The foundation and architecture of precision medicine in neurology and psychiatry

Failure modes and mitigations for Bayesian optimization of neuromodulation parameters

Objective.Precision medicine holds substantial promise for tailoring neuromodulation techniques to the symptomatology of individual patients. Precise selection of stimulation parameters for individual patients requires the development of robust optimization techniques. However, standard optimization approaches, like Bayesian optimization, have historically been assessed and developed for applications with far less noise than is typical in neuro-psychiatric outcome measures and with minimal focus on parameter safety.Approach.We conducted a literature review of individual effects in neurological and psychiatric applications to build a series of simulated patient responses of varying signal to noise ratio. Using these simulations, we assessed whether existing standards in Bayesian optimization are sufficient for robustly optimizing such effects.Main results.For effect sizes below a Cohen's d of 0.3, standard Bayesian optimization methods failed to consistently identify optimal parameters. This failure primarily results from over-sampling of the boundaries of the space as the number of samples increases, because the variance on the edges becomes disproportionately greater than in the remainder of parameter space. Using a combination of an input warp and a boundary avoiding Iterated Brownian-bridge kernel we demonstrated robust Bayesian optimization performance for problems with a Cohen's d effect size as low as 0.1.Significance.Our results demonstrate that caution should be taken when applying standard Bayesian optimization in neuromodulation applications with potentially low effect sizes, as standard algorithms are at high risk of converging to local rather than global optima. Mitigating techniques, like boundary avoidance, are effective and should be used to improve robustness.

The impact of real-world evidence in implementing and optimizing Alzheimer's disease care

Real-world evidence (RWE) can complement clinical trials by addressing gaps in how approved anti-amyloid therapies for early Alzheimer's disease (AD) are used in everyday practice. This article outlines strategies to generate RWE that bridge three key challenges in AD care: low detection rates of mild cognitive impairment (MCI), limited data on long-term safety and effectiveness, and a lack of personalized treatment strategies. With MCI detection rates among primary care providers as low as 6%-15%, we propose cost-effective triage tools using electronic health records to enhance early diagnosis and intervention. We also highlight the importance of understanding anti-amyloid therapy outcomes in diverse, real-world populations. Supported by FDA initiatives, pragmatic trials and observational studies using real-world data (RWD) can help develop predictive models that incorporate biomarkers and support precision medicine. These approaches aim to move AD care beyond one-size-fits-all treatment, guiding more tailored, effective strategies for patients.

Exploring interdisciplinary perspectives on the implementation of personalized medicine and patient-orchestrated care in Alzheimer's disease: A qualitative study within the ABOARD research project

BackgroundThe concepts of 'personalized medicine' and 'patient-orchestrated care' in Alzheimer's disease (AD) lack standard conceptualization, which presents challenges for collaborative and interdisciplinary care.ObjectiveWe explored the interpretations and perspectives of professionals involved in interdisciplinary work on a large-scale project, "ABOARD", with the aim to implement personalized medicine and patient-orchestrated care in AD.MethodsSemi-structured interviews were conducted with 30 professionals and audio-recorded. Two researchers independently coded the data inductively, followed by a thematic analysis.ResultsAccording to professionals across different disciplinary backgrounds (mean age 45.7 years; 53.3% female), personalized medicine pertains to the relevant options that an individual has, informed by biomedical and psychosocial factors, whereas patient-orchestrated care captures factors relevant to the decision-making process. Professionals differed in their views on patient-orchestrated care regarding its desirability and feasibility. The concepts were viewed as similar by professionals, as both involve personal preferences while ultimately assigning responsibility to the clinician. However, implementation challenges persist, and no thematic differences were found between clinicians and other AD-related professionals.ConclusionsAD professionals have shared interpretations and perspectives on implementation of personalized medicine but differed in their views on patient-orchestrated care. Personal preferences are seen as part of personalized medicine, but not yet reflected in definitions in the AD field and beyond. Critical discussions on the challenges and existing doubts are necessary for both personalized medicine and patient-orchestrated care. Multi-level implementation changes are needed for both concepts, which warrants stakeholder involvement as well as support and resources from the entire AD field.

What traditional neuropsychological assessment got wrong about mild traumatic brain injury. IV: clinical applications and future directions

PRIMARY OBJECTIVE

Part IV concludes this four-part review of 'What Traditional Neuropsychological Assessment Got Wrong About Mild Traumatic Brain Injury,' with a focus on clinical applications and future directions.

METHODS AND PROCEDURES

These reviews have highlighted the limitations of traditional neuropsychological assessment methods, particularly in the evaluation of the patient with mild traumatic brain injury (mTBI), and especially within the context of all of the 21st Century advances in neuroimaging, quantification and network neuroscience.

MAIN OUTCOME AND RESULTS

How advanced neuroimaging technology and contemporary network neuroscience can be applied to assessing the mTBI patient at this time along with neuroimaging of the future are reviewed. The current status of computerized neuropsychological test (CNT) development is reviewed as it applies to mTBI assessment. Likewise, how the future of various types of virtual reality (VR), artificial intelligence (AI), wearable sensors, and markerless gaming technology could enhance the mTBI CNT assessment tool box of the future is reviewed.

CONCLUSIONS

The review concludes with some aspirational statements about how improvements along with novel CNT methods could be developed and integrated with advanced neuroimaging technologies in the future to be tailored to meet the needs of the mTBI patient.

Alpha rhythm and Alzheimer's disease: Has Hans Berger's dream come true?

In this "centenary" paper, an expert panel revisited Hans Berger's groundbreaking discovery of human restingstate electroencephalographic (rsEEG) alpha rhythms (8-12 Hz) in 1924, his foresight of substantial clinical applications in patients with "senile dementia," and new developments in the field, focusing on Alzheimer's disease (AD), the most prevalent cause of dementia in pathological aging. Clinical guidelines issued in 2024 by the US National Institute on Aging-Alzheimer's Association (NIA-AA) and the European Neuroscience Societies did not endorse routine use of rsEEG biomarkers in the clinical workup of older adults with cognitive impairment. Nevertheless, the expert panel highlighted decades of research from independent workgroups and different techniques showing consistent evidence that abnormalities in rsEEG delta, theta, and alpha rhythms (< 30 Hz) observed in AD patients correlate with wellestablished AD biomarkers of neuropathology, neurodegeneration, and cognitive decline. We posit that these abnormalities may reflect alterations in oscillatory synchronization within subcortical and cortical circuits, inducing cortical inhibitory-excitatory imbalance (in some cases leading to epileptiform activity) and vigilance dysfunctions (e.g., mental fatigue and drowsiness), which may impact AD patients' quality of life. Berger's vision of using EEG to understand and manage dementia in pathological aging is still actual.

Lipidomics and genomics in mental health: insights into major depressive disorder, bipolar disorder, schizophrenia, and obsessive-compulsive disorder

INTRODUCTION

This systematic review explores the hypothesis that various lipid categories and lipid metabolism-related genomic variations link to mental disorders, seeking potential clinically useful markers.

METHODS

We searched PubMed, Scopus, and PsycInfo databases until October 12th, 2024, using terms related to lipidomics, lipid-related genomics, and different mental disorders, i.e., Major Depressive Disorder (MDD), Bipolar Disorder (BD), Schizophrenia (SCZ), and Obsessive-Compulsive Disorder (OCD). Eligible studies were assessed. Extracted data included author, year, methodology, outcomes, genes, and lipids linked to disorders. Bias and evidence certainty were evaluated. The systematic review adhered to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and a registered protocol (PROSPERO: CRD42023438862).

RESULTS

A total of 27 studies were included. SCZ showed alterations in 77 lipids, including triglycerides (TG), ceramides, and phosphatidylcholine, while MDD and BD exhibited 97 and 47 altered lipids, respectively, with overlap among disorders. Shared genes, such as ABCA13, DGKZ, and FADS, and pathways involving inflammation, lipid metabolism, and mitochondrial function were identified. OCD was associated with sphingolipid signaling and peroxisomal metabolism.

DISCUSSION

Lipid signatures in MDD, BD, and SCZ shed light on underlying processes. Further research is needed to validate biomarkers and refine their clinical applications in precision psychiatry.

Defining benefit: Clinically and biologically meaningful outcomes in the next-generation Alzheimer's disease clinical care pathway

To understand the potential benefits of emerging Alzheimer's disease (AD) therapies within and beyond clinical trial settings, there is a need to advance current outcome measurements into meaningful information relevant to all stakeholders. The relationship between the impact on disease biology and clinically measurable outcomes in cognition, function, and behavior must be considered when defining the meaningful benefit of early AD therapies. In this review, we discuss: (1) the lack of consideration for biomarkers in the current concept of meaningfulness in AD; (2) the lack of gold standards for determining minimal biologically and clinically important differences (MBCIDs) in AD trials; (3) how the treatment benefits of disease-modifying treatments are cumulative and increase over time; and (4) the different concepts of meaningfulness among key stakeholders. This review utilizes the future clinical biological framework of AD and aims to further integrate and expand the parameters of meaningful benefits toward a precision medicine framework. HIGHLIGHTS: Definition of meaningful benefit from Alzheimer's disease (AD) treatment varies across disease stage and stakeholder perspectives. Observable and meaningful outcomes must consider the clinical-biological nature of AD. Statistically significant effects or outcomes do not always equate to clinically meaningfulness. Assessment tools must reflect stage-specific subtle changes following treatment. Real-world evidence will support consensus, definition, and interpretation of clinical meaningfulness.

Resilience mechanisms underlying Alzheimer's disease

Alzheimer's disease (AD) consists of two main pathologies, which are the deposition of amyloid plaque as well as tau protein aggregation. Evidence suggests that not everyone who carries the AD-causing genes displays AD-related symptoms; they might never acquire AD as well. These individuals are referred to as non-demented individuals with AD neuropathology (NDAN). Despite the presence of extensive AD pathology in their brain, it was found that NDAN had better cognitive function than was expected, suggesting that they were more resilient (better at coping) to AD due to differences in their brains compared to other demented or cognitively impaired patients. Thus, identification of the mechanisms underlying resilience is crucial since it represents a promising therapeutic strategy for AD. In this review, we will explore the molecular mechanisms underpinning the role of genetic and molecular resilience factors in improving resilience to AD. These include protective genes and proteins such as APOE2, BDNF, RAB10, actin network proteins, scaffolding proteins, and the basal forebrain cholinergic system. A thorough understanding of these resilience mechanisms is crucial for not just comprehending the development of AD but may also open new treatment possibilities for AD by enhancing the neuroprotective pathway and targeting the pathogenic process.

Neuropsychiatric drug development: Perspectives on the current landscape, opportunities and potential future directions

Mental health represents a major challenge to our societies. One key difficulty associated with neuropsychiatric drug development is the lack of connection between the underlying biology and the disease. Nevertheless, there is growing optimism in this field with recent drug approvals (the first in decades) and renewed interest from pharmaceutical companies and investors. Here we review some of the most promising drug discovery and development endeavors currently deployed by industry. We also present elements illustrating the renewed interest from key stakeholders in neuropsychiatric drug development and provide potential future directions in this field.

Precision Emergency Medicine: A Systematic Review

Precision medicine, which customizes healthcare based on individual genetic, environmental, and lifestyle factors, has significantly advanced various medical fields. However, its adoption in emergency medicine remains limited despite the potential to enhance patient outcomes through more accurate diagnostics and personalized treatments. This systematic review examined current evidence on the application of precision medicine in emergency care by analyzing studies published between 2010 and 2024. Out of 218 records, 10 studies met the inclusion criteria, highlighting areas such as genomic applications, machine learning, point-of-care diagnostics, and biomarkers. The findings indicate that precision medicine can improve diagnostic accuracy and personalize patient care in emergency settings, although challenges such as time constraints, technological limitations, and the need for enhanced clinician training remain. Overcoming these barriers through interdisciplinary collaboration, investment in rapid diagnostic technologies, and comprehensive education programs is essential for effectively integrating precision medicine into emergency care. Ultimately, advancing precision emergency medicine holds promise for transforming emergency care into a more personalized and effective practice, thereby improving patient outcomes in acute situations.

Structural white matter connectivity differences independent of gray matter loss in mild cognitive impairment with neuropsychiatric symptoms: Early indicators of Alzheimer's disease using network-based statistics

BACKGROUND

Depression and circadian rhythm disruptions are non-cognitive neuropsychiatric symptoms (NPS) that can appear at any stage of the Alzheimer's disease (AD) continuum. Evidence suggests that NPS are linked to AD pathophysiology and hippocampal dysfunction.

OBJECTIVE

To examine structural white matter (WM) connectivity and its association with gray matter (GM) atrophy and to identify specific AD-related neural networks linked to NPS in individuals with mild cognitive impairment (MCI).

METHODS

Ninety-six older adults participants were divided into three groups based on the Global Depression Scale, Neuropsychiatric Inventory, Clinical Dementia Rating, and Mini-Mental Status Examination. Twelve individuals with MCI and NPS (MCI+) and 49 without NPS (MCI-) were classified, along with 35 age and gender-matched healthy individuals. Voxel-based morphometry and tract-based spatial statistics were employed to identify structural and microstructural alterations. Network-based statistics analyzed structural WM connectivity differences between MCI groups and healthy controls.

RESULTS

Significant structural WM connectivity and GM loss were exclusively observed in MCI+ individuals compared to controls. The hippocampus, amygdala, and sensory cortex showed GM atrophy (p < 0.05), while the thalamus, pallidum, putamen, caudate, hippocampus, and sensory and frontal cortices exhibited structural WM connectivity loss (p < 0.01). These data indicate early limbic system involvement even without GM atrophy.

CONCLUSIONS

Structural WM connectivity loss within the Papez circuit may precede and potentially predict GM atrophy in the temporal lobe of individuals with MCI+. These findings highlight the importance of investigating structural WM alterations in the prodromal phase of AD, which may inform diagnostic and therapeutic strategies in early AD.

Considering Biomarkers of Neurodegeneration in Alzheimer's Disease: The Potential of Circulating Cell-Free DNA in Precision Neurology

Neurodegenerative diseases, such as Alzheimer's disease (AD), are a growing public health crisis, exacerbated by an aging global population and the lack of effective early disease-modifying therapies. Early detection of neurodegenerative disorders is critical to delaying symptom onset and mitigating disease progression, but current diagnostic tools often rely on detecting pathology once clinical symptoms have emerged and significant neuronal damage has already occurred. While disease-specific biomarkers, such as amyloid-beta and tau in AD, offer precise insights, they are too limited in scope for broader neurodegeneration screening for these conditions. Conversely, general biomarkers like neurofilament light chain (NfL) provide valuable staging information but lack targeted insights. Circulating cell-free DNA (cfDNA), released during cell death, is emerging as a promising biomarker for early detection. Derived from dying cells, cfDNA can capture both general neurodegenerative signals and disease-specific insights, offering multi-layered genomic and epigenomic information. Though its clinical potential remains under investigation, advances in cfDNA detection sensitivity, standardized protocols, and reference ranges could establish cfDNA as a valuable tool for early screening. cfDNA methylation signatures, in particular, show great promise for identifying tissue-of-origin and disease-specific changes, offering a minimally invasive biomarker that could transform precision neurology. However, further research is required to address technological challenges and validate cfDNA's utility in clinical settings. Here, we review recent work assessing cfDNA as a potential early biomarker in AD. With continued advances, cfDNA could play a pivotal role in shifting care from reactive to proactive, improving diagnostic timelines and patient outcomes.

Unraveling the shared genetics of common epilepsies and general cognitive ability

PURPOSE

Cognitive impairment is prevalent among individuals with epilepsy, and increasing evidence indicates that genetic factors can underlie this relationship. However, the extent to which epilepsy subtypes differ in their genetic relationship with cognitive function, and information about the specific genetic variants involved remain largely unknown.

METHODS

We investigated the genetic relationship between epilepsies and general cognitive ability (COG) using complementary statistical tools, including linkage disequilibrium score (LDSC) regression, MiXeR and conjunctional false discovery rate (conjFDR). We analyzed genome-wide association study data on COG (n = 269,867) and common epilepsies (n = 27,559 cases, 42,436 controls), including the broad phenotypes 'all epilepsy', focal epilepsies and genetic generalized epilepsies (GGE), as well as specific subtypes. We functionally annotated the identified loci using several biological resources and validated the results in independent samples.

RESULTS

Using MiXeR, COG (11.2k variants) was estimated to be almost four times more polygenic than 'all epilepsy', GGE, juvenile myoclonic epilepsy (JME), and childhood absence epilepsy (CAE) (2.5k - 2.9k variants). The other epilepsy phenotypes were insufficiently powered for MiXeR analysis. We quantified extensive genetic overlap between COG and epilepsy types, but with varying negative genetic correlations (-0.23 to -0.04). COG was estimated to share 2.9k variants with both GGE and 'all epilepsy', and 2.3k variants with both JME and CAE. Using conjFDR, we identified 66 distinct loci shared between COG and epilepsies, including novel associations for GGE (27), 'all epilepsy' (5), JME (5) and CAE (5). The implicated genes were significantly expressed in multiple brain regions. The results were validated in independent samples (COG: p = 3.62 × 10-7; 'all epilepsy': p = 2.58 × 10-3).

CONCLUSION

Our study further dissects the substantial genetic basis shared between epilepsies and COG and identifies novel shared loci. An improved understanding of the genetic relationship between epilepsies and COG may lead to the development of novel comorbidity-targeted epilepsy treatments.

The role of parvalbumin interneuron dysfunction across neurodegenerative dementias

Parvalbumin-positive (PV+) basket neurons are fast-spiking, non-adapting inhibitory interneurons whose oscillatory activity is essential for regulating cortical excitation/inhibition balance. Their dysfunction results in cortical hyperexcitability and gamma rhythm disruption, which have recently gained substantial traction as contributing factors as well as potential therapeutic targets for the treatment of Alzheimer's Disease (AD). Recent evidence indicates that PV+ cells are also impaired in Frontotemporal Dementia (FTD) and Dementia with Lewy bodies (DLB). However, no attempt has been made to integrate these findings into a coherent pathophysiological framework addressing the contribution of PV+ interneuron dysfunction to the generation of cortical hyperexcitability and gamma rhythm disruption in FTD and DLB. To fill this gap, we epitomized the most recent evidence on PV+ interneuron impairment in AD, FTD, and DLB, focusing on its contribution to the generation of cortical hyperexcitability and gamma oscillatory disruption and their interplay with misfolded protein accumulation, neuronal death, and clinical symptoms' onset. Our work deepens the current understanding concerning the role of PV+ interneuron dysfunction across neurodegenerative dementias, highlighting commonalities and differences among AD, FTD, and DLB, thus paving the way for identifying novel biomarkers and potential therapeutic targets for the treatment of these diseases.

Effects of oral contraceptive pills on brain networks: A replication and extension

Neuroimaging research has identified significant effects of oral contraceptive pills (OCPs) on brain networks. A wide variety of approaches have been employed, largely in observational samples, with few converging results. This study therefore was designed to test for replication and extend this previous work using a randomized, double-blind, placebo-controlled crossover trial of the effects of OCPs on brain networks. Using functional MRI, we focused on brain regions identified in prior studies. Our analyses did not strictly replicate previously reported effects of OCPs on functional connectivity. Exploratory analyses suggested that traditional seed-based approaches may miss broader, network-level effects of OCPs on brain circuits. We applied data-driven, multivariate techniques to assess these network-level changes, A deeper understanding of neural effects of OCPs can be important in helping patients make informed decisions regarding contraception, mitigating unwanted side effects. Such information can also identify potentially confounding effects of OCPs in other neuroimaging investigations.

Fluid biomarkers in the context of amyloid-targeting disease-modifying treatments in Alzheimer's disease

Clinical management and therapeutics development for Alzheimer's disease (AD) have entered a new era, with recent approvals of monoclonal antibody therapies targeting the underlying pathophysiology of the disease and modifying its trajectory. Imaging and fluid biomarkers are becoming increasingly important in the clinical development of AD therapeutics. This review focuses on the evidence of fluid biomarkers from recent amyloid-β-targeting clinical trials, summarizing biomarker data across 12 trials. It further proposes a simple framework to put biomarker guidance in the context of amyloid-pathway-targeted disease modification, delineates factors that impact biomarker data in clinical trials, and highlights knowledge gaps and future directions. Increased knowledge and data on biomarkers in the context of disease progression and disease modification will help to better design future AD trials and guide the clinical management of patients on AD-modifying therapies, bringing us closer to the implementation of precision medicine in AD.

Genomic Insights into Dementia: Precision Medicine and the Impact of Gene-Environment Interaction

The diagnosis, treatment, and management of dementia provide significant challenges due to its chronic cognitive impairment. The complexity of this condition is further highlighted by the impact of gene-environment interactions. A recent strategy combines advanced genomics and precision medicine methods to explore the complex genetic foundations of dementia. Utilizing the most recent research in the field of neurogenetics, the importance of precise genetic data in explaining the variation seen in dementia patients can be investigated. Gene-environment interactions are important because they influence genetic susceptibilities and aid in the development and progression of dementia. Modified to each patient's genetic profile, precision medicine has the potential to detect groups at risk and make previously unheard-of predictions about the course of diseases. Precision medicine techniques have the potential to completely transform treatment and diagnosis methods. Targeted medications that target genetic abnormalities will probably appear, providing the possibility for more efficient and customized medical interventions. Investigating the relationship between genes and the environment may lead to preventive measures that would enable people to change their surroundings and minimize the risk of dementia, leading to the improved lifestyle of affected people. This paper provides a comprehensive overview of the genomic insights into dementia, emphasizing the pivotal role of precision medicine, and gene-environment interactions.

Recent Review on Biological Barriers and Host-Material Interfaces in Precision Drug Delivery: Advancement in Biomaterial Engineering for Better Treatment Therapies

Preclinical and clinical studies have demonstrated that precision therapy has a broad variety of treatment applications, making it an interesting research topic with exciting potential in numerous sectors. However, major obstacles, such as inefficient and unsafe delivery systems and severe side effects, have impeded the widespread use of precision medicine. The purpose of drug delivery systems (DDSs) is to regulate the time and place of drug release and action. They aid in enhancing the equilibrium between medicinal efficacy on target and hazardous side effects off target. One promising approach is biomaterial-assisted biotherapy, which takes advantage of biomaterials' special capabilities, such as high biocompatibility and bioactive characteristics. When administered via different routes, drug molecules deal with biological barriers; DDSs help them overcome these hurdles. With their adaptable features and ample packing capacity, biomaterial-based delivery systems allow for the targeted, localised, and prolonged release of medications. Additionally, they are being investigated more and more for the purpose of controlling the interface between the host tissue and implanted biomedical materials. This review discusses innovative nanoparticle designs for precision and non-personalised applications to improve precision therapies. We prioritised nanoparticle design trends that address heterogeneous delivery barriers, because we believe intelligent nanoparticle design can improve patient outcomes by enabling precision designs and improving general delivery efficacy. We additionally reviewed the most recent literature on biomaterials used in biotherapy and vaccine development, covering drug delivery, stem cell therapy, gene therapy, and other similar fields; we have also addressed the difficulties and future potential of biomaterial-assisted biotherapies.

2023 Society for Academic Emergency Medicine Consensus Conference on Precision Emergency Medicine: Development of a policy-relevant, patient-centered research agenda

OBJECTIVES

Precision medicine is data-driven health care tailored to individual patients based on their unique attributes, including biologic profiles, disease expressions, local environments, and socioeconomic conditions. Emergency medicine (EM) has been peripheral to the precision medicine discourse, lacking both a unified definition of precision medicine and a clear research agenda. We convened a national consensus conference to build a shared mental model and develop a research agenda for precision EM.

METHODS

We held a conference to (1) define precision EM, (2) develop an evidence-based research agenda, and (3) identify educational gaps for current and future EM clinicians. Nine preconference workgroups (biomedical ethics, data science, health professions education, health care delivery and access, informatics, omics, population health, sex and gender, and technology and digital tools), comprising 84 individuals, garnered expert opinion, reviewed relevant literature, engaged with patients, and developed key research questions. During the conference, each workgroup shared how they defined precision EM within their domain, presented relevant conceptual frameworks, and engaged a broad set of stakeholders to refine precision EM research questions using a multistage consensus-building process.

RESULTS

A total of 217 individuals participated in this initiative, of whom 115 were conference-day attendees. Consensus-building activities yielded a definition of precision EM and key research questions that comprised a new 10-year precision EM research agenda. The consensus process revealed three themes: (1) preeminence of data, (2) interconnectedness of research questions across domains, and (3) promises and pitfalls of advances in health technology and data science/artificial intelligence. The Health Professions Education Workgroup identified educational gaps in precision EM and discussed a training roadmap for the specialty.

CONCLUSIONS

A research agenda for precision EM, developed with extensive stakeholder input, recognizes the potential and challenges of precision EM. Comprehensive clinician training in this field is essential to advance EM in this domain.

Bio-Nano Toolbox for Precision Alzheimer's Disease Gene Therapy

Alzheimer's disease (AD) is the most burdensome aging-associated neurodegenerative disorder, and its treatment encounters numerous failures during drug development. Although there are newly approved in-market β-amyloid targeting antibody solutions, pathological heterogeneity among patient populations still challenges the treatment outcome. Emerging advances in gene therapies offer opportunities for more precise personalized medicine; while, major obstacles including the pathological heterogeneity among patient populations, the puzzled mechanism for druggable target development, and the precision delivery of functional therapeutic elements across the blood-brain barrier remain and limit the use of gene therapy for central neuronal diseases. Aiming for "precision delivery" challenges, nanomedicine provides versatile platforms that may overcome the targeted delivery challenges for AD gene therapy. In this perspective, to picture a toolbox for AD gene therapy strategy development, the most recent advances from benchtop to clinics are highlighted, possibly available gene therapy targets, tools, and delivery platforms are outlined, their challenges as well as rational design elements are addressed, and perspectives in this promising research field are discussed.

Apolipoprotein E in Alzheimer's disease trajectories and the next-generation clinical care pathway

Alzheimer's disease (AD) is a complex, progressive primary neurodegenerative disease. Since pivotal genetic studies in 1993, the ε4 allele of the apolipoprotein E gene (APOE ε4) has remained the strongest single genome-wide associated risk variant in AD. Scientific advances in APOE biology, AD pathophysiology and ApoE-targeted therapies have brought APOE to the forefront of research, with potential translation into routine AD clinical care. This contemporary Review will merge APOE research with the emerging AD clinical care pathway and discuss APOE genetic risk as a conduit to genomic-based precision medicine in AD, including ApoE's influence in the ATX(N) biomarker framework of AD. We summarize the evidence for APOE as an important modifier of AD clinical-biological trajectories. We then illustrate the utility of APOE testing and the future of ApoE-targeted therapies in the next-generation AD clinical-diagnostic pathway. With the emergence of new AD therapies, understanding how APOE modulates AD pathophysiology will become critical for personalized AD patient care.

TYK2 as a novel therapeutic target in Alzheimer's Disease with TDP-43 inclusions

Neuroinflammation is a pathological feature of many neurodegenerative diseases, including Alzheimer's disease (AD)1,2 and amyotrophic lateral sclerosis (ALS)3, raising the possibility of common therapeutic targets. We previously established that cytoplasmic double-stranded RNA (cdsRNA) is spatially coincident with cytoplasmic pTDP-43 inclusions in neurons of patients with C9ORF72-mediated ALS4. CdsRNA triggers a type-I interferon (IFN-I)-based innate immune response in human neural cells, resulting in their death4. Here, we report that cdsRNA is also spatially coincident with pTDP-43 cytoplasmic inclusions in brain cells of patients with AD pathology and that type-I interferon response genes are significantly upregulated in brain regions affected by AD. We updated our machine-learning pipeline DRIAD-SP (Drug Repurposing In Alzheimer's Disease with Systems Pharmacology) to incorporate cryptic exon (CE) detection as a proxy of pTDP-43 inclusions and demonstrated that the FDA-approved JAK inhibitors baricitinib and ruxolitinib that block interferon signaling show a protective signal only in cortical brain regions expressing multiple CEs. Furthermore, the JAK family member TYK2 was a top hit in a CRISPR screen of cdsRNA-mediated death in differentiated human neural cells. The selective TYK2 inhibitor deucravacitinib, an FDA-approved drug for psoriasis, rescued toxicity elicited by cdsRNA. Finally, we identified CCL2, CXCL10, and IL-6 as candidate predictive biomarkers for cdsRNA-related neurodegenerative diseases. Together, we find parallel neuroinflammatory mechanisms between TDP-43 associated-AD and ALS and nominate TYK2 as a possible disease-modifying target of these incurable neurodegenerative diseases.

Large-scale neurophysiology and single-cell profiling in human neuroscience

Advances in large-scale single-unit human neurophysiology, single-cell RNA sequencing, spatial transcriptomics and long-term ex vivo tissue culture of surgically resected human brain tissue have provided an unprecedented opportunity to study human neuroscience. In this Perspective, we describe the development of these paradigms, including Neuropixels and recent brain-cell atlas efforts, and discuss how their convergence will further investigations into the cellular underpinnings of network-level activity in the human brain. Specifically, we introduce a workflow in which functionally mapped samples of human brain tissue resected during awake brain surgery can be cultured ex vivo for multi-modal cellular and functional profiling. We then explore how advances in human neuroscience will affect clinical practice, and conclude by discussing societal and ethical implications to consider. Potential findings from the field of human neuroscience will be vast, ranging from insights into human neurodiversity and evolution to providing cell-type-specific access to study and manipulate diseased circuits in pathology. This Perspective aims to provide a unifying framework for the field of human neuroscience as we welcome an exciting era for understanding the functional cytoarchitecture of the human brain.

The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration

Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies. We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction, contributing to optimal brain health.

Personalized medicine in old age psychiatry and Alzheimer's disease

Elderly patients show us unfolded lives with unique individual characteristics. An increasing life span is associated with increasing physical and mental disease burden. Alzheimer's disease (AD) is an increasing challenge in old age. AD cannot be cured but it can be treated. The complexity of old age and AD offer targets for personalized medicine (PM). Targets for stratification of patients, detection of patients at risk for AD or for future targeted therapy are plentiful and can be found in several omic-levels.

Updating functional brain units: Insights far beyond Luria

This paper reviews Luria's model of the three functional units of the brain. To meet this objective, several issues were reviewed: the theory of functional systems and the contributions of phylogenesis and embryogenesis to the brain's functional organization. This review revealed several facts. In the first place, the relationship/integration of basic homeostatic needs with complex forms of behavior. Secondly, the multi-scale hierarchical and distributed organization of the brain and interactions between cells and systems. Thirdly, the phylogenetic role of exaptation, especially in basal ganglia and cerebellum expansion. Finally, the tripartite embryogenetic organization of the brain: rhinic, limbic/paralimbic, and supralimbic zones. Obviously, these principles of brain organization are in contradiction with attempts to establish separate functional brain units. The proposed new model is made up of two large integrated complexes: a primordial-limbic complex (Luria's Unit I) and a telencephalic-cortical complex (Luria's Units II and III). As a result, five functional units were delineated: Unit I. Primordial or preferential (brainstem), for life-support, behavioral modulation, and waking regulation; Unit II. Limbic and paralimbic systems, for emotions and hedonic evaluation (danger and relevance detection and contribution to reward/motivational processing) and the creation of cognitive maps (contextual memory, navigation, and generativity [imagination]); Unit III. Telencephalic-cortical, for sensorimotor and cognitive processing (gnosis, praxis, language, calculation, etc.), semantic and episodic (contextual) memory processing, and multimodal conscious agency; Unit IV. Basal ganglia systems, for behavior selection and reinforcement (reward-oriented behavior); Unit V. Cerebellar systems, for the prediction/anticipation (orthometric supervision) of the outcome of an action. The proposed brain units are nothing more than abstractions within the brain's simultaneous and distributed physiological processes. As function transcends anatomy, the model necessarily involves transition and overlap between structures. Beyond the classic approaches, this review includes information on recent systemic perspectives on functional brain organization. The limitations of this review are discussed.

Artificial intelligence in neurology: opportunities, challenges, and policy implications

Neurological conditions are the leading cause of disability and mortality combined, demanding innovative, scalable, and sustainable solutions. Brain health has become a global priority with adoption of the World Health Organization's Intersectoral Global Action Plan in 2022. Simultaneously, rapid advancements in artificial intelligence (AI) are revolutionizing neurological research and practice. This scoping review of 66 original articles explores the value of AI in neurology and brain health, systematizing the landscape for emergent clinical opportunities and future trends across the care trajectory: prevention, risk stratification, early detection, diagnosis, management, and rehabilitation. AI's potential to advance personalized precision neurology and global brain health directives hinges on resolving core challenges across four pillars-models, data, feasibility/equity, and regulation/innovation-through concerted pursuit of targeted recommendations. Paramount actions include swift, ethical, equity-focused integration of novel technologies into clinical workflows, mitigating data-related issues, counteracting digital inequity gaps, and establishing robust governance frameworks balancing safety and innovation.

Neuroscience meets behavior: A systematic literature review on magnetic resonance imaging of the brain combined with real-world digital phenotyping

A primary goal of neuroscience is to understand the relationship between the brain and behavior. While magnetic resonance imaging (MRI) examines brain structure and function under controlled conditions, digital phenotyping via portable automatic devices (PAD) quantifies behavior in real-world settings. Combining these two technologies may bridge the gap between brain imaging, physiology, and real-time behavior, enhancing the generalizability of laboratory and clinical findings. However, the use of MRI and data from PADs outside the MRI scanner remains underexplored. Herein, we present a Preferred Reporting Items for Systematic Reviews and Meta-Analysis systematic literature review that identifies and analyzes the current state of research on the integration of brain MRI and PADs. PubMed and Scopus were automatically searched using keywords covering various MRI techniques and PADs. Abstracts were screened to only include articles that collected MRI brain data and PAD data outside the laboratory environment. Full-text screening was then conducted to ensure included articles combined quantitative data from MRI with data from PADs, yielding 94 selected papers for a total of N = 14,778 subjects. Results were reported as cross-frequency tables between brain imaging and behavior sampling methods and patterns were identified through network analysis. Furthermore, brain maps reported in the studies were synthesized according to the measurement modalities that were used. Results demonstrate the feasibility of integrating MRI and PADs across various study designs, patient and control populations, and age groups. The majority of published literature combines functional, T1-weighted, and diffusion weighted MRI with physical activity sensors, ecological momentary assessment via PADs, and sleep. The literature further highlights specific brain regions frequently correlated with distinct MRI-PAD combinations. These combinations enable in-depth studies on how physiology, brain function and behavior influence each other. Our review highlights the potential for constructing brain-behavior models that extend beyond the scanner and into real-world contexts.

Predicting clinical progression trajectories of early Alzheimer's disease patients

BACKGROUND

Models for forecasting individual clinical progression trajectories in early Alzheimer's disease (AD) are needed for optimizing clinical studies and patient monitoring.

METHODS

Prediction models were constructed using a clinical trial training cohort (TC; n = 934) via a gradient boosting algorithm and then evaluated in two validation cohorts (VC 1, n = 235; VC 2, n = 421). Model inputs included baseline clinical features (cognitive function assessments, APOE ε4 status, and demographics) and brain magnetic resonance imaging (MRI) measures.

RESULTS

The model using clinical features achieved R2 of 0.21 and 0.31 for predicting 2-year cognitive decline in VC 1 and VC 2, respectively. Adding MRI features improved the R2 to 0.29 in VC 1, which employed the same preprocessing pipeline as the TC. Utilizing these model-based predictions for clinical trial enrichment reduced the required sample size by 20% to 49%.

DISCUSSION

Our validated prediction models enable baseline prediction of clinical progression trajectories in early AD, benefiting clinical trial enrichment and various applications.

STW-MD: a novel spatio-temporal weighting and multi-step decision tree method for considering spatial heterogeneity in brain gene expression data

Gene expression during brain development or abnormal development is a biological process that is highly dynamic in spatio and temporal. Previous studies have mainly focused on individual brain regions or a certain developmental stage. Our motivation is to address this gap by incorporating spatio-temporal information to gain a more complete understanding of brain development or abnormal brain development, such as Alzheimer's disease (AD), and to identify potential determinants of response. In this study, we propose a novel two-step framework based on spatial-temporal information weighting and multi-step decision trees. This framework can effectively exploit the spatial similarity and temporal dependence between different stages and different brain regions, and facilitate differential gene analysis in brain regions with high heterogeneity. We focus on two datasets: the AD dataset, which includes gene expression data from early, middle and late stages, and the brain development dataset, spanning fetal development to adulthood. Our findings highlight the advantages of the proposed framework in discovering gene classes and elucidating their impact on brain development and AD progression across diverse brain regions and stages. These findings align with existing studies and provide insights into the processes of normal and abnormal brain development.

Radiopharmaceuticals: navigating the frontier of precision medicine and therapeutic innovation

This review article explores the dynamic field of radiopharmaceuticals, where innovative developments arise from combining radioisotopes and pharmaceuticals, opening up exciting therapeutic possibilities. The in-depth exploration covers targeted drug delivery, delving into passive targeting through enhanced permeability and retention, as well as active targeting using ligand-receptor strategies. The article also discusses stimulus-responsive release systems, which orchestrate controlled release, enhancing precision and therapeutic effectiveness. A significant focus is placed on the crucial role of radiopharmaceuticals in medical imaging and theranostics, highlighting their contribution to diagnostic accuracy and image-guided curative interventions. The review emphasizes safety considerations and strategies for mitigating side effects, providing valuable insights into addressing challenges and achieving precise drug delivery. Looking ahead, the article discusses nanoparticle formulations as cutting-edge innovations in next-generation radiopharmaceuticals, showcasing their potential applications. Real-world examples are presented through case studies, including the use of radiolabelled antibodies for solid tumors, peptide receptor radionuclide therapy for neuroendocrine tumors, and the intricate management of bone metastases. The concluding perspective envisions the future trajectory of radiopharmaceuticals, anticipating a harmonious integration of precision medicine and artificial intelligence. This vision foresees an era where therapeutic precision aligns seamlessly with scientific advancements, ushering in a new epoch marked by the fusion of therapeutic resonance and visionary progress.

Neurodevelopmental and Neuropsychiatric Disorders

This chapter will focus on microglial involvement in neurodevelopmental and neuropsychiatric disorders, particularly autism spectrum disorder (ASD), schizophrenia and major depressive disorder (MDD). We will describe the neuroimmune risk factors that contribute to the etiopathology of these disorders across the lifespan, including both in early life and adulthood. Microglia, being the resident immune cells of the central nervous system, could play a key role in triggering and determining the outcome of these disorders. This chapter will review preclinical and clinical findings where microglial morphology and function were examined in the contexts of ASD, schizophrenia and MDD. Clinical evidence points out to altered microglial morphology and reactivity, as well as increased expression of pro-inflammatory cytokines, supporting the idea that microglial abnormalities are involved in these disorders. Indeed, animal models for these disorders found altered microglial morphology and homeostatic functions which resulted in behaviours related to these disorders. Additionally, as microglia have emerged as promising therapeutic targets, we will also address in this chapter therapies involving microglial mechanisms for the treatment of neurodevelopmental and neuropsychiatric disorders.

Analysis of Resting-State Functional Magnetic Resonance Imaging in Alzheimer's Disease

Alzheimer's disease (AD) has been characterized by widespread network disconnection among brain regions, widely overlapping with the hallmarks of the disease. Functional connectivity has been studied with an upward trend in the last two decades, predominantly in AD among other neuropsychiatric disorders, and presents a potential biomarker with various features that might provide unique contributions to foster our understanding of neural mechanisms of AD. The resting-state functional MRI (rs-fMRI) is usually used to measure the blood-oxygen-level-dependent signals that reflect the brain's functional connectivity. Nevertheless, the rs-fMRI is still underutilized, which might be due to the fairly complex acquisition and analytic methodology. In this chapter, we presented the common methods that have been applied in rs-fMRI literature, focusing on the studies on individuals in the continuum of AD. The key methodological aspects will be addressed that comprise acquiring, processing, and interpreting rs-fMRI data. More, we discussed the current and potential implications of rs-fMRI in AD.

Revolutionizing Neurological Disorder Treatment: Integrating Innovations in Pharmaceutical Interventions and Advanced Therapeutic Technologies

Neurological disorders impose a significant burden on individuals, leading to disabilities and a reduced quality of life. However, recent years have witnessed remarkable advancements in pharmaceutical interventions aimed at treating these disorders. This review article aims to provide an overview of the latest innovations and breakthroughs in neurological disorder treatment, with a specific focus on key therapeutic areas such as Alzheimer's disease, Parkinson's disease, multiple sclerosis, epilepsy, and stroke. This review explores emerging trends in drug development, including the identification of novel therapeutic targets, the development of innovative drug delivery systems, and the application of personalized medicine approaches. Furthermore, it highlights the integration of advanced therapeutic technologies such as gene therapy, optogenetics, and neurostimulation techniques. These technologies hold promise for precise modulation of neural circuits, restoration of neuronal function, and even disease modification. While these advancements offer hopeful prospects for more effective and tailored treatments, challenges such as the need for improved diagnostic tools, identification of new targets for intervention, and optimization of drug delivery methods will remain. By addressing these challenges and continuing to invest in research and collaboration, we can revolutionize the treatment of neurological disorders and significantly enhance the lives of those affected by these conditions.

Protocol for the Tallaght University Hospital Institute for Memory and Cognition-Biobank for Research in Ageing and Neurodegeneration

INTRODUCTION

Alzheimer's disease and other dementias affect >50 million individuals globally and are characterised by broad clinical and biological heterogeneity. Cohort and biobank studies have played a critical role in advancing the understanding of disease pathophysiology and in identifying novel diagnostic and treatment approaches. However, further discovery and validation cohorts are required to clarify the real-world utility of new biomarkers, facilitate research into the development of novel therapies and advance our understanding of the clinical heterogeneity and pathobiology of neurodegenerative diseases.

METHODS AND ANALYSIS

The Tallaght University Hospital Institute for Memory and Cognition Biobank for Research in Ageing and Neurodegeneration (TIMC-BRAiN) will recruit 1000 individuals over 5 years. Participants, who are undergoing diagnostic workup in the TIMC Memory Assessment and Support Service (TIMC-MASS), will opt to donate clinical data and biological samples to a biobank. All participants will complete a detailed clinical, neuropsychological and dementia severity assessment (including Addenbrooke's Cognitive Assessment, Repeatable Battery for Assessment of Neuropsychological Status, Clinical Dementia Rating Scale). Participants undergoing venepuncture/lumbar puncture as part of the clinical workup will be offered the opportunity to donate additional blood (serum/plasma/whole blood) and cerebrospinal fluid samples for longitudinal storage in the TIMC-BRAiN biobank. Participants are followed at 18-month intervals for repeat clinical and cognitive assessments. Anonymised clinical data and biological samples will be stored securely in a central repository and used to facilitate future studies concerned with advancing the diagnosis and treatment of neurodegenerative diseases.

ETHICS AND DISSEMINATION

Ethical approval has been granted by the St. James's Hospital/Tallaght University Hospital Joint Research Ethics Committee (Project ID: 2159), which operates in compliance with the European Communities (Clinical Trials on Medicinal Products for Human Use) Regulations 2004 and ICH Good Clinical Practice Guidelines. Findings using TIMC-BRAiN will be published in a timely and open-access fashion.

Circular and unified analysis in network neuroscience

Genuinely new discovery transcends existing knowledge. Despite this, many analyses in systems neuroscience neglect to test new speculative hypotheses against benchmark empirical facts. Some of these analyses inadvertently use circular reasoning to present existing knowledge as new discovery. Here, I discuss that this problem can confound key results and estimate that it has affected more than three thousand studies in network neuroscience over the last decade. I suggest that future studies can reduce this problem by limiting the use of speculative evidence, integrating existing knowledge into benchmark models, and rigorously testing proposed discoveries against these models. I conclude with a summary of practical challenges and recommendations.

Amyloid-related imaging abnormalities (ARIA): radiological, biological and clinical characteristics

Excess accumulation and aggregation of toxic soluble and insoluble amyloid-β species in the brain are a major hallmark of Alzheimer's disease. Randomized clinical trials show reduced brain amyloid-β deposits using monoclonal antibodies that target amyloid-β and have identified MRI signal abnormalities called amyloid-related imaging abnormalities (ARIA) as possible spontaneous or treatment-related adverse events. This review provides a comprehensive state-of-the-art conceptual review of radiological features, clinical detection and classification challenges, pathophysiology, underlying biological mechanism(s) and risk factors/predictors associated with ARIA. We summarize the existing literature and current lines of evidence with ARIA-oedema/effusion (ARIA-E) and ARIA-haemosiderosis/microhaemorrhages (ARIA-H) seen across anti-amyloid clinical trials and therapeutic development. Both forms of ARIA may occur, often early, during anti-amyloid-β monoclonal antibody treatment. Across randomized controlled trials, most ARIA cases were asymptomatic. Symptomatic ARIA-E cases often occurred at higher doses and resolved within 3-4 months or upon treatment cessation. Apolipoprotein E haplotype and treatment dosage are major risk factors for ARIA-E and ARIA-H. Presence of any microhaemorrhage on baseline MRI increases the risk of ARIA. ARIA shares many clinical, biological and pathophysiological features with Alzheimer's disease and cerebral amyloid angiopathy. There is a great need to conceptually link the evident synergistic interplay associated with such underlying conditions to allow clinicians and researchers to further understand, deliberate and investigate on the combined effects of these multiple pathophysiological processes. Moreover, this review article aims to better assist clinicians in detection (either observed via symptoms or visually on MRI), management based on appropriate use recommendations, and general preparedness and awareness when ARIA are observed as well as researchers in the fundamental understanding of the various antibodies in development and their associated risks of ARIA. To facilitate ARIA detection in clinical trials and clinical practice, we recommend the implementation of standardized MRI protocols and rigorous reporting standards. With the availability of approved amyloid-β therapies in the clinic, standardized and rigorous clinical and radiological monitoring and management protocols are required to effectively detect, monitor, and manage ARIA in real-world clinical settings.

Shifting the trajectory of therapeutic development for neurological and psychiatric disorders

Clinical trials for central nervous system disorders often enroll patients with unrecognized heterogeneous diseases, leading to costly trials that have high failure rates. Here, we discuss the potential of emerging technologies and datasets to elucidate disease mechanisms and identify biomarkers to improve patient stratification and monitoring of disease progression in clinical trials for neuropsychiatric disorders. Greater efforts must be centered on rigorously standardizing data collection and sharing of methods, datasets, and analytical tools across sectors. To address health care disparities in clinical trials, diversity of genetic ancestries and environmental exposures of research participants and associated biological samples must be prioritized.

Blood-based biomarkers for Alzheimer's disease: Current state and future use in a transformed global healthcare landscape

Timely detection of the pathophysiological changes and cognitive impairment caused by Alzheimer's disease (AD) is increasingly pressing because of the advent of biomarker-guided targeted therapies that may be most effective when provided early in the disease. Currently, diagnosis and management of early AD are largely guided by clinical symptoms. FDA-approved neuroimaging and cerebrospinal fluid biomarkers can aid detection and diagnosis, but the clinical implementation of these testing modalities is limited because of availability, cost, and perceived invasiveness. Blood-based biomarkers (BBBMs) may enable earlier and faster diagnoses as well as aid in risk assessment, early detection, prognosis, and management. Herein, we review data on BBBMs that are closest to clinical implementation, particularly those based on measures of amyloid-β peptides and phosphorylated tau species. We discuss key parameters and considerations for the development and potential deployment of these BBBMs under different contexts of use and highlight challenges at the methodological, clinical, and regulatory levels.

Data Mining of Microarray Datasets in Translational Neuroscience

Data mining involves the computational analysis of a plethora of publicly available datasets to generate new hypotheses that can be further validated by experiments for the improved understanding of the pathogenesis of neurodegenerative diseases. Although the number of sequencing datasets is on the rise, microarray analysis conducted on diverse biological samples represent a large collection of datasets with multiple web-based programs that enable efficient and convenient data analysis. In this review, we first discuss the selection of biological samples associated with neurological disorders, and the possibility of a combination of datasets, from various types of samples, to conduct an integrated analysis in order to achieve a holistic understanding of the alterations in the examined biological system. We then summarize key approaches and studies that have made use of the data mining of microarray datasets to obtain insights into translational neuroscience applications, including biomarker discovery, therapeutic development, and the elucidation of the pathogenic mechanisms of neurodegenerative diseases. We further discuss the gap to be bridged between microarray and sequencing studies to improve the utilization and combination of different types of datasets, together with experimental validation, for more comprehensive analyses. We conclude by providing future perspectives on integrating multi-omics, to advance precision phenotyping and personalized medicine for neurodegenerative diseases.

Personalized infection prevention and control: a concept whose time has arrived

Personalized medicine has been progressively implemented in several diagnostic and therapeutic patients' algorithms, based on the common assumption that tailoring interventions, practices, and/or therapies to individual patients' clinical, biological, epidemiological, and genetic characteristics would optimize their effectiveness and reduce adverse effects. The potential benefit of the precision medicine approach has been recently considered for possible implementation in the field of infection prevention and control. The commentary explores available evidence and assesses possible future scenarios where, through advanced modeling approaches, we would be able to provide personalized prediction algorithms identifying at-risk patients who deserve the implementation of tailored preventive measures.

Application of Advanced Technologies-Nanotechnology, Genomics Technology, and 3D Printing Technology-In Precision Anesthesia: A Comprehensive Narrative Review

There has been increasing interest and rapid developments in precision medicine, which is a new medical concept and model based on individualized medicine with the joint application of genomics, bioinformatics engineering, and big data science. By applying numerous emerging medical frontier technologies, precision medicine could allow individualized and precise treatment for specific diseases and patients. This article reviews the application and progress of advanced technologies in the anesthesiology field, in which nanotechnology and genomics can provide more personalized anesthesia protocols, while 3D printing can yield more patient-friendly anesthesia supplies and technical training materials to improve the accuracy and efficiency of decision-making in anesthesiology. The objective of this manuscript is to analyze the recent scientific evidence on the application of nanotechnology in anesthesiology. It specifically focuses on nanomedicine, precision medicine, and clinical anesthesia. In addition, it also includes genomics and 3D printing. By studying the current research and advancements in these advanced technologies, this review aims to provide a deeper understanding of the potential impact of these advanced technologies on improving anesthesia techniques, personalized pain management, and advancing precision medicine in the field of anesthesia.

Integrative metabolomics science in Alzheimer's disease: Relevance and future perspectives

Alzheimer's disease (AD) is determined by various pathophysiological mechanisms starting 10-25 years before the onset of clinical symptoms. As multiple functionally interconnected molecular/cellular pathways appear disrupted in AD, the exploitation of high-throughput unbiased omics sciences is critical to elucidating the precise pathogenesis of AD. Among different omics, metabolomics is a fast-growing discipline allowing for the simultaneous detection and quantification of hundreds/thousands of perturbed metabolites in tissues or biofluids, reproducing the fluctuations of multiple networks affected by a disease. Here, we seek to critically depict the main metabolomics methodologies with the aim of identifying new potential AD biomarkers and further elucidating AD pathophysiological mechanisms. From a systems biology perspective, as metabolic alterations can occur before the development of clinical signs, metabolomics - coupled with existing accessible biomarkers used for AD screening and diagnosis - can support early disease diagnosis and help develop individualized treatment plans. Presently, the majority of metabolomic analyses emphasized that lipid metabolism is the most consistently altered pathway in AD pathogenesis. The possibility that metabolomics may reveal crucial steps in AD pathogenesis is undermined by the difficulty in discriminating between the causal or epiphenomenal or compensatory nature of metabolic findings.

Artificial intelligence-driven drug development against autoimmune diseases

Artificial intelligence (AI)-based predictive models are being used to foster a precision medicine approach to treat complex chronic diseases such as autoimmune and autoinflammatory disorders (AIIDs). In the past few years the first models of systemic lupus erythematosus (SLE), primary Sjögren syndrome (pSS), and rheumatoid arthritis (RA) have been produced by molecular profiling of patients using omic technologies and integrating the data with AI. These advances have confirmed a complex pathophysiology involving multiple proinflammatory pathways and also provide evidence for shared molecular dysregulation across different AIIDs. I discuss how models are used to stratify patients, assess causality in pathophysiology, design drug candidates in silico, and predict drug efficacy in virtual patients. By relating individual patient characteristics to the predicted properties of millions of drug candidates, these models can improve the management of AIIDs through more personalized treatments.

Turning the Spotlight to Cholinergic Pharmacotherapy of the Human Language System

Even though language is essential in human communication, research on pharmacological therapies for language deficits in highly prevalent neurodegenerative and vascular brain diseases has received little attention. Emerging scientific evidence suggests that disruption of the cholinergic system may play an essential role in language deficits associated with Alzheimer's disease and vascular cognitive impairment, including post-stroke aphasia. Therefore, current models of cognitive processing are beginning to appraise the implications of the brain modulator acetylcholine in human language functions. Future work should be directed further to analyze the interplay between the cholinergic system and language, focusing on identifying brain regions receiving cholinergic innervation susceptible to modulation with pharmacotherapy to improve affected language domains. The evaluation of language deficits in pharmacological cholinergic trials for Alzheimer's disease and vascular cognitive impairment has thus far been limited to coarse-grained methods. More precise, fine-grained language testing is needed to refine patient selection for pharmacotherapy to detect subtle deficits in the initial phases of cognitive decline. Additionally, noninvasive biomarkers can help identify cholinergic depletion. However, despite the investigation of cholinergic treatment for language deficits in Alzheimer's disease and vascular cognitive impairment, data on its effectiveness are insufficient and controversial. In the case of post-stroke aphasia, cholinergic agents are showing promise, particularly when combined with speech-language therapy to promote trained-dependent neural plasticity. Future research should explore the potential benefits of cholinergic pharmacotherapy in language deficits and investigate optimal strategies for combining these agents with other therapeutic approaches.

Differential response to donepezil in MRI subtypes of mild cognitive impairment

BACKGROUND

Donepezil is an approved therapy for the treatment of Alzheimer's disease (AD). Results across clinical trials have been inconsistent, which may be explained by design-methodological issues, the pathophysiological heterogeneity of AD, and diversity of included study participants. We investigated whether response to donepezil differs in mild cognitive impaired (MCI) individuals demonstrating different magnetic resonance imaging (MRI) subtypes.

METHODS

From the Hippocampus Study double-blind, randomized clinical trial, we included 173 MCI individuals (donepezil = 83; placebo = 90) with structural MRI data, at baseline and at clinical follow-up assessments (6-12-month). Efficacy outcomes were the annualized percentage change (APC) in hippocampal, ventricular, and total grey matter volumes, as well as in the AD cortical thickness signature. Participants were classified into MRI subtypes as typical AD, limbic-predominant, hippocampal-sparing, or minimal atrophy at baseline. We primarily applied a subtyping approach based on continuous scale of two subtyping dimensions. We also used the conventional categorical subtyping approach for comparison.

RESULTS

Donepezil-treated MCI individuals showed slower atrophy rates compared to the placebo group, but only if they belonged to the minimal atrophy or hippocampal-sparing subtypes. Importantly, only the continuous subtyping approach, but not the conventional categorical approach, captured this differential response.

CONCLUSIONS

Our data suggest that individuals with MCI, with hippocampal-sparing or minimal atrophy subtype, may have improved benefit from donepezil, as compared with MCI individuals with typical or limbic-predominant patterns of atrophy. The newly proposed continuous subtyping approach may have advantages compared to the conventional categorical approach. Future research is warranted to demonstrate the potential of subtype stratification for disease prognosis and response to treatment.

TRIAL REGISTRATION

ClinicalTrial.gov NCT00403520. Submission Date: November 21, 2006.