Multiple system atrophy (MSA) is a progressive neurological disorder that affects movement and automatic body functions such as blood pressure control. This study followed 17 people with MSA for one year to examine phosphorylated alpha-synuclein, an abnormal protein found in skin nerves. Levels of this protein increased over time and were associated with greater problems in blood pressure regulation and other autonomic symptoms. Skin biopsy results also detected the abnormal protein in two participants whose cerebrospinal fluid tests were negative. These findings suggest that skin biopsies could provide a minimally invasive way to help diagnose MSA, monitor its progression, and identify participants for clinical trials.

Fig.Ìý1

Participant enrollment and classification flow diagram. A total of 23 participants were screened for study inclusion, among whom five were excluded during screening due to advanced multiple system atrophy (MSA) or incidental magnetic resonance imaging findings. Eighteen participants were enrolled, of whom one withdrew prior to follow-up. Seventeen participants completed the 12-month follow-up visit. Final diagnostic classifications based on longitudinal clinical assessments and biomarker data included MSA (nÌý = 10 patients), Parkinson’s disease or dementia with Lewy bodies (PD/DLB,Ìýn = 5), and inconclusive diagnosis (n = 2)

]]> /valiant/2026/09/26/nucleus-basalis-functional-connectivity-aberrations-in-temporal-lobe-epilepsy-and-improvements-after-successful-epilepsy-surgery/ Sat, 26 Sep 2026 21:07:00 +0000 /valiant/?p=7518 Cavender, Addison C.; Doss, Derek J.; Makhoul, Ghassan S.; Lerma, Bruno Hidalgo Monroy; Reda, Anas; Withers, C. Price; Liao, Emily; Wang, Kate; González, Hernán F. J.; Goodale, Sarah E.; Johnson, Graham W.; Cutting, Laurie E.; Bick, Sarah K.; Chang, Catie; Morgan, Victoria L.; Englot, Dario J. (2026).Ìý.ÌýEpilepsia.Ìý

Temporal lobe epilepsy (TLE) can disrupt brain networks involved in attention, alertness, and thinking. This study examined whether these networks recover after epilepsy surgery, particularly in patients who become seizure-free. Brain scans from 75 patients with TLE and 106 healthy controls showed reduced connectivity involving the nucleus basalis of Meynert (NBM), a brain region important for attention and cognitive function. After surgery, this connectivity improved and became similar to that of healthy controls, with greater recovery seen in patients who were seizure-free. These findings suggest that stopping seizures may allow some disrupted brain networks to recover and that NBM connectivity could help track recovery after epilepsy surgery.

FIGURE 1

Nucleus basalis of Meynert (NBM) exhibits reduced connectivity in patients with temporal lobe epilepsy compared to controls. Violin plots show FisherÌýz-transformed Pearson correlations between ipsilateral NBM node time series (A, B) and the contralateral node time series (C, D) with the frontoparietal association cortex (FPAC) as well as all other nodes in the brain (whole-brain). Data include 75 patients with temporal lobe epilepsy and 106 healthy controls. Voxelwise connectivity maps show threshold-free cluster enhancement (TFCE)-correctedÌýt-statistics, illustrating reductions in ipsilateral (E) and contralateral (F) NBM connectivity in preoperative patients compared to controls. Negative contrasts (patients < controls) are shown, and no positive differences (patients > controls) survived correction. One patient in panel A had functional connectivity (FC) = −7.60, which falls outside the plotted axis range and thus was not shown for visualization purposes. Group differences were assessed using independent samplesÌýt-tests with false discovery rate correction. *p < .05, **p < .01, ***p < .001. C, contralateral; I, ipsilateral.

Prenatal alcohol exposure (PAE) can affect how the brain develops, but its effects on the shape and structure of the brain’s outer layer, or cortex, are not fully understood. Using MRI scans from 121 people aged 7–21 years, including 56 with PAE, we examined several features of cortical structure and how they changed with age. Youth with PAE showed differences in brain folding, groove depth, and structural complexity in specific brain regions compared with unexposed youth. Some of these features also developed differently with age in the PAE group. Overall, the findings suggest that measures of brain folding, particularly gyrification, may be more sensitive to the effects of prenatal alcohol exposure than commonly studied measures such as cortical thickness.

Fig. 1. A: Distribution of ages for participants with PAE (blue) or unexposed (salmon). MRI scans are represented by dots with horizontal lines connecting scans of the same participant. There are 163 scans (73 PAE) from 123 participants (58 PAE). 84 participants had 1 scan, 38 participants had 2, and 1 participant had 3 scans. Figure was created using ggplot2 in R (). B: Flowchart depicting the number of scans (and participants) at each stage of data acquisition and processing. Note that there were no significant group differences in the quality of T1-weighted data or MaCRUISE segmentations.

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)ÌýwithÌýfunctional 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.