A Connectivity-Based Psychometric Prediction Framework for Brain-Behavior Relationship Studies

From neural activity to behavioral engagement: temporal dynamics as their "common currency" during music

The human cortex is highly dynamic as manifest in its vast ongoing temporal repertoire. Similarly, human behavior is also variable over time with, for instance, fluctuating response times. How the brain's ongoing dynamics relates to the fluctuating dynamics of behavior such as emotions remains yet unclear, though. We measure median frequency (MF) in a dynamic way (D-MF) to investigate the dynamics in both EEG neural activity and the subjects' continuous behavioral assessment of their perceived emotional engagement changes during five different music pieces. Our main findings are: (i) significant differences in the frequency dynamics, e.g., D-MF, of the subjects' behavioral engagement ratings between the five music pieces, (ii) significant differences in the, e.g., D-MF, of the music pieces' EEG-based neural activity, and (iii) there is a unidirectional relationship from neural to behavioral during the five music pieces as measured through correlation and Granger causality between their respective D-MF's. Together, we demonstrate that neural dynamics relates to behavioral dynamics through the shared fluctuations in their dynamics. This highlights the key role of dynamics in connecting neural and behavioral activity as their "common currency" (Northoff et al. 2020, 2024).

Transdiagnostic Connectome-Based Prediction of Response Inhibition

Response inhibition (RI) deficits are a core feature across diagnostic categories of mental disorders. However, it remains unclear whether the brain networks underlying different forms of RI deficits are disorder-shared or disorder-specific, and how they interact with aberrant brain connectivity across disorders. Connectome-based predictive modeling (CPM) provides a novel approach for exploring the brain networks associated with RI abnormalities across diagnostic categories of mental disorders. Publicly available resting-state functional magnetic resonance imaging data from individuals with schizophrenia (n = 47), bipolar disorder (n = 47), and attention-deficit/hyperactivity disorder (n = 40), as well as healthy controls (n = 121), were utilized to construct whole-brain network predictive models for different forms of RI (action cancellation and action restraint). The brain networks of different forms of RI were further compared with abnormal brain networks in the diagnostic groups. Action restraint and action cancellation exhibited both shared and distinct brain networks. There was a dissociation in the relationship between the brain networks underlying different forms of RI and the aberrant connectivity patterns observed across diagnostic categories. Our models successfully predicted action restraint performance across diagnostic categories, whereas the model failed to effectively predict action cancellation due to the influence of disease-related aberrant connectivity on the brain networks underlying action cancellation. Nevertheless, the action cancellation model demonstrated generalizability to novel, healthy participants (n = 220) from an independent dataset. Our study clarifies the complex relationship between deficits in RI and the neuropathology of mental disorders and provides a foundation for more accurate cognitive assessment and targeted interventions. Our findings highlight the importance of refining RI constructs and emphasize the value of applying connectome methods to reveal cross-diagnostic neural mechanisms.

How measurement noise limits the accuracy of brain-behaviour predictions

Major efforts in human neuroimaging strive to understand individual differences and find biomarkers for clinical applications by predicting behavioural phenotypes from brain imaging data. To identify generalisable and replicable brain-behaviour prediction models, sufficient measurement reliability is essential. However, the selection of prediction targets is predominantly guided by scientific interest or data availability rather than psychometric considerations. Here, we demonstrate the impact of low reliability in behavioural phenotypes on out-of-sample prediction performance. Using simulated and empirical data from four large-scale datasets, we find that reliability levels common across many phenotypes can markedly limit the ability to link brain and behaviour. Next, using 5000 participants from the UK Biobank, we show that only highly reliable data can fully benefit from increasing sample sizes from hundreds to thousands of participants. Our findings highlight the importance of measurement reliability for identifying meaningful brain-behaviour associations from individual differences and underscore the need for greater emphasis on psychometrics in future research.

Connectivity-based neuromarker for children's inhibitory control ability and its relevance to body mass index

Preserving a normal body mass index (BMI) is crucial for the healthy growth and development of children. As a core aspect of executive functions, inhibitory control plays a pivotal role in maintaining a normal BMI, which is key to preventing issues of childhood obesity. By studying individual variations in inhibitory control performance and its associated connectivity-based neuromarker in a sample of primary school students (N = 64; 9-12 yr), we aimed to unravel the pathway through which inhibitory control impacts children's BMI. Utilizing resting-state functional MRI scans and a connectivity-based psychometric prediction framework, we found that enhanced inhibitory control abilities were primarily associated with increased functional connectivity in brain structures vital to executive functions, such as the superior frontal lobule, superior parietal lobule, and posterior cingulate cortex. Conversely, inhibitory control abilities displayed a negative relationship with functional connectivity originating from reward-related brain structures, such as the orbital frontal and ventral medial prefrontal lobes. Furthermore, we revealed that both inhibitory control and its corresponding neuromarker can moderate the association between food-related delayed gratification and BMI in children. However, only the neuromarker of inhibitory control maintained its moderating effect on children's future BMI, as determined in the follow-up after one year. Overall, our findings shed light on the potential mechanisms of how inhibitory control in children impacts BMI, highlighting the utility of the connectivity-based neuromarker of inhibitory control in the context of childhood obesity.

Neural processing of speech comprehension in noise predicts individual age using fNIRS-based brain-behavior models

Speech comprehension in noise depends on complex interactions between peripheral sensory and central cognitive systems. Despite having normal peripheral hearing, older adults show difficulties in speech comprehension. It remains unclear whether the brain's neural responses could indicate aging. The current study examined whether individual brain activation during speech perception in different listening environments could predict age. We applied functional near-infrared spectroscopy to 93 normal-hearing human adults (20 to 70 years old) during a sentence listening task, which contained a quiet condition and 4 different signal-to-noise ratios (SNR = 10, 5, 0, -5 dB) noisy conditions. A data-driven approach, the region-based brain-age predictive modeling was adopted. We observed a significant behavioral decrease with age under the 4 noisy conditions, but not under the quiet condition. Brain activations in SNR = 10 dB listening condition could successfully predict individual's age. Moreover, we found that the bilateral visual sensory cortex, left dorsal speech pathway, left cerebellum, right temporal-parietal junction area, right homolog Wernicke's area, and right middle temporal gyrus contributed most to prediction performance. These results demonstrate that the activations of regions about sensory-motor mapping of sound, especially in noisy conditions, could be sensitive measures for age prediction than external behavior measures.

ConnSearch: A framework for functional connectivity analysis designed for interpretability and effectiveness at limited sample sizes

Functional connectivity studies increasingly turn to machine learning methods, which typically involve fitting a connectome-wide classifier, then conducting post hoc interpretation analyses to identify the neural correlates that best predict a dependent variable. However, this traditional analytic paradigm suffers from two main limitations. First, even if classifiers are perfectly accurate, interpretation analyses may not identify all the patterns expressed by a dependent variable. Second, even if classifiers are generalizable, the patterns implicated via interpretation analyses may not replicate. In other words, this traditional approach can yield effective classifiers while falling short of most neuroscientists' goals: pinpointing the neural correlates of dependent variables. We propose a new framework for multivariate analysis, ConnSearch, which involves dividing the connectome into components (e.g., groups of highly connected regions) and fitting an independent model for each component (e.g., a support vector machine or a correlation-based model). Conclusions about the link between a dependent variable and the brain are based on which components yield predictive models rather than on interpretation analysis. We used working memory data from the Human Connectome Project (N = 50-250) to compare ConnSearch with four existing connectome-wide classification/interpretation methods. For each approach, the models attempted to classify examples as being from the high-load or low-load conditions (binary labels). Relative to traditional methods, ConnSearch identified neural correlates that were more comprehensive, had greater consistency with the WM literature, and better replicated across datasets. Hence, ConnSearch is well-positioned to be an effective tool for functional connectivity research.

The challenges and prospects of brain-based prediction of behaviour

Relating individual brain patterns to behaviour is fundamental in system neuroscience. Recently, the predictive modelling approach has become increasingly popular, largely due to the recent availability of large open datasets and access to computational resources. This means that we can use machine learning models and interindividual differences at the brain level represented by neuroimaging features to predict interindividual differences in behavioural measures. By doing so, we could identify biomarkers and neural correlates in a data-driven fashion. Nevertheless, this budding field of neuroimaging-based predictive modelling is facing issues that may limit its potential applications. Here we review these existing challenges, as well as those that we anticipate as the field develops. We focus on the impacts of these challenges on brain-based predictions. We suggest potential solutions to address the resolvable challenges, while keeping in mind that some general and conceptual limitations may also underlie the predictive modelling approach.

Cross-cohort replicability and generalizability of connectivity-based psychometric prediction patterns

An increasing number of studies have investigated the relationships between inter-individual variability in brain regions' connectivity and behavioral phenotypes, making use of large population neuroimaging datasets. However, the replicability of brain-behavior associations identified by these approaches remains an open question. In this study, we examined the cross-dataset replicability of brain-behavior association patterns for fluid cognition and openness predictions using a previously developed region-wise approach, as well as using a standard whole-brain approach. Overall, we found moderate similarity in patterns for fluid cognition predictions across cohorts, especially in the Human Connectome Project Young Adult, Human Connectome Project Aging, and Enhanced Nathan Kline Institute Rockland Sample cohorts, but low similarity in patterns for openness predictions. In addition, we assessed the generalizability of prediction models in cross-dataset predictions, by training the model in one dataset and testing in another. Making use of the region-wise prediction approach, we showed that first, a moderate extent of generalizability could be achieved with fluid cognition prediction, and that, second, a set of common brain regions related to fluid cognition across cohorts could be identified. Nevertheless, the moderate replicability and generalizability could only be achieved in specific contexts. Thus, we argue that replicability and generalizability in connectivity-based prediction remain limited and deserve greater attention in future studies.

Asymmetric generalizability of multimodal brain-behavior associations across age-groups

Machine learning methods have increasingly been used to map out brain-behavior associations (BBA), and to predict out-of-scanner behavior of unseen subjects. Given the brain changes that occur in the context of aging, the accuracy of these predictions is likely to depend on how similar the training and testing data sets are in terms of age. To this end, we examined how well BBAs derived from an age-group generalize to other age-groups. We partitioned the CAM-CAN data set (N = 550) into the young, middle, and old age-groups, then used the young and old age-groups to construct prediction models for 11 behavioral outcomes using multimodal neuroimaging features (i.e., structural and resting-state functional connectivity, and gray matter volume/cortical thickness). These models were then applied to all three age-groups to predict their behavioral scores. When the young-derived models were used, a graded pattern of age-generalization was generally observed across most behavioral outcomes-predictions are the most accurate in the young subjects in the testing data set, followed by the middle and then old-aged subjects. Conversely, when the old-derived models were used, the disparity in the predictive accuracy across age-groups was mostly negligible. These findings hold across different imaging modalities. These results suggest the asymmetric age-generalization of BBAs-old-derived BBAs generalized well to all age-groups, however young-derived BBAs generalized poorly beyond their own age-group.

Linking interindividual variability in brain structure to behaviour

What are the brain structural correlates of interindividual differences in behaviour? More than a decade ago, advances in structural MRI opened promising new avenues to address this question. The initial wave of research then progressively led to substantial conceptual and methodological shifts, and a replication crisis unveiled the limitations of traditional approaches, which involved searching for associations between local measurements of neuroanatomy and behavioural variables in small samples of healthy individuals. Given these methodological issues and growing scepticism regarding the idea of one-to-one mapping of psychological constructs to brain regions, new perspectives emerged. These not only embrace the multivariate nature of brain structure-behaviour relationships and promote generalizability but also embrace the representation of the relationships between brain structure and behavioural data by latent dimensions of interindividual variability. Here, we examine the past and present of the study of brain structure-behaviour associations in healthy populations and address current challenges and open questions for future investigations.

Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity

Algorithmic biases that favor majority populations pose a key challenge to the application of machine learning for precision medicine. Here, we assessed such bias in prediction models of behavioral phenotypes from brain functional magnetic resonance imaging. We examined the prediction bias using two independent datasets (preadolescent versus adult) of mixed ethnic/racial composition. When predictive models were trained on data dominated by white Americans (WA), out-of-sample prediction errors were generally higher for African Americans (AA) than for WA. This bias toward WA corresponds to more WA-like brain-behavior association patterns learned by the models. When models were trained on AA only, compared to training only on WA or an equal number of AA and WA participants, AA prediction accuracy improved but stayed below that for WA. Overall, the results point to the need for caution and further research regarding the application of current brain-behavior prediction models in minority populations.