Neuroimaging Measures Research Articles

Predictive Coding in Music Perception: Emotional Influences and Therapeutic Implications in Depression

This literature review examines the intricate relationship between music prediction and its emotional influence on depression. Drawing on theoretical frameworks such as predictive coding and empirical evidence from neuroimaging, psychophysiological studies, and clinical trials, the review explores how the brain's anticipation and processing of musical cues evoke emotional responses. Foundational work and recent advances underscore the role of hierarchical predictive networks in modulating emotional experiences during music listening. In healthy individuals, the balance between predictability and surpriseespecially as influenced by rhythmic complexity and cultural learningcreates a rich palette of emotional reactions. In contrast, individuals with depression often exhibit distorted predictive processes, characterized by overly rigid and negatively biased expectations, which may reinforce depressive cognitions and emotional dysregulation. Music-based interventions, as evidenced by systematic reviews, have shown promise in alleviating depressive symptoms by engaging both psychological and physiological pathways. However, gaps remainparticularly regarding the impact of violated musical predictions on emotional processing in depression. Future research integrating neuroimaging and psychophysiological measures is essential to better understand these mechanisms and to optimize music-based therapeutic strategies. This review thus highlights both the potential and current limitations of leveraging musics predictive properties for improving mental health outcomes in depression.

Acute dentate nucleus deep brain stimulation modulates corticomotor excitability in chronic stroke survivors.

Acute dentate nucleus deep brain stimulation modulates corticomotor excitability in chronic stroke survivors.

Deciphering the Heterogeneity of Schizophrenia: A Multimodal and Multivariate Neuroimaging Framework for Unveiling Brain-Symptom Relationships and Underlying Subtypes.

Schizophrenia manifests large heterogeneities in either symptoms or brain abnormalities. However, the neurobiological basis of symptomatic diversity remains poorly understood. We hypothesized that schizophrenia's diverse symptoms arise from the interplay of structural and functional alterations across multiple brain regions, rather than isolated abnormalities in a single area. A total of 495 schizophrenia patients and 507 healthy controls from 8 sites were recruited. Five symptomatic dimensions of schizophrenia patients were derived from the Positive and Negative Syndrome Scale. Multivariate canonical correlation analysis was introduced to identify symptom-related multimodal magnetic resonance imaging composite indicators (MRICIs) derived from gray matter volume, functional connectivity strength, and white matter fractional anisotropy. The intergroup differences in MRICIs were compared, and the paired-wise correlations between symptom dimensions and MRICIs were resolved. Finally, K-means clustering was used to identify the underlying biological subtypes of schizophrenia based on MRICIs. Canonical correlation analysis identified 15 MRICIs in schizophrenia that were specifically contributed by the neuroimaging measures of multiple regions, respectively. These MRICIs can effectively characterize the complexity of symptoms, showing correlations within and across symptom dimensions, and were consistent across both first-episode and chronic patients. Additionally, some of these indicators could moderately differentiate schizophrenia patients from healthy controls. K-means clustering identified 2 schizophrenia subtypes with distinct MRICI profiles and symptom severity. Symptom-guided multimodal and multivariate MRICIs could decode the symptom heterogeneity of schizophrenia patients and might be considered as potential biomarkers for schizophrenia.

Brain Age Prediction in Type II GM1 Gangliosidosis.

GM1 gangliosidosis is an inherited, progressive, and fatal neurodegenerative lysosomal storage disorder with no approved treatment. We calculated a predicted brain ages and Brain Structures Age Gap Estimation (BSAGE) for 81 MRI scans from 41 Type II GM1 gangliosidosis patients and 897 MRI scans from 556 neurotypical controls (NC) utilizing BrainStructuresAges , a machine learning MRI analysis pipeline. NC showed whole brain aging at a rate of 0.83 per chronological year compared with 1.57 in juvenile GM1 patients and 12.25 in late-infantile GM1 patients, accurately reflecting the clinical trajectories of the two disease subtypes. Accelerated and distinct brain aging was also observed throughout midbrain structures including the thalamus and caudate nucleus, hindbrain structures including the cerebellum and brainstem, and the ventricles in juvenile and late-infantile GM1 patients compared to NC. Predicted brain age and BSAGE both correlated with cross-sectional and longitudinal clinical assessments, indicating their importance as a surrogate neuroimaging outcome measures for clinical trials in GM1 gangliosidosis.

Altered Brain-Behavior Association During Resting State is a Potential Psychosis Risk Marker.

Alterations in cognitive and neuroimaging measures in psychosis may reflect altered brain-behavior interactions patterns accompanying the symptomatic manifestation of the disease. Using graph connectivity-based approaches, we tested the brain-behavior association between cognitive functioning and functional connectivity at different stages of psychosis. We collected resting-state fMRI of 204 neurotypical controls (NC) in two independent cohorts, 43 patients with chronic psychosis (PSY), and 22 subjects with subthreshold psychotic symptoms (STPS). In NC, we calculated graph connectivity metrics and tested their associations with neuropsychological scores. Replicable associations were tested in PSY and STPS and externally validated in three cohorts of 331, 371, and 232 individuals, respectively. NC showed a positive correlation between the degree centrality of a right prefrontal-cingulum-striatal circuit and total errors on Wisconsin Card Sorting Test. Conversely, PSY and STPS showed negative correlations. External replications confirmed both associations while highlighting the heterogeneity of STPS. Group differences in either centrality or cognition alone were not equally replicable. In four independent cohorts totaling 1,203 participants, we identified a replicable alteration of the brain-behavior association in different stages of psychosis. These results highlight the high replicability of multimodal markers and suggest the opportunity for longitudinal investigations that may test this marker for early risk identification.

Lifetime depression, sleep disruption and brain structure in the UK Biobank cohort.

Lifetime depression, sleep disruption and brain structure in the UK Biobank cohort.

High-dimensional mediation analysis reveals the mediating role of physical activity patterns in genetic pathways leading to AD-like brain atrophy

BackgroundAlzheimer’s disease (AD) is a complex disorder that affects multiple biological systems including cognition, behavior and physical health. Unfortunately, the pathogenic mechanisms behind AD are not yet clear and the treatment options are still limited. Despite the increasing number of studies examining the pairwise relationships between genetic factors, physical activity (PA), and AD, few have successfully integrated all three domains of data, which may help reveal mechanisms and impact of these genomic and phenomic factors on AD. We use high-dimensional mediation analysis as an integrative framework to study the relationships among genetic factors, PA and AD-like brain atrophy quantified by spatial patterns of brain atrophy.ResultsWe integrate data from genetics, PA and neuroimaging measures collected from 13,425 UK Biobank samples to unveil the complex relationship among genetic risk factors, behavior and brain signatures in the contexts of aging and AD. Specifically, we used a composite imaging marker, Spatial Pattern of Abnormality for Recognition of Early AD (SPARE-AD) that characterizes AD-like brain atrophy, as an outcome variable to represent AD risk. Through GWAS, we identified single nucleotide polymorphisms (SNPs) that are significantly associated with SPARE-AD as exposure variables. We employed conventional summary statistics and functional principal component analysis to extract patterns of PA as mediators. After constructing these variables, we utilized a high-dimensional mediation analysis method, Bayesian Mediation Analysis (BAMA), to estimate potential mediating pathways between SNPs, multivariate PA signatures and SPARE-AD. BAMA incorporates Bayesian continuous shrinkage prior to select the active mediators from a large pool of candidates. We identified a total of 22 mediation pathways, indicating how genetic variants can influence SPARE-AD by altering physical activity. By comparing the results with those obtained using univariate mediation analysis, we demonstrate the advantages of high-dimensional mediation analysis methods over univariate mediation analysis.ConclusionThrough integrative analysis of multi-omics data, we identified several mediation pathways of physical activity between genetic factors and SPARE-AD. These findings contribute to a better understanding of the pathogenic mechanisms of AD. Moreover, our research demonstrates the potential of the high-dimensional mediation analysis method in revealing the mechanisms of disease.

Prediction of mental health risk in adolescents.

Prospective prediction of mental health risk in adolescence can facilitate early preventive interventions. Here, using psychosocial questionnaires and neuroimaging measures from over 11,000 children in the Adolescent Brain and Cognitive Development Study, we trained neural network models to stratify general psychopathology risk. The model trained on current symptoms accurately predicted which participants would convert into the highest psychiatric illness risk group in the following year (area under the receiver operating characteristic curve = 0.84). The model trained solely on potential etiologies or disease mechanisms achieved an area under the receiver operating characteristic curve of 0.75 without relying on the child's current symptom burden. Sleep disturbances emerged as the most influential predictor of high-risk status, surpassing adverse childhood experiences and family mental health history. Including neuroimaging measures did not enhance predictive performance. These findings suggest that artificial intelligence models trained on readily available psychosocial questionnaires can effectively predict future psychiatric risk while highlighting potential targets for intervention. This is a promising step toward artificial intelligence-based mental health screening for clinical decision support systems.

Linking obesity-associated genotype to child language development: the role of early-life neurology-related proteomics and brain myelination.

Linking obesity-associated genotype to child language development: the role of early-life neurology-related proteomics and brain myelination.

Neuroimaging meets neurophysiology: vascular endothelial growth factor and regional cerebral blood flow in early Alzheimer's disease.

Angiogenesis may play an important role in the pathophysiology of Alzheimer's disease (AD). Previous in vitro and in vivo studies suggest a link between angiogenesis and cerebral blood flow (CBF) in AD; however, this has never been studied clinically. In this sample of study participants with early AD (n = 15), serum vascular endothelial growth factor (VEGF), an angiogenesis biomarker, was negatively associated with regional CBF (rCBF) in the angular gyrus even after bootstrapping at a repetition of 5,000 and controlling for age, sex, and diagnosis (β = -0.015, SE = 0.006, P = 0.02, f2 = 0.27, Pbootstrapped = 0.049). Sex-stratified subgroup analyses showed a strong negative correlation between rCBF in the angular gyrus and log-VEGF in males (n = 7; r = -0.78, P = 0.04), but not in females (n = 8; r = -0.16, P = 0.7). These results support an association between angiogenesis and CBF in early AD that should be further investigated in longitudinal studies and may have relevance for future therapeutic interventions in AD.NEW & NOTEWORTHY This manuscript supports the findings from previous in vitro and in vivo Alzheimer's disease (AD) studies where angiogenesis was associated with cerebral blood flow (CBF) changes. Using both neuroimaging and neurophysiology measures, this study showed the association between CBF and blood vascular endothelial growth factor (VEGF) in people with early AD, suggesting further investigation into angiogenesis and CBF as potential therapeutic targets for AD.

Deep medullary veins: a promising neuroimaging marker for neurodegeneration in multiple sclerosis.

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system (CNS). Recent studies have shown that different forms of vascular abnormalities may be related to the pathogenesis of MS. Susceptibility-weighted imaging (SWI) can directly image intracranial venules. The aim of this study was to investigate the association between deep medullary veins (DMVs) and the degree of neurodegeneration in patients with MS. In this prospective cross-sectional study, 34 patients with MS and 30 age-matched healthy controls (HCs) were recruited. The count and score of DMVs, which can reflect the visibility and continuity of DMVs were evaluated based on SWI. The differences between the group with a high DMV score (DMV >10) and the group with a low DMV score (DMV ≤10) were assessed. The association of DMV change with neurodegeneration neuroimaging markers [including amount and volume of white matter lesion (WML), degree of cortical atrophy, whole-brain atrophy, and deep gray matter (DGM) atrophy] and clinical Expanded Disability Status Scale (EDSS) were observed in patients with MS. It was found that compared with controls, patients with MS (n=34) had a significantly lower DMV count (P<0.001) and a significantly higher DMV score (P<0.001). The low- and high-DMV score groups differed significantly in terms of EDSS (P=0.048) and neurodegeneration neuroimaging indicators, including WML volume (P=0.015), brain parenchymal fraction (BPF) (P=0.047), thalamic fraction (P=0.036), and caudate fraction (P=0.015). In the correlation analysis of the MS group, DMV count was negatively correlated with the number of WMLs (r=-0.535; P=0.001) and the WML volume (r=-0.416; P=0.014) but positively correlated with the neuroimaging measurements reflecting the degree of whole-brain atrophy and DGM atrophy. Furthermore, the DMV score was positively correlated with EDSS (r=0.450; P=0.008), number of WMLs (r=0.490; P=0.003), and WML volumes (r=0.635; P=0.001) but negatively correlated with the neuroimaging measurements reflecting the degree of whole-brain atrophy and DGM atrophy. Reduced DMV visibility and continuity could reflect the severity of neurodegeneration in patients with MS. DMV count and score may be imaging indicators for assessing the severity of MS.

Bias in data-driven replicability analysis of univariate brain-wide association studies

Recent studies have used big neuroimaging datasets to answer an important question: how many subjects are required for reproducible brain-wide association studies? These data-driven approaches could be considered a framework for testing the reproducibility of several neuroimaging models and measures. Here we test part of this framework, namely estimates of statistical errors of univariate brain-behaviour associations obtained from resampling large datasets with replacement. We demonstrate that reported estimates of statistical errors are largely a consequence of bias introduced by random effects when sampling with replacement close to the full sample size. We show that future meta-analyses can largely avoid these biases by only resampling up to 10% of the full sample size. We discuss implications that reproducing mass-univariate association studies requires tens-of-thousands of participants, urging researchers to adopt other methodological approaches.

Semiparametric Confidence Sets for Arbitrary Effect Sizes in Longitudinal Neuroimaging.

The majority of neuroimaging inference focuses on hypothesis testing rather than effect estimation. With concerns about replicability, there is growing interest in reporting standardized effect sizes from neuroimaging group-level analyses. Confidence sets are a recently developed approach to perform inference for effect sizes in neuroimaging but are restricted to univariate effect sizes and cross-sectional data. Thus, existing methods exclude increasingly common multigroup or nonlinear longitudinal associations of biological brain measurements with inter- and intra-individual variations in diagnosis, development, or symptoms. We broadly generalize the confidence set approach by developing a method for arbitrary effect sizes in longitudinal studies. Our method involves robust estimation of the effect size image and spatial and temporal covariance function based on generalized estimating equations. We obtain more efficient effect size estimates by concurrently estimating the exchangeable working covariance and using a nonparametric bootstrap to determine the joint distribution of effect size across voxels used to construct confidence sets. These confidence sets identify regions of the image where the lower or upper simultaneous confidence interval is above or below a given threshold with high probability. We evaluate the coverage and simultaneous confidence interval width of the proposed procedures using realistic simulations and perform longitudinal analyses of aging and diagnostic differences of cortical thickness in Alzheimer's disease and diagnostic differences of resting-state hippocampal activity in psychosis. This comprehensive approach along with the visualization functions integrated into the pbj R package offers a robust tool for analyzing repeated neuroimaging measurements.

A comparative diagnostic study using clinical and multimodal assessment, including functional neuroimaging and oculomotricity tools, to differentiate ADHD in young patients from healthy control group.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that may persist into adulthood, with no established objective diagnostic tool yet. This study aims to propose a multimodal objective assessment tool involving clinical assessments, functional neuroimaging, and oculomotricity measurement for ADHD in young adults. Seventy-one medication-naïve patients and 71 healthy controls (HCs) aged 18 to 28 underwent clinical interviews, Conners' Adult ADHD Rating Scale (CAARS) questionnaire, functional near-infrared spectroscopy (fNIRS), oculomotricity task, and Conners' Continuous Performance Task (CPT) 3rd edition. Student's t-tests with Bonferroni's correction were performed to compare the performance between groups, and logistic regression was used for classification. ADHD patients had significantly lower frontal hemodynamic response during verbal fluency task (VFT) (P = 0.0003), more anticipatory eye movements during overlap task (P = 0.0006), higher latency (P < 0.0001), anticipatory (P < 0.0001), and errors (P < 0.0001) during anti-saccade task, as well as higher commission errors (P < 0.0001) and standard deviation in hit reaction time (HRT) (P = 0.0018). The multivariate logistic regression model featuring these seven parameters from the three objective tests (fNIRS-VFT, oculomotricity, and CPT) yielded an area under the receiver operating characteristic curve (AUC) value of 0.892 (95% confidence interval (CI): 0.840-0.944), with sensitivity and specificity of 80.28% and 84.51%, respectively. This multimodal assessment offered an accurate diagnostic tool for ADHD in young adults and laid the foundation for future machine-learning approaches.

Effect of Triheptanoin on Caudate Atrophy and Motor Scores in Patients With Early-Stage Huntington Disease: A Phase II Study.

Brain energy deficiency occurs at the early stage of Huntington disease (HD). Triheptanoin, a drug that targets the Krebs cycle, can restore a normal brain energetic profile in patients with HD. In this study, we aimed at assessing its efficacy on clinical and neuroimaging structural measures in HD. We conducted a 6-month bicentric (Paris, Leiden) double-blind randomized controlled trial followed by a 6-month open-label phase, between June 2015 and December 2019. We enrolled 107 patients at the early stage of HD-total motor score (TMS) of the UHDRS between 5 and 40. Participants received triheptanoin 1 g/kg/day or placebo (ratio 1/1). The primary outcome was the rate of caudate atrophy at 6 months using the caudate boundary shift integral (cBSI) method. Main secondary outcomes were cBSI at 12 months, TMS, and diffusion imaging at 6 and 12 months. Analysis was conducted using ANOVA, and data were presented with a 95% CI. To perform a 12-month comparison, we used the placebo arm of a 12-month randomized controlled trial conducted in parallel, using the double robust propensity score method. One hundred patients were randomized (mean age 49 years, 52% women). Fourteen patients withdrew from the study, including 10 because of gastrointestinal effects. There was no difference in cBSI at 6 months between groups (mean 0.026 [0.018-0.033] vs 0.023 [0.014-0.032]). TMS at 12 months was stable in patients treated with triheptanoin for 12 months (mean 0.6 [-1.1 to 2.1]), whereas it increased in patients initially on placebo (2.5 [1.2-3.8]). Compared with the external placebo control group, caudate atrophy decreased by approximately 50% (0.038 [0.028-0.048] vs 0.070 [0.057-0.082]) and TMS stabilized (0.66 [-1.07 to 2.48] vs 2.65 [1.38-3.89]) in patients treated with triheptanoin for 12 months. There was no effect of triheptanoin on caudate atrophy over 6 months. Compared with the external placebo group, triheptanoin was associated with motor stability and decreased caudate atrophy in patients treated for 12 months, but the post hoc nature of these findings is a major limitation. clinicaltrials.gov NCT02453061, May 25, 2015. First patient enrolled on June 29, 2015. This study provides Class I evidence that triheptanoin does not slow caudate atrophy compared with placebo over 6 months in patients with early HD.

Cross-Sectional Comparison of Structural MRI Markers of Impairment in a Diverse Cohort of Older Adults.

Neurodegeneration is presumed to be the pathological process measure most proximal to clinical symptom onset in Alzheimer Disease (AD). Structural MRI is routinely collected in research and clinical trial settings. Several quantitative MRI-based measures of atrophy have been proposed, but their low correspondence with each other has been previously documented. The purpose of this study was to identify which commonly used structural MRI measure (hippocampal volume, cortical thickness in AD signature regions, or brain age gap [BAG]) had the best correspondence with the Clinical Dementia Rating (CDR) in an ethno-racially diverse sample. 2870 individuals recruited by the Healthy and Aging Brain Study-Health Disparities completed both structural MRI and CDR evaluation. Of these, 1887 individuals were matched on ethno-racial identity (Mexican American [MA], non-Hispanic Black [NHB], and non-Hispanic White [NHW]) and CDR (27% CDR > 0). We estimated brain age using two pipelines (DeepBrainNet, BrainAgeR) and then calculated BAG as the difference between the estimated brain age and chronological age. We also quantified their hippocampal volumes using HippoDeep and cortical thicknesses (both an AD-specific signature and average whole brain) using FreeSurfer. We used ordinal regression to evaluate associations between neuroimaging measures and CDR and to test whether these associations differed between ethno-racial groups. Higher BAG (pDeepBrainNet = 0.0002; pBrainAgeR = 0.00117) and lower hippocampal volume (p = 0.0015) and cortical thickness (p < 0.0001) were associated with worse clinical status (higher CDR). AD signature cortical thickness had the strongest relationship with CDR (AICDeepBrainNet = 2623, AICwhole cortex = 2588, AICBrainAgeR = 2533, AICHippocampus = 2293, AICSignature Cortical Thickness = 1903). The relationship between CDR and atrophy measures differed between ethno-racial groups for both BAG estimates and hippocampal volume, but not for cortical thickness. We interpret the lack of an interaction between ethno-racial identity and AD signature cortical thickness on CDR as evidence that cortical thickness effectively captures sources of disease-related atrophy that may differ across racial and ethnic groups. Cortical thickness had the strongest association with CDR. These results suggest that cortical thickness may be a more sensitive and generalizable marker of neurodegeneration than hippocampal volume or BAG in ethno-racially diverse cohorts.

Impulsivity behaviors and white matter mediate the relationship between genetic risk for cannabis use disorder and early cannabis use in adolescents.

Cannabis use disorder (CUD) is strongly influenced by genetic factors; however the mechanisms underpinning this association are not well understood. This study investigated whether a polygenic risk score (PRS) based on a genome-wide association study for CUD in adults predicts cannabis use in adolescents and whether the association can be explained by inter-individual variation in structural properties of brain white matter or risk-taking behaviors. Longitudinal and cross-sectional analyses using data from the IMAGEN cohort, a European longitudinal study integrating genetic, neuroimaging and behavioral measures. We measured associations between PRS for CUD, novelty and sensation seeking traits and fractional anisotropy (FA) of white matter tracts. Mediation modeling explored whether novelty seeking and FA mediated the association between the PRS and cannabis use. Participants were assessed at 14 (n = 1762), 19 (n = 1175) and 23 (n = 1139) years old. European School Survey Project on Alcohol and Other Drugs, substance use risk profile scale, Fagerstrom Test for Nicotine Dependence, temperament and character inventory, Kirby Monetary Questionnaire, diffusor tensor imaging and CUD-PRS. CUD-PRS was associated with adolescent total cannabis exposure [P < 0.001, beta = 0.098 (95% confidence interval = 0.059, 0.137)] as well as with other substance use measures [alcohol P = 0.002, beta = 0.058 (0.020, 0.096); cigarettes smoked P < 0.001, beta = 0.086 (0.044, 0.128); fargestrom score P < 0.001, beta = 0.062 (0.028, 0.096); drug score P < 0.001, beta = 0.106 (0.065, 0.147)]. CUD-PRS was also associated with impulsivity, risk-taking behaviors [impulsivity P < 0.001, beta = 0.106 (0.060, 0.142); sensation seeking P < 0.001, beta = 0.094 (0.0523, 0.1357); novelty seeking P < 0.001, beta = 0.105 (0.064, 0.146); discounting task P < 0.001, beta = 0.051 (0.013, 0.089)] and average FA [P < 0.001, beta = -0.010 (-0.015, -0.005)]. Longitudinal mediation models showed that these behaviors and brain measures could mediate the association of PRS with cannabis use [overall indirect effect for novelty seeking P < 0.001, beta = 0.048 (0.028, 0.068); impulsivity P = 0.016, beta = 0.019 (0.004, 0.035); sensation seeking P < 0.001, beta = 0.034 (0.017, 0.05)]. The genetic risk of adult cannabis use disorder appears to be associated with substance use behavior and white matter structure as early as age 14. The observed mediation effect is consistent with the notion that genetic risk increases novelty seeking in a way that leads to more cannabis use in adolescents.

Cerebrospinal fluid pressure dynamics as a biomechanical marker for quantification of spinal cord compression: Conceptual framework and systematic review of clinical trials.

In patients with acute spinal cord injury (SCI) and degenerative cervical myelopathy (DCM), spinal cord compression is considered a main contributor to spinal cord damage, associated with cerebrospinal fluid (CSF) space obstruction. CSF pressure (CSFP) dynamics are studied as a potential indirect biomechanical marker for spinal cord compression, and as a proxy to estimate spinal cord perfusion pressure (SCPP). Evidence for safety and feasibility of CSFP dynamics in clinical trials as well as interrelations with neuroimaging and intraspinal pressure, and relation to preclinical CSFP models. Systematic review. This review followed PRISMA guidelines, risk of bias assessment with ROBINS-I tool, PROSPERO registration (CRD42024545629). 11 relevant papers were identified (n=212 patients, n=194 intraoperative, n=18 bedside). Risk of bias for safety reporting was low-moderate. Intraoperative CSFP assessments were commonly performed in acute SCI. CSFP was assessed to calculate SCPP (7/11), to evaluate effects from surgical decompression (5/11) and for therapeutic CSF drainage (3/11). The adverse event rate associated with the intrathecal catheter was 8% (n=15/194). The preliminary safety and feasibility profile of CSFP assessments in spinal cord compression encourages clinical application. However, a deeper risk-benefit analysis is limited as the clinical value is not yet determined, given challenges of defining disease specific critical CSFP and SCPP thresholds. The interrelation between measures of CSFP and neuroimaging is yet to be proven. Targeted preclinical studies are essential to improve our understanding of complex CSFP-cord compression interrelations.

AD BLOOD BIOMARKERS IN DIVERSE COMMUNITY SETTINGS: A LONGITUDINAL PERSPECTIVE FROM THE ARIC STUDY

Abstract Alzheimer’s disease and related dementias (ADRD) feature a prolonged preclinical stage spanning decades, with the transition from mid- to late-life marking the critical period for onset and accumulation of pathological brain changes that may lead to physical and cognitive disability. Identifying individuals at risk for cognitive and physical decline during preclinical stages when interventions or disease modifying treatments are more likely to be effective is needed. Blood biomarkers of ADRD pathology and neurodegeneration are promising cost-effective and non-invasive options to fill this gap. To date, however, there are limited data on temporal changes in blood biomarkers and their associations with cognitive, mobility, and neuroimaging outcomes in diverse community-based cohorts. This symposium will feature 23 years of data from the well-established community-based Atherosclerosis Risk in Communities (ARIC) Study cohort which assayed blood biomarkers of amyloid-β (Aβ)42/40, phosphorylated tau at threonine 181 (p-Tau181), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP) using Quanterix Simoa assays on stored specimens at up to 3 timepoints in midlife and late-life in ~1,800 participants. This session will present findings on temporal blood biomarker changes from mid- to late-life and associated risk and protective factors of biomarker changes; mid- to late-life changes in blood biomarkers and associations with late-life neuroimaging measures of neurodegeneration, cerebrovascular disease, and amyloid deposition; and the associations of late-life blood biomarkers with prevalent and incident mobility and cognitive impairment.

Transdiagnostic Network Localization of Social, Language, and Motor Symptoms in Patients With Frontotemporal Lobar Degeneration.

Frontotemporal lobar degeneration (FTLD) includes different clinical syndromes with distinct patterns of symptoms and neuroanatomical locations of neurodegeneration. However, FTLD is clinically heterogeneous (with overlapping symptoms across several domains) and neuroanatomically heterogeneous (with brain atrophy in different locations in different patients). Traditional methods struggle to fully account for this heterogeneity. In this study, we use a relatively new neuroimaging approach, atrophy network mapping, to localize clinical symptoms in patients with FTLD to specific brain networks transdiagnostically. Data were obtained from the Frontotemporal Lobar Degeneration Neuroimaging Initiative and 4-Repeat Tauopathy Neuroimaging Initiative. Inclusion required T1-weighted MRI and a diagnosis of behavioral-variant frontotemporal dementia (bvFTD), semantic-variant primary progressive aphasia (svPPA), nonfluent primary progressive aphasia (nfvPPA), progressive supranuclear palsy Richardson syndrome (PSP-rs), corticobasal syndrome (CBS), or normal cognition. Measures of social cognition (Interpersonal Reactivity Index, Revised Self-Monitoring Scale), language (Boston Naming Test, Animal Fluency), and motor function (Unified Parkinson Disease Rating Scale Part III, PSP Rating Scale) were correlated with neuroimaging measures, including cortical thickness, volume, and atrophy network mapping, a newer method that localizes regions connected to brain atrophy using a functional connectome from cognitively normal persons (n = 1,000). Fifty-seven patients with bvFTD (age 61.2 ± 6.8 years, 35% female), 41 PSP-rs (age 69.7 ± 7.4 years, 54% female), 39 CBS (age 66.2 ± 6.2 years, 51% female), 37 svPPA (age 63.0 ± 6.0 years, 46% female), and 36 nfvPPA (age 68.3 ± 7.3 years, 53% female) and 135 healthy age-matched controls (age 63.3 ± 7.4 years, 58% female) were included. Compared with atrophy alone, atrophy network mapping showed more consistent neuroimaging results across patients with the same clinical syndrome (Dice index mean 0.68 vs 0.11, paired t = 263.1, df = 4,452, p < 0.001), more strongly explained social cognition (F(1, 84) = 10.2, p = 0.002) and motor symptoms (F(1, 185) = 91.3, p < 0.001) across different syndromes, and showed novel neuroanatomical associations, with the temporal parietal junction relating to social cognition; Wernicke area relating to language symptoms; and association sensorimotor cortex, thalamus, and cerebellum relating to motor symptoms. Atrophy network mapping can improve understanding of brain-behavior relationships in clinically and neuroanatomically heterogeneous disorders such as FTLD.