Brain age of rhesus macaques over the lifespan.

Through the application of machine learning algorithms to neuroimaging data the brain age methodology was shown to provide a useful individual-level biological age prediction and identify key brain regions responsible for the prediction. In this study, we present the methodology of constructing a rhesus macaque brain age model using a machine learning algorithm and discuss the key predictive brain regions in comparison to the human brain, to shed light on cross-species primate similarities and differences. Structural information of the brain (e.g., parcellated volumes) from brain magnetic resonance imaging of 43 rhesus macaques were used to develop brain atlas-based features to build a brain age model that predicts biological age. The best-performing model used 22 selected features and achieved an R2 of 0.72. We also identified interpretable predictive brain features including Right Fronto-orbital Cortex, Right Frontal Pole, Right Inferior Lateral Parietal Cortex, and Bilateral Posterior Central Operculum. Our findings provide converging evidence of the parallel and comparable brain regions responsible for both non-human primates and human biological age prediction.

Fractal Dimension Analysis in Neurological Disorders: An Overview.

Fractal analysis has emerged as a powerful tool for characterizing irregular and complex patterns found in the nervous system. This characterization is typically applied by estimating the fractal dimension (FD), a scalar index that describes the topological complexity of the irregular components of the nervous system, both at the macroscopic and microscopic levels, that may be viewed as geometric fractals. Moreover, temporal properties of neurophysiological signals can also be interpreted as dynamic fractals. Given its sensitivity for detecting changes in brain morphology, FD has been explored as a clinically relevant marker of brain damage in several neuropsychiatric conditions as well as in normal and pathological cerebral aging. In this sense, evidence is accumulating for decreases in FD in Alzheimer's disease, frontotemporal dementia, Parkinson's disease, multiple sclerosis, and many other neurological disorders. In addition, it is becoming increasingly clear that fractal analysis in the field of clinical neurology opens the possibility of detecting structural alterations in the early stages of the disease, which highlights FD as a potential diagnostic and prognostic tool in clinical practice.

"What will you do after?": Lessons from Academia and the World Beyond.

Determining post-PhD career options is a challenge for many Psychology PhD graduates. Here I provide a comprehensive overview of the diverse career trajectories available to graduates, drawing from interviews with 53 PhD graduates conducted as part of the two-volume Academia and the World Beyond book series. From these, I conducted a hierarchical qualitative classification to categorise and characterise potential career paths. The findings reveal a spectrum of opportunities, from traditional academic roles to "academic adjacent" and "skill-transfer" careers. This work underscores the versatility of Psychology doctoral training, providing skills that can support a wide array of career possibilities. The results serve as a guide for current and prospective PhD students-and their mentors-emphasising the variety of professional contexts where doctoral training is beneficial.

Subjective memory measures: Metamemory questionnaires currently in use.

Subjective memory evaluation is important for assessing memory abilities and complaints alongside objective measures. In research and clinical settings, questionnaires are used to examine perceived memory ability, memory complaints, and memory beliefs/knowledge. Although they provide a structured measure of self-reported memory, there is some debate as to whether subjective evaluation accurately reflects memory abilities. Specifically, the disconnect between subjective and objective memory measures remains a long-standing issue within the field. Thus, it is essential to evaluate the benefits and limitations of questionnaires that are currently in use. This review encompasses three categories of metamemory questionnaires: self-efficacy, complaints, and multidimensional questionnaires. Factors influencing self-evaluation of memory including knowledge and beliefs about memory, ability to evaluate memory, recent metamemory experiences, and affect are examined. The relationship between subjective and objective memory measures is explored, and considerations for future development and use of metamemory questionnaires are provided.

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.

Rare and extreme outcomes in risky choice.

Many real-world decisions involving rare events also involve extreme outcomes. Despite this confluence, decisions-from-experience research has only examined the impact of rarity and extremity in isolation. With rare events, people typically choose as if they underestimate the probability of a rare outcome happening. Separately, people typically overestimate the probability of an extreme outcome happening. Here, for the first time, we examine the confluence of these two biases in decisions-from-experience. In a between-groups behavioural experiment, we examine people's risk preferences for rare extreme outcomes and for rare non-extreme outcomes. When outcomes are both rare and extreme, people's risk preferences shift away from traditional risk patterns for rare events: they show reduced underweighting for events that are both rare and extreme. We simulate these results using a small-sample model of decision-making that accounts for both the underweighting of rare events and the overweighting of extreme events. These separable influences on risk preferences suggest that to understand real-world risk for rare events we must also consider the extremity of the outcomes.

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 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.

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.

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.

Using Evidence-Based Learning Strategies to Improve Medical Education.

Medical education research has been adopting principles from psychology to improve student learning. Here is an overview and illustrative examples of six evidence-based learning strategies that have been thoroughly researched and validated in the psychology literature: spacing, interleaving, retrieval practice, elaboration, dual coding, and concrete examples. For each of these, their use within medical education and considerations that may influence efficacy are discussed. Medical education researchers should collaborate more with psychology researchers in transdisciplinary teams to better implement these strategies and more directly benefit from advances made in the psychology literature.

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).

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.

Quantifying numerical and spatial reliability of hippocampal and amygdala subdivisions in FreeSurfer.

On-going, large-scale neuroimaging initiatives can aid in uncovering neurobiological causes and correlates of poor mental health, disease pathology, and many other important conditions. As projects grow in scale with hundreds, even thousands, of individual participants and scans collected, quantification of brain structures by automated algorithms is becoming the only truly tractable approach. Here, we assessed the spatial and numerical reliability for newly deployed automated segmentation of hippocampal subfields and amygdala nuclei in FreeSurfer 7. In a sample of participants with repeated structural imaging scans (N = 928), we found numerical reliability (as assessed by intraclass correlations, ICCs) was reasonable. Approximately 95% of hippocampal subfields had "excellent" numerical reliability (ICCs ≥ 0.90), while only 67% of amygdala subnuclei met this same threshold. In terms of spatial reliability, 58% of hippocampal subfields and 44% of amygdala subnuclei had Dice coefficients ≥ 0.70. Notably, multiple regions had poor numerical and/or spatial reliability. We also examined correlations between spatial reliability and person-level factors (e.g., participant age; T1 image quality). Both sex and image scan quality were related to variations in spatial reliability metrics. Examined collectively, our work suggests caution should be exercised for a few hippocampal subfields and amygdala nuclei with more variable reliability.

Artificial Intelligence for Dementia Research Methods Optimization.

INTRODUCTION: Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater. METHODS: We summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research. RESULTS: We present an overview of ML algorithms most frequently used in dementia research and highlight future opportunities for the use of ML in clinical practice, experimental medicine, and clinical trials. We discuss issues of reproducibility, replicability and interpretability and how these impact the clinical applicability of dementia research. Finally, we give examples of how state-of-the-art methods, such as transfer learning, multi-task learning, and reinforcement learning, may be applied to overcome these issues and aid the translation of research to clinical practice in the future. DISCUSSION: ML-based models hold great promise to advance our understanding of the underlying causes and pathological mechanisms of dementia.

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.

Biased confabulation in risky choice.

When people make risky decisions based on past experience, they must rely on memory. The nature of the memory representations that support these decisions is not yet well understood. A key question concerns the extent to which people recall specific past episodes or whether they have learned a more abstract rule from their past experience. To address this question, we examined the precision of the memories used in risky decisions-from-experience. In three pre-registered experiments, we presented people with risky options, where the outcomes were drawn from continuous ranges (e.g., 100-190 or 500-590), and then assessed their memories for the outcomes experienced. In two preferential tasks, people were more risk seeking for high-value than low-value options, choosing as though they overweighted the outcomes from more extreme ranges. Moreover, in two preferential tasks and a parallel evaluation task, people were very poor at recalling the exact outcomes encountered, but rather confabulated outcomes that were consistent with the outcomes they had seen and were biased towards the more extreme ranges encountered. This common pattern suggests that the observed decision bias in the preferential task reflects a basic cognitive process to overweight extreme outcomes in memory. These results highlight the importance of the edges of the distribution in providing the encoding context for memory recall. They also suggest that episodic memory influences decision-making through gist memory and not through direct recall of specific instances.

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.

Young Adults with a Parent with Dementia Show Early Abnormalities in Brain Activity and Brain Volume in the Hippocampus: A Matched Case-Control Study.

Having a parent with Alzheimer's disease (AD) and related dementias confers a risk for developing these types of neurocognitive disorders in old age, but the mechanisms underlying this risk are understudied. Although the hippocampus is often one of the earliest brain regions to undergo change in the AD process, we do not know how early in the lifespan such changes might occur or whether they differ early in the lifespan as a function of family history of AD. Using a rare sample, young adults with a parent with late-onset dementia, we investigated whether brain abnormalities could already be detected compared with a matched sample. Moreover, we employed simple yet novel techniques to characterize resting brain activity (mean and standard deviation) and brain volume in the hippocampus. Young adults with a parent with dementia showed greater resting mean activity and smaller volumes in the left hippocampus compared to young adults without a parent with dementia. Having a parent with AD or a related dementia was associated with early aberrations in brain function and structure. This early hippocampal dysfunction may be due to aberrant neural firing, which may increase the risk for a diagnosis of dementia in old age.