This study looked at how to make very high-resolution 7T diffusion MRI scans more accurate and reliable. Diffusion MRI is used to study the brain’s white matter pathways, but at such high field strength the images can be distorted, which can reduce anatomical accuracy and make results less reproducible. The researchers tested different ways of collecting and correcting the scans by using five healthy adults who each had two MRI sessions. They compared several methods, ranging from uncorrected scans to more advanced approaches that used multiple phase-encoding directions, which are different ways of collecting the image data to help correct distortion. They then checked how well each method lined up with standard anatomical MRI images and how consistent the measurements were when the scan was repeated. All of the correction methods improved image accuracy compared with uncorrected scans, but using a full set of reversed phase-encoding images worked better than the common approach of using only one reversed reference image. The best results came from using a four-direction phase-encoding scheme, which produced the most accurate images and the most reproducible measurements. This approach also allowed the researchers to reconstruct both large and very fine white matter pathways with high quality. Overall, the study shows that collecting diffusion MRI data in multiple directions is important for getting dependable results from high-resolution 7T brain scans.

Fig. 1.��Methodology. The highly oversampled acquisition (top) enabled creation of subset combinations of nine time-equivalent 10��min acquisitions (bottom). In the schematic, short blocks denote b��=��0 volumes and long blocks denote diffusion-weighted volumes (DWIs). Color encodes the phase-encoding axis (blue��=��AP/PA; red��=��LR/RL), and the shading direction within each block indicates phase-encoding polarity (e.g., AP vs PA; LR vs RL). A full 10��min acquisition includes 64 uniformly distributed diffusion weighted directions (with b��=��0 images interspersed every 16 volumes). This is repeated once for each of the four PE directions (AP, PA, RL, and LR). The nine corrected acquisitions depicted here fall into four categories: (1–4) single reverse PE b��=��0 (PA, AP, RL, LR); (5–6) full blip-up/blip-down with unique DWIs (AP-PA, RL-LR); (7–8) full blip-up/blip-down with repeated DWIs (AP-PAr, RL-LRr); (9) -way PE (AP-PA-RL-LR). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

]]> /valiant/2026/05/26/association-of-mri-visible-perivascular-spaces-with-longitudinal-cognitive-decline-over-a-decade/ Tue, 26 May 2026 21:10:06 +0000 /valiant/?p=6783 Kohno, Kyoko.; Sun, Yunyi.; LeFevre, James D.; Robb, W. Hudson.; Jackson, T. Bryan.; Liu, Dandan.; Vyas, Yukti.; Sweely, Benjamin.; Pechman, Kimberly R.; Shashikumar, Niranjana.; Peterson, Amalia.; Landman, Bennett.; Davis, Larry Taylor.; Hohman, Timothy J.; Jefferson, Angela L. (2026).��.��Neurology, 106(9), e214803.��

Cerebral small vessel disease is a common cause of dementia-related brain damage, and it affects the brain’s tiny blood vessels. Several MRI signs of this disease often appear together, which makes it hard to tell which ones are most important for thinking skills. One of these signs is enlarged perivascular spaces, which are small fluid-filled spaces around blood vessels that can be seen on MRI. Earlier work from the same group showed that enlarged perivascular spaces in the basal ganglia, a deep brain region, were linked to poorer thinking ability at a single point in time, even after accounting for other signs of small vessel disease. In this study, the researchers followed 750 adults in the 91������ Memory and Aging Project, a long-term study of aging, for about five years on average, with some participants followed for as long as 11 years. At the beginning of the study, everyone had a brain MRI to measure several markers of small vessel disease, including perivascular spaces, white matter hyperintensities, lacunes, and microbleeds. The participants also completed repeated thinking and memory tests over time. The results showed that people with more enlarged perivascular spaces in the basal ganglia at the start tended to decline more over time in several abilities, including naming, processing speed, executive function, visual organization, and memory. When the researchers compared the different MRI markers directly, basal ganglia perivascular spaces still predicted worse long-term executive function and visual-spatial skills on their own. Overall, the findings suggest that enlarged perivascular spaces in the basal ganglia may be an early warning sign of later decline in specific thinking abilities as people age.

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Figure 2��Basal Ganglia PVS Volume Fraction and Longitudinal Neuropsychological Outcomes

Solid blue line reflects unadjusted values of neuropsychological outcomes (y-axis) corresponding to basal ganglia PVS volume fraction (x-axis). Shading reflects a 95% CI. PVS = MRI-visible perivascular spaces. Significant differences were seen among Animal Naming, Boston Naming Test, Coding, Executive Function, Visual Organization, and Episodic Memory annual changes (p��= 0.047, 0.03, 0.009, 0.0001, 0.04, 0.004).

Multiple system atrophy, or MSA, is a fast-moving brain disease that can be hard to diagnose early and monitor over time. This study looked at whether a special kind of MRI scan called quantitative susceptibility mapping, or QSM, can detect abnormal iron buildup in the brain, since iron imbalance may play a role in MSA and could be useful for tracking the disease. The researchers scanned 38 people with MSA, including 10 with early-stage disease who were followed again after 12 months, along with 43 people with Parkinson’s disease and 23 healthy adults of similar age. They measured iron-related changes in several brain regions, including the substantia nigra, globus pallidus, putamen, and dentate nucleus. Compared with both Parkinson’s disease and healthy controls, people with MSA had higher iron-related signal changes in the globus pallidus and substantia nigra, with smaller changes in the putamen. A more sensitive analysis that focused on higher values within each region detected these differences better than simple median measurements, suggesting it was better at picking up small, localized areas of iron buildup. Higher iron levels in the globus pallidus were also linked to worse clinical symptoms. In the 12-month follow-up, iron-related changes increased in the substantia nigra and globus pallidus, showing that these abnormalities can worsen over time. Overall, the findings suggest that QSM MRI may be a useful way to help diagnose MSA earlier, follow disease progression, and evaluate treatments aimed at reducing iron-related damage.

Fig. 1.��Representative QSM images from each diagnostic group.Quantitative susceptibility maps (QSM) for representative female participants matched for age across cohorts: a healthy control (HC, 67 years), Parkinson’s disease (PD, 62 years), PD from the bioMUSE study (69 years), multiple system atrophy (MSA) from bioMUSE (65 years), and MSA from the cross-sectional cohort (72 years). The PD (bioMUSE) participant was initially enrolled as MSA but later reclassified as PD based on longitudinal clinical evaluation. The MSA (bioMUSE) participant represents an early-stage case, whereas the MSA (cross-sectional) participant had more advanced disease. Each row displays a different subcortical region: the putamen (PT) and globus pallidus (GP) (top), the substantia nigra (SN) (middle), and the dentate nucleus (DN) (bottom). The leftmost column shows the QSM overlaid on the T1-weighted image to provide anatomical context. The second column shows the zoomed QSM for the HC participant with atlas-derived ROI outlines in red and anatomical labels. The remaining columns show the zoomed QSM for each diagnostic group. White arrows indicate the structures of interest in each row. Increases in magnetic susceptibility (brighter signal) are visible in the PT, GP, and SN in MSA participants compared with HC and PD. Susceptibility values are displayed in parts per million (ppm) using a grayscale colormap windowed from −0.1 to 0.2 ppm.

Tooth loss has been linked to memory problems and faster cognitive decline in older adults, but it is not known whether losing teeth is also associated with changes in brain structure, especially in white matter, the brain tissue that carries signals between regions and can be affected by inflammation and blood vessel problems. In this study, researchers followed 375 participants from the Baltimore Longitudinal Study of Aging for an average of 4.8 years and compared clinically measured tooth loss with changes seen on MRI brain scans and diffusion tensor imaging (DTI), a type of scan that shows the health of white matter. The participants, who had an average age of 65.5 years, were tracked over as long as 12 years. The results showed that people with more tooth loss were more likely to already have signs of brain changes, including a larger fourth ventricle, which is a fluid-filled space in the brain, smaller brain volumes in temporal regions, more abnormalities in deep white matter, and lower white matter integrity in the corpus callosum, the major fiber tract connecting the two sides of the brain. Over time, each lost tooth was linked to a faster decline in white matter health in the corpus callosum and corona radiata, suggesting ongoing damage to these pathways. Tooth loss was also associated with higher levels of blood markers related to inflammation, such as white blood cells and neutrophils, and with lower albumin, a protein that can reflect overall health. However, these inflammation markers did not explain the brain imaging findings. The study suggests that tooth loss may be a warning sign of worsening white matter health in aging, even apart from the inflammation measures examined here.

Fig. 1.��Study design.��Legend: Created in BioRender. Greig, E. (2026)��.

]]> /valiant/2026/05/26/what-needs-to-be-standardized-for-reliable-reproducible-and-robust-tractography/ Tue, 26 May 2026 20:24:44 +0000 /valiant/?p=6764 Legarreta, Jon Haitz.; Schiavi, Simona.; Tang, Wei.; Banks, Garrett.; Cieslak, Matthew.; Schilling, Kurt.; De Luca, Alberto.; Tournier, Jacques-Donald.; Kruper, John.; Rheault, Francois.; Sotiropoulos, Stamatios N.; Pestilli, Franco.; Veraart, Jelle.; Yang, Joseph Yuan-Mou.; Descoteaux, Maxime.; Heilbronner, Sarah.; Rokem, Ariel. (2026).��.��GigaScience, 15.��

Tractography is an important tool for mapping how different parts of the brain are connected. It uses brain imaging data to trace white matter pathways, but because the field is changing quickly, different research groups often use different methods and software. This lack of standardization can lead to inconsistent results, making studies harder to reproduce and limiting use in clinical settings. Differences in how data are collected, how brain images are aligned and processed, and natural anatomical variation between people, age groups, and even species all add to the problem. Another challenge is that there is no broad agreement on the best way to perform tractography, which makes it harder to build reliable automated quality checks and slows clinical translation. This article reviews the main challenges in standardizing tractography and highlights the parts of the process that most need consistent methods so the results can be more reliable, reproducible, and useful across studies and applications.

Figure 1

Summary of main challenges and suggested standardization solutions toward reliable, reproducible, and robust tractography.

]]> /valiant/2026/05/26/functional-connectivity-between-the-uncinate-fasciculus-and-frontotemporal-semantic-system-supports-reading-comprehension-in-adolescents/ Tue, 26 May 2026 16:45:47 +0000 /valiant/?p=6748 Nguyen, Tin Q.; Harriott, Emily M.; Gao, Yurui; Burgess, Andrea N.; Cavender, Addie C.; Lou, Chenglin; Schilling, Kurt G.; Landman, Bennett A.; Gore, John C.; Cutting, Laurie E. (2025).��.��Imaging Neuroscience, 3.��

Skilled reading depends on several brain systems working together, including those for recognizing words, understanding meaning, and controlling attention and thinking. Scientists know a lot about the brain’s “wiring” for reading, but less about how these connections actually function. In this study, we looked at whether the left uncinate fasciculus, a white matter pathway connecting frontal and temporal brain regions and thought to be important for understanding meaning, affects how word recognition relates to reading comprehension. Fifty-three children and adolescents, ages 10 to 14, completed brain scans while resting and took tests of word recognition and reading comprehension. We measured how strongly the uncinate fasciculus was functionally connected to nearby gray matter regions involved in semantic memory, meaning-based knowledge, and cognitive control by examining correlations in resting brain activity signals. The results showed that stronger connectivity in this pathway was linked to a weaker reliance of reading comprehension on word recognition skill. In other words, children with stronger communication in this meaning-related brain network seemed better able to understand text even when word recognition was less strong. These findings suggest that semantic brain systems connected through the uncinate fasciculus may help readers use flexible, meaning-based strategies to support comprehension, and they highlight the importance of studying how white matter and gray matter work together in the developing reading brain.

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Fig. 1

Visualization of the analysis pipeline. (a) Pearson’s correlations between white matter (WM) and gray matter (GM) time series were computed to create each participant’s functional connectivity matrix, with rows representing 48 WM bundles from the Eve atlas and columns representing 82 GM regions from the Brodmann’s Areas (BA) atlas. Reproduced with permission from Gao et al. (2021). (b) Functional connectivity indices between the uncinate fasciculus (UF) and GM regions were derived from the WM-GM functional connectivity matrices across participants. (c) Functional connectivity indices with the uncinate fasciculus were filtered by structurally linked GM regions of interest (ROIs). (d) Regression analysis was conducted to examine differences in WJ Passage Comprehension scores related to the interaction between WJ Basic Reading scores and UF-GM ROI functional connectivity (FC). (e) Multiple comparisons were corrected using False Discovery Rate (FDR). (f) Results surviving multiple comparison correction, including significant interaction effects and UF-GM ROI functional connectivity patterns, were visualized.

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)��and��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.