-based single-subject statistics). Of these, 20 papers reported using a single-subject test-retest analysis to assess reliability. Thus, we found that a relatively modest number of papers reporting single-subject results quantified single-subject reliability. These varied substantially in acquisition parameters, task design, and reliability measures, creating significant challenges for making comparisons across studies. Future endeavors to optimize the localization of language networks in individuals will benefit from the standardization and broader reporting of reliability metrics for different tasks and acquisition parameters.

Functional networks in the infant brain during sleep and wake states

Functional brain networks are assessed differently earlier versus later in development: infants are almost universally scanned asleep, whereas adults are typically scanned awake. Observed differences between infant and adult functional networks may thus reflect differing states of consciousness rather than or in addition to developmental changes. We explore this question by comparing functional networks in functional magnetic resonance imaging (fMRI) scans of infants during natural sleep and awake movie-watching. As a reference, we also scanned adults during awake rest and movie-watching. Whole-brain functional connectivity was more similar within the same state (sleep and movie in infants; rest and movie in adults) compared with across states. Indeed, a classifier trained on patterns of functional connectivity robustly decoded infant state and even generalized to adults; interestingly, a classifier trained on adult state did not generalize as well to infants. Moreover, overall similarity between infant and adult functional connectivity was modulated by adult state (stronger for movie than rest) but not infant state (same for sleep and movie). Nevertheless, the connections that drove this similarity, particularly in the frontoparietal control network, were modulated by infant state. In sum, infant functional connectivity differs between sleep and movie states, highlighting the value of awake fMRI for studying functional networks over development.

A phenomenological model of whole brain dynamics using a network of neural oscillators with power-coupling

We present a general, trainable oscillatory neural network as a large-scale model of brain dynamics. The model has a cascade of two stages - an oscillatory stage and a complex-valued feedforward stage - for modelling the relationship between structural connectivity and functional connectivity from neuroimaging data under resting brain conditions. Earlier works of large-scale brain dynamics that used Hopf oscillators used linear coupling of oscillators. A distinctive feature of the proposed model employs a novel form of coupling known as power coupling. Oscillatory networks based on power coupling can accurately model arbitrary multi-dimensional signals. Training the lateral connections in the oscillator layer is done by a modified form of Hebbian learning, whereas a variation of the complex backpropagation algorithm does training in the second stage. The proposed model can not only model the empirical functional connectivity with remarkable accuracy (correlation coefficient between simulated and empirical functional connectivity- 0.99) but also identify default mode network regions. In addition, we also inspected how structural loss in the brain can cause significant aberration in simulated functional connectivity and functional connectivity dynamics; and how it can be restored with optimized model parameters by an in silico perturbational study.

Effective connectivity between deep brain stimulation targets in individuals with treatment-resistant depression

The therapeutic effect of deep brain stimulation on patients with treatment-resistant depression is strongly dependent on the connectivity of the stimulation region with other regions associated with depression. The aims of this study are to characterize the effective connectivity between the brain regions playing important roles in depression and further investigate the underlying pathophysiological mechanisms of treatment-resistant depression and the mechanisms involving deep brain stimulation. Thirty-three individuals with treatment-resistant depression and 29 healthy control subjects were examined. All subjects underwent resting-state functional MRI scanning. The coupling parameters reflecting the causal interactions among deep brain stimulation targets and medial prefrontal cortex were estimated using spectral dynamic causal modelling. Our results showed that compared to the healthy control subjects, in the left hemisphere of treatment-resistant depression patients, the nucleus accumbens was inhibited by the inferior thalamic peduncle and excited the ventral caudate and the subcallosal cingulate gyrus, which in turn excited the lateral habenula. In the right hemisphere, the lateral habenula inhibited the ventral caudate and the nucleus accumbens, both of which inhibited the inferior thalamic peduncle, which in turn inhibited the cingulate gyrus. The ventral caudate excited the lateral habenula and the cingulate gyrus, which excited the medial prefrontal cortex. Furthermore, these effective connectivity links varied between males and females, and the left and right hemispheres. Our findings suggest that intrinsic excitatory/inhibitory connections between deep brain stimulation targets are impaired in treatment-resistant depression patients, and that these connections are sex dependent and hemispherically lateralized. This knowledge can help to better understand the underlying mechanisms of treatment-resistant depression, and along with tractography, structural imaging, and other relevant clinical information, may assist to determine the appropriate region for deep brain stimulation therapy in each treatment-resistant depression patient.

Genomic Structural Equation Modeling Reveals Latent Phenotypes in the Human Cortex with Distinct Genetic Architecture

Genetic contributions to human cortical structure manifest pervasive pleiotropy. This pleiotropy may be harnessed to identify unique genetically-informed parcellations of the cortex that are neurobiologically distinct from functional, cytoarchitectural, or other cortical parcellation schemes. We investigated genetic pleiotropy by applying genomic structural equation modeling (SEM) to map the genetic architecture of cortical surface area (SA) and cortical thickness (CT) for the 34 brain regions recently reported in the ENIGMA cortical GWAS. Genomic SEM uses the empirical genetic covariance estimated from GWAS summary statistics with LD score regression (LDSC) to discover factors underlying genetic covariance, which we are denoting genetically informed brain networks (GIBNs). Genomic SEM can fit a multivariate GWAS from summary statistics for each of the GIBNs, which can subsequently be used for LD score regression (LDSC). We found the best-fitting model of cortical SA identified 6 GIBNs and CT identified 4 GIBNs. The multivariate GWASs of these GIBNs identified 74 genome-wide significant (GWS) loci (p<5×10-8), including many previously implicated in neuroimaging phenotypes, behavioral traits, and psychiatric conditions. LDSC of GIBN GWASs found that SA-derived GIBNs had a positive genetic correlation with bipolar disorder (BPD), and cannabis use disorder, indicating genetic predisposition to a larger SA in the specific GIBN is associated with greater genetic risk of these disorders. A negative genetic correlation was observed with attention deficit hyperactivity disorder (ADHD), major depressive disorder (MDD), and insomnia, indicating genetic predisposition to a larger SA in the specific GIBN is associated with lower genetic risk of these disorders. CT GIBNs displayed a negative genetic correlation with alcohol dependence. Jointly modeling the genetic architecture of complex traits and investigating multivariate genetic links across phenotypes offers a new vantage point for mapping the cortex into genetically informed networks.

Psychometric properties of the Turkish version of the moral distress-appraisal scale for nurses

Moral distress causes frustration, guilt, anger, stress, sadness, anxiety, fear, burnout, insecurity, and depression in nurses, and this is reflected in their work performances. Therefore, internationally validated tools and methodological studies are needed to measure moral distress among nurses. This study aims to evaluate the psychometric properties of the Turkish version of the Moral Distress-Appraisal Scale (MD-APPS) among nurses in Turkey. Psychometric properties of the Turkish version of the Moral Distress-Appraisal Scale (MD-APPS), which included internal consistency reliability and construct validity with factor analysis, were examined in a sample of 420 nurses working in different hospitals in Turkey completed the study between February and July 2022. The content validity index of the Turkish version of the MD-APPS based on expert opinions was 0.90. Through exploratory factor analysis for construct validity, a two-factor structure was obtained as in the original scale. The variance explained by these two factors was 56.67 %. Confirmatory factor analysis showed that the scale was valid, while internal consistency coefficient and test-retest results demonstrated that the scale was reliable. The Turkish version of the MD-APPS is a valid and reliable tool for evaluating moral distress among nurses.

Optimizing network neuroscience computation of individual differences in human spontaneous brain activity for test-retest reliability

A rapidly emerging application of network neuroscience in neuroimaging studies has provided useful tools to understand individual differences in intrinsic brain function by mapping spontaneous brain activity, namely intrinsic functional network neuroscience (ifNN). However, the variability of methodologies applied across the ifNN studies-with respect to node definition, edge construction, and graph measurements-makes it difficult to directly compare findings and also challenging for end users to select the optimal strategies for mapping individual differences in brain networks. Here, we aim to provide a benchmark for best ifNN practices by systematically comparing the measurement reliability of individual differences under different ifNN analytical strategies using the test-retest design of the Human Connectome Project. The results uncovered four essential principles to guide ifNN studies: (1) use a whole brain parcellation to define network nodes, including subcortical and cerebellar regions; (2) construct functional networks using spontaneous brain activity in multiple slow bands; and (3) optimize topological economy of networks at individual level; and (4) characterize information flow with specific metrics of integration and segregation. We built an interactive online resource of reliability assessments for future ifNN (https://ibraindata.com/research/ifNN).

Childhood trauma is linked to abnormal static-dynamic brain topology in adolescents with major depressive disorder

Childhood trauma is a leading risk factor for adolescents developing major depressive disorder (MDD); however, the underlying neuroimaging mechanisms remain unclear. This study aimed to investigate the association among childhood trauma, MDD and brain dysfunctions by combining static and dynamic brain network models. We recruited 46 first-episode drug-naïve adolescent MDD patients with childhood trauma (MDD-CT), 53 MDD patients without childhood trauma (MDD-nCT), and 90 healthy controls (HCs) for resting-state functional magnetic resonance imaging (fMRI) scans; all participants were aged 13-18 years. Compared to the HCs and MDD-nCT groups, the MDD-CT group exhibited significantly higher global and local efficiency in static brain networks and significantly higher temporal correlation coefficients in dynamic brain network models at the whole-brain level, and altered the local efficiency of default mode network (DMN) and temporal correlation coefficients of DMN, salience (SAN), and attention (ATN) networks at the local perspective. Correlation analysis indicated that altered brain network features and clinical symptoms, childhood trauma, and particularly emotional neglect were highly correlated in adolescents with MDD. This study may provide new evidence for the dysconnectivity hypothesis regarding the associations between childhood trauma and MDD in adolescents from the perspectives of both static and dynamic brain topology.

Reliability and validity of the Diabetic foot self-care questionnaire in Turkish patients

AIM

This study was conducted to examine the psychometric, properties of the Turkish version of the Diabetic, Foot Self-Care, Questionnaire.

METHODS

This was a descriptive- methodological study conducted on 193 diabetes patients. Data were collected, using Descriptive, Information Form and Diabetic Foot Self-Care Questionnaire. Data were analyzed using exploratory factor analysis, item-total score correlation, Cronbach's alpha, and test-retest analysis.

RESULTS

The Diabetic, Foot Self-Care, Questionnaire consists of 16 items and three sub-dimensions. The three sub-dimensions recorded a variance of 58.137%. The total Cronbach's alpha coefficient of the Turkish version of the Diabetic, Foot Self-Care, Questionnaire was 0.87, and Cronbach's alpha values of its sub-dimensions were 0.71 and 0.88. The two-month test-retest credibility interpreted with intra-class correlation was 0.97.

CONCLUSIONS

It has been shown that the questionnaire is a valid and reliable tool for assessing foot self-care behavior in diabetic patients.

Evaluating functional brain organization in individuals and identifying contributions to network overlap

Individual differences in the spatial organization of resting state networks have received increased attention in recent years. Measures of individual-specific spatial organization of brain networks and overlapping network organization have been linked to important behavioral and clinical traits and are therefore potential biomarker targets for personalized psychiatry approaches. To better understand individual-specific spatial brain organization, this paper addressed three key goals. First, we determined whether it is possible to reliably estimate weighted (non-binarized) resting state network maps using data from only a single individual, while also maintaining maximum spatial correspondence across individuals. Second, we determined the degree of spatial overlap between distinct networks, using test-retest and twin data. Third, we systematically tested multiple hypotheses (spatial mixing, temporal switching, and coupling) as candidate explanations for why networks overlap spatially. To estimate weighted network organization, we adopt the Probabilistic Functional Modes (PROFUMO) algorithm, which implements a Bayesian framework with hemodynamic and connectivity priors to supplement optimization for spatial sparsity/independence. Our findings showed that replicable individual-specific estimates of weighted resting state networks can be derived using high quality fMRI data within individual subjects. Network organization estimates using only data from each individual subject closely resembled group-informed network estimates (which was not explicitly modeled in our individual-specific analyses), suggesting that cross-subject correspondence was largely maintained. Furthermore, our results confirmed the presence of spatial overlap in network organization, which was replicable across sessions within individuals and in monozygotic twin pairs. Intriguingly, our findings provide evidence that network overlap is indicative of linear additive coupling. These results suggest that regions of network overlap concurrently process information from all contributing networks, potentially pointing to the role of overlapping network organization in the integration of information across multiple brain systems.

Brain-wide functional connectivity artifactually inflates throughout fMRI scans: a problem and solution

The fMRI blood oxygen level-dependent (BOLD) signal is a mainstay of neuroimaging assessment of neuronal activity and functional connectivity in vivo. Thus, a chief priority is maximizing this signal's reliability and validity. To this end, the fMRI community has invested considerable effort into optimizing both experimental designs and physiological denoising procedures to improve the accuracy, across-scan reproducibility, and subject discriminability of BOLD-derived metrics like functional connectivity. Despite these advances, we discover that a substantial and ubiquitous defect remains in fMRI datasets: functional connectivity throughout the brain artifactually inflates during the course of fMRI scans - by an average of more than 70% in 15 minutes of scan time - at spatially heterogeneous rates, producing both spatial and temporal distortion of brain connectivity maps. We provide evidence that this inflation is driven by a previously unrecognized time-dependent increase of non-neuronal, systemic low-frequency oscillation (sLFO) blood flow signal during fMRI scanning. This signal is not removed by standard denoising procedures such as independent component analysis (ICA). However, we demonstrate that a specialized sLFO denoising procedure - Regressor Interpolation at Progressive Time Delays (RIPTiDe) - can be added to standard denoising pipelines to significantly attenuate functional connectivity inflation. We confirm the presence of sLFO-driven functional connectivity inflation in multiple independent fMRI datasets - including the Human Connectome Project - as well as across resting-state, task, and sleep-state conditions, and demonstrate its potential to produce false positive findings. Collectively, we present evidence for a previously unknown physiological phenomenon that spatiotemporally distorts estimates of brain connectivity in human fMRI datasets, and present a solution for mitigating this artifact.

Insights from personalized models of brain and behavior for identifying biomarkers in psychiatry

A main goal in translational neuroscience is to identify neural correlates of psychopathology ("biomarkers") that can be used to facilitate diagnosis, prognosis, and treatment. This goal has led to substantial research into how psychopathology symptoms relate to large-scale brain systems. However, these efforts have not yet resulted in practical biomarkers used in clinical practice. One reason for this underwhelming progress may be that many study designs focus on increasing sample size instead of collecting additional data within each individual. This focus limits the reliability and predictive validity of brain and behavioral measures in any one person. As biomarkers exist at the level of individuals, an increased focus on validating them within individuals is warranted. We argue that personalized models, estimated from extensive data collection within individuals, can address these concerns. We review evidence from two, thus far separate, lines of research on personalized models of (1) psychopathology symptoms and (2) fMRI measures of brain networks. We close by proposing approaches uniting personalized models across both domains to improve biomarker research.

Appetitive and aversive cue reactivities differentiate neural subtypes of alcohol drinkers

Craving reflects the subjective urge to use drugs and can be triggered by both positive and negative emotional states. No studies have systematically investigated the relative roles of these mechanisms in the pathophysiology of substance misuse. Here, we performed meta-analyses of drug cue-elicited reactivity and win and loss processing in the monetary incentive delay task to identify distinct neural correlates of appetitive and aversive responses to drug cues. We then characterized the appetitive and aversive cue responses in seventy-six alcohol drinkers performing a cue craving task during fMRI. Imaging data were processed according to published routines. The appetitive circuit involved medial cortical regions and the ventral striatum, and the aversive circuit involved the insula, caudate and mid-cingulate cortex. We observed a significant correlation of cue-elicited activity (β estimates) of the appetitive and aversive circuit. However, individuals varied in appetitive and aversive cue responses. From the regression of appetitive (y) vs. aversive (x) β, we identified participants in the top 1/3 each of those with positive and negative residuals as "approach" (n = 15) and "avoidance" (n = 11) and the others as the "mixed" (n = 50) subtype. In clinical characteristics, the avoidance subtype showed higher sensitivity to punishment and, in contrast, the approach subtype showed higher levels of sensation seeking and alcohol expectancy for social and physical pressure. The findings highlighted distinct neural underpinnings of appetitive and aversive components of cue-elicited reactivity and provided evidence for potential subtypes of alcohol drinkers.

Accounting for temporal variability in functional magnetic resonance imaging improves prediction of intelligence

Neuroimaging-based prediction methods for intelligence have seen a rapid development. Among different neuroimaging modalities, prediction using functional connectivity (FC) has shown great promise. Most literature has focused on prediction using static FC, with limited investigations on the merits of such analysis compared to prediction using dynamic FC or region-level functional magnetic resonance imaging (fMRI) times series that encode temporal variability. To account for the temporal dynamics in fMRI, we propose a bi-directional long short-term memory (bi-LSTM) approach that incorporates feature selection mechanism. The proposed pipeline is implemented via an efficient algorithm and applied for predicting intelligence using region-level time series and dynamic FC. We compare the prediction performance using different fMRI features acquired from the Adolescent Brain Cognitive Development (ABCD) study involving nearly 7000 individuals. Our detailed analysis illustrates the consistently inferior performance of static FC compared to region-level time series or dynamic FC for single and combined rest and task fMRI experiments. The joint analysis of task and rest fMRI leads to improved intelligence prediction under all models compared to using fMRI from only one experiment. In addition, the proposed bi-LSTM pipeline based on region-level time series identifies several shared and differential important brain regions across fMRI experiments that drive intelligence prediction. A test-retest analysis of the selected regions shows strong reliability across cross-validation folds. Given the large sample size of ABCD study, our results provide strong evidence that superior prediction of intelligence can be achieved by accounting for temporal variations in fMRI.

Connectivity gradients in spontaneous brain activity at multiple frequency bands

The intrinsic organizational structure of the brain is reflected in spontaneous brain oscillations. Its functional integration and segregation hierarchy have been discovered in space by leveraging gradient approaches to low-frequency functional connectivity. This hierarchy of brain oscillations has not yet been fully understood, since previous studies have mainly concentrated on the brain oscillations from a single limited frequency range (~ 0.01-0.1 Hz). In this work, we extended the frequency range and performed gradient analysis across multiple frequency bands of fast resting-state fMRI signals from the Human Connectome Project and condensed a frequency-rank cortical map of the highest gradient. We found that the coarse skeletons of the functional organization hierarchy are generalizable across the multiple frequency bands. Beyond that, the highest integration levels of connectivity vary in the frequency domain across different large-scale brain networks. These findings are replicated in another independent dataset and demonstrated that different brain networks can integrate information at varying rates, indicating the significance of examining the intrinsic architecture of spontaneous brain activity from the perspective of multiple frequency bands.

Relating white matter microstructure in theoretically defined addiction networks to relapse in alcohol use disorder

Theoretical models group maladaptive behaviors in addiction into neurocognitive domains such as incentive salience (IS), negative emotionality (NE), and executive functioning (EF). Alterations in these domains lead to relapse in alcohol use disorder (AUD). We examine whether microstructural measures in the white matter pathways supporting these domains are associated with relapse in AUD. Diffusion kurtosis imaging data were collected from 53 individuals with AUD during early abstinence. We used probabilistic tractography to delineate the fornix (IS), uncinate fasciculus (NE), and anterior thalamic radiation (EF) in each participant and extracted mean fractional anisotropy (FA) and kurtosis fractional anisotropy (KFA) within each tract. Binary (abstained vs. relapsed) and continuous (number of days abstinent) relapse measures were collected over a 4-month period. Across tracts, anisotropy measures were typically (i) lower in those that relapsed during the follow-up period and (ii) positively associated with the duration of sustained abstinence during the follow-up period. However, only KFA in the right fornix reached significance in our sample. The association between microstructural measures in these fiber tracts and treatment outcome in a small sample highlights the potential utility of the three-factor model of addiction and the role of white matter alterations in AUD.