Using functional connectivity models to characterize relationships between working and episodic memory

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.

Brain-phenotype models fail for individuals who defy sample stereotypes

Individual differences in brain functional organization track a range of traits, symptoms and behaviours1-12. So far, work modelling linear brain-phenotype relationships has assumed that a single such relationship generalizes across all individuals, but models do not work equally well in all participants13,14. A better understanding of in whom models fail and why is crucial to revealing robust, useful and unbiased brain-phenotype relationships. To this end, here we related brain activity to phenotype using predictive models-trained and tested on independent data to ensure generalizability15-and examined model failure. We applied this data-driven approach to a range of neurocognitive measures in a new, clinically and demographically heterogeneous dataset, with the results replicated in two independent, publicly available datasets16,17. Across all three datasets, we find that models reflect not unitary cognitive constructs, but rather neurocognitive scores intertwined with sociodemographic and clinical covariates; that is, models reflect stereotypical profiles, and fail when applied to individuals who defy them. Model failure is reliable, phenotype specific and generalizable across datasets. Together, these results highlight the pitfalls of a one-size-fits-all modelling approach and the effect of biased phenotypic measures18-20 on the interpretation and utility of resulting brain-phenotype models. We present a framework to address these issues so that such models may reveal the neural circuits that underlie specific phenotypes and ultimately identify individualized neural targets for clinical intervention.

Neuropsychological, Metabolic, and Connectivity Underpinnings of Semantic Interference Deficits Using the LASSI-L

BACKGROUND

LASSI-L is a novel neuropsychological test specifically designed for the early diagnosis of Alzheimer's disease (AD) based on semantic interference.

OBJECTIVE

To examine the cognitive and neural underpinnings of the failure to recover from proactive semantic and retroactive semantic interference.

METHODS

One hundred and fifty-five patients consulting for memory loss were included. Patients underwent neuropsychological assessment, including the LASSI-L, and FDG-PET imaging. They were categorized as subjective memory complaints (SMC) (n=32), pre-mild cognitive impairment (MCI) due to AD (Pre-MCI) (n=39), MCI due to AD (MCI-AD) (n=71), and MCI without evidence of neurodegeneration (MCI-NN) (n=13). Voxel-based brain mapping and metabolic network connectivity analyses were conducted.

RESULTS

A significant group effect was found for all the LASSI-L scores. LASSI-L scores measuring failure to recover from proactive semantic interference and retroactive semantic interference were predicted by other neuropsychological tests with a precision of 64.1 and 44.8%. The LASSI-L scores were associated with brain metabolism in the bilateral precuneus, superior, middle and inferior temporal gyri, fusiform, angular, superior and inferior parietal lobule, superior, middle and inferior occipital gyri, lingual gyrus, and posterior cingulate. Connectivity analysis revealed a decrease of node degree and centrality in posterior cingulate in patients showing frPSI.

CONCLUSION

Episodic memory dysfunction and the involvement of the medial temporal lobe, precuneus and posterior cingulate constitute the basis of the failure to recover from proactive semantic interference and retroactive semantic interference. These findings support the role of the LASSI-L in the detection, monitoring and outcome prediction during the early stages of AD.

Using functional connectivity models to characterize relationships between working and episodic memory

INTRODUCTION

Working memory is a critical cognitive ability that affects our daily functioning and relates to many cognitive processes and clinical conditions. Episodic memory is vital because it enables individuals to form and maintain their self-identities. Our study analyzes the extent to which whole-brain functional connectivity observed during completion of an N-back memory task, a common measure of working memory, can predict both working memory and episodic memory.

METHODS

We used connectome-based predictive models (CPMs) to predict 502 Human Connectome Project (HCP) participants' in-scanner 2-back memory test scores and out-of-scanner working memory test (List Sorting) and episodic memory test (Picture Sequence and Penn Word) scores based on functional magnetic resonance imaging (fMRI) data collected both during rest and N-back task performance. We also analyzed the functional brain connections that contributed to prediction for each of these models.

RESULTS

Functional connectivity observed during N-back task performance predicted out-of-scanner List Sorting scores and to a lesser extent out-of-scanner Picture Sequence scores, but did not predict out-of-scanner Penn Word scores. Additionally, the functional connections predicting 2-back scores overlapped to a greater degree with those predicting List Sorting scores than with those predicting Picture Sequence or Penn Word scores. Functional connections with the insula, including connections between insular and parietal regions, predicted scores across the 2-back, List Sorting, and Picture Sequence tasks.

CONCLUSIONS

Our findings validate functional connectivity observed during the N-back task as a measure of working memory, which generalizes to predict episodic memory to a lesser extent. By building on our understanding of the predictive power of N-back task functional connectivity, this work enhances our knowledge of relationships between working memory and episodic memory.