Vascular risk factors can contribute to damage in the brain’s white matter, which contains nerve fibers that connect different brain regions. However, it is unclear how individual risk factors affect specific white matter pathways. This study examined the independent effects of four major vascular risk factors—high blood pressure, heart disease, diabetes, and body mass index (BMI)—using diffusion MRI data from 2,961 participants across five research cohorts. Researchers assessed microscopic changes in 48 white matter pathways using several measures of tissue structure and water movement. High blood pressure and heart disease showed the strongest and most widespread associations with poorer white matter health. Associations with diabetes became weaker in additional analyses, while findings for BMI varied depending on the measure examined. These results suggest that high blood pressure and heart disease may have particularly important relationships with white matter health, highlighting the potential value of managing these conditions to help preserve brain structure.

FIGURE 1

White matter tractography templates. Forty-eight white matter tractography templates were used in this study and can be grouped into TC (A), association (B), projection (C), and limbic tracts (D). IFG, inferior frontal gyrus; IFOF, inferior fronto-occipital fasciculus; ILF, inferior longitudinal fasciculus; IPL, inferior parietal lobe; M1, primary motor cortex; PMd, dorsal premotor cortex; PMv, ventral premotor; S1, primary somatosensory cortex; SLF, superior longitudinal fasciculus; SLF-TP, temporoparietal superior longitudinal fasciculus; SMA, supplementary motor area; SPL, superior parietal lobe; TC, transcallosal; UF, uncinate fasciculus.

]]> /valiant/2026/08/24/distinct-brain-systems-support-afferent-and-efferent-autonomic-activity/ Mon, 24 Aug 2026 20:00:00 +0000 /valiant/?p=7394 Min, Jungwon; Liu, Gengshuo; Dahl, Martin J.; Lee, Tae-Ho; Nashiro, Kaoru; Yoo, Hyun Joo; Cho, Christine; Bogdan, Paul; Chang, Catie; Lehrer, Paul M.; Thayer, Julian F.; Mather, Mara. (2026). . Social Cognitive and Affective Neuroscience, 21(1), nsag054.

The brain and autonomic nervous system, which controls automatic functions such as heart rate, continuously communicate with each other. However, it is often unclear whether specific brain regions are responding to signals from the body or sending signals that influence the body. Using brain imaging and heart rate variability (HRV), a measure of changes in the time between heartbeats, this study examined brain–heart communication in younger and older adults during emotion regulation and rest. During emotion regulation, activity in the insula and cingulate cortex was associated with lower HRV. During rest, several brain regions, including the posterior insula, responded after decreases in HRV, suggesting that they receive information related to changes in the body. In contrast, activity in the anterior insula and cingulate cortex occurred before increases in HRV, suggesting a role in sending signals that influence heart activity. The results support a possible feedback loop in which decreased HRV activates brain regions that receive bodily signals, which then activate regions involved in regulating HRV. This pattern also differed with age, as the brain regions involved in receiving and sending signals were more distinct in younger adults but overlapped more in older adults. These findings provide insight into how the brain and heart coordinate and how this communication may change with aging.

Figure 1

Two types of RMSSD time series paired with whole-brain BOLD signals. Modeling individual BOLD time series at TR with RMSSD time series over two 10-second windows before and after TRs allows us to investigate bidirectional relationships between RMSSD and brain BOLD activity. By correlating at-TR BOLD signals with before-TR RMSSD while accounting for after-TR RMSSD, we can examine how changes in RMSSD influence changes in BOLD activity. Likewise, by correlating at-TR BOLD signals with after-TR RMSSD while accounting for before-TR RMSSD, we can investigate how changes in RMSSD are influenced by changes in BOLD activity.

]]> /valiant/2026/08/24/advancing-fair-and-explainable-machine-learning-for-neuroimaging-dementia-pattern-classification-in-multi-racial-and-multi-ethnic-populations/ Mon, 24 Aug 2026 19:52:39 +0000 /valiant/?p=7386 Ho, Ngoc-Huynh; Charisis, Sokratis; Honnorat, Nicolas; Brandigampala, Sachintha Ransara; Wang, Di; Heckbert, Susan R.; Fox, Peter T.; Martinez, David; Wang, David H.; Hughes, Timothy M.; Archer, Derek B.; Hohman, Timothy J.; Seshadri, Sudha; Davatzikos, Christos; Habes, Mohamad. (2026). . Nature Communications, 17(1), 8026.

Dementia affects millions of people worldwide and is projected to triple by 2050, making early and accurate diagnosis important for treatment and quality of life. However, artificial intelligence models used to classify dementia from MRI scans may not perform equally well across racial and ethnic groups. This study examined differences in dementia classification among 6,584 Non-Hispanic White, 1,263 Non-Hispanic African American, and 713 Hispanic White individuals. The researchers found significant differences in model performance, particularly when a model trained using data from one group was applied to another. To reduce these differences, they evaluated RegAlign, a machine-learning approach that uses a small amount of data from underrepresented groups to help the model adapt while accounting for differences between groups and dementia categories. RegAlign substantially reduced performance gaps, especially between Non-Hispanic White and Hispanic populations. These findings highlight the importance of using diverse training data and fairness-focused machine-learning methods to improve the accuracy and consistency of MRI-based dementia classification across different racial and ethnic populations.

Fig. 1: Baseline performance on group-balanced subsets for assessing disparities across multi-racial and multi-ethnic populations.

Enlarged perivascular spaces (ePVS) are fluid-filled spaces around small blood vessels in the brain that can become more visible with aging and small vessel disease and may reflect reduced clearance of waste from the brain. Measuring ePVS manually on MRI scans is time-consuming and impractical for large studies. To address this, researchers developed DORES, a deep learning tool that automatically identifies and measures ePVS using two types of brain MRI images. The model was developed using data from the 91³Ô¹ÏÍø Memory and Aging Project and evaluated against expert manual measurements and three other automated tools. DORES showed good performance in identifying ePVS in both white matter and the basal ganglia, a group of structures deep within the brain, and its estimates of ePVS number and volume agreed well with expert measurements. Testing on an independent Alzheimer’s disease imaging dataset showed somewhat lower performance, as was also observed with the other automated methods. Results also varied depending on the type of MRI scanner used, suggesting that scanner differences can affect measurement consistency. Overall, DORES provides a promising automated approach for measuring ePVS in older adults, although scanner-related differences should be considered when applying the method across multiple research sites.

Fig. 1.ÌýRepresentative White Matter ePVS Segmentations of DORES Performance in VMAP.

T₁-weighted axial scans were selected to illustrate DORES performance at the 25th (top row; DiceÌý=Ìý0.55), 50th (middle row; DiceÌý=Ìý0.63), and 75th (bottom row; DiceÌý=Ìý0.73) percentiles of white matter regional Dice scores. The first column displays the raw, skull-stripped images in the white matter. The second column displays the corresponding segmentation overlays. Manual segmentations are shown in blue, whereas DORES predictions are shown in red. Voxels where manual and DORES segmentations overlap appear purple. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

]]> /valiant/2026/08/24/is-correction-for-gradient-nonlinearity-necessary-in-a-brain-diffusion-tensor-mri-clinical-study/ Mon, 24 Aug 2026 18:54:20 +0000 /valiant/?p=7352 Kanakaraj, Praitayini; Yao, Tianyuan; Li, Zhiyuan; Newlin, Nancy R.; Kim, Michael E.; Gao, Chenyu; Yu, Tian; Krishnan, Aravind; Rogers, Baxter P.; Hohman, Tim; Jefferson, Angela L.; Shashikumar, Niranjana; Pechman, Kimberly R.; Davis, L. Taylor; Moyer, Daniel; Schilling, Kurt G.; Archer, Derek; Anderson, Adam; Landman, Bennett A. (2026). . PLOS ONE, 21(7), e0350808.

Diffusion tensor imaging (DTI) is an MRI technique used to study the structure of brain tissue, particularly white matter. However, imperfections in the MRI system’s magnetic field gradients can slightly alter diffusion measurements across different parts of the brain and may bias results if not corrected. This study examined the effects of correcting these gradient nonlinearities (GNL) and whether they could influence conclusions about aging and neurological conditions. We analyzed 948 imaging sessions from the 91³Ô¹ÏÍø Memory & Aging Project, including measurements of white and gray matter. GNL correction produced changes of about 1% in fractional anisotropy and 3.3% in mean diffusivity, two measures of brain tissue microstructure, as well as changes of about 5 degrees in the estimated direction of nerve fibers, affecting at least 20% of the brain. Some brain regions, particularly superior, occipital, and parietal areas, were more affected, while larger-scale structural measurements changed by up to 12%. Although the effects were generally small at the individual level, they could become statistically important in large studies, particularly those involving multiple MRI scanners or sites. These findings suggest that GNL effects should be considered and, when possible, corrected or measured in large brain imaging studies to improve the reliability of comparisons across individuals, scanners, and clinical groups.

Fig 1.Ìý(a) The MCI clinical cohort comprises 327 participants, each undergoing up to four diffusion MRI sessions acquired on Scanner A (blue) or Scanner B (green).

(b) Maps of the diagonal elements of the gradient nonlinearity tensor, L_xx, L_yy, and L_zz, estimated from empirical phantom field maps for each scanner (FOV 384 × 384 × 384 mm). These entries describe the local scaling of nominal gradients applied along the x, y, and z axes and illustrate scanner-dependent spatial variation in effective diffusion weighting. (c) At a representative brain location (marked in panel b), the sphere plot shows the angular deviation between nominal and GNL-corrected b-vectors (example shown for Scanner B), and the line plot shows the corresponding effective b-values across diffusion volumes for Scanner A (blue) and Scanner B (green) relative to the nominal b-value (orange). Together, these panels summarize the cohort, the underlying gradient nonlinearity fields, and their impact on the effective diffusion encoding used in our analyses. Our study analyzes the effects of GNL on this clinical cohort.

Accurately identifying theÌýepileptogenic zone (EZ)—the area of the brain where seizures begin—is essential for planning surgery in people withÌýmedication-resistant epilepsy. While combiningÌýelectroencephalography (EEG)Ìý·É¾±³Ù³óÌýfunctional magnetic resonance imaging (fMRI)Ìýcan help locate this region, the technique requires specialized equipment and is not widely available. In this study, the researchers developed a new fMRI-based measure called theÌýdynamic functional connectivity (dFC) peak index, which aims to identify seizure-related brain activity without the need for simultaneous EEG. They evaluated the method in 62 patients with focal epilepsy, most of whom hadÌýtemporal lobe epilepsy (TLE), and compared the results with those from 109 healthy volunteers. The dFC peak index was elevated in brain regions known to be involved in temporal lobe epilepsy, particularly the medial temporal lobe. Patients whose surgeries removed areas with higher dFC peak index values were more likely to have better seizure outcomes, including those whose standard MRI scans did not show visible abnormalities. These findings suggest that the dFC peak index may provide valuable additional information for identifying the epileptogenic zone and could help guide surgical planning for people with medication-resistant epilepsy.

FIGURE 1

Negative dFCÌýpeaks. (A) Description of negative dFC peaks. Temporally Z-scored timeseries for the DMNÌýand a region are shown. The two timeseries are multiplied together at each timepoint to calculate the dFC timeseries between the two regions. Negative dFC peaks are highlighted with black circles, with solid circles denoting peaks of interest (DMN deactivation) and dotted circles denoting peaks of no interest (DMN activation). (B) Examples of negative dFC peaks following interictal epileptic discharges in a patient with left medial temporal lobe epilepsy (top) and right lateral temporal lobe epilepsy (bottom). The EEG-based interictal epileptic discharge activation maps are shown on the left along with a map of the DMN. The timeseries for the DMN, the activated region from the interictal discharges, and the dFC between the two regions are shown in red, yellow, and blue, respectively. DMN, default mode network; dFC, dynamic functional connectivity; EEG: electroencephalography; fMRI, functional magnetic resonance imaging.

The two halves of the brain are not perfectly identical, and these differences inÌýwhite matter—the bundles of nerve fibers that connect different brain regions—are thought to support specialized functions such as language and spatial reasoning. Although previous studies have examined white matter asymmetry, most have been limited by small sample sizes, narrow age ranges, or a focus on only a few brain pathways. In this study, the researchers analyzed brain imaging data from more thanÌý35,000 healthy individualsÌýranging in age from birth to 100 years, creating the most comprehensive maps to date of white matter asymmetry across the lifespan. They examined 30 major white matter pathways and measured multiple features related to both their microscopic tissue structure and overall anatomy. The results showed that asymmetry is present in every pathway studied, but its direction and degree vary depending on the specific pathway and the structural feature being measured. The patterns of asymmetry also changed throughout life, with distinct developmental changes in childhood and adolescence and a general trend toward greater asymmetry with advancing age, particularly in later adulthood. These findings provide a valuable reference for understanding how white matter develops and changes over the lifespan and may help researchers better identify brain changes associated with healthy aging and neurological disorders.

FIGURE 1

Overview of the study datasets, white matter features, and analytical framework. (A) Age distributions for each of the 50 contributing datasets (violin plots), illustrating broad coverage from 0 to 100 years. Color encodes the number of participants per dataset (log scale). (B) Features extracted for each of the 30 bilateral pathways. Microstructural indices (e.g., Fractional Anisotropy, and Mean, Axial and Radial diffusivities; FA, MD, AD, and RD) summarize tissue organization and axonal/myelin density; macrostructural indices (e.g., tract volume and length) capture pathway size and geometry. Macrostructural cartoon reproduced under CC-BY from YehÌý.Ìý(C) Analysis pipeline. For each participant, white matter pathways were segmented, and features were extracted. A Lateralization Index (LI) was calculated for each tract-feature pair. These LIs were used as input for a normative modeling framework (GAMLSS) to generate age-specific population centile curves.

Changes in the brain’sÌýlimbic white matter—the nerve fiber pathways involved in memory, learning, and emotion—are common in aging andÌýAlzheimer’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 involvedÌýCDH19, a gene linked toÌýoligodendrocytes, the cells responsible for producing myelin, the protective coating that surrounds nerve fibers. Several other genes, includingÌýRORA,ÌýFAM107B, andÌýKC6, 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 andÌýAlzheimer’s disease (AD)Ìýhas 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, andÌýAPOE ε4Ìýstatus—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 withÌýmild 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 theÌýtemporal lobe, a brain region important for memory. In contrast, among participants with MCI, higher fat intake was associated with slower enlargement of theÌýinferior 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.

Ìý

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 participantsÌýβÌý=Ìý−18.7,ÌýpÌý=Ìý0.008; MCI participantsÌýβÌý=Ìý11.2, pÌý=Ìý0.43. (B) Associations between Pfat and temporal lobe volume, stratified by cognitive status; CU participantsÌýβÌý=Ìý−47.2,ÌýpÌý=Ìý0.007; MCI participantsÌýβÌý=Ìý50.9,ÌýpÌý=Ìý0.21. (C) Associations between Pfat and inferior lateral ventricle volume, stratified by cognitive status; CU participantsÌýβÌý=Ìý−1.89,ÌýpÌý=Ìý0.27; MCI participantsÌýβÌý=Ìý−22.5,ÌýpÌý=Ìý0.006. CU, cognitively unimpaired; MCI, mild cognitive impairment; MRI, magnetic resonance imaging; Pfat, percentage of energy from fat; Tfat, total fat intake.

The human brain depends on a network of connections to work properly, and white matter is the part that carries signals between different brain regions, much like a communication highway. When these pathways are disrupted, they are linked to many neurological, psychiatric, and developmental disorders. Doctors already use growth charts to track how children grow, and researchers have also created reference charts for whole-brain size and gray matter, but until now there has not been a similar standard for white matter. This study fills that gap by creating lifespan reference charts for human brain white matter. The researchers analyzed and standardized 35,120 brain scans from studies around the world to show how white matter pathways normally develop from birth to age 100, including growth, maturation, and later decline. These charts provide a baseline for healthy brain development and aging, so researchers and clinicians can compare an individual’s brain with typical patterns and identify unusual changes linked to disease. Because the charts are open access, they can also be used broadly in future clinical and neuroscience research.

Fig. 1: Global WM brain charts across the human lifespan.

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