Troubled past: A critical psychometric assessment of the self-report Survey of Autobiographical Memory (SAM)

TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance

We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography and associated pointwise tissue microstructure measurements. By employing a point cloud representation, TractGeoNet can directly utilize tissue microstructure and positional information from all points within a fiber tract without the need to average or bin data along the streamline as traditionally required by dMRI tractometry methods. To improve regression performance, we propose a novel loss function, the Paired-Siamese Regression loss, which encourages the model to focus on accurately predicting the relative differences between regression label scores rather than just their absolute values. In addition, to gain insight into the brain regions that contribute most strongly to the prediction results, we propose a Critical Region Localization algorithm. This algorithm identifies highly predictive anatomical regions within the white matter fiber tracts for the regression task. We evaluate the effectiveness of the proposed method by predicting individual performance on two neuropsychological assessments of language using a dataset of 20 association white matter fiber tracts from 806 subjects from the Human Connectome Project Young Adult dataset. The results demonstrate superior prediction performance of TractGeoNet compared to several popular regression models that have been applied to predict individual cognitive performance based on neuroimaging features. Of the twenty tracts studied, we find that the left arcuate fasciculus tract is the most highly predictive of the two studied language performance assessments. Within each tract, we localize critical regions whose microstructure and point information are highly and consistently predictive of language performance across different subjects and across multiple independently trained models. These critical regions are widespread and distributed across both hemispheres and all cerebral lobes, including areas of the brain considered important for language function such as superior and anterior temporal regions, pars opercularis, and precentral gyrus. Overall, TractGeoNet demonstrates the potential of geometric deep learning to enhance the study of the brain's white matter fiber tracts and to relate their structure to human traits such as language performance.

Intrinsic functional connectivity among memory networks does not predict individual differences in narrative recall

Individuals differ greatly in their ability to remember the details of past events, yet little is known about the brain processes that explain such individual differences in a healthy young population. Previous research suggests that episodic memory relies on functional communication among ventral regions of the default mode network ("DMN-C") that are strongly interconnected with the medial temporal lobes. In this study, we investigated whether the intrinsic functional connectivity of the DMN-C subnetwork is related to individual differences in memory ability, examining this relationship across 243 individuals (ages 18-50 years) from the openly available Cambridge Center for Aging and Neuroscience (Cam-CAN) dataset. We first estimated each participant's whole-brain intrinsic functional brain connectivity by combining data from resting-state, movie-watching, and sensorimotor task scans to increase statistical power. We then examined whether intrinsic functional connectivity predicted performance on a narrative recall task. We found no evidence that functional connectivity of the DMN-C, with itself, with other related DMN subnetworks, or with the rest of the brain, was related to narrative recall. Exploratory connectome-based predictive modeling (CBPM) analyses of the entire connectome revealed a whole-brain multivariate pattern that predicted performance, although these changes were largely outside of known memory networks. These results add to emerging evidence suggesting that individual differences in memory cannot be easily explained by brain differences in areas typically associated with episodic memory function.

The positive dimension of schizotypy is associated with self-report measures of autobiographical memory and future thinking but not experimenter-scored indices

ABSTRACTThe ability to remember our past and to imagine the future are critical to our sense of self. Previous research has indicated that they are disrupted in schizophrenia. However, it is unclear (i) whether this is found when examining experimenter-scored indices of content and/or participants' self-report of phenomenological characteristics, and (ii) how these abilities might be related to symptoms. This study sought to address these questions by taking a dimensional approach and measuring positive and negative schizotypal experiences in healthy people (n = 90). Participants were given cue words. For some, they remembered an event from the past and for others they generated an event in the future. No significant relationships were found with any aspect of schizotypy when participants' descriptions were scored by the experimenter according to a standardised episodic content measure. In contrast, several significant positive correlations were observed for past memory and future thinking when examining the positive dimension of schizotypy and participants' ratings, particularly to sensory characteristics of the experience and mental pre- or reliving. These results indicate enhanced subjective experiences of autobiographical memory and future thinking in those who report delusional and hallucinatory-like occurrences, which might be linked to mental imagery or metacognitive alterations.

Taking stock of the past: A psychometric evaluation of the Autobiographical Interview

Autobiographical memory (AM) involves a rich phenomenological re-experiencing of a spatio-temporal event from the past, which is challenging to objectively quantify. The Autobiographical Interview (AI; Levine et al. Psychology and Aging, 17(4), 677-689, 2002) is a manualized performance-based assessment designed to quantify episodic (internal) and semantic (external) features of recalled and verbally conveyed prior experiences. The AI has been widely adopted, yet has not undergone a comprehensive psychometric validation. We investigated the reliability, validity, association to individual differences measures, and factor structure in healthy younger and older adults (N = 352). Evidence for the AI's reliability was strong: the subjective scoring protocol showed high inter-rater reliability and previously identified age effects were replicated. Internal consistency across timepoints was robust, suggesting stability in recollection. Central to our validation, internal AI scores were positively correlated with standard, performance-based measures of episodic memory, demonstrating convergent validity. The two-factor structure for the AI was not well supported by confirmatory factor analysis. Adjusting internal and external detail scores for the number of words spoken (detail density) improved trait estimation of AM performance. Overall, the AI demonstrated sound psychometric properties for inquiry into the qualities of autobiographical remembering.

Intrinsic functional connectivity in medial temporal lobe networks is associated with susceptibility to misinformation

Memory is notoriously fallible and susceptible to misinformation. Yet little is known about the underlying cognitive and neural mechanisms that render individuals vulnerable to this type of false memory. The current experiments take an individual differences approach to examine whether susceptibility to misinformation reflects stable underlying factors related to memory retrieval. In Study 1, we report for the first time the existence of substantial individual variability in susceptibility to misinformation in the context of repeated memory retrieval, when the misinformation effect is most pronounced. This variability was not related to an individual's tendency to adopt an episodic retrieval style during remembering (trait mnemonics). In Study 2, we next examined whether susceptibility to misinformation is related to intrinsic functional connectivity in medial temporal lobe (MTL) networks known to coordinate memory reactivation during event retrieval. Stronger resting-state functional connectivity between the MTL and cortical areas associated with visual memory reactivation (occipital cortex) was associated with better protection from misinformation. Together, these results reveal that while memory distortion is a universal property of our reconstructive memory system, susceptibility to misinformation varies at the individual level and may depend on one's ability to reactivate visual details during memory retrieval.

How Imagination and Memory Shape the Moral Mind

Interdisciplinary research has proposed a multifaceted view of human cognition and morality, establishing that inputs from multiple cognitive and affective processes guide moral decisions. However, extant work on moral cognition has largely overlooked the contributions of episodic representation. The ability to remember or imagine a specific moment in time plays a broadly influential role in cognition and behavior. Yet, existing research has only begun exploring the influence of episodic representation on moral cognition. Here, we evaluate the theoretical connections between episodic representation and moral cognition, review emerging empirical work revealing how episodic representation affects moral decision-making, and conclude by highlighting gaps in the literature and open questions. We argue that a comprehensive model of moral cognition will require including the episodic memory system, further delineating its direct influence on moral thought, and better understanding its interactions with other mental processes to fundamentally shape our sense of right and wrong.

Development of a Japanese version of the Autobiographical Recollection Test: convergent validity with self-reported scales and memory details

While numerous studies have examined the characteristics of specific autobiographical memories, until recently, no questionnaire has asked how individuals remember their past in general. We developed a Japanese version of the Autobiographical Recollection Test (ART), which consists of seven components (vividness, narrative coherence, reliving, rehearsal, scene, visual imagery, and life story relevance) and surveys the general characteristics of autobiographical remembering. Confirmatory factor analysis and item response theory showed that the Japanese version of the ART had sufficient psychometric properties and generally correlated as hypothesised with self-report questionnaires as a measure of convergent validity. While the short version of the Japanese ART correlated positively with the internal details (episodic elements) of autobiographical narratives, the full version did not correlate with internal details. We discuss the use of ART for future research examining individual and cultural differences in autobiographical remembering.