A predictor-informed multi-subject bayesian approach for dynamic functional connectivity.

Dynamic functional connectivity investigates how the interactions among brain regions vary over the course of an fMRI experiment. Such transitions between different individual connectivity states can be modulated by changes in underlying physiological mechanisms that drive functional network dynamics, e.g., changes in attention or cognitive effort. In this paper, we develop a multi-subject Bayesian framework where the estimation of dynamic functional networks is informed by time-varying exogenous physiological covariates that are simultaneously recorded in each subject during the fMRI experiment. More specifically, we consider a dynamic Gaussian graphical model approach where a non-homogeneous hidden Markov model is employed to classify the fMRI time series into latent neurological states. We assume the state-transition probabilities to vary over time and across subjects as a function of the underlying covariates, allowing for the estimation of recurrent connectivity patterns and the sharing of networks among the subjects. We further assume sparsity in the network structures via shrinkage priors, and achieve edge selection in the estimated graph structures by introducing a multi-comparison procedure for shrinkage-based inferences with Bayesian false discovery rate control. We evaluate the performances of our method vs alternative approaches on synthetic data. We apply our modeling framework on a resting-state experiment where fMRI data have been collected concurrently with pupillometry measurements, as a proxy of cognitive processing, and assess the heterogeneity of the effects of changes in pupil dilation on the subjects' propensity to change connectivity states. The heterogeneity of state occupancy across subjects provides an understanding of the relationship between increased pupil dilation and transitions toward different cognitive states.

Performance on a contour integration task as a function of contour shape in schizophrenia and controls.

Contour Integration (CI) is the ability to integrate elemental features into objects and is a basic visual process essential for object perception and recognition, and for functioning in visual environments. It is now well documented that people with schizophrenia (SZ), in addition to having cognitive impairments, also have several visual perceptual deficits, including in CI. Here, we retrospectively characterize the performance of both SZ and neurotypical individuals (NT) on a series of contour shapes, made up of Gabor elements, that varied in terms of closure and curvature. Participants in both groups performed a CI training task that included 7 different families of shapes (Lines, Ellipse, Blobs, Squiggles, Spiral, Circle and Letters) for up to 40 sessions. Two parameters were manipulated in the training task: Orientation Jitter (OJ, i.e., orientation deviations of individual Gabor elements from ideal for each shape) and Inducer Number (IN, i.e., number of Gabor elements defining the shape). Results show that both OJ and IN thresholds significantly differed between the groups, with higher (OJ) and lower (IN) thresholds observed in the controls. Furthermore, we found significant effects as a function of the contour shapes, with differences between groups emerging with contours that were considered more complex, e.g., due to having a higher degree of curvature (Blobs, Spiral, Letters). These data can inform future work that aims to characterize visual integration impairments in schizophrenia.

Active Mutual Conjoint Estimation of Multiple Contrast Sensitivity Functions.

Recent advances in nonparametric Contrast Sensitivity Function (CSF) estimation have yielded a new tradeoff between accuracy and efficiency not available to classical parametric estimators. An additional advantage of this new framework is the ability to independently tune multiple aspects of the estimator to seek further improvements. Machine Learning CSF (MLCSF) estimation with Gaussian processes allows for design optimization in the kernel, acquisition function and underlying task representation, to name a few. This paper describes a novel kernel for CSF estimation that is more flexible than a kernel based on strictly functional forms. Despite being more flexible, it can result in a more efficient estimator. Further, trial selection for data acquisition that is generalized beyond pure information gain can also improve estimator quality. Finally, introducing latent variable representations underlying general CSF shapes can enable simultaneous estimation of multiple CSFs, such as from different eyes, eccentricities or luminances. The conditions under which the new procedures perform better than previous nonparametric estimation procedures are presented and quantified.

Contrast response function estimation with nonparametric Bayesian active learning.

Multidimensional psychometric functions can typically be estimated nonparametrically for greater accuracy or parametrically for greater efficiency. By recasting the estimation problem from regression to classification, however, powerful machine learning tools can be leveraged to provide an adjustable balance between accuracy and efficiency. Contrast sensitivity functions (CSFs) are behaviorally estimated curves that provide insight into both peripheral and central visual function. Because estimation can be impractically long, current clinical workflows must make compromises such as limited sampling across spatial frequency or strong assumptions on CSF shape. This article describes the development of the machine learning contrast response function (MLCRF) estimator, which quantifies the expected probability of success in performing a contrast detection or discrimination task. A machine learning CSF can then be derived from the MLCRF. Using simulated eyes created from canonical CSF curves and actual human contrast response data, the accuracy and efficiency of the machine learning contrast sensitivity function (MLCSF) was evaluated to determine its potential utility for research and clinical applications. With stimuli selected randomly, the MLCSF estimator converged slowly toward ground truth. With optimal stimulus selection via Bayesian active learning, convergence was nearly an order of magnitude faster, requiring only tens of stimuli to achieve reasonable estimates. Inclusion of an informative prior provided no consistent advantage to the estimator as configured. MLCSF achieved efficiencies on par with quickCSF, a conventional parametric estimator, but with systematically higher accuracy. Because MLCSF design allows accuracy to be traded off against efficiency, it should be explored further to uncover its full potential.

Testing the Informativeness of Diverse Measures of Auditory Processing for Clinical Audiological Practice in Middle-Aged Adults in Mexico.

PURPOSE: Standard clinical audiologic assessment is limited in its ability to capture variance in self-reported hearing difficulty. Additionally, the costs associated with clinical testing in audiology create financial barriers for hearing health care in developing countries like Mexico. This study used an open-source Spanish-language tool called PART (Portable Automated Rapid Testing) to test the hypothesis that a battery of assessments of auditory processing can complement standard clinical audiological assessment to better capture the variance of self-reported hearing difficulty. METHOD: Forty-three adults between 40 and 69 years of age were tested in Mexico City using a traditional clinical pure-tone audiogram, cognitive screening, and a battery of PART-based auditory processing assessments including a speech-on-speech competition spatial release from masking task. Results were compared to self-reported hearing difficulty, assessed with a Spanish-language adaptation of the Hearing Handicap Inventory for the Elderly-Screening Version (HHIE-S). RESULTS: Several measures from the PART battery exhibited stronger correlations with self-reported hearing difficulties than the pure-tone audiogram. The spatial release from masking task best captured variance in HHIE-S scores and remained significant after controlling for the effects of age, audibility, and cognitive score. CONCLUSIONS: The spatial release from masking task can complement traditional clinical measures to better account for patient's self-reported hearing difficulty. Open-source access to this test in PART supports its implementation for Spanish speakers in clinical settings around the world at low cost. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.24470140.

Unicorn, Hare, or Tortoise? Using Machine Learning to Predict Working Memory Training Performance.

People differ considerably in the extent to which they benefit from working memory (WM) training. Although there is increasing research focusing on individual differences associated with WM training outcomes, we still lack an understanding of which specific individual differences, and in what combination, contribute to inter-individual variations in training trajectories. In the current study, 568 undergraduates completed one of several N-back intervention variants over the course of two weeks. Participants' training trajectories were clustered into three distinct training patterns (high performers, intermediate performers, and low performers). We applied machine-learning algorithms to train a binary tree model to predict individuals' training patterns relying on several individual difference variables that have been identified as relevant in previous literature. These individual difference variables included pre-existing cognitive abilities, personality characteristics, motivational factors, video game experience, health status, bilingualism, and socioeconomic status. We found that our classification model showed good predictive power in distinguishing between high performers and relatively lower performers. Furthermore, we found that openness and pre-existing WM capacity to be the two most important factors in distinguishing between high and low performers. However, among low performers, openness and video game background were the most significant predictors of their learning persistence. In conclusion, it is possible to predict individual training performance using participant characteristics before training, which could inform the development of personalized interventions.

Investigating the Role of Individual Differences in Adherence to Cognitive Training.

Consistent with research across several domains, intervention adherence is associated with desired outcomes. Our study investigates adherence, defined by participants' commitment to, persistence with, and compliance with an intervention's regimen, as a key mechanism underlying cognitive training effectiveness. We examine this relationship in a large and diverse sample comprising 4,775 adults between the ages of 18 and 93. We test the predictive validity of individual difference factors, such as age, gender, cognitive capability (i.e., fluid reasoning and working memory), grit, ambition, personality, self-perceived cognitive failures, socioeconomic status, exercise, and education on commitment to and persistence with a 20-session cognitive training regimen, as measured by the number of sessions completed. Additionally, we test the relationship between compliance measures: (i) spacing between training sessions, as measured by the average time between training sessions, and (ii) consistency in the training schedule, as measured by the variance in time between training sessions, with performance trajectories on the training task. Our data suggest that none of these factors reliably predict commitment to, persistence with, or compliance with cognitive training. Nevertheless, the lack of evidence from the large and representative sample extends the knowledge from previous research exploring limited, heterogenous samples, characterized by older adult populations. The absence of reliable predictors for commitment, persistence, and compliance in cognitive training suggests that nomothetic factors may affect program adherence. Future research will be well served to examine diverse approaches to increasing motivation in cognitive training to improve program evaluation and reconcile the inconsistency in findings across the field.

Improving comparability across cognitive training trials for brain aging: A focus on interoperability.

Cognitive training may promote healthy brain aging and prevent dementia, but results from individual studies are inconsistent. There are disagreements on how to evaluate cognitive training interventions between clinical and basic scientists. Individual labs typically create their own assessment and training materials, leading to difficulties reproducing methods. Here, we advocate for improved interoperability: the exchange and cooperative development of a consensus for cognitive training design, analysis, and result interpretation. We outline five guiding principles for improving interoperability: (i) design interoperability, developing standard design and analysis models; (ii) material interoperability, promoting sharing of materials; (iii) interoperability incentives; (iv) privacy and security norms, ensuring adherence to accepted ethical standards; and (v) interpretability prioritization, encouraging a shared focus on neurobiological mechanisms to improve clinical relevance. Improving interoperability will allow us to develop scientifically optimized, clinically useful cognitive training programs to slow/prevent brain aging. HIGHLIGHTS: Interoperability facilitates progress via resource sharing and comparability.Better interoperability is needed in cognitive training for brain aging research.We adapt an interoperability framework to cognitive training research.We suggest five guiding principles for improved interoperability.We propose an open-source pipeline to facilitate interoperability.

Locus coeruleus integrity predicts ease of attaining and maintaining neural states of high attentiveness.

The locus coeruleus (LC), a small subcortical structure in the brainstem, is the brain's principal source of norepinephrine. It plays a primary role in regulating stress, the sleep-wake cycle, and attention, and its degradation is associated with aging and neurodegenerative diseases associated with cognitive deficits (e.g., Parkinson's, Alzheimer's). Yet precisely how norepinephrine drives brain networks to support healthy cognitive function remains poorly understood - partly because LC's small size makes it difficult to study noninvasively in humans. Here, we characterized LC's influence on brain dynamics using a hidden Markov model fitted to functional neuroimaging data from healthy young adults across four attention-related brain networks and LC. We modulated LC activity using a behavioral paradigm and measured individual differences in LC magnetization transfer contrast. The model revealed five hidden states, including a stable state dominated by salience-network activity that occurred when subjects actively engaged with the task. LC magnetization transfer contrast correlated with this state's stability across experimental manipulations and with subjects' propensity to enter into and remain in this state. These results provide new insight into LC's role in driving spatiotemporal neural patterns associated with attention, and demonstrate that variation in LC integrity can explain individual differences in these patterns even in healthy young adults.

Contrast Response Function Estimation with Nonparametric Bayesian Active Learning.

Multidimensional psychometric functions can typically be estimated nonparametrically for greater accuracy or parametrically for greater efficiency. By recasting the estimation problem from regression to classification, however, powerful machine learning tools can be leveraged to provide an adjustable balance between accuracy and efficiency. Contrast Sensitivity Functions (CSFs) are behaviorally estimated curves that provide insight into both peripheral and central visual function. Because estimation can be impractically long, current clinical workflows must make compromises such as limited sampling across spatial frequency or strong assumptions on CSF shape. This paper describes the development of the Machine Learning Contrast Response Function (MLCRF) estimator, which quantifies the expected probability of success in performing a contrast detection or discrimination task. A machine learning CSF can then be derived from the MLCRF. Using simulated eyes created from canonical CSF curves and actual human contrast response data, the accuracy and efficiency of the MLCSF was evaluated in order to determine its potential utility for research and clinical applications. With stimuli selected randomly, the MLCSF estimator converged slowly toward ground truth. With optimal stimulus selection via Bayesian active learning, convergence was nearly an order of magnitude faster, requiring only tens of stimuli to achieve reasonable estimates. Inclusion of an informative prior provided no consistent advantage to the estimator as configured. MLCSF achieved efficiencies on par with quickCSF, a conventional parametric estimator, but with systematically higher accuracy. Because MLCSF design allows accuracy to be traded off against efficiency, it should be explored further to uncover its full potential. Precis: Machine learning classifiers enable accurate and efficient contrast sensitivity function estimation with item-level prediction for individual eyes.

Improving memory for unusual events with wakeful reactivation.

Memory consists of multiple processes, from encoding information, consolidating it into short- and long- term memory, and later retrieving relevant information. Targeted memory reactivation is an experimental method during which sensory components of a multisensory representation (such as sounds or odors) are 'reactivated', facilitating the later retrieval of unisensory attributes. We examined whether novel and unpredicted events benefit from reactivation to a greater degree than normal stimuli. We presented participants with everyday objects, and 'tagged' these objects with sounds (e.g., animals and their matching sounds) at different screen locations. 'Oddballs' were created by presenting unusual objects and sounds (e.g., a unicorn with a heartbeat sound). During a short reactivation phase, participants listened to a replay of normal and oddball sounds. Participants were then tested on their memory for visual and spatial information in the absence of sounds. Participants were better at remembering the oddball objects compared to normal ones. Importantly, participants were also better at recalling the locations of oddball objects whose sounds were reactivated, compared to objects whose sounds that were not presented again. These results suggest that episodic memory benefits from associating objects with unusual cues, and that reactivating those cues strengthen the entire multisensory representation, resulting in enhanced memory for unisensory attributes.

Development and validation of a Spanish-language spatial release from masking task in a Mexican population.

This study validates a new Spanish-language version of the Coordinate Response Measure (CRM) corpus using a well-established measure of spatial release from masking (SRM). Participants were 96 Spanish-speaking young adults without hearing complaints in Mexico City. To present the Spanish-language SRM test, we created new recordings of the CRM with Spanish-language Translations and updated the freely available app (PART; https://ucrbraingamecenter.github.io/PART_Utilities/) to present materials in Spanish. In addition to SRM, we collected baseline data on a battery of non-speech auditory assessments, including detection of frequency modulations, temporal gaps, and modulated broadband noise in the temporal, spectral, and spectrotemporal domains. Data demonstrate that the newly developed speech and non-speech tasks show similar reliability to an earlier report in English-speaking populations. This study demonstrates an approach by which auditory assessment for clinical and basic research can be extended to Spanish-speaking populations for whom testing platforms are not currently available.

Effects of Gamification on Assessment of Spatial Release From Masking.

PURPOSE: Difficulty understanding speech in noise is a common communication problem. Clinical tests of speech in noise differ considerably from real-world listening and offer patients limited intrinsic motivation to perform well. In order to design a test that captures motivational aspects of real-world communication, this study investigated effects of gamification, or the inclusion of game elements, on a laboratory spatial release from masking test. METHOD: Fifty-four younger adults with normal hearing completed a traditional laboratory and a gamified test of spatial release from masking in counterbalanced order. Masker level adapted based on performance, with the traditional test ending after 10 reversals and the gamified test ending when participants solved a visual puzzle. Target-to-masker ratio thresholds (TMRs) with colocated maskers, separated maskers, and estimates of spatial release were calculated after the 10th reversal for both tests and from the last six reversals of the adaptive track from the gamified test. RESULTS: Thresholds calculated from the 10th reversal indicated no significant differences between the traditional and gamified tests. A learning effect was observed with spatially separated maskers, such that TMRs were better for the second test than the first, regardless of test order. Thresholds calculated from the last six reversals of the gamified test indicated better TMRs in the separated condition compared to the traditional test. CONCLUSIONS: Adding gamified elements to a traditional test of spatial release from masking did not negatively affect test validity or estimates of spatial release. Participants were willing to continue playing the gamified test for an average of 30.2 reversals of the adaptive track. For some listeners, performance in the separated condition continued to improve after the 10th reversal, leading to better TMRs and greater spatial release from masking at the end of the gamified test compared to the traditional test. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.22028789.

Consistency of preferred retinal locus across tasks and participants trained with a simulated scotoma.

After loss of central vision following retinal pathologies such as macular degeneration (MD), patients often adopt compensatory strategies including developing a "preferred retinal locus" (PRL) to replace the fovea in tasks involving fixation. A key question is whether patients develop multi-purpose PRLs or whether their oculomotor strategies adapt to the demands of the task. While most MD patients develop a PRL, clinical evidence suggests that patients may develop multiple PRLs and switch between them according to the task at hand. To understand this, we examined a model of central vision loss in normally seeing individuals and tested whether they used the same or different PRLs across tasks after training. Nineteen participants trained for 10 sessions on contrast detection while in conditions of gaze-contingent, simulated central vision loss. Before and after training, peripheral looking strategies were evaluated during tasks measuring visual acuity, reading abilities and visual search. To quantify strategies in these disparate, naturalistic tasks, we measured and compared the amount of task-relevant information at each of 8 equally spaced, peripheral locations, while participants performed the tasks. Results showed that some participants used consistent viewing strategies across tasks whereas other participants' strategies differed depending on task. This novel method allows quantification of peripheral vision use even in relatively ecological tasks. These results represent one of the first examinations of peripheral viewing strategies across tasks in simulated vision loss. Results suggest that individual differences in peripheral looking strategies following simulated central vision loss may model those developed in pathological vision loss.

A Novel Hidden Markov Approach to Studying Dynamic Functional Connectivity States in Human Neuroimaging.

Introduction: Hidden Markov models (HMMs) are a popular choice to extract and examine recurring patterns of activity or functional connectivity in neuroimaging data, both in terms of spatial patterns and their temporal progression. Although many diverse HMMs have been applied to neuroimaging data, most have defined states based on activity levels (intensity-based [IB] states) rather than patterns of functional connectivity between brain areas (connectivity-based states), which is problematic if we want to understand connectivity dynamics: IB states are unlikely to provide comprehensive information about dynamic connectivity patterns. Methods: We addressed this problem by introducing a new HMM that defines states based on full functional connectivity (FFC) profiles among brain regions. We empirically explored the behavior of this new model in comparison to existing approaches based on IB or summed functional connectivity states using the Human Connectome Project unrelated 100 functional magnetic resonance imaging "resting-state" dataset. Results: Our FFC model discovered connectivity states with more distinguishable (i.e., unique and separable from each other) patterns than previous approaches, and recovered simulated connectivity-based states more faithfully than the other models tested. Discussion: Thus, if our goal is to extract and interpret connectivity states in neuroimaging data, our new model outperforms previous methods, which miss crucial information about the evolution of functional connectivity in the brain. Impact statement Hidden Markov models (HMMs) can be used to investigate brain states noninvasively. Previous models "recover" connectivity from intensity-based hidden states, or from connectivity "summed" across nodes. In this study, we introduce a novel connectivity-based HMM and show how it can reveal true connectivity hidden states under minimal assumptions.

Supplementing a widely available weight loss program with gamified inhibitory control training: A randomized pilot study.

Background: Increasing evidence suggests that targeting self-regulatory processes may improve obesity treatment outcomes. Incorporating gamification principles in inhibitory control training may promote sustained training adherence and resulting benefits. This pilot study evaluated the preliminary efficacy of supplementing an evidence-based weight management program (WW) with sustained gamified inhibitory control training (PolyRules!) on change in Body Mass Index (BMI) among adults with overweight/obesity. Methods: 30 adults with overweight/obesity (M age 49.9 ± 12.4, 86.7% female; 23.3% Hispanic, mean BMI 35.3 ± 6.3) were randomly assigned to receive WW with or without PolyRules! for 12 weeks. The primary outcome was change in BMI from baseline to post-intervention across study arms. Implementation and process indicators were captured to inform larger trials. Results: Average change in BMI was -0.9 in the WW arm and -1.2 in the WW + PolyRules! arm (Cohen's d = 0.26). In the WW + PolyRules! arm, increased training was associated with greater decreases in BMI (r = -0.506, p = 0.0454). WW + PolyRules! participants completed an average of 60.4% sessions and reported positive experiences. There was no difference in frequency of food (d = -0.02) and weight tracking (d = -0.19) between arms. Conclusions: Studies in larger samples should evaluate training-related effects on weight. Supplementing WW with gamified inhibitory training appears feasible, with no detrimental effect on engagement.

The benefits of simultaneous tDCS and working memory training on transfer outcomes: A systematic review and meta-analysis.

BACKGROUND: Transcranial direct current stimulation (tDCS) has shown potential as an effective aid to facilitate learning. A popular application of this technology has been in combination with working memory training (WMT) in order to enhance transfer effects to other cognitive measures after training. OBJECTIVE: This meta-analytic review aims to synthesize the existing literature on tDCS-enhanced WMT to quantify the extent to which tDCS can improve performance on transfer tasks after training. Furthermore, we were interested to evaluate the moderating effects of assessment time point (immediate post-test vs. follow-up) and transfer distance, i.e., the degree of similarity between transfer and training tasks. METHODS: Using robust variance estimation, we performed a systematic meta-analysis of all studies to date that compared WMT with tDCS to WMT with sham in healthy adults. All procedures conformed to PRISMA guidelines. RESULTS: Across 265 transfer measures in 18 studies, we found a small positive net effect of tDCS on improving overall performance on transfer measures after WMT. These effects were sustained at follow-up, which ranged from 1 week to one year after training, with a median of 1 month. Additionally, although there were no significant differences as a function of transfer distance, effects were most pronounced for non-trained working memory tasks. CONCLUSIONS: This review provides evidence that tDCS can be effective in promoting learning over and above WMT alone, and can durably improve performance on trained and untrained measures for weeks to months after the initial training and stimulation period. In particular, boosting performance on dissimilar working memory tasks may present the most promising target for tDCS-augmented WMT.

Comparing random dot motion in MATLAB vs. Inquisit Millisecond.

Random Dot Motion (RDM) displays refer to clouds of independently moving dots that can be parametrically manipulated to provide a perception of the overall cloud moving coherently in a specified direction of motion. As a well-studied probe of motion perception, RDMs have been widely employed to understand underlying neural mechanisms of motion perception, perceptual decision-making, and perceptual learning, among other processes. Despite their wide use, RDM stimuli implementation is highly dependent on the parameters and the generation algorithm of the stimuli; both can greatly influence behavioral performance on RDM tasks. With the advent of the COVID pandemic and an increased need for more accessible platforms, we aimed to validate a novel RDM paradigm on Inquisit Millisecond, a platform for the online administration of cognitive and neuropsychological tests and assessments. We directly compared, in the same participants using the same display, a novel RDM paradigm on both Inquisit Millisecond and MATLAB with Psychtoolbox. We found that psychometric functions of Coherence largely match between Inquisit Millisecond and MATLAB, as do the effects of Duration. These data demonstrate that the Millisecond RDM provides data largely consistent with those previously found in laboratory-based systems, and the present findings can serve as a reference point for expected thresholds for when these procedures are used remotely on different platforms.

Remote auditory assessment using Portable Automated Rapid Testing (PART) and participant-owned devices.

Remote testing of auditory function can be transformative to both basic research and hearing healthcare; however, historically, many obstacles have limited remote collection of reliable and valid auditory psychometric data. Here, we report performance on a battery of auditory processing tests using a remotely administered system, Portable Automatic Rapid Testing. We compare a previously reported dataset collected in a laboratory setting with the same measures using uncalibrated, participant-owned devices in remote settings (experiment 1, n = 40) remote with and without calibrated hardware (experiment 2, n = 36) and laboratory with and without calibrated hardware (experiment 3, n = 58). Results were well-matched across datasets and had similar reliability, but overall performance was slightly worse than published norms. Analyses of potential nuisance factors such as environmental noise, distraction, or lack of calibration failed to provide reliable evidence that these factors contributed to the observed variance in performance. These data indicate feasibility of remote testing of suprathreshold auditory processing using participants' own devices. Although the current investigation was limited to young participants without hearing difficulties, its outcomes demonstrate the potential for large-scale, remote hearing testing of more hearing-diverse populations both to advance basic science and to establish the clinical viability of auditory remote testing.