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/2026/04/29/bridging-histology-and-tractography-first-in-vivo-visualization-of-short-range-prefrontal-connections-informed-by-primate-tract-tracing/ Wed, 29 Apr 2026 02:38:51 +0000 /valiant/?p=6524 Amandola, Matthew; Kim, Michael E.; Rheault, François; Landman, Bennett; Schilling, Kurt (2026).��.��Human Brain Mapping, 47(5), e70520.��

For many years, studies in non-human primates have shown that the��prefrontal cortex (PFC)—a part of the brain involved in decision-making, planning, and complex thinking—contains a dense network of��short-range connections(local wiring between nearby brain regions). However, studying these fine connections in living humans has been difficult because non-invasive imaging methods like��diffusion tractography��(a technique that estimates brain pathways by tracking water movement) can produce inaccurate results, including false connections.

In this study, researchers developed a new approach to map these local brain connections more reliably in living humans. They combined high-resolution tractography with prior knowledge from��histology (microscopic studies of brain tissue, considered a gold standard for anatomical detail) to guide their analysis. Using brain scans from over 1,000 individuals, they were able to map 91 specific short-range connections within and between five key regions of the PFC. Their method showed strong agreement with known anatomical data, achieving over 80% precision (correctly identified connections) and over 70% accuracy (overall correctness compared to histological findings). Importantly, the results captured not only general patterns of connectivity but also subtle details that had previously only been observed in invasive studies.

The study also found that these brain connections are highly consistent within the same person over time, yet vary meaningfully between individuals—suggesting each person has a stable but unique “wiring pattern” in their PFC. Overall, this work demonstrates that combining detailed anatomical knowledge with advanced imaging can significantly improve our ability to map the human brain’s internal connections. This opens new possibilities for understanding how local brain circuits support thinking and behavior, and how they may be altered in neurological or psychiatric conditions.

FIGURE 1

(a.) Schematic depicting the interconnections of the prefrontal cortex (PFC). Each dot represents a connection with histological precedence. Red = dl-PFC (dorsolateral prefrontal), blue = vl-PFC (ventrolateral prefrontal), orange = orbitofrontal, purple = F. Pole (frontal pole), green = ACC (anterior cingulate). (b.) Table overview of histologically supported connections. + = consistently shown in histological literature.

This study looks at how changes in brain structure are linked to the return of depression in older adults. Specifically, it focuses on��white matter hyperintensities (WMH)�����Ի���hypointensities (WMh)—areas in brain scans that appear unusually bright or dark and are thought to reflect small blood vessel damage and increased vascular (blood flow–related) risk. These markers are commonly seen in older individuals and have been associated with late-life depression (LLD), but it has been unclear whether changes in these brain features over time contribute to depression coming back after recovery.

To investigate this, researchers followed 223 older adults (average age about 67), including people whose depression had improved (remitted LLD) and a comparison group without depression. Brain scans were taken every 8 months over two years to track changes in WMh. During this period, about half of the participants who had recovered from depression experienced a relapse. The researchers found that people who relapsed already had higher levels of WMh at the start of the study compared to those without depression. However, the��rate at which these brain changes increased over time was not significantly different between groups. When looking more closely, individuals who started with high WMh levels and also showed faster accumulation over time had nearly three times the risk of relapse compared to those with low levels and slow changes.

Overall, the findings suggest that having a higher burden of these brain changes at baseline is an important risk factor for depression returning in older adults, while the speed of progression alone may be less informative. However, people with both high initial levels and rapid increases may be at especially high risk and could benefit from closer monitoring and care to help prevent relapse.

Figure. 1��Mixed effects model predictions. (A), (B): Results of the model comparing HC vs. remLLD. (C), (D): Results comparing HC vs. REM vs. EarlyREL vs. LateREL. EarlyREL and LateREL represent individuals that relapse within 250 days of baseline and after 250 days of baseline, respectively. Both models adjusted for time (days since baseline), vascular disease burden using CIRS-G, age at baseline, ICV at baseline, sex, education, race, study site, group, and time * group effects.

Most brain-based models of reading focus on a network of regions in the left side of the brain that help us understand words, sentences, and whole texts, but they tend to ignore the role of emotion. In reality, emotional factors—especially��arousal��(how intense or exciting a word or phrase feels)—can influence how we process what we read. While earlier brain imaging studies have shown that emotional words or full passages can activate areas involved in both emotion and understanding a story (sometimes called building a “situation model,” or a mental picture of what’s happening), it has been unclear how smaller units, like phrases within a passage, affect the brain. It is also not well understood how differences between individuals in responding to emotional intensity might relate to reading ability, particularly in children.

In this study, researchers used��functional magnetic resonance imaging (fMRI)—a technique that measures brain activity by tracking blood flow—to examine how 86 third-grade students processed emotional content while reading. They analyzed how changes in emotional intensity at the phrase level (based on ratings from a standardized word database) influenced brain activity as the children read full passages. The results showed that more emotionally intense phrases led to increased activity in several brain regions involved in emotion and understanding context, including the��amygdala��(important for processing emotions), the��striatum��(linked to motivation and reward), the��posterior insula(involved in internal bodily and emotional awareness), and the��dorsomedial prefrontal cortex (dmPFC)��(a region associated with thinking about situations and making sense of narratives). Importantly, greater activity in the dmPFC was also linked to better reading comprehension skills.

Overall, the findings suggest that emotional intensity within a text plays a meaningful role in how the brain processes language, even at the level of individual phrases. This highlights the importance of including emotional factors in models of reading and suggests that using more emotionally engaging reading materials could help improve comprehension, especially for children who are still developing their reading skills.

Fig. 1.��Sample stimuli presentation. As an attention check, children were asked to indicate consecutively repeated stimuli using a thumb button press. Not pictured is the blank screen (variable jitter) between each stimulus.

This study investigates how��cerebrospinal fluid (CSF)—the fluid that surrounds and cushions the brain and spinal cord—moves within the brain, and how this movement is influenced by the body’s automatic (autonomic) functions, such as heart rate and breathing. CSF flow is important because it helps remove waste and maintain brain health. While previous research has linked CSF movement to sleep and brain activity, the researchers wanted to isolate the role of the��autonomic nervous system��(the system that controls involuntary processes like heartbeat and respiration).

To do this, they used fMRI scans to observe fluid-related signals in the brain while changing levels of carbon dioxide (CO₂) in participants’ blood—a method that affects blood vessel tone, breathing, and heart function without directly altering brain activity. They found that changes in CSF movement could not be explained simply by physical or mechanical factors. Instead, variations in��heart-rate variability��(natural fluctuations in the time between heartbeats) played a key role in driving slow CSF flow, independent of breathing. Additionally, changes in CO₂ levels mainly affected how frequently heart rate and breathing patterns fluctuated, rather than how strong those fluctuations were.

Overall, the findings suggest that CSF movement is strongly influenced by autonomic regulation, and that both higher and lower-than-normal CO₂ levels can disrupt this process. This highlights a new way to study and potentially control brain fluid dynamics—by adjusting CO₂ levels—without relying on sleep or direct neural activity, offering potential insights into brain health and disease.

��

Fig 1: The predictions of CSF flow dynamics across capnias is based on three different physiological pathways: vascular tone, sympathetic tone, and neuronal activity. According to the vascular-tone theory, CSF fluctuations should be maximal at normocapnia. According to the neuronal-activity theory, CSF fluctuations should be maximized at hypocapnia. Lastly, according to the sympathetic-tone theory, CSF fluctuations should be maximized at hypercapnia. These theories will be tested using empirical data involving different capnias, at which all three variables will be altered.

]]> /valiant/2026/03/26/neuroimaging-phewas-and-molecular-phenotyping-implicate-psmc3-in-alzheimers-disease/ Thu, 26 Mar 2026 18:43:37 +0000 /valiant/?p=6307 Xavier Bledsoe; Ting-Chen Wang; Yiyang Wu; Derek Archer; Hung Hsin Chen; Adam C. Naj; William S. Bush; Timothy J. Hohman; Logan Dumitrescu; Jennifer E. Below; Eric R. Gamazon (2026).��.��Alzheimer’s & Dementia, 22(2), e71217.��

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.

The brain undergoes major structural and functional changes from childhood through adolescence. Research suggests that neurodevelopment happens in a hierarchical way, meaning different brain regions and networks mature at different rates. However, less is known about how the brain’s intrinsic spatiotemporal propagations—patterns showing how activity spreads across the brain over time—develop during this period. This study examined how these activity patterns change from childhood to early adulthood.

Using a recently developed method that measures time-lagged dynamic propagations, the researchers analyzed how brain activity travels along three major axes of brain organization: the sensory–association (S-A) axis, which connects basic sensory regions to higher-order thinking areas; the task-positive to default network (TP-D) axis, which reflects shifts between attention-focused networks and the default mode network active during rest and internal thought; and the somatomotor–visual (SM-V) axis, which links movement and visual processing regions. The results showed that these propagation patterns gradually become more adult-like over development. As children mature, they spend more time in S-A and TP-D propagation states, while the occurrence of SM-V propagation states decreases.

Importantly, top-down propagations along the S-A axis—meaning activity flowing from higher-order cognitive regions to sensory regions—increased with age and were better predictors of cognitive performance than bottom-up propagations, which flow from sensory areas upward. These findings were replicated in two independent datasets, the Human Connectome Project Development cohort and the Nathan Kline Institute Rockland Sample, supporting the robustness and generalizability of the results. Overall, the study provides new insight into how large-scale functional brain dynamics develop during youth and how these changes support cognitive abilities.

Fig. 1: Spatiotemporal propagation patterns and their neurodevelopmental change from children to early adulthood.

A��The first three propagation patterns derived from the reference cohort (HCP-A), represent group-level reference propagation patterns. Each row displays a full propagation cycle for the recurring spatiotemporal patterns: sensorimotor to association (S-A), task-positive to default mode networks (TP-D), and somatomotor to visual networks (SM-V). The patterns are depicted through their temporal phase cycle, ranging from 0 to 2π.��B��Explained variance ratios of the first six propagation patterns from CPCA. The light blue line represents the youth cohort (HCP-D) and the dark line represents the reference adult cohort (HCP-A).��C��Between-cohort similarity matrix showing the pairwise Pearson’s correlation of the propagation patterns across youth (HCP-D) and adult (HCP-A) propagation patterns. We also confirmed cross-cohort similarity using HCP Young Adult cohort (N = 892, age 21-35, Figure.��)].��D��Reliability of propagation patterns, assessed by the discriminability for HCP-D and HCP-A cohorts.��E��Age-related similarity of propagation patterns to adult reference. Dots represent the spatial correlations of the propagation pattern between individuals in the youth cohort and the group-level adult reference. The regression line illustrates the developmental trend across age. Age effect was assessed using a Spearman correlation, with��p��values adjusted for multiple comparisons using the false-discovery-rate (FDR) correction. Significant age-related increases were observed for the S-A (pFDR <0.001), TP-D (pFDR <0.001) and SM-V (pFDR = 0.002) propagation patterns. Statistical significance is denoted by asterisks (*: pFDR <0.05).��F��Age prediction using the first three dynamic patterns. A combination of the first three dominant propagation patterns in the PLSR model predicts age with a Spearman’s correlation ρ of 0.80 and a mean absolute error (MAE) of 1.87 years.

In diffusion magnetic resonance imaging, or dMRI, an incomplete field of view (FOV) means that part of the brain is missing from the scan. This can seriously affect analyses of white matter connectivity, including tractography, which maps the pathways of nerve fiber bundles across the brain. Although previous studies have used deep generative models to estimate or “impute” the missing regions, it is still unclear how to best use additional information from paired multi-modality data, such as combining dMRI with structural T1-weighted (T1w) MRI, to improve the quality of reconstruction and support downstream analyses.

To address this, the researchers developed a new framework that imputes missing dMRI regions by integrating diffusion features from the acquired portion of the scan with information about the complete brain anatomical structure derived from paired imaging data. The idea is that using anatomical guidance from other modalities can improve how the missing diffusion signals are reconstructed. They tested the framework on two cohorts from different sites, including a total of 96 participants, and compared it with a baseline method that treated T1w and dMRI information equally without specifically leveraging their complementary roles.

The proposed framework significantly improved imputation quality, as measured by the angular correlation coefficient, and improved the accuracy of downstream tractography, as measured by the Dice score. These results suggest that carefully integrating paired multi-modality data leads to more accurate reconstruction of incomplete dMRI scans. By improving whole-brain tractography, this approach may reduce uncertainty in analyses of white matter bundles, particularly those relevant to neurodegenerative diseases.

Fig. 1.

Visualization (left) and histogram (right) of 103 real cases of dMRI scans with incomplete FOV that failed quality assurance. In the left figure, horizontal regions indicate the distribution of the incomplete part of FOV with an estimated position of a brain mask. The total cutoff distance from the incomplete FOV to the top of the brain is estimated using a corresponding and registered T1w image. Its histogram is presented in the right figure.

Multiple sclerosis (MS) causes lesions, or areas of damage, in the brain that can be seen on multicontrast magnetic resonance (MR) images. Automatically segmenting, or outlining, these lesions using deep learning (DL) can improve speed and consistency compared to manual tracing by experts. Although many DL methods perform well on data similar to what they were trained on, they often struggle when tested on new datasets from different hospitals or scanners, a problem known as poor out-of-domain generalization.

To address this issue, the researchers developed a new method called UNISELF. The goal of UNISELF is to achieve high segmentation accuracy within the original training domain while also performing well on data from different sources. UNISELF introduces a test-time self-ensembled lesion fusion strategy, which combines multiple predictions at test time to improve accuracy. It also uses test-time instance normalization (TTIN) of latent features, meaning it adjusts internal feature representations during testing to better handle domain shifts and missing input contrasts, such as when certain MR image types are unavailable.

The model was trained using data from the ISBI 2015 longitudinal MS segmentation challenge. On the official test dataset, UNISELF ranked among the top-performing methods. Importantly, when evaluated on out-of-domain datasets with different scanners, imaging protocols, and missing contrasts—including the MICCAI 2016 dataset, the UMCL dataset, and a private multisite dataset—UNISELF outperformed other benchmark models trained on the same ISBI data. These results suggest that UNISELF is both accurate and robust to real-world variations in MR imaging, making it a promising tool for automated MS lesion segmentation across diverse clinical settings.

Fig. 1.��An illustration of the spatial augmentation, network input, and network output during training in UNISELF.