Collaborative imagination synchronizes representations of the future and fosters social connection in the present.

From close friends to people on a first date, imagining a shared future appears fundamental to relationships. Yet, no previous research has conceptualized the act of imagination as a socially constructed process that affects how connected we feel to others. The present studies provide a framework for investigating imagination as a collaborative process in which individuals cocreate shared representations of hypothetical events-what we call collaborative imagination. Across two preregistered studies (N = 244), we provide evidence that collaborative imagination of a shared future fosters social connection in novel dyads-beyond imagining a shared future individually or shared experience in general. Subjective ratings and natural language processing of participants' imagined narratives illuminate the representational features of imagined events shaped by collaborative imagination. Together, the present findings have the potential to shift how we view the structure and function of imagination with implications for better understanding interpersonal relationships and collective cognition.

Cerebellar Theta Burst Stimulation Impairs Working Memory.

Working memory refers to the process of temporarily storing and manipulating information. The role of the cerebellum in working memory is thought to be achieved through its connections with the prefrontal cortex. Previous studies showed that theta burst stimulation (TBS), a form of repetitive transcranial magnetic stimulation, of the cerebellum changes its functional connectivity with the prefrontal cortex. Specifically, excitatory intermittent TBS (iTBS) increases, whereas inhibitory continuous TBS (cTBS) decreases this functional connectivity. We hypothesized that iTBS on the cerebellum will improve working memory, whereas cTBS will disrupt it. Sixteen healthy participants (10 women) participated in this study. Bilateral cerebellar stimulation was applied with a figure-of-eight coil at 3 cm lateral and 1 cm below the inion. The participants received iTBS, cTBS, and sham iTBS in three separate sessions in random order. Within 30 min after TBS, the participants performed four working memory tasks: letter 1-Back and 2-Back, digit span forward, and digit span backward. Repeated measures analysis of variance revealed a significant effect of the type of stimulation (iTBS/cTBS/Sham) on performance in the digit span backward task (p = 0.02). The planned comparison showed that the cTBS condition had significantly lower scores than the sham condition (p = 0.01). iTBS and cTBS did not affect performance in the 1- and 2-Back and the digit span forward tasks compared to sham stimulation. The findings support the hypothesis that the cerebellum is involved in working memory, and this contribution may be disrupted by cTBS.

The effects of emotion on retrospective duration memory using virtual reality.

Our memories for temporal duration may be colored by the emotions we experience during an event. While emotion generally enhances some aspects of memory, temporal duration has been shown to be particularly susceptible to emotion-induced distortions. However, prior work has faced difficulty when studying this phenomenon, having to make some trade-offs on ecological validity or experimental control. Here, we sought to bridge this gap by studying the effects of emotion on temporal duration memory using virtual reality. In the present study, a final sample of 69 participants experienced a series of negative-emotional and neutral worlds within virtual reality. Following this, participants provided ratings of emotionality (arousal, valence, pleasantness) and retrospective duration estimates (i.e., remembered time). We hypothesized that negative events would be recalled as having a greater duration than neutral events (H1). We additionally hypothesized that negative, but not neutral, events would be recalled as being longer than the true duration (H2). The results supported H1 while failing to provide evidence in support of H2. Together, the results bolster the importance of emotion, especially negative emotion, in shaping how we remember the temporal unfolding of the past.

The retrograde effects of negative emotion on memory for conceptually related events: a registered report.

Emotional events are often remembered better than neutral ones; however, emotion can also spill over and affect our memory for neutral experiences that precede an emotional event. Theories suggest that emotion can retroactively enhance memory for preceding neutral events that are considered high-priority while impairing memory for events deemed low-priority. However, the impact of conceptual relationships (i.e., semantic connections) between preceding neutral information and emotional events on memory for the preceding information has received little attention. This study investigated the influence of conceptual relatedness on the retroactive effects of emotion on memory. Participants sequentially encoded pairs of images that were high or low in conceptual relatedness, each comprising a neutral object followed by either a negative or neutral image. Participants returned the next day for a recognition memory assessment. The results indicated an interactive effect of emotion and conceptual relatedness on memory: In a "discovery" sample, memory was poorer for images preceding conceptually unrelated negative (vs. neutral) images, while the opposite pattern was seen for conceptually related images. In a "replication" sample, these effects were partially replicated, with the former impairment effect statistically observed but not the latter augmentation effect. Hence, conceptual relatedness affects how negative emotion influences memory.

Handedness effects on imagery of dominant- versus non-dominant-hand movements: An electroencephalographic investigation.

Mental representations of our bodies are thought to influence how we interact with our surroundings. We can examine these mental representations through motor imagery, the imagination of movement using scalp EEG recordings. The visual modality of motor imagery emphasises 'seeing' the imagined movement and is associated with increased activity in the alpha rhythm (8-14 Hz) measured over the occipital regions. The kinaesthetic modality emphasises 'feeling' the movement and is associated with decreased activity in the mu rhythm (8-14 Hz) measured over the sensorimotor cortices. These two modalities can be engaged in isolation or together. We recorded EEG activity while 37 participants (17 left-hand dominant) completed an objective hand motor imagery task. Left-handers exhibited significant activity differences between occipital and motor regions only during imagery of right-hand (non-dominant-hand) movements. This difference was primarily driven by less oscillatory activity in the mu rhythm, which may reflect a shift in imagery strategy wherein participants placed more effort into generating the kinaesthetic sensations of non-dominant-hand imagery. Spatial features of 8-14 Hz activity generated from principal component analysis (PCA) provide further support for a strategy shift. Right-handers also exhibited significant differences between alpha and mu activity during imagery of non-dominant movements. However, this difference was not primarily driven by either rhythm, and no differences were observed in the group's PCA results. Together, these findings indicate that individuals imagine movement differently when it involves their dominant versus non-dominant hand, and left-handers may be more flexible in their motor imagery strategies.

Fade In, Fade Out: Do Shifts in Visual Perspective Predict the Consistency of Real-World Memories?

Memories of our personal past are not exact accounts of what occurred. Instead, memory reconstructs the past in adaptive-though not always faithful-ways. Using a naturalistic design, we asked how the visual perspective adopted in the mind's eye when recalling the past-namely, an "own eyes" versus "observer" perspective-relates to the stability of autobiographical memories. We hypothesized that changes in visual perspective over time would predict poorer consistency of memories. Young adults (N = 178) rated the phenomenology of and freely recalled self-selected memories of everyday events at two time points (10 weeks apart). Multilevel linear modeling revealed, as expected, that greater shifts in visual perspective over time predicted lower memory consistency, particularly for emotional details. Our results offer insight into the factors that predict the fidelity of memories for everyday events. Moreover, our results may elucidate new metrics that are useful in interpreting eyewitness testimony or experiences relayed in clinical contexts.

Memory and Consciousness-Usually in Tandem but Sometimes Apart.

Episodic memory, the ability to remember specific events from one's personal past, has been the subject of research for several decades, with a particular emphasis on its relationship with consciousness. In the December 2022 issue of Cognitive and Behavioral Neurology , Budson, Richman, and Kensinger shed new light on this complex topic with a comprehensive exploration of consciousness. In this commentary, I present three propositions about the relationship between episodic memory and consciousness: (1) Episodic memory is usually associated with conscious retrieval; (2) it is possible to have consciousness without episodic memory; and (3) episodic memory can be accessed without conscious retrieval. Drawing from studies conducted with nonhuman animals, I provide evidence to support each of these propositions and discuss how they relate to the theory presented by Budson et al (2000). Although some of my propositions differ from their views, their work has been valuable in stimulating ongoing discussions to advance our understanding of memory.

Beyond body image: what body schema and motor imagery can tell us about the way patients with anorexia nervosa experience their body.

AIM: Recent evidence suggests that the body image disturbance often observed in patients with anorexia nervosa also extends to the body schema. According to the embodiment approach, the body schema is not only involved in motor execution, but also in tasks that only require a mental simulation of a movement such as motor imagery, mental rotation of bodies, and visuospatial perspective-taking. The aim of the present study was to assess the ability of patients with anorexia to mentally simulate movements. METHODS: The sample included 52 patients with acute anorexia and 62 healthy controls. All participants completed three tests of explicit motor imagery, a mental rotation test and a test of visuospatial perspective-taking. RESULTS: Patients with anorexia nervosa, with respect to controls, reported greater difficulties in imagining movements according to a first-person perspective, lower accuracy in motor imagery, selective impairment in the mental rotation of human figures, and reduced ability in assuming a different egocentric visuospatial perspective. CONCLUSION: These results are indicative of a specific alteration in motor imagery in patients with anorexia nervosa. Interestingly, patients' difficulties appear to be limited to those tasks which specifically rely on the body schema, while patients and controls performed similarly in the 3D objects mental rotation task.

Artificial intelligence for dementia research methods optimization.

Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.

Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia.

With the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice. HIGHLIGHTS: Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real-world validation, and interdisciplinary collaboration are required.

Artificial intelligence for dementia-Applied models and digital health.

INTRODUCTION: The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as "deep phenotyping" cohorts with multi-omics health data become available. METHODS: This narrative review synthesizes understanding of applied models and digital health technologies, in terms of dementia risk prediction, diagnostic discrimination, prognosis, and progression. Machine learning approaches show evidence of improved predictive power compared to standard clinical risk scores in predicting dementia, and the potential to decompose large numbers of variables into relatively few critical predictors. RESULTS: This review focuses on key areas of emerging promise including: emphasis on easier, more transparent data sharing and cohort access; integration of high-throughput biomarker and electronic health record data into modeling; and progressing beyond the primary prediction of dementia to secondary outcomes, for example, treatment response and physical health. DISCUSSION: Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g., online), or naturalistic (e.g., watch-based accelerometry).

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review.

INTRODUCTION: Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. METHODS: We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. RESULTS: A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. DISCUSSION: The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. HIGHLIGHTS: There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias.

Long-Term Connectome Analysis Reveals Reshaping of Visual, Spatial Networks in a Model With Vascular Dementia Features.

BACKGROUND: Connectome analysis of neuroimaging data is a rapidly expanding field that offers the potential to diagnose, characterize, and predict neurological disease. Animal models provide insight into biological mechanisms that underpin disease, but connectivity approaches are currently lagging in the rodent. METHODS: We present a pipeline adapted for structural and functional connectivity analysis of the mouse brain, and we tested it in a mouse model of vascular dementia. RESULTS: We observed lacunar infarctions, microbleeds, and progressive white matter change across 6 months. For the first time, we report that default mode network activity is disrupted in the mouse model. We also identified specific functional circuitry that was vulnerable to vascular stress, including perturbations in a sensorimotor, visual resting state network that were accompanied by deficits in visual and spatial memory tasks. CONCLUSIONS: These findings advance our understanding of the mouse connectome and provide insight into how it can be altered by vascular insufficiency.

Cortical complexity estimation using fractal dimension: A systematic review of the literature on clinical and nonclinical samples.

Fractal geometry has recently been proposed as a useful tool for characterizing the complexity of the brain cortex, which is likely to derive from the recurrence of sulci-gyri convolution patterns. The index used to describe the cortical complexity is called fractal dimensional (FD) and was employed by different research exploring the neurobiological correlates of distinct pathological and nonpathological conditions. This review aims to describe the literature on the application of this index, summarize the heterogeneities between studies and inform future research on this topic. Sixty-two studies were included in the systematic review. The main research lines concern neurodevelopment, aging and the neurobiology of specific psychiatric and neurological disorders. Overall, the included papers indicate that cortical complexity is likely to reduce during aging and in various pathological processes affecting the brain. Nevertheless, the high heterogeneity between studies strongly prevents the possibility of drawing conclusions. Further research considering this index besides other morphological values is needed to better clarify the role of FD in characterizing the cortical structure.

Memory rehabilitation: restorative, specific knowledge acquisition, compensatory, and holistic approaches.

Memory impairment following an acquired brain injury can negatively impact daily living and quality of life-but can be reduced by memory rehabilitation. Here, we review the literature on four approaches for memory rehabilitation and their associated strategies: (1) the restorative approach, aimed at a return to pre-morbid functioning, (2) the knowledge acquisition approach, involving training on specific information relevant to daily life, (3) the compensatory approach, targeted at improving daily functioning, and (4) the holistic approach, in which social, emotional, and behavioral deficits are addressed alongside cognitive consequences of acquired brain injury. Each memory rehabilitation approach includes specific strategies such as drill and practice (restorative), spaced retrieval (knowledge acquisition), memory aids (compensatory), or a combination of psychotherapy and cognitive strategies (holistic). Past research has demonstrated mixed support for the use of restorative strategies to improve memory function, whereas knowledge acquisition strategies show promising results on trained tasks but little generalization to untrained tasks and activities of daily living. Compensatory strategies remain widely used but require intensive training to be effectively employed. Finally, the holistic approach is becoming more widespread due to improvements in psychosocial wellbeing, yet there are considerable resource and cost requirements. Several factors can influence rehabilitation outcomes including metacognition and emotional disturbances. Considerations for future research to improve the applicability of strategies for memory rehabilitation include assessing memory impairment severity, examining memory needs in daily life, and exploring the long-term effects of memory rehabilitation.

Emotional arousal impairs association memory: roles of prefrontal cortex regions.

The brain processes underlying impairing effects of emotional arousal on associative memory were previously attributed to two dissociable routes using high-resolution fMRI of the MTL (Madan et al. 2017). Extrahippocampal MTL regions supporting associative encoding of neutral pairs suggested unitization; conversely, associative encoding of negative pairs involved compensatory hippocampal activity. Here, whole-brain fMRI revealed prefrontal contributions: dmPFC was more involved in hippocampal-dependent negative pair learning and vmPFC in extrahippocampal neutral pair learning. Successful encoding of emotional memory associations may require emotion regulation/conflict resolution (dmPFC), while neutral memory associations may be accomplished by anchoring new information to prior knowledge (vmPFC).

T2 heterogeneity as an in vivo marker of microstructural integrity in medial temporal lobe subfields in ageing and mild cognitive impairment.

A better understanding of early brain changes that precede loss of independence in diseases like Alzheimer's disease (AD) is critical for development of disease-modifying therapies. Quantitative MRI, such as T2 relaxometry, can identify microstructural changes relevant to early stages of pathology. Recent evidence suggests heterogeneity of T2 may be a more informative MRI measure of early pathology than absolute T2. Here we test whether T2 markers of brain integrity precede the volume changes we know are present in established AD and whether such changes are most marked in medial temporal lobe (MTL) subfields known to be most affected early in AD. We show that T2 heterogeneity was greater in people with mild cognitive impairment (MCI; n = 49) compared to healthy older controls (n = 99) in all MTL subfields, but this increase was greatest in MTL cortices, and smallest in dentate gyrus. This reflects the spatio-temporal progression of neurodegeneration in AD. T2 heterogeneity in CA1-3 and entorhinal cortex and volume of entorhinal cortex showed some ability to predict cognitive decline, where absolute T2 could not, however further studies are required to verify this result. Increases in T2 heterogeneity in MTL cortices may reflect localised pathological change and may present as one of the earliest detectible brain changes prior to atrophy. Finally, we describe a mechanism by which memory, as measured by accuracy and reaction time on a paired associate learning task, deteriorates with age. Age-related memory deficits were explained in part by lower subfield volumes, which in turn were directly associated with greater T2 heterogeneity. We propose that tissue with high T2 heterogeneity represents extant tissue at risk of permanent damage but with the potential for therapeutic rescue. This has implications for early detection of neurodegenerative diseases and the study of brain-behaviour relationships.

A brief primer on the PhD supervision relationship.

Becoming a successful academic and securing a principal investigator (PI) position at a research-intensive university requires many distinct skills. Beyond some form of technical skills and domain-specific knowledge, some of these skills include time management, scientific writing, public speaking, and project management. Training prior to the PI position involved some of these latter skills, and perhaps even some degree of trainee supervision, but PhD-level supervision and the associated responsibilities do not arrive until one becomes a PI. Many academic skills are learned 'on the job', but few more so than PhD supervision. While I myself have limited PhD supervision experience, I have reviewed the literature on PhD student-supervisor relationship and here present a brief primer.

Age-related decrements in cortical gyrification: Evidence from an accelerated longitudinal dataset.

Cortical gyrification has been found to decrease due to aging, but thus far this has only been examined in cross-sectional samples. Interestingly, the topography of these age-related differences in gyrification follows a distinct gradient along the cortex relative to age effects on cortical thickness, likely suggesting a different underlying neurobiological mechanism. Here I examined several aspects of gyrification in an accelerated longitudinal dataset of 280 healthy adults aged 45-92 with an interval between first and last MRI sessions of up to 10 years (total of 815 MRI sessions). Results suggest that age changes in sulcal morphology underlie these changes in gyrification.

Exploring the Facets of Emotional Episodic Memory: Remembering "What," "When," and "Which".

Our memories can differ in quality from one event to the next, and emotion is one important explanatory factor. Still, the manner in which emotion impacts episodic memory is complex: Whereas emotion enhances some aspects of episodic memory-particularly central aspects-it dampens memory for peripheral/contextual information. Extending previous work, we examined the effects of emotion on one often overlooked aspect of memory, namely, temporal context. We tested whether emotion would impair memory for when an event occurred. Participants (N = 116 adults) watched videos wherein negative and neutral images were inserted. Consistent with prior work, results showed that emotion enhanced and impaired memory, respectively, for "what" and "which." Unexpectedly, emotion was associated with enhanced accuracy for "when": We found that participants estimated that neutral images occurred relatively later, but there was no such bias for negative images. By examining multiple features of episodic memory, we provide a holistic characterization of the myriad effects of emotion.