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Abstract Background This systematic review synthesizes the most recent neuroimaging procedures and machine learning approaches for the prediction of conversion from mild cognitive impairment to Alzheimer’s disease dementia. Methods We systematically searched PubMed, SCOPUS, and Web of Science databases following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) systematic review guidelines. Results Our search returned 2572 articles, 56 of which met the criteria for inclusion in the final selection. The multimodality framework and deep learning techniques showed potential for predicting the conversion of MCI to AD dementia. Conclusion Findings of this systematic review identified that the possibility of using neuroimaging data processed by advanced learning algorithms is promising for the prediction of AD progression. We also provided a detailed description of the challenges that researchers are faced along with future research directions. The protocol has been registered in the International Prospective Register of Systematic Reviews– CRD42019133402 and published in the Systematic Reviews journal.

Cognitive aging is a complex and dynamic process characterized by changes due to genetics and environmental factors, including lifestyle choices and environmental exposure, which contribute to the heterogeneity observed in cognitive outcomes. This heterogeneity is particularly pronounced among older adults, with some individuals maintaining stable cognitive function while others experience complex, non-linear changes, making it difficult to identify meaningful decline accurately. Current research methods range from population-level modeling to individual-specific assessments. In this work, we review these methodologies and propose that population subtyping should be considered as a viable alternative. This approach relies on early individual-specific detection methods that can lead to an improved understanding of changes in individual cognitive trajectories. The improved understanding of cognitive trajectories through population subtyping can lead to the identification of meaningful changes and the determination of timely, effective interventions. This approach can aid in informing policy decisions and in developing targeted interventions that promote cognitive health, ultimately contributing to a more personalized understanding of the aging process within society and reducing the burden on healthcare systems.

Background: Targeting the dorsolateral prefrontal cortex (DLPFC) with repeated sessions of theta-burst stimulation (TBS) is a promising treatment for youth depression. However, it remains unclear whether TBS selectively acts upon inhibitory and/or excitatory measures. Such insight could aid with the development of biomarker driven approaches to delivering TBS in youth with depression. Methods: Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) was used to assess the neurophysiological impact of a 4-week bilateral DLPFC-targeted TBS trial followed by a cognitive exercise for the treatment of depression in youth. In 26 youth with depression (aged 16–24 years old), TMS-evoked potentials (TEPs) were assessed at baseline and after treatment completion from the bilateral DLPFC and inferior parietal lobules (IPL). Results: Following treatment, there were significant changes in TEPs both local and distal to the stimulated treatment sites. The amplitude of the left DLPFC N100 response was significantly reduced. Also, there was a significant association between changes in the right DLPFC N45 response and reduction in depressive and rumination scores. Additionally, the amplitude of the right IPL N100 response was reduced. Moreover, the modulation of the right IPL N100 response was significantly associated with a reduction in depressive scores. Limitations: The primary limitation was the lack of a sham treatment arm. Conclusions: Bilateral DLPFC TBS may exert its therapeutic impact by modulating the neurophysiology of regions and networks both local and distal to the treatment site. Collectively, our findings offer novel insight into the potential therapeutic mechanisms of TBS for the treatment of youth depression.

Cognitive reserve reflects the brain’s intrinsic adaptive capacity against the neurodegenerative effects of aging. The maintenance or enhancement of the brain’s cognitive reserve plays a crucial role in mitigating the severity of pathologies associated with aging. A new movement, social prescribing, which focuses on prescribing lifestyle activities as a treatment for patients, is growing in popularity as a solution against aging pathologies. However, few studies have demonstrated a clear impact of lifestyle activities on individual cognitive health, outside of floor and ceiling effects. Understanding who benefits from which lifestyle factors remains unclear. Here, we investigated the potential effects of lifestyle activities on individuals’ cognitive health from more than 3,530 older adults using a stratification method and advanced analysis technique. Our stratification methods allowed us to observe a new result: older adults who had relatively average cognitive scores were not impacted by lifestyle factors. By comparison, older adults with very high or very low cognitive scores were highly impacted by lifestyle factors. These findings expand the orchid and dandelion theory to the aging field, regarding the biological sensitivity of individuals to harmful and protective environmental effects. Our discoveries demonstrate the role of individual differences in the aging process and its importance for social prescribing programs.

Abstract Background Alzheimer’s disease (AD) is a neurodegenerative disorder commonly associated with deficits of cognition and changes in behavior. Mild cognitive impairment (MCI) is the prodromal stage of AD that is defined by slight cognitive decline. Not all with MCI progress to AD dementia. Thus, the accurate prediction of progression to Alzheimer’s, particularly in the stage of MCI could potentially offer developing treatments to delay or prevent the transition process. The objective of the present study is to investigate the most recent neuroimaging procedures in the domain of prediction of transition from MCI to AD dementia for clinical applications and to systematically discuss the machine learning techniques used for the prediction of MCI conversion. Methods Electronic databases including PubMed, SCOPUS, and Web of Science will be searched from January 1, 2017, to the date of search commencement to provide a rapid review of the most recent studies that have investigated the prediction of conversion from MCI to Alzheimer’s using neuroimaging modalities in randomized trial or observational studies. Two reviewers will screen full texts of included papers using predefined eligibility criteria. Studies will be included if addressed research on AD dementia and MCI, explained the results in a way that would be able to report the performance measures such as the accuracy, sensitivity, and specificity. Only studies addressed Alzheimer’s type of dementia and its early-stage MCI using neuroimaging modalities will be included. We will exclude other forms of dementia such as vascular dementia, frontotemporal dementia, and Parkinson’s disease. The risk of bias in individual studies will be appraised using an appropriate tool. If feasible, we will conduct a random effects meta-analysis. Sensitivity analyses will be conducted to explore the potential sources of heterogeneity. Discussion The information gathered in our study will establish the extent of the evidence underlying the prediction of conversion to AD dementia from its early stage and will provide a rigorous and updated synthesis of neuroimaging modalities allied with the data analysis techniques used to measure the brain changes during the conversion process. Systematic review registration PROSPERO,CRD42019133402

Introduction: Rates of dementia are projected to increase over the coming years as global populations age. Without a treatment to slow the progression of dementia, many health policies are focusing on preventing dementia by slowing the rate of cognitive decline with age. However, it is unclear which lifestyle changes in old age meaningfully reduce the rate of cognitive decline associated with aging.Objectives: Use existing, multi-year longitudinal health data to determine if engagement in a variety of different lifestyle activities can slow the rate of cognitive decline as older adults age.Method: Data from the English Longitudinal Study of Aging was analyzed using a quasi-experimental, efficient matched-pair design inspired by the clinical trial methodology. Changes in short-term memory scores were assessed over a multi-year interval for groups who undertook one of 11 different lifestyle activities, compared to control groups matched across confounding socioeconomic and lifestyle factors.Results: Two factors, moderate-intensity physical activity and learning activities, resulted in significant positive impact on cognitive function.Conclusion: Our analysis brings cognitive benefit arguments in favor of two lifestyle activities, moderate-intensity physical activity and learning activities, while rejecting other factors advanced by the literature such as vigorous-intensity physical activity. Those findings justify and encourage the development of new lifestyle health programs by health authorities and bring forward the new health system solution, social prescribing.

The number of patients suffering from dementia is expected to more than triple by the year 2040, and this represents a major challenge to publicly-funded healthcare systems throughout the world. One of the most effective prevention mechanisms against dementia lies in increasing brain- and cognitive-reserve capacity, which has been found to reduce the behavioral severity of dementia symptoms as neurological degeneration progresses. To date though, most of the factors known to enhance this reserve stem from largely immutable history factors, such as level of education and occupational attainment. Here, we review the potential for basic lifestyle activities, including physical exercise, meditation and musical experience, to contribute to reserve capacity and thus reduce the incidence of dementia in older adults. Relative to other therapies, these activities are low cost, are easily scalable and can be brought to market quickly and easily. Overall, although preliminary evidence is promising at the level of randomized control trials, the state of research on this topic remains underdeveloped. As a result, several important questions remain unanswered, including the amount of training required to receive any cognitive benefit from these activities and the extent to which this benefit continues following cessation. Future research directions are discussed for each lifestyle activity, as well as the potential for these and other lifestyle activities to serve as both a prophylactic and a therapeutic treatment for dementia.

BackgroundDigital health represents an important strategy in the future of health care delivery. Over the past decade, mobile health has accelerated the agency of health care users. Despite prevailing excitement about the potential of digital health, questions remain on efficacy, uptake, usability, and patient outcome. This challenge is confounded by 2 industries, digital and health, which have vastly different approaches to research, design, testing, and implementation. In this regard, there is a need to examine prevailing design approaches, weigh their benefits and challenges toward implementation, and recommend a path forward that synthesizes the needs of this complex stakeholder group. ObjectiveIn this review, we aimed to study prominent digital health intervention design approaches that mediate the digital health space. In doing so, we sought to examine the origins, perceived benefits, contrasting nuances, challenges, and typical use-case scenarios of each methodology. MethodsA narrative review of digital health design approaches was performed between September 2020 and April 2021 by referencing keywords such as “digital health design,” “mHealth design,” “e-Health design,” “agile health,” and “agile healthcare.” The studies selected after screening were those that discussed the design and implementation of digital health design approaches. A total of 120 studies were selected for full-text review, of which 62 (51.6%) were selected for inclusion in this review. ResultsA review identifying the 5 overarching digital health design approaches was compiled: user-centered design, person-based design, human-centered design, patient-centered design, and patient-led design. The findings were synthesized in a narrative structure discussing the origins, advantages, disadvantages, challenges, and potential use-case scenarios. ConclusionsDigital health is experiencing the growing pains of rapid expansion. Currently, numerous design approaches are being implemented to harmonize the needs of a complex stakeholder group. Whether the end user is positioned as a person, patient, or user, the challenge to synthesize the constraints and affordances of both digital design and health care, built equally around user satisfaction and clinical efficacy, remains paramount. Further research that works toward a transdisciplinarity in digital health may help break down friction in this field. Until digital health is viewed as a hybridized industry with unique requirements rather than one with competing interests, the nuances that each design approach posits will be difficult to realize in a real-world context. We encourage the collaboration of digital and health experts within hybrid design teams, through all stages of intervention design, to create a better digital health culture and design ethos.