This review explains how advanced brain imaging techniques are improving the way Alzheimer’s disease (AD) is diagnosed and managed. A key tool is��PET imaging (positron emission tomography), which allows doctors to see specific biological changes in the brain while a person is still alive. Different types of PET scans highlight different aspects of the disease.��Amyloid PET��detects amyloid-β plaques—abnormal protein buildups that are a hallmark of Alzheimer’s—and is now especially important because some new treatments require confirmation that these plaques are present before therapy can begin.��Tau PET��images another protein, tau, which forms tangles inside brain cells and is closely linked to disease severity; this makes it useful for determining how advanced the disease is and for understanding unusual symptoms. Meanwhile,��18F-FDG PET��measures how the brain uses glucose (its main energy source), helping doctors distinguish Alzheimer’s from other types of dementia based on patterns of reduced brain activity.

The review highlights that these imaging methods are becoming more widely available and are increasingly used together with clinical evaluations and other biomarkers (such as those found in blood or cerebrospinal fluid). Improved quantitative techniques—methods that provide precise, repeatable measurements—also allow doctors to track disease progression and monitor how well treatments are working over time. Overall, molecular imaging is shifting Alzheimer’s diagnosis toward a more biology-based approach, enabling earlier and more accurate detection and supporting more personalized treatment strategies.

FIGURE 1.

¹⁸F-FDG PET scans of patients without (A) and with (B) AD. (A) Maximum-intensity-projection image showing absence of gross atrophy or pathology. (B) Maximum-intensity-projection image showing characteristic hypometabolism in posterior cingulate, precuneus, and temporoparietal cortices, with relative preservation of metabolism in sensorimotor cortex. This pattern often produces appearance of person wearing headphones, sometimes referred to as “earmuff” or “headphone” sign.

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This study looked at how genetic differences linked to Alzheimer’s disease (AD) may influence the brain, aiming to better understand how these genes actually lead to changes seen in patients. While previous research has identified many AD-related genes, it is still unclear how these genes affect brain structure and function. To explore this, the researchers used a functional genomics approach, meaning they examined how genetic variants influence gene activity (gene expression) and, in turn, brain features seen on imaging scans. They connected known AD genes to specific brain characteristics using a tool called the NeuroimaGene Atlas, and compared these predicted effects with real-world brain imaging data from patients. They also analyzed genetic covariance, which looks at how different traits share common genetic influences, to identify links between brain features and risk factors like family history of dementia.

The results suggest that a gene called PSMC3, which plays a role in breaking down unwanted or damaged proteins, may be important in the development of Alzheimer’s disease. Changes in AD-related genes were linked to differences in key brain areas involved in memory and thinking, such as the frontal cortex (important for decision-making and cognition), as well as changes in cerebrospinal fluid (the fluid surrounding the brain and spinal cord). The study also found shared genetic influences between Alzheimer’s risk and features of the hippocampus, a brain region critical for memory. Interestingly, higher activity of the PSMC3 gene was associated with better cognitive performance and lower levels of amyloid beta, a protein that builds up abnormally in Alzheimer’s disease. Overall, these findings help connect genetic risk factors to specific brain changes, offering a clearer picture of how Alzheimer’s disease develops and pointing to potential targets for future research and treatment.

FIGURE 1

Schematic overview of the analytical framework. A, Grid summarizing primary data resources integrated in the study. B, Directed acyclic graph illustrating TWAS analyses and downstream imputation of neuroimaging features via NeuroimaGene. C, Visualization of genetic covariance analyses comparing the genetic architecture of clinical AD and parental AD with neuroimaging-derived features. D, Logistic regression models evaluating associations between��neuroimaging features and parental AD status. E, Integration of clinical neuroimaging data linking brain features to AD status. F, Composite synthesis comparing the neuroimaging features obtained across transcriptomic, genetic covariance, parental history, and clinical approaches. AD, Alzheimer’s disease; Dx, diagnosis; TWAS, transcriptome-wide association study; UKBB, UK Biobank.

Studying proteins in body fluids, known as biofluid proteomics, can help scientists better understand the biological changes that occur in Alzheimer’s disease and related dementias, collectively called ADRDs. One protein of interest is oligodendrocyte myelin glycoprotein, or OMG. OMG is found mainly in the brain and is involved in myelination, the process that forms the protective coating around nerve fibers. However, its exact role in disease mechanisms, its usefulness as a biomarker, and its potential as a treatment target in ADRDs are not fully understood.

In this study, researchers first observed that lower levels of OMG in the blood were linked to higher levels of cortical amyloid deposition, a buildup of amyloid plaques in the brain that is a hallmark of Alzheimer’s disease, in two community-based groups. They then examined OMG more extensively using high-throughput proteomics data from sixteen independent cohorts across North America, Europe, and Asia. These included both cross-sectional studies, which look at people at a single time point, and longitudinal studies, which follow people over many years. The analysis included multiple biofluids such as blood plasma and cerebrospinal fluid (CSF), as well as brain tissue samples, and used different proteomic technologies.

The results showed that lower plasma OMG levels were associated with amyloid buildup, poorer brain structure, dementia, and multiple sclerosis. Lower OMG was also found in people who later developed dementia over follow-up periods ranging from 7 to 20 years. Proteomic patterns in CSF and brain tissue suggested that OMG is linked to neuroprotective processes, especially those that maintain axonal structural integrity, which is essential for healthy communication between nerve cells. In addition, two-sample Mendelian randomization, a genetic method used to assess potential causal relationships, indicated that higher OMG levels may protect against several neurodegenerative diseases.

Overall, these findings suggest that OMG plays an important role in neurodegenerative resilience in older adults and that its level in the blood may serve as a reliable indicator of this protective effect.

Fig. 1

Study overview. The current study leveraged proteomics from the Baltimore Longitudinal study of Aging (BLSA), the Atherosclerosis risk in Communities study (ARIC), the Emory AD Research Center (Emory-ADRC; EADRC), a Stanford University cohort (i.e., participants enrolled in the Iqbal Farrukh and Asad Jamal Stanford ad Research Center, the Stanford Aging and Memory study, the Stanford Biomarkers in PD study, and the Stanford Center for Memory Disorders cohort study), the Religious Orders study/Rush Memory and Aging Project (ROSMAP), the Knight ad Research Center (Knight-ADRC; KADRC), a Hong Kong AD cohort (HKADC), the Women’s Health Initiative (WHI), the UK Biobank (UKB), the AD Neuroimaging Initiative (ADNI), the National Institute for Longevity Sciences-Longitudinal study of Aging (NILS-LSA), the Whitehall II cohort, the Cardiovascular Health study (CHS), the Generation Scotland study (GenS), the Johns Hopkins multiple sclerosis Center (JHMSC), and the Johns Hopkins Neurology cohort (JHN). *previously computed results were obtained for HKADC, WHI, CHS, and GenS. JHMSC analyses examined prevalent multiple sclerosis, not dementia

This study followed cognitively normal adults over time to determine which magnetic resonance imaging (MRI) biomarkers best predict future cognitive impairment. Researchers examined 154 different MRI-based measurements in 509 participants from the Baltimore Longitudinal Study of Aging who were age 50 or older and cognitively normal at the start of the study. Participants underwent repeated cognitive testing and 3 Tesla (3T) MRI scans, including T1- and T2-weighted imaging to assess brain structure and diffusion tensor imaging (DTI) to measure white matter microstructural integrity. The analyses accounted for factors such as age and other confounders and also examined differences by sex and amyloid beta (Aβ) status, a biological marker associated with Alzheimer’s disease.

Over an average follow-up of 4.6 years, individuals who later developed cognitive impairment showed greater declines in white matter integrity compared to those who remained cognitively stable. These changes were especially pronounced in major white matter tracts, including the corpus callosum, cingulum bundle, and inferior fronto-occipital fasciculus, which are pathways that connect different brain regions. To a lesser extent, thinning and atrophy in the temporal lobe were also linked to later impairment. The associations between brain changes and future cognitive decline were stronger in men and in individuals who were amyloid-positive.

Overall, the findings suggest that early changes in white matter microstructure, as measured by DTI, are particularly sensitive indicators of future mild cognitive impairment (MCI) and dementia. Certain MRI metrics may therefore be especially useful for identifying risk in people who are still cognitively normal.

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FIGURE 1

Study overview. Participants were selected from the BLSA neuroimaging substudy based on cognitively normal (CN) status and age 50 or older at baseline. The study data included longitudinal cognitive assessments, clinical diagnoses (Dx), 3T magnetic resonance imaging scans, and baseline plasma biomarkers related to Alzheimer’s disease and related dementias, specifically amyloid beta 42/40, collected between 2008 and 2019. The subsequently impaired (SI) group (also CN at baseline) included individuals who later developed mild cognitive impairment (MCI) or dementia or were “Impaired, not MCI/dementia.” Impairment onset dates ranged from 2012 to 2019 (≈1- to 9-year interval).

“SuperAgers” are adults age 80 and older whose memory abilities are similar to those of middle-aged adults. Because memory usually declines with age, researchers are interested in understanding what makes SuperAgers different. In this study, we examined whether differences in the apolipoprotein E (APOE) gene are associated with being a SuperAger. The APOE gene has different forms, called alleles—most commonly APOE-ε2, APOE-ε3, and APOE-ε4. The APOE-ε4 allele is known to increase risk for Alzheimer’s disease, while APOE-ε2 is often considered protective.

We analyzed data from 18,080 participants across eight research cohorts. Using standardized clinical diagnoses and cognitive test scores measuring memory, executive function (skills like planning and problem-solving), and language, we identified SuperAgers, cognitively normal controls, and individuals with Alzheimer’s disease dementia within different age groups. We examined these patterns separately in non-Hispanic White (NHW) and non-Hispanic Black (NHB) participants.

Among NHW participants, SuperAgers were significantly less likely to carry the APOE-ε4 allele and more likely to carry the APOE-ε2 allele compared to both individuals with Alzheimer’s disease and cognitively normal controls, including those over age 80. A similar pattern was observed in NHB SuperAgers, suggesting fewer APOE-ε4 alleles and more APOE-ε2 alleles, although the smaller sample size meant that not all comparisons were statistically significant.

Overall, the results provide strong evidence that APOE allele frequency is related to SuperAger status. However, more research—especially with larger samples of NHB SuperAgers—is needed to determine whether the biological mechanisms that support exceptional cognitive resilience differ across racial groups.

FIGURE 1

Flow diagram for participant classification of SuperAgers, cases, and controls. (A) Flowchart depicting inclusion and exclusion criteria for identifying SuperAgers, AD dementia cases, controls. (B) Flowchart depicting selection order of SuperAgers, cases, and controls. Age range of participants indicated by line segment with arrows on each end. Age of participant classification is indicated by position of shorter, labeled line segments. Closed circles at the end of line segments indicate inclusion of age, such that age range is less-than-or-equal-to or greater-than-or-equal-to the age with which the circle aligns, while open circles indicate exclusion of age, such that age range is less-than or greater-than the age with which the circle aligns. Sequence of selection is indicated by line height, higher lines indicating earlier selection. AD, Alzheimer’s disease; ADSP-PHC, Alzheimer’s Disease Sequencing Project – Phenotype Harmonization Consortium; CN, cognitively normal; EXF, executive functioning; LAN, language; MEM, memory.

New methods can estimate the age at which amyloid buildup first begins in the brain, known as estimated amyloid onset age or EAOA, using amyloid positron emission tomography, a brain imaging technique that detects amyloid plaques linked to Alzheimer’s disease. This study examined the genetic factors that influence EAOA to better understand the earliest biological changes in Alzheimer’s disease. Using harmonized amyloid PET data from 4,216 participants, researchers performed genome-wide survival analyses, tissue-specific gene expression studies, and genetic covariance analyses. They found that genetic variants in apolipoprotein E, or APOE, as well as ABCA7 and RASGEF1C, were linked to earlier amyloid onset. Individuals with the APOE ε4 ε4 and ε3 ε4 genotypes developed amyloid buildup about 6.3 and 5 years earlier than those with the ε3 ε3 genotype, while the ε2 variant appeared protective against early onset. A specific genetic variant called rs4147929, which affects how much ABCA7 is expressed in the brain, was associated with amyloid onset occurring about 4 years earlier and with lower ABCA7 expression, which in turn was linked to greater amyloid pathology seen at autopsy. The study also found shared genetic risk between earlier amyloid onset and several immune-related diseases. Together, these findings highlight APOE, ABCA7, and RASGEF1C as important genetic contributors to early amyloid accumulation and suggest potential biological targets for intervention at the earliest stages of Alzheimer’s disease.

FIGURE 1

A, Genome-wide survival analysis without��APOE��covariate. The��APOE��loci was the only significant loci (top SNV: rs429538; HR ��=�� 3.17,��p ��=�� 6.56��×��10⁻¹⁷⁵). A priori significance (5��×��10⁻⁸) is indicated by the red line. B, Genome-wide survival analysis with��APOE��ε2 and ε4 status included as covariates. Three loci passed significance rs4147929 (HR ��=�� 1.31,��p��=��2.87��×��10⁻⁸, minor allele��=��A, major allele��=��G, BP��=��1063444), rs3752246 (HR ��=�� 1.31,��p��=��3.76��×��10⁻⁸, minor allele��=��C, major allele��=��G, BP��=��1056493), and rs374637031 (HR��=��1.57,��p��=��4.95��×��10⁻⁸, minor allele��=��A, major allele��=��G, BP��=��106714314). rs4147929 is intronic in��ABCA7��on chromosome 19 while rs3752246 is a missense variant in��ABCA7. rs374637031 was not found in any SNV databases. A priori significance (5��×��10⁻⁸) is indicated by the red line.��APOE, apolipoprotein E; BP, base pair; HR, hazard ratio; SNV, single nucleotide variant.

Amyloid deposition, the buildup of amyloid beta protein in the brain, begins decades before clinical symptoms appear in Alzheimer disease. To understand early biological changes linked to amyloid, this study combined blood transcriptomics, which measure gene expression in whole blood, with positron emission tomography PET imaging that detects brain amyloid. The analysis included 1,739 cognitively unimpaired participants from the Anti Amyloid Treatment in Asymptomatic Alzheimer Disease A4 study. Whole blood RNA sequencing data were used to define groups of co expressed genes called gene modules, and linear regression models tested whether module expression was associated with amyloid PET burden while adjusting for age, sex, education, and apolipoprotein E APOE ε2 and ε4 genotypes.

Among 18 gene modules examined, one module enriched for histone genes located on chromosome 6 was significantly associated with amyloid burden, with higher amyloid levels linked to lower expression of this histone gene cluster. Histone genes encode proteins involved in DNA packaging and regulation of gene expression. In addition, estimated immune cell proportions derived from the blood transcriptomic data showed suggestive associations, including lower predicted levels of activated natural killer NK cells and CD4 positive activated memory T cells with higher amyloid deposition.

Overall, these findings identify the histone gene cluster on chromosome 6 and specific immune cell signatures as blood based correlates of brain amyloid accumulation in preclinical Alzheimer disease, suggesting that peripheral gene expression and immune changes may reflect early disease processes long before symptoms develop.

FIGURE 1

Gene expression modules determined with Weighted Gene Co-expression Network Analysis analysis. The gene module is presented along the��y-axis. The number of genes is presented along the��x-axis.

We analyzed positron emission tomography (PET) scans from multiple studies to understand how amyloid and tau proteins, which are linked to Alzheimer’s disease, vary across different stages of cognitive decline. The study included over 10,000 participants, ranging from cognitively unimpaired individuals to those with mild cognitive impairment or dementia. Amyloid levels tended to increase with age among people without symptoms or with mild impairment, while very high amyloid levels were most common in those with dementia. In a subset of participants with tau PET scans, tau protein levels increased with both amyloid levels and clinical severity, with complex patterns related to age. Importantly, within each clinical stage, there was a wide range of amyloid and tau levels, showing that brain pathology can vary greatly even among people with similar cognitive status. This work demonstrates that PET scans can be standardized across different studies and tracers, revealing the heterogeneous nature of amyloid and tau accumulation along the continuum of cognitive decline.

FIGURE 1

Amyloid and tau positron emission tomography (PET) staging. (A) Distribution of amyloid PET status, where positivity was determined as ≥25 CL, and amyloid PET staging, determined using six CL-based bins in the study sample (n��=��10,396). (B) Breakdown of tau PET and biological staging for subset of individuals from (A) who also underwent tau PET imaging (n��=��3295). Tau-positive individuals were assessed for concordance with Braak hierarchical staging. Those who followed the hierarchical staging were grouped into four hierarchical tau PET stages (e.g., T−, T12+, T34+, and T56+). These tau PET stages were then used to operationalize the PET-based biological stages for AD (Jack et��al.), which require amyloid positivity for disease staging. Boxes with a dashed outline were excluded from staging schemas. A±, amyloid PET positivity; CL, Centiloid; T±, tau PET positivity.

The Uniform Data Set (UDS) is a large, standardized collection of clinical and cognitive data used in Alzheimer’s disease research. In version 3 of the UDS, the neuropsychological test battery was updated, replacing several tests that had been used in earlier versions (UDS 1 and 2). Because these changes make it difficult to directly compare cognitive performance over time, this study focused on validating new cognitive domain scores—specifically for memory, executive functioning, and language—that were designed to be comparable across all UDS versions.

To do this, the researchers developed and evaluated co-calibrated and harmonized domain scores, which adjust results from different test versions onto a common scale. These scores were compared with a more traditional statistical approach called equipercentile equating using a smaller group of participants who had completed tests from both UDS eras. The researchers also examined whether the harmonized scores showed expected relationships with brain MRI measures and with APOE genotype, a genetic factor known to be associated with Alzheimer’s disease risk, across different UDS versions.

The study included nearly 50,000 participants, with about one quarter having data from both the earlier and later UDS versions. The co-calibrated domain scores closely matched results from the traditional equating method. Importantly, the harmonized scores showed meaningful and statistically significant associations with brain imaging findings and APOE genotype, supporting their biological and clinical validity. At the same time, the study found clear differences in raw domain scores across UDS versions, underscoring the need for harmonization.

Overall, this work shows that co-calibrated and harmonized cognitive domain scores make it possible to compare memory, executive function, and language performance across different versions of the UDS. These scores help preserve long-term continuity in Alzheimer’s research and are now publicly available through the National Alzheimer’s Coordinating Center.

FIGURE 2

Co-calibrated scores at enrollment, grouped by dementia diagnosis and UDS span era. *p��<��0.05; **p��<��0.001; ***p��<��0.0001.

Alzheimer’s disease (AD), especially in its late-onset form, often impairs episodic memory—the ability to remember past events or learn new information. Most research on memory in AD relies on laboratory tasks, like recalling word lists or short stories, but it is unclear how well these tasks reflect everyday memory challenges, such as remembering conversations. This review aimed to summarize what is known about conversational memory in AD, which refers to memory for what is said during conversations one participates in or overhears, including the content of utterances and their corresponding responses.

Following scoping review guidelines, the authors screened 8,351 records across multiple databases and retained 121 studies on conversation and three specifically on conversational memory in people with AD. None of the studies directly examined memory for spontaneous conversations. Most studies did report some contextual details of conversations, like how structured the interaction was or the number and type of conversational partners, but often left out important factors such as hearing or vision status (missing in 67% of studies) and diagnostic details (missing in 33%). Research primarily focused on verbal behaviors, including repetitions, disfluencies, and ambiguous statements, while only 29% addressed nonverbal behaviors such as gestures, facial expressions, or eye gaze.

Overall, the review highlights a major gap in understanding conversational memory in AD. Studying this type of memory could be important for helping individuals maintain independence, participate effectively in healthcare decisions, and preserve social well-being. The authors suggest that future research should explore conversational memory in more depth and provide guidance for designing studies that can capture this everyday cognitive function.

Figure 2. Percentage of reports on conversation with an affiliation to a country. Reports may be affiliated to more than one country, and two reports did not include the country of affiliation. Therefore, the total percentage does not equal 100%. Countries represented in affiliations are as follows: AU = Australia, BE = Belgium, BR = Brazil, CA = Canada, CN = China, DK = Denmark, EC = Ecuador, FI = Finland, FR = France, IN = India, IE = Ireland, IL = Israel, IT = Italy, JP = Japan, NL = Netherlands, NZ = New Zealand, NO = Norway, PK = Pakistan, PT = Portugal, SI = Slovenia, ZA = South Africa, ES = Spain, SE = Sweden, TW = Taiwan, TR = Turkey, GB = United Kingdom, US = United States of America, VE = Venezuela. All other countries are displayed in gray. This figure was prepared using the WorldMapR package ().