Biology subjects

Iturria-Medina, Y.

Publications and source records attributed to Iturria-Medina, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

Machine-learning MRI stratification of genetic frontotemporal dementia for clinical trial enrichment

BACKGROUNDGenetic frontotemporal dementia (FTD) shows large differences in symptom profiles, brain atrophy patterns, and progression rate, making clinical trials difficult to design and power. There is a need for biomarkers that can model disease progression, identify biologically distinct groups, and support efficient trial enrichment. METHODSWe applied contrastive trajectory inference (cTI), a machine-learning method, to structural MRI, white matter hyperintensity, and demographic data from 736 participants in the GENFI cohort, including non-carriers and carriers of C9orf72, GRN, or MAPT mutations. cTI produced an individual "genetic FTD progression score" (0-1) and grouped mutation carriers into data-driven subtypes. We tested construct validity using correlations between progression score and cognitive/functional measures, examined subtype differences in brain-behavior coupling, plasma neurofilament light (NfL), and longitudinal decline, and compared cTI-based trial enrichment against age, cortical thickness and NfL using analytic and simulation-based power analyses. RESULTSGenetic FTD progression scores correlated strongly with global dementia severity and multiple cognitive domains (all p < 0.001), confirming robust clinical scoring. Two mutation-carrier subtypes emerged: a Progressive Track (Subtype 2) with strong associations between progression score and cognitive/functional impairment, rising NfL, and faster longitudinal decline; and a Dissociated Track (Subtype 3) with comparable levels of structural variation but weak or absent clinical and NfL changes, suggesting relative biological stability. Baseline subtype membership added prognostic value for future decline in processing speed and language beyond baseline severity. Notably, for C9orf72 and GRN, cTI-informed enrichment reduced required recruited sample size per arm by about 61-75% compared with unenriched designs, and outperformed enrichment using age, cortical thickness or NfL in both analytic and simulation-based power analyses. CONCLUSIONSMachine-learning stratification of genetic FTD reveals a progressive and a dissociated disease track and provides individualized progression scores that closely track clinical status. cTI progression scores offer a powerful tool for trial enrichment, enabling smaller, more efficient prevention and early-intervention trials than conventional MRI or NfL markers alone.

neuroscience↗

bioRxiv2025

MRI-based classifier to identify close-to-onset cases in C9orf72 genetic frontotemporal dementia

Predicting symptom onset in genetic frontotemporal dementia (FTD) is crucial for advancing targeted interventions and clinical trial design. Brain changes begin years before clinical symptoms emerge, making neuroimaging a strong candidate for onset prediction. However, FTD is highly heterogeneous, encompassing diverse molecular pathologies, affected brain networks, and symptom trajectories. This variability limits the predictive power of any single imaging biomarker and underscores the need for an integrative, multimodal approach to improve prediction accuracy and generalizability. We used machine learning to integrate diverse neuroimaging features, identifying a robust signature for risk stratification. We analyzed T1-weighted and T2-weighted MRI scans from 71 symptomatic C9orf72 carriers, 90 presymptomatic carriers, and 69 healthy controls from the GENFI cohort. We used FreeSurfer to measure cortical thickness and subcortical volumes, and BISON to quantify white matter hyperintensities (WMH). We applied Principal Component Analysis for dimensionality reduction and trained a random forest classifier to distinguish symptomatic carriers from controls. The model was subsequently applied to the presymptomatic cohort to identify individuals whose brain patterns resembled those of symptomatic cases, under the hypothesis that greater similarity indicated a higher risk of conversion. We validated the model with neuropsychological data and a two-year longitudinal follow-up. The classifier distinguished symptomatic C9orf72 carriers from controls with 87.0% accuracy. When applied to presymptomatic carriers, the model identified 21.1% of the cohort as having brain features comparable to those of symptomatic cases. This "high-risk group" showed significant neuropsychological weaknesses in executive function, language and social cognition compared to the non high-risk group. The model accurately predicted clinical conversion within a two-year period with 84.5% accuracy, a 70% sensitivity and a 93.3% negative predictive value. Our findings demonstrate the utility of a machine learning approach using multi-modal MRI to identify presymptomatic C9orf72 carriers at high risk of disease onset within the next two years. By capturing subtle neuroanatomical patterns associated with disease processes, this approach offers a promising method for stratifying genetic FTD carriers prior to symptom onset. Such predictive models could optimize patient selection in future clinical trials.

neuroscience↗

bioRxiv2025

Molecular and neuropathological determinants of neuronal dysfunction in Alzheimer's disease

The biological basis of neuronal excitatory/inhibitory (E/I) imbalance in Alzheimers disease (AD) remains unclear. Using a comprehensive cohort with ante-mortem functional neuroimaging and post-mortem molecular data from the same participants, we mapped individual, whole-brain E/I imbalances through biophysical modeling. E/I ratios in regions supporting higher-order cognitive functions were significantly associated with cognitive performance and decline, with mediation by global neuropathological burden. We also observed a significant inverted U-shaped relationship between E/I ratios and neurofibrillary tangle severity, peaking at the limbic stage (Braak III-IV) in 14 brain areas, including the bilateral hippocampus and superior frontal gyrus. In addition, we identified 89 genes and 101 proteins that predict regional E/I ratios, with pathways related to synaptic signaling and immune response overrepresented. The generalizability of these molecular predictors was confirmed in two independent cohorts, achieving good classification performance for neuropathology severity and AD dementia. Lastly, the estimated E/I imbalances in AD aligned with whole-brain distributions of microglia and oligodendrocyte precursor cells, suggesting that spatial cellular organization contributes to vulnerability to neuronal dysfunction. Overall, this study provides critical insights into the cellular, molecular, and neuropathological signatures of circuit-level dysfunction in AD.

neuroscience↗

bioRxiv2025

Characterizing Post-Mortem Brain Molecular Taxonomy of Cognitive Resilience and Translating it to Living Humans

Here, we define cognitive resilience as slower or faster cognitive decline after we regress out the effects of common brain neuropathologies. Its understanding could provide important insights into the biology underlying cognitive health, enabling the development of more effective strategies to prevent cognitive decline and dementia. However, this requires the development of a practical method to quantify resilience and measure it in living individuals, as well as identifying heterogenous pathways associated with resilience in different individuals. Here, we approach this problem by using a data-driven framework to quantify and characterize molecular signatures underlying cognitive resilience. Using multimodal contrastive trajectory inference (mcTI) on bulk RNA sequencing and tandem mass tag (TMT) proteomic data from 898 post- mortem brain samples from the Religious Orders Study and the Rush Memory and Aging Project (ROSMAP), we derived individual-level molecular pseudotime values reflecting the molecular path from high to low resilience across individuals. Additionally, we identified two distinct molecular subtypes of resilience, each characterized by unique transcriptomic and proteomic signatures, and differing associations with several phenotypes. To translate our brain-derived pseudotime and subtypes to living individuals, we developed prediction models with paired genetics, ante-mortem blood omics, clinical, psychosocial, imaging and device data from the same individuals, demonstrating the potential to predict brain molecular resilience profiles in living persons. Our findings establish a framework for quantifying resilience based on multi- level molecular signatures, identify molecularly distinct resilience subtypes, and demonstrate the feasibility of translating brain-derived molecular profiles to living individuals--laying the groundwork for the development of targeted resilience-promoting interventions in cognitive aging.

neuroscience↗

bioRxiv2025

Whole-Brain Cell-Cell Interaction Axes Explaining Tissue Vulnerability Across the Neurodegenerative Spectrum

Disrupted cell-cell communication represents a fundamental mechanism underlying neurodegeneration, yet how specific intercellular signaling patterns relate to regional brain vulnerability remains poorly understood. Here, we map whole-brain intercellular interaction networks and their spatial correspondence with tissue damage across 13 neurodegenerative conditions, including early- and late-onset Alzheimers disease, presenilin-1 mutations, clinical and pathological subtypes of frontotemporal lobar degeneration, Parkinsons disease, dementia with Lewy bodies, and amyotrophic lateral sclerosis. By integrating multiregional single-nucleus and bulk RNA-seq data with curated cell-cell interaction databases and structural MRI, we reconstruct over 1,000 whole-brain maps of ligand-receptor interactions and quantify their associations with regional atrophy patterns. Multivariate analysis identifies three dominant axes of intercellular communication that explain regional vulnerability to neurodegeneration. Notably, the first axis involves neuron-astrocyte-microglia interactions, explaining atrophy patterns shared by frontotemporal lobar degeneration and Alzheimers disease subtypes. Two complementary axes involving neurons, endothelial cells, and astrocytes explain patterns specific to mutations in PS1 and Parkinsons disease. Importantly, validation in an independent post-mortem cohort (N = 375) confirms that late-onset Alzheimers disease-associated cell-cell interactions predict observed frontal cortex atrophy. These results establish a systematic framework linking local intercellular communication networks to spatial patterns of neurodegeneration, revealing both shared and disease-specific molecular pathways that drive regional brain vulnerability and identifying cellular interaction targets for precision therapeutic interventions.

neuroscience↗

bioRxiv2025

Patterns of Structural Disconnection Driving Proprioceptive Deficits After Stroke

BackgroundStroke is a leading cause of death and disability worldwide, with proprioceptive impairments affecting up to 64% of survivors. These impairments hinder sensorimotor function and motor recovery, significantly impacting post-stroke disability and quality of life. Proprioception depends on an integrated network of brain regions but remains underexplored due to limitations in clinical assessments, making it difficult to identify precise links between stroke- related damage and functional deficits. To address this, we combined quantitative proprioceptive measurements with Connectome-based Lesion-Symptom Mapping (CLSM) to identify white matter (WM) disconnection patterns underlying proprioceptive deficits following sensorimotor stroke. MethodsIn this observational study, we investigated the relationship between WM disconnection and proprioceptive deficits in 39 chronic stroke survivors with paretic arm function (>6 months post-stroke; 13 females; aged 35-81) using CLSM and kinematic assessments. Lesions were manually delineated from 3T MRI scans, and proprioception was quantified using the Arm Position Matching (APM) task on the KINARM Exoskeleton. Patient-specific voxel-wise WM disconnection maps were generated using the Tractography Lesion Assessment Standard (TractLAS), which quantifies disconnection relative to a healthy WM connectome. Proprioceptive scores were regressed against disconnection maps using voxel-wise linear regressions (family-wise error corrected, controlled for age and sex). ResultsOur disconnectome-based approach identified a network of regions where proprioceptive deficits were significantly associated with WM disconnection (d = 0.55-1, p < .005 FWE, t = 3.48-6.35). These included tracts previously implicated in proprioceptive function (superior longitudinal fasciculus, middle longitudinal fasciculus, and arcuate fasciculus) and beyond (medial lemniscus, spinothalamic tract, posterior thalamic radiation). ConclusionWe provide evidence that post-stroke proprioceptive impairments arise from network- wide WM disconnection in several key tracts that mediate proprioceptive function. This study highlights the benefits of using CLSM to assess stroke-related proprioceptive deficits and offers a framework for network-informed assessments of functional impairments that can be used for targeted therapies post-stroke.

neuroscience↗

bioRxiv2025

Brain Multi-Omic Subtypes of Neuroticism Reveal Molecular Signatures linked to Alzheimer's Disease

ImportanceMolecular mechanisms linking neuroticism with Alzheimers disease traits are unknown. ObjectiveTo identify molecular subtypes of neuroticism and examine their association with ADRD traits. DesignThree ongoing cohort studies were used; Religious Orders Study (ROS), Rush Memory and Aging Project (MAP) and Minority Aging Research Study (MARS), that began enrollment in 1994, 1997, and 2004, respectively. SettingOlder priests, nuns, and brothers from across the U.S. (ROS), older adults (MAP) and older African-American adults (MARS) from across the greater Chicago metropolitan area. Participants1,028 decedents with multi-omic data from the dorsolateral prefrontal cortex (DLPFC), the anterior cingulate cortex (AC), and the posterior cingulate gyrus (PCG). Exposure(s)Eight layers of omics (DNA methylation and histone acetylation from DLPFC; RNA seq from AC, DLPFC, and PCG, single-nucleus RNA, TMT proteomics and metabolomics from DLPFC) and Neuroticism using the 12-item version from the NEO Five-Factor Inventory. Main outcome(s) and measure(s)Person-specific multi-omic molecular pseudotime representing molecular progression from low to high phenotypic expression of neuroticism, and three multi-omic brain molecular subtypes of neuroticism which represent distinct omic pathways from no/low neuroticism to high neuroticism that differ by their omic constituents. Participants are exclusively assigned to the subtype which aligns mostly with their multi-omic profile. ResultsThe top drivers of subtype differentiation were transcriptomic alterations across three brain regions (DLPFC, AC, PCG) which extensively and differentially characterized the subtypes. The subtypes were also differentially associated with AD pathology, temporal lobe atrophy, and AD dementia, with subtype N1 showing the strongest associations. Conclusions and RelevanceNeuroticism may be driven by three distinct molecular subtypes, with subtype N1 driving ADRD-related associations, N2 showing some ADRD associations, and N3 being completely independent of these outcomes. Our data provide novel insights into the biology of individual differences in predispositions of neuroticism and its associations with ADRD traits. Key pointsO_ST_ABSQuestionC_ST_ABSWhat are the brain multi-omics molecular signatures linking neuroticism with Alzheimers diseases and related dementias (AD/ADRDs)? FindingsWe identified three distinct brain multi-omic molecular subtypes reflecting different molecular pathways underlying neuroticism. Top omic features of the subtypes were extensively and differentially characterized by transcriptomic alterations across three brain regions - dorsolateral prefrontal cortex, anterior cingulate cortex, and posterior cingulate gyrus. Subtype N1 was strongly associated with AD pathology, AD dementia, and temporal lobe atrophy. MeaningThe association we typically observe between phenotypic neuroticism and ADRD clinical traits might be largely driven by a molecular pathway underlying this trait.

neuroscience↗

bioRxiv2025

White Matter Hyperintensities Precede other Biomarkers in GRN Frontotemporal Dementia

INTRODUCTIONIncreased white matter hyperintensities (WMHs) have been reported in genetic frontotemporal dementia (FTD) in small studies, but the sequence of WMH abnormalities relative to other biomarkers is unclear. METHODSUsing a large dataset (n=763 GENFI2 participants), we measured WMHs and examined them across genetic FTD variants and stages. Cortical and subcortical volumes were parcellated, and serum neurofilament light chain (NfL) levels were measured. Biomarker progression was assessed with discriminative event-based and regression modeling. RESULTSSymptomatic GRN carriers showed elevated WMHs, primarily in the frontal lobe, while no significant increase was observed in C9orf72 or MAPT carriers. WMH abnormalities preceded NfL elevation, ventricular enlargement, and cortical atrophy. Longitudinally, baseline WMHs predicted subcortical changes, while subcortical volumes did not predict WMH changes, suggesting WMHs may precede neurodegeneration. DISCUSSIONWMHs are elevated in a subset of GRN-related FTD. When present, they appear early and should be considered in disease progression models. HighlightsO_LIElevated WMH volumes in symptomatic GRN carriers, but not in other mutations. C_LIO_LIWMH accumulation is mostly observed in the frontal lobe. C_LIO_LIWMH abnormalities appear early in GRN-FTD, before NfL, atrophy, and ventriculomegaly. C_LIO_LILongitudinally, WMH volumes can predict subcortical changes, but not vice versa. C_LIO_LIWMHs are key early markers in GRN-FTD and should be included in progression models. C_LI RESEARCH IN CONTEXTO_ST_ABSSystematic reviewC_ST_ABSWe systematically reviewed the literature on white matter hyperintensities (WMHs) in frontotemporal dementia (FTD) using PubMed. While a few small studies reported increased WMHs in GRN mutation carriers, their sample sizes were limited, and they did not assess the timing of WMHs within disease progression or their temporal relationship to other biomarkers. InterpretationWe identified a sequence of key biomarkers in GRN-related FTD and demonstrated that WMHs are among the earliest biomarkers, preceding cortical and subcortical atrophy as well as blood biomarkers. This aligns with neuropathological evidence of early white matter involvement in FTLD-GRN. Additionally, using a larger dataset, we validated previous reports of elevated WMHs in GRN carriers, confirming their reliability. Future directionsFuture studies should integrate WMHs into FTD progression models to enhance early diagnosis. Understanding why only a subset of GRN carriers exhibit high WMH volumes remains a key research priority.

neuroscience↗

bioRxiv2025

Translating the Post-Mortem Brain Multi-Omics Molecular Taxonomy of Alzheimer's Dementia to Living Humans

Alzheimers disease (AD) dementia is characterized by significant molecular and phenotypic heterogeneity, which confounds its mechanistic understanding, diagnosis, and effective treatment. In this study, we harness the most comprehensive dataset of paired ante-mortem blood omics, clinical, psychological, and post-mortem brain multi-omics data and neuroimaging to extensively characterize and translate the molecular taxonomy of AD dementia to living individuals. First, utilizing a comprehensive integration of eight complementary molecular layers from brain multi-omics data (N = 1,189), we identified three distinct molecular AD dementia subtypes exhibiting strong associations with cognitive decline, sex, psychological traits, brain morphology, and characterized by specific cellular and molecular drivers involving immune, vascular, and oligodendrocyte precursor cells. Next, in a significant translational effort, we developed predictive models to convert these advanced brain-derived molecular profiles (AD dementia pseudotimes and subtypes) into blood-, MRI- and psychological traits-based markers. The translation results underscore both the promise of these models and the opportunities for further enhancement. Our findings enhance the understanding of AD heterogeneity, underscore the value of multi-scale molecular approaches for elucidating causal mechanisms, and lay the groundwork for the development of novel therapies in living persons that target multi-level brain molecular subtypes of AD dementia.

neuroscience↗

bioRxiv2025

Multi-omic subtypes of Alzheimer's dementia are differentially associated with psychological traits

ImportancePsychological traits reflecting neuroticism, depressive symptoms, loneliness, and purpose in life are risk factors of AD dementia; however, the underlying biologic mechanisms of these associations remain largely unknown. ObjectiveTo examine whether one or more multi-omic brain molecular subtypes of AD is associated with neuroticism, depressive symptoms, loneliness, and/or purpose in life. DesignTwo cohort-based studies; Religious Orders Study (ROS) and Rush Memory and Aging Project (MAP), both ongoing longitudinal clinical pathological studies that began enrollment in 1994 and 1997. SettingOlder priests, nuns, and brothers from across the U.S. (ROS) and older adults from across the greater Chicago metropolitan area (MAP). Participants822 decedents with multi-omic data from the dorsolateral prefrontal cortex. Exposure(s)Pseudotime, representing molecular distance from no cognitive impairment (NCI) to AD dementia, and three multi-omic brain molecular subtypes of AD dementia representing 3 omic pathways from no cognitive impairment (NCI) to AD dementia that differ by their omic constituents. Main outcome(s) and measure(s)We first ran four separate linear regressions with neuroticism, depressive symptoms, loneliness, purpose in life as the outcomes, and pseudotime as the predictor, adjusting for age, sex and education. We then ran four separate analyses of covariance (ANCOVAs) with Bonferroni-corrected post-hoc tests to test whether the three multi-omic AD subtypes are differentially associated with the four traits, adjusting for the same covariates. ResultPseudotime was positively associated (p<0.05) with neuroticism and loneliness. AD subtypes were differentially associated with the traits: AD subtypes 1 and 3 were associated with neuroticism; AD subtype 2 with depressive symptoms; AD subtype 3 with loneliness, and AD subtype 2 with purpose in life. Conclusions and RelevanceThree multi-omic brain molecular subtypes of AD dementia differentially share omic features with four psychological risk factors of AD dementia. Our data provide novel insights into the biology underlying well-established associations between psychological traits and AD dementia. Key pointsO_ST_ABSQuestionC_ST_ABSAre three distinct multi-omic brain molecular subtypes of Alzheimers disease (AD) dementia associated with four well-established psychological AD risk factors (neuroticism, depressive symptoms, loneliness and purpose in life)? FindingsWe found differential associations: AD subtypes 1 and 3 were associated with neuroticism, AD subtype 2 was associated with depressive symptoms, AD subtype 3 was associated with loneliness; and AD subtype 2 was associated with purpose in life. MeaningPsychological risk factors might be associated with AD dementia via shared multi-omic molecular pathways.

molecular biology↗

bioRxiv2025

Cell type transcriptomics reveal shared genetic mechanisms in Alzheimer's and Parkinson's disease

Historically, Alzheimers disease (AD) and Parkinsons disease (PD) have been investigated as two distinct disorders of the brain. However, a few similarities in neuropathology and clinical symptoms have been documented over the years. Traditional single gene-centric genetic studies, including GWAS and differential gene expression analyses, have struggled to unravel the molecular links between AD and PD. To address this, we tailor a pattern-learning framework to analyze synchronous gene co-expression at sub-cell-type resolution. Utilizing recently published single-nucleus AD (70,634 nuclei) and PD (340,902 nuclei) datasets from postmortem human brains, we systematically extract and juxtapose disease-critical gene modules. Our findings reveal extensive molecular similarities between AD and PD gene cliques. In neurons, disrupted cytoskeletal dynamics and mitochondrial stress highlight convergence in key processes; glial modules share roles in T-cell activation, myelin synthesis, and synapse pruning. This multi-module sub-cell-type approach offers insights into the molecular basis of shared neuropathology in AD and PD.

neuroscience↗

bioRxiv2024

Sex and APOE4-specific links between cardiometabolic risk factors and white matter alterations in individuals with a family history of Alzheimer's disease

INTRODUCTIONWhite matter (WM) alterations are among the earliest changes in Alzheimers disease (AD), yet limited work has comprehensively characterized the effects of AD risk factors on WM. METHODSIn older adults with a family history of AD, we investigated the sex-specific and APOE genotype-related relationships between WM microstructure and risk factors. Multiple MRI-derived metrics were integrated using a multivariate approach based on the Mahalanobis distance (D2). The links between WM D2 and cognition were also explored. RESULTSWM D2 in several regions was associated with high systolic blood pressure, BMI, and glycated hemoglobin, and low cholesterol, in both males and females. APOE4+ displayed a distinct risk pattern, with LDL-cholesterol having a detrimental effect only in carriers, and this pattern was linked to immediate memory performance. Myelination was the main mechanism underlying WM alterations. DISCUSSIONOur findings reveal that combined exposure to multiple cardiometabolic risk factors negatively impacts microstructural health, which may subsequently affect cognition. Notably, APOE4 carriers exhibited a different risk pattern, especially in the role of LDL, suggesting distinct underlying mechanisms in this group.

neuroscience↗

bioRxiv2024

Evaluating permutation-based inference for partial least squares analysis of neuroimaging data

Partial least squares (PLS) is actively leveraged in neuroimaging work, typically to map latent variables (LVs) representing brain-behaviour associations. LVs are considered statistically significant if they tend to capture more covariance than LVs derived from permuted data, with a Procrustes rotation applied to map each set of permuted LVs to the space defined by the originals, creating an "apples to apples" comparison. Yet, it has not been established whether applying the rotation makes the permutation test more sensitive to whether true LVs are present in a dataset, and it is unclear if significance alone is sufficient to fully characterize a PLS decomposition, given that complementary metrics like strength and split-half stability may offer non-redundant information about the LVs. Accordingly, we performed PLS analyses across a range of simulated datasets with known latent effects, observing that the Procrustes rotation systematically weakened the null distributions for the first LV. By extension, the first LV was nearly always significant, regardless of whether the effect was weak, undersampled, noisy, or simulated at all. But, if no rotation was applied, all possible LVs tended to be significant as we increased the sample size of UK Biobank datasets. Meanwhile, LV strength and stability metrics accurately tracked our confidence that effects were present in simulated data, and allowed for a more nuanced assessment of which LVs may be relevant in the UK Biobank. We end by presenting a list of considerations for researchers implementing PLS permutation testing, and by discussing promising alternative tests which may alleviate the concerns raised by our findings.

neuroscience↗

bioRxiv2024

Association between Adolescent Brain Morphometry and Cognitive Function: Insights from a Cross-Sectional Analysis of ABCD Data from 9 to 15 Years Old

Significant changes occur in brain structure and cognitive abilities during adolescence. Investigating their association can provide insight into brain-based cognitive development, yet previous studies were limited by narrow brain measures, small samples, and lacking focus on age-related variation. Here, we analyzed a large cohort (N = 8,534, age 9-15) from the Adolescent Brain Cognitive Development dataset. Using structural MRI and diffusion imaging, we derived 16 regional structural measures and integrated them via morphometric similarity networks to characterize 16,563 regional, connectivity, and hub features. We applied large-scale computational models to investigate their associations with performance on seven cognitive subtests and general intelligence (g), as well as age-related changes. Brain areas most strongly associated with cognitive ability also showed the greatest age-related variability in these associations, located primarily in the frontal, temporal, and occipital lobes. Structural MRI measures exhibited stronger associations with cognition and greater age-related variability than diffusion-derived metrics, while global hub measures showed stronger and more variable associations than local measures. Overall, our study provides a comprehensive and reliable understanding of brain structure-cognition associations during adolescence.

neuroscience↗

bioRxiv2024

Beyond brain size: disentangling the effect of sex and brain size on brain morphometry and cognitive functioning

It is imperative to study sex differences in brain morphology and function. However, there are major observable and unobservable confounding factors that can contribute to the estimated differences. Males have larger head sizes than females. Head size differences not only act as a confounding factor in studying sex differences in the brain, but also impact its anatomy and functioning. In this work, we seek to disentangle the effect of head size from sex in studying sex differentiated aging trajectories, its relation to canonical functional networks and cytoarchitectural classes, brain allometry, cognition. Using the UK Biobank (UKBB) neuroimaging data (N = 35,732 participants, 19,281 females, 44-82 years of age), we created a subsample (N = 11,294) where females (N = 5,657) and males were matched by their total intracranial volume (TIV) and age, a subsample that maintains the UKBB sample distribution, one matched only by age, and one that exaggerated the TIV difference between sexes. We then modeled the aging trajectories at both regional and vertex-wise levels in the four subsamples, and compared the estimations of the models. Our results show that when females and males have the same head size, the overall sex estimations tend towards zero, suggesting that most of the variability results from head size differences. Our approach also revealed bidirectional sex differences in brain neuroanatomy previously masked by the effect of head size. Further, the scaling relationship between regional and total brain volume remains fairly consistent across the lifespan and is not sex differentiated overall. We evaluated how the results of cognitive tests with perceived sex differences are influenced and explained by head size and found that although the correlation between TIV and cognitive scores is low, the matching process changes the direction of the effect sizes of differences between sexes in "verbal and numerical reasoning" and "working memory" cognitive domains. Taken together, employing a matching approach that is widely used in causal modeling studies, we provide new evidence for disentanglement of sex differences in the brain from head size as a biological confound.

neuroscience↗

bioRxiv2024

Microstructural Correlates of Cognitive and Motor Functioning Revealed via Multimodal Multivariate Analysis

Recent advances in cognitive neuroscience emphasise the importance of healthy white matter (WM) for optimal behavioural functioning. It is now widely accepted that brain connectivity via WM contributes to the emergence of behaviour. However, the association between the microstructure of WM fibres and behaviour is poorly understood due to the indirect and overlapping nature of methods used to assess microstructure. Here, we used the Mahalanobis Distance (D2) to integrate 10 metrics of WM derived from multimodal neuroimaging that have strong ties to microstructure. The D2 metric was chosen because it accounts for metrics' covariance as it measures the voxelwise distance between every subject and the average; thus providing a robust multiparametric assessment of microstructure. We used multivariate correlation to examine voxelwise WM-behaviour associations with two cognitive and two motor tasks, which allowed us to compare within and across behavioural domains. We observed that behaviour is organised in cognitive, motor, and integrative components that have widespread associations with WM, from frontal to parietal regions. Notably, the decomposition of these factors shows that tasks traditionally labelled as cognitive also contain motor components that map onto motor-related WM patterns, and that motor tasks likewise contain non-motor components linked to distinct WM microstructural features. Our results highlight the complex nature of the links between microstructure and behaviour, and support the relevance of multivariate modelling when examining brain-behaviour associations.

neuroscience↗

bioRxiv2024

MVComp toolbox: MultiVariate Comparisons of brain MRI features accounting for common information across metrics

Multivariate approaches have recently gained in popularity to address the physiological unspecificity of neuroimaging metrics and to better characterize the complexity of biological processes underlying behavior. However, commonly used approaches are biased by the intrinsic associations between variables, or they are computationally expensive and may be more complicated to implement than standard univariate approaches. Here, we propose using the Mahalanobis distance (D2), an individual-level measure of deviation relative to a reference distribution that accounts for covariance between metrics. To facilitate its use, we introduce an open-source python-based tool for computing D2 relative to a reference group or within a single individual: the MultiVariate Comparison (MVComp) toolbox. The toolbox allows different levels of analysis (i.e., group-or subject-level), resolutions (e.g., voxel-wise, ROI-wise) and dimensions considered (e.g., combining MRI metrics or WM tracts). Several example cases are presented to showcase the wide range of possible applications of MVComp and to demonstrate the functionality of the toolbox. The D2 framework was applied to the assessment of white matter (WM) microstructure at 1) the group-level, where D2 can be computed between a subject and a reference group to yield an individualized measure of deviation. We observed that clustering applied to D2 in the corpus callosum yields parcellations that highly resemble known topography based on neuroanatomy, suggesting that D2 provides an integrative index that meaningfully reflects the underlying microstructure. 2) At the subject level, D2 was computed between voxels to obtain a measure of (dis)similarity. The loadings of each MRI metric (i.e., its relative contribution to D2) were then extracted in voxels of interest to showcase a useful option of the MVComp toolbox. These relative contributions can provide important insights into the physiological underpinnings of differences observed. Integrative multivariate models are crucial to expand our understanding of the complex brain-behavior relationships and the multiple factors underlying disease development and progression. Our toolbox facilitates the implementation of a useful multivariate method, making it more widely accessible.

neuroscience↗

bioRxiv2023

Transcriptomic signatures of Abeta- and tau-induced neuronal dysfunction reveal inflammatory processes at the core of Alzheimer's disease pathophysiology

Molecular mechanisms enabling pathology-induced neuronal dysfunction in Alzheimers disease (AD) remain elusive. Here, we use mechanistic computational models to infer the combined influence of PET-measured A{beta} and tau burdens on fMRI-derived neuronal activity and to subsequently identify the transcriptomic spatial correlates of AD pathophysiology. Our results reveal overrepresented genes and biological processes that participate in synaptic degeneration and interact with A{beta} and tau deposits. Furthermore, we confirmed the central role of the immune system and neuroinflammatory pathways within AD pathogenesis; microglia were significantly enriched in the gene set associated with A{beta} and tau synergistic influences on neuronal activity. Lastly, our computational approach unveiled drug candidates with the potential to halt or reduce the observed pathological effects on neuronal activity, including existing medication for cancer, immune disorders, and cardiovascular diseases, many currently under clinical evaluation in AD. Overall, these findings support the notion that the AD brain experiences functional changes intricately associated with a diverse spectrum of molecular processes.

neuroscience↗