Memory decline is one of the hallmark features of��Alzheimer’s disease (AD), but measurable changes in memory often occur only after significant changes have already taken place in the brain. This study investigated whether brain characteristics seen in��midlife��share genetic links with memory performance later in life, with the goal of identifying earlier markers of Alzheimer’s-related cognitive decline. The researchers analyzed genetic data from more than 24,000 older adults alongside brain imaging and genetic data from over 33,000 middle-aged participants in the UK Biobank. They found that brain features related to the structure and microscopic organization of the��medial temporal lobe—a region critical for memory—and the��frontal lobe��showed the strongest shared genetic links with memory performance. They also identified shared genetic patterns involving the��default mode network, a network of brain regions that is important for memory and is known to be affected early in Alzheimer’s disease. These findings suggest that changes in specific brain regions during midlife may reflect genetic pathways that contribute to later-life memory decline and Alzheimer’s disease, potentially helping researchers identify earlier biomarkers and new targets for treatment.

FIGURE 1

Genetic covariance between imaging-derived phenotypes (IDPs) and memory performance. Volcano plots show strength of genetic covariance of diffusion (top panel), structural (middle panel), and functional (bottom panel) IDPs with cross-sectional memory performance (MEM) for all, impaired, and unimpaired individuals, including the��APOEregion in the analyses. Colors highlight significant genetic covariance with memory (FDR-corrected��p��<��0.05), with green indicating positive covariance and red indicating negative covariance. Data point shape for structural IDPs indicates the atlas used to generate the cortical measure, with circles representing the Desikan-Killiany atlas, squares representing the Destrieux atlas, diamonds representing the Desikan-Killiany-Tourville (DKT) atlas, and triangles representing other smaller atlases (e.g., thalamic nuclei, hippocampal subfields). Data point shape for functional IDPs indicates the dimensionality of group ICA, with diamonds representing 100-dimensional ICA and circles representing 25-dimensional ICA. Top IDPs for each modality and analysis that survived FDR correction are labeled. Diffusion measures highlight regions such as the fornix and corona radiata; structural measures highlight regions such as the lingual gyrus, anterior cingulate gyrus and sulcus, fusiform gyrus, and superior temporal gyrus and sulcus. Table����provides the mapping between original UKB IDP names and our intuitive names. Ant., anterior; L, left hemisphere; Lat., lateral; Post., posterior; R, right hemisphere; Sup., superior.

Changes in the brain’s��limbic white matter—the nerve fiber pathways involved in memory, learning, and emotion—are common in aging and��Alzheimer’s disease (AD), but the genetic factors that influence these changes are not well understood. This study analyzed brain imaging and genetic data from 2,614 older adults across seven research cohorts, including many participants with cognitive impairment. The researchers found that differences in limbic white matter structure are strongly influenced by genetics and identified six regions of the genome associated with these brain changes. One of the strongest signals involved��CDH19, a gene linked to��oligodendrocytes, the cells responsible for producing myelin, the protective coating that surrounds nerve fibers. Several other genes, including��RORA,��FAM107B, and��KC6, were also associated with cognitive performance and the brain changes seen in Alzheimer’s disease. The findings further suggest that biological pathways related to insulin signaling, immune function, and cardiovascular health may contribute to white matter changes during aging. Overall, the study provides new evidence that the structure of limbic white matter is influenced by genetics and identifies several genes and biological pathways that may play a role in Alzheimer’s disease and age-related cognitive decline.

FIGURE 1

SNP heritability estimates for limbic white matter (WM) microstructure. SNP heritability of 35 FW-corrected dMRI metrics from seven WM tracts in the limbic system. Abbreviations: AxD, axial diffusivity; dMRI, diffusion magnetic resonance imaging; FA, fractional anisotropy; FW_corr, free-water corrected; MD, mean diffusivity; RD, radial diffusivity; WM, white matter.

The relationship between dietary fat and��Alzheimer’s disease (AD)��has remained unclear, with previous studies reporting mixed results. This study examined whether fat intake was associated with changes in brain structure over time and whether those associations differed based on factors such as cognitive status, sex, and��APOE ε4��status—a genetic variant that increases the risk of developing Alzheimer’s disease. The researchers followed 758 participants for an average of 4.6 years, including individuals with normal cognition and those with��mild cognitive impairment (MCI), an early stage of cognitive decline. Among cognitively unimpaired participants, a higher percentage of calories from fat was associated with faster shrinkage of the��temporal lobe, a brain region important for memory. In contrast, among participants with MCI, higher fat intake was associated with slower enlargement of the��inferior lateral ventricle, a change that is often linked to brain atrophy. This association appeared to be driven primarily by women and individuals carrying the APOE ε4 genetic variant. The findings suggest that the relationship between dietary fat and brain health may differ depending on a person’s stage of cognitive decline and underlying risk factors. The authors note that, in higher-risk groups, the slower progression of some brain changes may reflect a compensatory response rather than a protective effect of a high-fat diet.

��

FIGURE 1

Pfat × Cognitive status and Tfat × Cognitive status on longitudinal structural MRI variables. Lines reflect structural MRI variables corresponding to Pfat and Tfat. The gray shaded area reflects 95% confidence interval. (A) Associations between Tfat and hippocampal volume, stratified by cognitive status; CU participants��β��=��−18.7,��p��=��0.008; MCI participants��β��=��11.2, p��=��0.43. (B) Associations between Pfat and temporal lobe volume, stratified by cognitive status; CU participants��β��=��−47.2,��p��=��0.007; MCI participants��β��=��50.9,��p��=��0.21. (C) Associations between Pfat and inferior lateral ventricle volume, stratified by cognitive status; CU participants��β��=��−1.89,��p��=��0.27; MCI participants��β��=��−22.5,��p��=��0.006. CU, cognitively unimpaired; MCI, mild cognitive impairment; MRI, magnetic resonance imaging; Pfat, percentage of energy from fat; Tfat, total fat intake.

New treatments for Alzheimer’s disease that target��amyloid-beta (Aβ)—a protein that builds up in the brain—are changing how the disease is diagnosed and managed. This study examined real-world data from Mayo Clinic health records (2019–2025) to see how testing and treatment patterns have shifted with the introduction of a drug called��lecanemab, which is given by infusion.

After insurance coverage expanded, use of lecanemab increased rapidly. At the same time, there were notable changes in how patients are tested: traditional methods like��cerebrospinal fluid (CSF) testing��declined, while blood-based tests—especially��plasma p-tau217��(a marker linked to Alzheimer’s-related brain changes)—rose sharply. Brain scans using��PET imaging��to detect amyloid also increased. All patients who received lecanemab were confirmed to have amyloid buildup through PET or CSF testing.

The study also found that women were more likely to test positive for amyloid across different testing methods. Genetic testing showed that many patients carried the��APOE-ε4 variant, a gene associated with higher Alzheimer’s risk, but those with two copies of this variant were less likely to start lecanemab treatment. Overall, the findings show that the arrival of anti-amyloid therapies is rapidly reshaping both diagnostic approaches and treatment use in real-world clinical care.

FIGURE 1

Regulatory milestones of Alzheimer’s disease biomarkers and treatments from 2012 through 2025. Aβ, amyloid-beta; AD, Alzheimer’s disease; CMS, Centers for Medicare & Medicaid Services; CSF, cerebrospinal fluid; PET, positron emission tomography; pTau, phosphorylated tau.

]]> /valiant/2026/04/29/using-diffusion-mri-to-relate-hippocampal-subfield-microstructure-to-delayed-verbal-memory-in-cognitively-intact-individuals-at-genetic-risk-for-developing-alzheimers-disease/ Wed, 29 Apr 2026 02:52:27 +0000 /valiant/?p=6544 VanGilder, Jennapher Lingo; Hooyman, Andrew; Hakhu, Sasha; Schilling, Kurt G.; Hu, Leland S.; Zhou, Yuxiang; Caselli, Richard J.; Baxter, Leslie C.; Beeman, Scott C. (2026).��.��Experimental Gerontology, 218, 113112.��

This study explores how subtle changes in the brain may help identify people at risk for��Alzheimer’s disease (AD)��before symptoms appear. The researchers focused on the��hippocampus, a brain region important for memory, and compared older adults who carry the��APOE ε4 gene variant��(a known genetic risk factor for AD) with those who do not. Using advanced brain imaging techniques, including��diffusion MRI��methods that examine the brain’s��microstructure��(the fine, internal organization of brain tissue), they looked at how these features relate to memory performance.

The results showed that overall hippocampal size did not differ in a meaningful way. However, more detailed microstructural measures—especially a metric called��orientation dispersion (ODI), which reflects how nerve fibers are organized—were linked to better verbal memory performance in people with the APOE ε4 variant. In particular, higher ODI in a specific hippocampal subregion (the left subiculum) was associated with better recall of spoken information.

These findings suggest that looking at the brain’s microstructure, rather than just its size, may provide earlier and more sensitive clues about cognitive changes in people at genetic risk for Alzheimer’s disease.

Fig. 1.��Shown are the absolute values of log-transformed raw��p-values for the APOE ε4 interaction across 10 hippocampal regions of interest (i.e., left and right CA1, CA2–3, CA4, subiculum, and whole hippocampus), assessed for ODI, NDI, FA, MD, and volumetric metrics in relation to CFT recall and AVLT scores. Higher the magnitudes on the graph correspond to smaller p-values. The dashed line represents the threshold for statistical significance after Bonferroni correction for 10 comparisons (p��=��0.005). Notably, only the left subiculum was associated with AVLT, indicating significant interaction effects that persist beyond multiple comparison correction.

]]> /valiant/2026/03/26/pet-imaging-in-alzheimer-disease-in-the-era-of-antiamyloid-therapy-in-the-united-states-clinical-utility-quantification-and-policy-landscape/ Thu, 26 Mar 2026 19:04:23 +0000 /valiant/?p=6321 Ty Skyles; Samantha M. Bouchal; Anna Giarratana; Jacob Wengler; Ian Hart; Erin Greig; Harmanjeet Singh; Steve S. Huang; Felipe Martinez; Ba Nguyen; Clifford H. Shin; Ming Yang; Ephraim Parent; W. Hudson Robb; Ana M. Franceschi; Brian Burkett; Derek Johnson; Mary Ellen Koran (2026).��.��Journal of Nuclear Medicine Technology, 54(1), 10–17.��

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.