Regional Brain Volumes Research Articles

Disproportional smaller fornix with altered microstructure in pediatric multiple sclerosis shown by high-resolution fluid-suppressed diffusion tractography.

Diffusion tensor imaging (DTI) in adults with multiple sclerosis (MS) has identified marked volume and diffusion abnormalities of the fornix, the main white matter (WM) output tract of the hippocampus. To determine if the fornix is affected in pediatric-onset MS (POMS) using the same DTI protocols used in adult-onset MS (AOMS), which would suggest its early involvement in the disease course. High-resolution, fluid-suppressed diffusion tractography was used to identify the fornix in 11 POMS patients (13-19 years old) and 26 controls. Fornix volume and diffusion metrics were compared between groups and with other total/regional brain volumes, and then correlated with cognitive/clinical scores. POMS showed lower fornix volumes (-26%) compared to controls, which was greater than proportional losses in total and other regional brain volumes. Notably, the hippocampus volume was not lower in POMS. DTI yielded lower fractional anisotropy (-7%) and higher mean (+12%), axial (+7%), and radial (+16%) diffusivities in POMS. There were no significant correlations between fornix volume/diffusion metrics and cognitive/clinical scores. Diffusion tractography showed marked injury to the fornix in POMS that precedes injury to connected gray matter such as hippocampus, implicating the fornix as an early brain region affected in MS.

Characterization of cognitive decline in long-duration type 1 diabetes by cognitive, neuroimaging and pathological examinations.

We aimed to characterize factors associated with the under-studied complication of cognitive decline in aging people with long-duration type 1 diabetes (T1D). Joslin "Medalists" (n = 222; T1D ≥ 50 years) underwent cognitive testing. Medalists (n = 52) and age-matched non-diabetic controls (n = 20) underwent neuro- and retinal imaging. Brain pathology (n = 26) was examined. Relationships amongst clinical, cognitive and neuroimaging parameters were evaluated. Compared to controls, Medalists had worse psychomotor function and recall, which associated with female gender, lower visual acuity, reduced physical activity, longer diabetes duration and higher inflammatory cytokines. On neuroimaging, compared to controls, Medalists had significantly lower total and regional brain volumes, equivalent to 9 years of accelerated aging, but small vessel disease markers did not differ. Reduced brain volumes associated with female sex, reduced psychomotor function, worse visual acuity, longer diabetes duration and higher inflammation, but not with glycemic control. Worse cognitive function, lower brain volumes, and diabetic retinopathy correlated with thinning of the outer retinal nuclear layer. Worse baseline visual acuity associated with declining psychomotor function in longitudinal analysis. Brain volume mediated the association between visual acuity and psychomotor function by 57%. Brain pathologies showed decreased volumes, but predominantly mild vascular or Alzheimer's-related pathology. This first comprehensive study of cognitive function, neuroimaging and pathology in aging T1D individuals demonstrated that cognitive decline was related to parenchymal rather than neurovascular abnormalities, unlike type 2 diabetes, suggestive of accelerated aging in T1D. Improving visual acuity could perhaps be an important preventive measure against cognitive decline in people with T1D.

Relationship Between Cognitive Abilities and Lower-Limb Movements: Can Analyzing Gait Parameters and Movements Help Detect Dementia? A Systematic Review

Identifying and diagnosing cognitive impairment remains challenging. Some diagnostic procedures are invasive, expensive, and not always accurate. Meanwhile, evidence suggests that cognitive impairment is associated with changes in gait parameters. Certain gait parameters manifesting differences between people with and without cognitive impairment are more pronounced when adding a secondary task (dual-task scenario). In this systematic review, the capability of gait analysis to identify cognitive impairment is investigated. Twenty-three studies published between 2014 and 2024 met the inclusion criteria. A significantly lower gait speed and cadence as well as higher gait variability were found in people with mild cognitive impairment (MCI) and/or dementia, compared with the group with no cognitive impairment. While dual tasks appeared to amplify the differences between the two populations, the type of secondary tasks (e.g., calculations and recalling phone numbers) had an effect on gait changes. The activity and volume of different brain regions were also different between the two populations during walking. In conclusion, while this systematic review supported the potential of using gait analysis to identify cognitive impairment, there are a number of parameters researchers need to consider such as gait variables to be studied, types of dual tasks, and analysis of brain changes while performing the movement tasks.

Compositional brain scores capture Alzheimer's disease-specific structural brain patterns along the disease continuum.

Traditional multivariate methods for neuroimaging studies overlook the interdependent relationship between brain features. This study addresses this gap by analyzing relative brain volumetric patterns to capture how Alzheimer's disease (AD) and genetics influence brain structure along the disease continuum. This study analyzed data from participants across the AD continuum from the Alzheimer's and Families (ALFA) and Alzheimer's Disease Neuroimaging Initiative (ADNI) studies. Compositional data analysis (CoDA) was exploited to examine relative brain volumetric variations that (1) were linked to different AD stages compared to cognitively unimpaired amyloid-β-negative (CU A-) individuals and (2) varied by AD genetic risk. Disease stage-specific compositional brain scores were identified, differentiating CU A- individuals from those in more advanced stages. Genetic risk-stratified models revealed a broader genetic landscape affecting brain morphology in AD, beyond the well-known apolipoprotein E ε4 allele. CoDA emerges as an alternative multivariate framework to deepen understanding of AD-related structural changes and support targeted interventions for those at higher genetic risk. Compositional data analysis (CoDA) revealed the relative variation of brain region volumes, captured in compositional brain scores, capable of discerning between cognitively unimpaired amyloid-β-negative individuals and subjects within other disease-stage groups along the Alzheimer's disease (AD) continuum. CoDA also uncovered the genetic vulnerability of specific brain regions at each stage of the disease along the continuum. CoDA is capable of integrating magnetic resonance imaging data from two different cohorts without stringent requirements for harmonization. This translates as an advantage, compared to traditional methods, and strengthens the reliability of cross-study comparisons by standardizing the data despite different labeling agreements, facilitating collaborative and large-scale research. The algorithm is sensitive to AD-specific effects, as the main compositional brain scores display little overlap with the age-specific compositional brain score. CoDA provides a more accurate analysis of brain imaging data addressing its compositional nature, which can influence the development of targeted approaches, opening new avenues for enhancing brain health.

Neonatal Nutrition and Brain Structure at 7 Years in Children Born Very Preterm

Neonatal protein intake following very preterm birth has long lasting effects on brain development. However, it is uncertain whether these effects are associated with improved or impaired brain maturation. To assess the association of neonatal protein intake following very preterm birth with brain structure at 7 years of age. This cohort study involved children born very preterm before or after a change in neonatal intensive care unit nutritional protocol that increased protein intake at the National Women's Hospital in Auckland, New Zealand. The children completed magnetic resonance imaging (MRI) scanning at 7 years. There were 128 children who were initially eligible. MRI data were ineligible for analysis if excessive head motion or clinical brain abnormalities were present. Data were collected from July 2012 to January 2016, and data analysis took place from January 2017 to March 2024. Neonatal intensive care unit nutritional protocol. Those who were born before the protocol change took place (July 2005 to December 2006) were in the old protocol group, while those who were born after the protocol change (January 2007 to October 2008) were in the new protocol group. Observers were blind to participant grouping. All actual enteral and parenteral intakes of protein, fat, energy, and breast milk for days 1 to 7 and days 1 to 14, and growth velocity to postnatal day 28 were calculated for each infant. Preplanned outcomes were group comparisons between regional brain volumes and diffusion parameters of major white matter tracts along with analyses with both groups combined exploring associations of nutrition with brain metrics. Data from 99 children were analyzed, including 42 in the old protocol group (26 female [55%]; mean [SD] gestational age at birth, 27 [2] weeks) and 57 in the new protocol group (27 female [47%]; mean [SD] gestational age at birth, 26 [2] weeks). Protein intake differed between the groups at both 7 days (old protocol: mean [SD] intake, 17 [2] g/kg-1; new protocol: mean [SD] intake, 21 [2] g/kg-1) and 14 days after birth (old protocol: mean [SD] intake, 41 [6] g/kg-1; new protocol: mean [SD] intake, 45 [7] g/kg-1). The new protocol group had smaller brain volume as a percentage of intercranial volume than the old protocol group (mean [SD], 80% [4%] vs 86% [7%]) but absolute brain volumes were similar. The new protocol group had significantly thinner lateral occipital and lateral parietal cortices than the old protocol group. With both groups combined, those with greater protein, fat, energy, and breast milk intake had more mature diffusion tensor metrics (higher fractional anisotropy and less diffusion) across multiple tracts, although this finding did not reach statistical significance for every tract. In this cohort of children born very preterm, children with greater neonatal protein intake had a more mature profile of brain metrics assessed with MRI at 7 years of age. These results contribute to the ongoing evaluation of optimal nutrition for infants born very preterm and suggest that the protein intake experienced by the new protocol group may promote brain maturation in a way that is still observable at 7 years of age.

Plasma N-terminal tau fragment is an amyloid-dependent biomarker in Alzheimer's disease.

Novel fluid biomarkers for tracking neurodegeneration specific to Alzheimer's disease (AD) are greatly needed. Using two independent well-characterized cohorts (n=881 in total), we investigated the group differences in plasma N-terminal tau (NT1-tau) fragments across different AD stages and their association with cross-sectional and longitudinal amyloid beta (Aβ) plaques, tau tangles, brain atrophy, and cognitive decline. Plasma NT1-tau significantly increased in symptomatic AD and displayed positive associations with Aβ PET (positron emission tomography) and tau PET. Higher baseline NT1-tau levels predicted greater tau PET, with 2- to 10-year intervals and faster longitudinal Aβ PET increases, AD-typical neurodegeneration, and cognitive decline. Plasma NT1-tau showed negative correlations with baseline regional brain volume and thickness, superior to plasma brain-derived tau (BD-tau) and neurofilament light (NfL) in Aβ-positive participants. This study suggests that plasma NT1-tau is an Aβ-dependent biomarker and outperforms BD-tau and NfL in detecting cross-sectional neurodegeneration in the AD continuum. Plasma N-terminal tau (NT1-tau) was specifically increased in the A+/T+ stage. Plasma NT1-tau was positively associated with greater amyloid beta (Aβ) and tau PET (positron emission tomography) accumulations. Higher plasma NT1-tau predicted greater tau burden and faster Aβ increases. Plasma NT1-tau was more related to neurodegeneration than plasma brain-derived tau (BD-tau) and neurofilament light (NfL).

Modulating effects of fitness and physical activity on Alzheimer's disease: Implications from a six-month randomized controlled sports intervention.

Physical activity and fitness are major targets in Alzheimer's disease (AD) preventive research. However, current research is heterogeneous and often disregards the relationship between these parameters and disease outcomes. To assess the effects of physical activity and fitness on AD within the context of a multicomponent sports intervention. 46 participants with early-stage AD (mean age 70 ± 7 years, 18 women, mean Montreal Cognitive Assessment (MoCA) score 19±5) were included in a six-month randomized controlled trial (Dementia-MOVE), participating in either a multicomponent sports intervention or a control condition with a psychoeducational program. The modulating effect of fitness and physical activity changes on AD outcome parameters such as cognition, function and cerebral brain structure from 3T-MRI were examined using multiple linear regression analyses. An increase in VO2max was associated with assignment to the intervention group (p = 0.016), lower baseline fitness (p = 0.001), and an increased rate of physical activity (p = 0.046). Only in the intervention group, ΔVO2max had a beneficial modulating effect on the MoCA score (p = 0.039), the executive functions (p = 0.017) and regional brain volumes of the temporal lobe, e.g., the hippocampus (p = 0.044). High daily step count was associated with preserved executive functions (p = 0.001), and caregivers' quality of life (p ≤ 0.001) in the overall sample. Our results confirm that multicomponent exercise improves cardiorespiratory fitness in AD, which is associated with advantageous developments in cognitive performance and preservation of brain structure. These findings suggest that especially patients with comparably worse cognition and fitness benefit and should be encouraged for activity engagement.

Impact of high maternal body mass index on fetal cerebral cortical and cerebellar volumes.

Maternal obesity increases a child's risk of neurodevelopmental impairment. However, little is known about the impact of maternal obesity on fetal brain development. We prospectively recruited 20 healthy pregnant women across the range of pre-pregnancy or first-trimester body mass index (BMI) and performed fetal brain magnetic resonance imaging (MRI) of their healthy singleton fetuses. We examined correlations between early pregnancy maternal BMI and regional brain volume of living fetuses using volumetric MRI analysis. Of 20 fetuses, there were 8 males and 12 females (median gestational age at MRI acquisition was 24.3weeks, range: 19.7-33.3weeks, median maternal age was 33.3 years, range: 22.0-37.4 years). There were no significant differences in clinical demographics between overweight (OW, 25≤BMI<30)/obese (OB, BMI≥30 kg/m2) (n=12) and normal BMI (18.5≤BMI<25) (n=8) groups. Fetuses in the OW/OB group had significantly larger left cortical plate (p=0.0003), right cortical plate (p=0.0002), and whole cerebellum (p=0.049) compared to the normal BMI group. In the OW/OB BMI group, cortical plate volume was larger relative to other brain regions after 28weeks. This pilot study supports the concept that maternal obesity impacts fetal brain volume, detectable via MRI in living fetuses using quantitative analysis.

Plasma proteomic signatures of social isolation and loneliness associated with morbidity and mortality

Abstract The biology underlying the connection between social relationships and health is largely unknown. Here, leveraging data from 42,062 participants across 2,920 plasma proteins in the UK Biobank, we characterized the proteomic signatures of social isolation and loneliness through proteome-wide association study and protein co-expression network analysis. Proteins linked to these constructs were implicated in inflammation, antiviral responses and complement systems. More than half of these proteins were prospectively linked to cardiovascular disease, type 2 diabetes, stroke and mortality during a 14 year follow-up. Moreover, Mendelian randomization (MR) analysis suggested causal relationships from loneliness to five proteins, with two proteins (ADM and ASGR1) further supported by colocalization. These MR-identified proteins (GFRA1, ADM, FABP4, TNFRSF10A and ASGR1) exhibited broad associations with other blood biomarkers, as well as volumes in brain regions involved in interoception and emotional and social processes. Finally, the MR-identified proteins partly mediated the relationship between loneliness and cardiovascular diseases, stroke and mortality. The exploration of the peripheral physiology through which social relationships influence morbidity and mortality is timely and has potential implications for public health.

Normative modeling of brain MRI data identifies small subcortical volumes and associations with cognitive function in youth with fetal alcohol spectrum disorder (FASD).

To quantify regional subcortical brain volume anomalies in youth with fetal alcohol spectrum disorder (FASD), assess the relative sensitivity and specificity of abnormal volumes in FASD vs. a comparison group, and examine associations with cognitive function. Participants: 47 children with FASD and 39 typically-developing comparison participants, ages 8-17years, who completed physical evaluations, cognitive and behavioral testing, and an MRI brain scan. A large normative MRI dataset that controlled for sex, age, and intracranial volume was used to quantify the developmental status of 7 bilateral subcortical regional volumes. Z-scores were calculated based on volumetric differences from the normative sample. T-tests compared subcortical volumes across groups. Percentages of atypical volumes are reported as are sensitivity and specificity in discriminating groups. Lastly, Pearson correlations examined the relationships between subcortical volumes and neurocognitive performance. Participants with FASD demonstrated lower mean volumes across a majority of subcortical regions relative to the comparison group with prominent group differences in the bilateral hippocampi and bilateral caudate. More individuals with FASD (89%) had one or more abnormally small volume compared to 72% of the comparison group. The bilateral hippocampi, bilateral putamen, and right pallidum were most sensitive in discriminating those with FASD from the comparison group. Exploratory analyses revealed associations between subcortical volumes and cognitive functioning that differed across groups. In this sample, youth with FASD had a greater number of atypically small subcortical volumes than individuals without FASD. Findings suggest MRI may have utility in identifying individuals with structural brain anomalies resulting from PAE.

Voxel-based Morphometry of Alzheimer’s Disease Using a Localizer Image: A Comparative Study with Magnetization Prepared Rapid Acquisition with Gradient Echo

Magnetization prepared rapid acquisition with gradient echo (MPRAGE) sequence is a gold-standard technique for voxel-based morphometry (VBM) because of high spatial resolution and excellent tissue contrast, especially between gray matter (GM) and white matter (WM). Despite its benefits, MPRAGE exhibits distinct challenge for VBM in some patients with neurological disease because of long scan time and motion artifacts. Speedily acquired localizer images may alleviate this problem. This study aimed to evaluate the feasibility of VBM using 3D Fast Low Angle Shot image captured for localizer (L3DFLASH). Consecutive 13 patients with pathologically confirmed Alzheimer's disease (AD) (82 ± 9 years) and 21 healthy controls (HC) (79 ± 4 years) were included in this study. Whole-brain L3DFLASH and MPRAGE were captured and preprocessed using the Computational Anatomy Toolbox 12 (CAT12). Agreement with MPRAGE was evaluated for L3DFLASH using regional normalized volume for segmented brain areas. In addition to brain volume difference on VBM and Bland-Altman analysis, atrophic pattern of AD on VBM was evaluated using L3DFLASH and MPRAGE. Acquisition time was 18s for L3DFLASH and 288s for MPRAGE. There was a slight systematic difference in all regional normalized volumes from L3DFLASH and MPRAGE. For the whole cohort, GM volume measured from MPRAGE was greater than that from L3DFLASH in most of the region on VBM. When AD and HC were compared, AD-related atrophic pattern was demonstrated in both L3DFLASH and MPRAGE on VBM, although the difference was noted in significant clusters between them. Although systematic difference was noted in regional brain volume measured from L3DFLASH and MPRAGE, AD-related atrophic pattern was preserved in L3DFLASH on VBM. VBM, using speedily acquired localizer image, may provide limited but useful information for evaluating brain atrophy.

Baseline gray matter volume associates with working memory performance after prefrontal transcranial direct current stimulation.

Working memory is crucial for daily life and is often impaired in neuropsychiatric conditions. Attempts to enhance it using transcranial direct current stimulation (tDCS) have shown mixed results, possibly due to large inter-individual variability. This study assessed whether baseline regional brain volume was associated with working memory performance following tDCS. Healthy participants were randomly assigned to three bilateral tDCS protocols (sham, 1.5 mA, and 3 mA) over the dorsolateral prefrontal cortex (dlPFC) (anode-left, cathode-right) for 20 minutes, in a within-subjects design with a 2-week interval, followed by emotional and non-emotional 3-back tasks. Baseline volumetric data were used to extract gray matter volumes of defined regions of interest; the dlPFC, the medial PFC (mPFC), and the posterior cingulate cortex (PCC) bilaterally. Data from thirty-nine participants (69.2 % female, mean age: 24.56 years) across 112 tDCS sessions were analyzed. Findings revealed no significant association between working memory performance post sham-tDCS and gray matter volume. However, larger baseline cortical volumes across all regions were associated with slower reaction times and lower accuracy for the non-emotional task at 1.5 mA, whereas non-significant results were observed at 3 mA. For the emotional task, only a significant association for reaction time after 3 mA and left dlPFC and right PCC were found. Findings highlight not only the association between individual baseline gray matter, but also the impact of methodological choices, such as current intensity and outcome, on the effect of tDCS. Future research should aim to further explore individual variability and methodological factors to deepen our understanding of the mechanisms underlying tDCS.

PREDICTING TIME TO COGNITIVE IMPAIRMENT USING BRAIN AGE

Abstract The neurological changes that occur during the asymptomatic pre-clinical phase of cognitive impairment (CI) can be quantified using brain age (BA) measures and leveraged to identify those at risk of clinical manifestation. We employ a machine learning survival model to predict future CI among healthy older adults using cognitive, demographic, genetic, and neuroimaging data including regional brain volume and anatomically specific measures of BA. Participants were 2,718 adults from NACC (1,795 females, mean age=69.14±10.91). Converters (N=343) were diagnosed with CI during the study, and non-converters (N=2,375) were CN across all visits. We predicted time to conversion (days) from baseline MRIs (185 brain volumes and regional BA measures) and 69 clinical variables. The model was trained on 1,846 converters and 252 non-converters, validated on 32 converters and 238 non-converters, and tested on 150 converters and 200 non-converters with 5-fold cross-validation. The model’s predictive accuracy (c-index) was 0.82 for training, 0.83 for validation, and 0.78 in the test set. Important features for prediction included age, neurocognitive test scores, education, history of hypertension and stroke, BA, and volumes of the right hippocampus, right precentral gyrus, bilateral white matter, and left inferior temporal gyrus. Products of this model allow us to compare a) the risk, and b) the BA biomarkers across population subgroups with heightened CI risk such as racial minorities and people with fewer years of education. The ability to accurately predict future cognitive impairment (CI) benefits those most in need of therapeutic interventions and reduces the burden of CI on healthcare systems.

Vascular risk factors are associated with grey matter atrophy in secondary progressive multiple sclerosis.

Comorbidities including vascular risk factors can be associated with whole and regional brain atrophy in multiple sclerosis (MS). This has been examined in mixed MS cohorts in prospective or observational studies; however, the association between vascular comorbidities (VCM) in secondary progressive MS (SPMS) and brain atrophy has been less well studied. The aim was to investigate the cross-sectional and longitudinal association between VCM, comorbidity burden and brain atrophy in SPMS. Post hoc analysis of 445 participants from the MS-Secondary Progressive multi-arm trial (MS-SMART)-a multi-arm multicentre phase-2b randomised placebo-controlled trial of three agents in SPMS (NCT01910259). VCM (hypertension, hyperlipidaemia) but also asthma, hypothyroidism and osteoporosis were recorded. Regional and whole brain volume (WBV), and percentage brain volume change were calculated using SIENAX and SIENA, respectively. Multiple linear regression was used to investigate the cross-sectional and longitudinal relationships between VCM, overall comorbidity count and whole brain, grey matter (GM) and white matter (WM) atrophy. The cohort was predominantly female (67%), mean age 55 with median EDSS 6.0. In total, 13% and 9% had hypertension and hyperlipidaemia, respectively. In cross-sectional regression models, VCM was associated with decreased cortical GM volume [(hypertension β = -0.30, 95%CI -0.54 to -0.06, p = 0.01) (hyperlipidaemia β = -0.37, 95%CI -0.64 to -0.09, p = 0.008)]; but not WBV. Having ≥2 comorbidities was also associated with decreased cortical GM volume (β = -0.36, 95%CI -0.61 to -0.10, p = 0.007). No relationship was observed between VCM/comorbidity count and whole brain or GM atrophy rate over 96 weeks. People with SPMS with VCM or increased overall comorbidity burden showed reduced whole brain and especially cortical grey matter volumes, but no significant impact on subsequent 2-year atrophy rate was detected.

Discovery, Replicability, and Generalizability of a Left Anterior Hippocampus' Morphological Network Linked toSelf-Regulation.

The human hippocampus is a key region in cognitive and emotional processing, but also a vulnerable and plastic region. Accordingly, there is a great interest in understanding how variability in the hippocampus' structure relates to variability in behavior in healthy and clinical populations. In this study, we aimed to link interindividual variability in subregional hippocampal networks (i.e., the brain grey matter networks of hippocampal subregions) to variability in behavioral phenotype. To do so, we used a multiblock multivariate approach mapping the association between grey matter volume in hippocampal subregions, grey matter volume in the whole brain regions, and behavioral variables in healthy adults. To ensure the robustness and generalizability of the findings, we implemented a cross-cohort discovery and validation framework. This framework utilized two independent cohorts: the Human Connectome Project Young Adult (HCP-YA) cohort and the Human Connectome Project Aging (HCP-A) cohort, enabling us to assess the replicability and generalizability of hippocampal-brain-behavior phenotypes across different age groups in the population. Our results highlighted a left anterior hippocampal morphological network including the left amygdala and the posterior midline structures whose expression relates to higher self-regulation, life satisfaction, and better performance at standard neuropsychological tests. The cross-cohort generalizability of the hippocampus-brain-behavior mapping demonstrates its relevance beyond a specific population sample. Our previous work in developmental populations showed that the hippocampus' head co-maturates with most of the brain during childhood. The current data-driven study further suggests that grey matter volume in the left hippocampal head network would be particularly relevant for self-regulation abilities in adults that influence a range of life outcomes. Future studies should thus investigate the factors influencing the development of this morphological network across childhood, as well as its relationship to neurocognitive phenotypes in various brain diseases.

Assessment of Regional Brain Volume Measurements with Different Brain Extraction and Bias Field Correction Methods in Neonatal MRI

Proper selection and application of preprocessing steps are crucial for obtaining accurate segmentation in brain Magnetic Resonance Imaging (MRI). The aim of this study is to evaluate the impact brain extraction (BE) and bias field correction (BFC) methods have on regional brain volume (RBV) measurements of preterm neonates’ T2w MRI at term-equivalent age (TEA). Five BE methods (Manual, BET2, SWS, HD-BET, SynthStrip) were applied together with two BFC methods (SPM-BFC and N4ITK), before segmenting the neonatal brain into eight tissue classes (cortical grey matter, white matter, cerebral spinal fluid, deep nuclear grey matter, hippocampus, amygdala, cerebellum, and brainstem) using an automated segmentation software (MANTiS). Quantitative assessments were conducted, including the coefficient of variation (CV), coefficient of joint variation (CJV), Dice coefficient (DC), and RBV. HD-BET, together with N4ITK, showed the highest performance (mean ± standard deviation) regarding CV of 0.047 ± 0.005 (white matter) and 0.070 ± 0.005 (grey matter), CJV of 0.662 ± 0.095, DC of 0.942 ± 0.063, and RBV without significant differences (except in the brainstem) from the manual segmentation. Therefore, such combination of methods is recommended for improved skull-stripping accuracy, intensity homogeneity, and reproducibility of RBV of T2w MRI at TEA.

A multi-stage feature selection method to improve classification of potential super-agers and cognitive decliners using structural brain MRI data-a UK biobank study.

Cognitive aging is described as the age-related decline in areas such as memory, executive function, reasoning, and processing speed. Super-Agers, adults over 80 years old, have cognitive function performance comparable to middle-aged adults. To improve cognitive reserve and potentially decrease Alzheimer's disease (AD) risk, it is essential to contrast changes in regional brain volumes between "Positive-Agers" who have superior cognitive performance compared to their age peers but are not 80 years old yet and aging adults who show cognitive decline (i.e., "Cognitive Decliners"). Using longitudinal cognitive tests over 7-9 years in UK Biobank, principal component analysis (PCA) was first applied to four cognitive domains to create a general cognition (GC) composite score. The GC score was then used to identify latent cognitive groups. Given cognitive groups as the target variable and structural magnetic resonance imaging (sMRI) data and demographics as predictors, we developed a multi-stage feature selection algorithm to identify the most important features. We then trained a Random Forest (RF) classifier on the final set of 54 selected sMRI and covariate predictors to distinguish between Positive-Agers and Cognitive Decliners. The RF model achieved an AUC of 73%. The top 6 features were age, education, brain total surface area, the area of pars orbitalis, mean intensity of the thalamus, and superior frontal gyrus surface area. Prediction of cognitive trajectory types using sMRI may improve our understanding of successful cognitive aging.

Comparing Intra- and Inter-individual Correlational Brain Connectivity from Functional and Structural Neuroimaging Data.

Inferring brain connectivity from inter-individual correlations has been applied to various neuroimaging modalities, such as glucose metabolic activity measured by positron emission tomography (PET) and brain structures assessed using MRI. The variability that drives these inter-individual correlations is generally attributed to individual differences, potentially influenced by factors like genetics, life experiences, and biological sex. However, it remains unclear whether long-term within-individual effects, such as aging, and state-like effects also contribute to the correlated structures, and how intra-individual correlations are compared to inter-individual correlations. In this study, we analyzed longitudinal data spanning a wide age range, examining regional brain volumes using structural MRI, and regional brain functions using both regional homogeneity (ReHo) of resting-state functional MRI and glucose metabolic activity measured with Fludeoxyglucose (18F) FDG-PET. In a first dataset from a single individual scanned over 15 years, we found that intra-individual correlations in both ReHo and regional volumes resembled resting-state functional connectivity. In a second dataset, involving multiple longitudinal points and participants for FDG-PET and MRI, we replicated these findings, showing that both intra- and inter-individual correlations were strongly associated with resting-state functional connectivity. Correlations in functional measures (i.e., ReHo or FDG-PET) showed greater similarity with resting-state connectivity than structural measures. Moreover, matrices from the same modality showed higher similarity between the two datasets, indicating modality specific contributions. These results suggest that multiple factors may contribute to both inter- and intra-individual correlational measures of connectivity. Understanding or controlling for these factors could enhance the interpretability of the inter-individual connectivity measures.

Machine learning based metabolomic and genetic profiles for predicting multiple brain phenotypes

BackgroundIt is unclear regarding the association between metabolomic state/genetic risk score(GRS) and brain volumes and how much of variance of brain volumes is attributable to metabolomic state or GRS.MethodsOur analysis included 8635 participants (52.5% females) aged 40–70 years at baseline from the UK Biobank. Metabolomic profiles were assessed using nuclear magnetic resonance at baseline (between 2006 and 2010). Brain volumes were measured using magnetic resonance imaging between 2014 and 2019. Machine learning was used to generate metabolomic state and GRS for each of 21 brain phenotypes.ResultsIndividuals in the top 20% of metabolomic state had 2.4–35.7% larger volumes of 21 individual brain phenotypes compared to those in the bottom 20% while the corresponding number for GRS ranged from 1.5 to 32.8%. The proportion of variance of brain volumes (R [2]) explained by the corresponding metabolomic state ranged from 2.2 to 19.4%, and the corresponding number for GRS ranged from 0.8 to 8.7%. Metabolomic state provided no or minimal additional prediction values of brain volumes to age and sex while GRS provided moderate additional prediction values (ranging from 0.8 to 8.8%). No significant interplay between metabolomic state and GRS was observed, but the association between metabolomic state and some regional brain volumes was stronger in men or younger individuals. Individual metabolomic profiles including lipids and fatty acids were strong predictors of brain volumes.ConclusionsIn conclusion, metabolomic state is strongly associated with multiple brain volumes but provides minimal additional prediction value of brain volumes to age + sex. Although GRS is a weaker contributor to brain volumes than metabolomic state, it provides moderate additional prediction value of brain volumes to age + sex. Our findings suggest metabolomic state and GRS are important predictors for multiple brain phenotypes.

MRI Assessment of Healthy Aging Trajectories in the Marmoset Brain

AbstractBackgroundThe common marmoset (Callithrix jacchus) is an important animal model in neuroscience and neurological diseases (e.g., Alzheimer’s disease ‐ AD), as they present primate‐specific evolutionary features such as an expanded frontal cortex. We established a new consortium with funding support from the National Institute on Aging to generate, characterize, and validate MArmosets as Research MOdels of AD (MARMO‐AD). This consortium develops and studies gene‐edited marmoset models carrying genetic risk for AD, comparing them against wild‐type aging marmosets from birth throughout their lifespan, using non‐invasive longitudinal assessments. Here, we aim to characterize healthy aging trajectories of regional brain volume in a population of marmosets.MethodsWe imaged a cohort of 59 marmosets (45 males, 14 females) across the lifespan (8 to 150 months) using a dedicated 9.4T 30cm bore MRI scanner (Bruker BioSpin Corp, Billerica). The animals were anesthetized under isoflurane and maintained under normal physiological conditions. High‐resolution (250 µm isotropic) T1‐, T2‐, and diffusion‐weighted structural MRI were acquired. The brain images were aligned and registered to the Marmoset Brain Mapping V3 template. The brain was segmented into cortical (CTX) and subcortical (SUB CTX) grey matter, white matter (WM), and cerebral spinal fluid (CSF), and voxel‐based‐morphometry was used to quantify regional brain volume in the left and right hemispheres.ResultsWe discovered a decrease in grey matter volume in both males and females with age (Figure 1), reflected by the reduction in several cortical and subcortical brain regions in both sexes. Overall, we discovered that age affects female marmoset brains (38 CTX and 5 SUB CTX regions) more than males (15 CTX and 2 SUB CTX regions). We found no significant age‐dependent changes in WM and CSF.ConclusionsOur work is the first to thoroughly describe the normal aging of the marmoset brain, a valuable model for age‐related neuropathologies (e.g., AD). This research will establish normative baselines for changes in regional marmoset brain volume with aging that will be used to evaluate our genetically engineered marmoset models of AD, which is the goal of the MARMO‐AD consortium.