Biology subjects

Karim, H. T.

Publications and source records attributed to Karim, H. T..

5 recordsLinked to original sources

bioRxiv2026

Network Localization of Anxiety as an Early Manifestation of Alzheimer's Disease-related Neurodegeneration

Objective Anxiety is a prominent neuropsychiatric symptom in preclinical Alzheimer's disease (AD), but the neural circuitry linking anxiety and AD remains uncharacterized. We assessed whether anxiety in preclinical AD is independently associated with AD-related neurodegeneration and localizes to a common anxiety network. Methods In cognitively unimpaired (CU) older adults with 3T MRI and amyloid-beta PET (N=1,775), we examined whether an established AD-related atrophy network is associated with anxiety (STAI-6). Then, using Atrophy Network Mapping (ANM), which maps individual cortical atrophy patterns onto a normative functional connectome (N=1000), we derived an anxiety network. We tested its 1) spatial convergence with the AD network and with a causal anxiety network derived from lesion and neuromodulation data, 2) specificity relative to depression, 3) generalizability in samples from independent cohorts with AD and Parkinson's disease (total n=878), and 4) association with longitudinal outcomes. Results In CU older adults, greater AD-network atrophy was associated with higher anxiety, independent of depression, cognition, cortical atrophy, and amyloid burden. Anxiety localized to a network closely resembling the AD network (r=0.74, p=0.04) and the causal anxiety network (r=0.72, p=0.02). This convergence extended across the AD continuum and to the Parkinson's disease cohort and was specific to anxiety relative to depression. Greater anxiety-network atrophy was associated with cognitive decline over 4.6 years. Conclusion Anxiety localizes to a brain circuit resembling the AD-related atrophy network, is consistent across the AD continuum and may extend beyond AD. These findings support anxiety as an independent marker of early AD-related neurodegeneration.

neuroscience↗

bioRxiv2026

Quantifying Uncertainty in Brain Age Predictions via Conformal Prediction

Youth can exhibit signs of delayed or accelerated neurodevelopment, measurable via brain magnetic resonance imaging (MRI). Brain age prediction seeks to estimate brain age at an individual level using machine learning models fitted on healthy individuals. The brain age gap (BAG): the difference between brain age and chronological age, has been studied as a potential biomarker. However, BAG suffers from known limitations, including dependence on chronological age, regression to the mean, and challenges in interpretability. As an alternative framework, we introduce brain age intervals (BAIs) as a normative interval on an individual's chronological age, based on MRI measures. BAIs can be derived using a recent statistical framework called conformal prediction that yields prediction intervals with guaranteed coverage. We train several brain age prediction models on structural and functional MRI scans from the Reproducible Brain Charts dataset (aged 6-22) and estimated BAIs. The BAIs demonstrated stable empirical coverage close to the nominal 90% level (median coverage=91%, width=7.36 years, root mean square error (RMSE)=2.20 years across 100 repeated train-test splits). Associations between interval coverage and clinical measures (p-factor, parental education) were weak, with only a modest directional signal for parental education. Together, these findings demonstrate the feasibility of uncertainty-aware brain age modeling in youth populations while highlighting the need for larger and more diverse samples to detect meaningful clinical and environmental associations.

neuroscience↗

bioRxiv2026

CREB: Consistent Reference External Batch Harmonization

AO_SCPLOWBSTRACTC_SCPLOWMachine learning models using functional magnetic resonance imaging (fMRI) are becoming increasingly popular - these models often rely on training data from multiple, large, and publicly available datasets. It is often necessary to harmonize these data across sites and sequences, and algorithms like ComBat are frequently applied to correct for these differences. This has been shown to improve model performance and generalizability. However, applying traditional ComBat necessitates harmonizing all data (train, validation, test, and other unseen external test sets) simultaneously, which leads to potential data leakage and limits application to new unseen data. We introduce Consistent Reference External Batch (CREB) harmonization, a novel extension of ComBat that learns the prior distribution of site effects exclusively from a designated training set. This learned prior serves as a consistent, easily deployable reference point that employs the empirical Bayes framework to update the site effect for any new, external unseen data. This approach enables training, validation, and test sets to be harmonized separately, thereby preventing data leakage, ensuring the integrity of downstream analyses, and application to new unseen data. CREB is different from traditional ComBat in which each sites prior distribution is estimated at once, but this cannot be applied to unseen data or data from sites not included in the original set of data. We tested CREB with train data from 2846 participants (ages 18-97 years) across 9 different studies and test data from 1113 participants (ages 18-88 years) from 3 studies. We evaluated the performance of harmonization with functional connectivity and gray matter volume. We show that CREB can effectively harmonize the test data to the train data, and have comparable performance to ComBat. CREB is able to conduct this harmonization in a two-step procedure that prevents leakage and is deployable to new unseen data. Finally, we tested whether CREB could similarly preserve biological variance (e.g., whether age associations were preserved after harmonization). We found that CREB, like ComBat could preserve age associations with both functional connectivity and gray matter volume measures. CREB provides an easily deployable, robust harmonization method to standardize data to a common reference distribution, making it uniquely suitable for training generalizable machine learning models.

bioengineering↗

bioRxiv2025

Basal Ganglia Dopamine Availability and Immune Activation Interact and Relate to Anhedonia Severity among Youth with Depression

Anhedonia emerges in adolescence, has putative substrates in neural reward and dopamine systems, and is a hallmark of poor course in depression. Psychoneuroimmunology models suggest altered immune and reward systems may jointly underlie its development. When dopamine availability (rather than function) is considered, immune activation may be understood as a moderator amplifying how lower dopamine levels translate into motivational deficits. This cross-sectional study analyzed data from 55 youth with current depression (Mage=21.4 years; Female=75%). Plasma was assayed for relative quantification of 92 immune proteins, followed by dimensionality reduction via principal component (PC) analysis, forming five PC scores. Dopamine availability was indexed by basal ganglia tissue iron, quantified by MRI R2, a measure detectable in youth. Anhedonia was assessed using the Snaith-Hamilton Pleasure Scale (SHAPS), thought to capture overall anhedonia, and Positive Valence Systems Scale (PVSS), which provides overall, anticipatory, and consummatory anhedonia scores. PC1 scores--whose loadings indicated broadly distributed immune protein elevations consistent with general immune activation--moderated the relationship between basal ganglia dopamine availability and anhedonia: At higher PC1 levels, lower dopamine availability was linked to greater overall anhedonia (as measured by SHAPS) and anticipatory anhedonia, but not to consummatory anhedonia. Lower PC2 scores were associated with greater PVSS overall and consummatory anhedonia. Analyses examining individual basal ganglia regions suggest potential differences in dopaminergic-immune interactions across these regions, with effects most consistently observed in the putamen. Findings highlight nuanced neuroimmune pathways underlying anhedonia components in youth and support inflammation-based models of depression emphasizing dopaminergic-immune interplay.

neuroscience↗

bioRxiv2022

Causal feature selection using a knowledge graph combining structured knowledge from the biomedical literature and ontologies: a use case studying depression as a risk factor for Alzheimer's disease

BackgroundCausal feature selection is essential for estimating effects from observational data. Identifying confounders is a crucial step in this process. Traditionally, researchers employ content-matter expertise and literature review to identify confounders. Uncontrolled confounding from unidentified confounders threatens validity, conditioning on intermediate variables (mediators) weakens estimates, and conditioning on common effects (colliders) induces bias. Additionally, without special treatment, erroneous conditioning on variables combining roles introduces bias. However, the vast literature is growing exponentially, making it infeasible to assimilate this knowledge. To address these challenges, we introduce a novel knowledge graph (KG) application enabling causal feature selection by combining computable literature-derived knowledge with biomedical ontologies. We present a use case of our approach specifying a causal model for estimating the total causal effect of depression on the risk of developing Alzheimers disease (AD) from observational data. MethodsWe extracted computable knowledge from a literature corpus using three machine reading systems and inferred missing knowledge using logical closure operations. Using a KG framework, we mapped the output to target terminologies and combined it with ontology-grounded resources. We translated epidemiological definitions of confounder, collider, and mediator into queries for searching the KG and summarized the roles played by the identified variables. We compared the results with output from a complementary method and published observational studies and examined a selection of confounding and combined role variables in-depth. ResultsOur search identified 128 confounders, including 58 phenotypes, 47 drugs, 35 genes, 23 collider, and 16 mediator phenotypes. However, only 31 of the 58 confounder phenotypes were found to behave exclusively as confounders, while the remaining 27 phenotypes played other roles. Obstructive sleep apnea emerged as a potential novel confounder for depression and AD. Anemia exemplified a variable playing combined roles. ConclusionOur findings suggest combining machine reading and KG could augment human expertise for causal feature selection. However, the complexity of causal feature selection for depression with AD highlights the need for standardized field-specific databases of causal variables. Further work is needed to optimize KG search and transform the output for human consumption. HighlightsO_LIKnowledge of causal variables and their roles is essential for causal inference. C_LIO_LIWe show how to search a knowledge graph (KG) for causal variables and their roles. C_LIO_LIThe KG combines literature-derived knowledge with ontology-grounded knowledge. C_LIO_LIWe design queries to search the KG for confounder, collider, and mediator roles. C_LIO_LIKG search reveals variables in these roles for depression and Alzheimers disease. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/500549v4_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@1c3f417org.highwire.dtl.DTLVardef@1ce1703org.highwire.dtl.DTLVardef@148d645org.highwire.dtl.DTLVardef@eb55e7_HPS_FORMAT_FIGEXP M_FIG C_FIG Statement of SignificanceO_ST_ABSProblemC_ST_ABSExtensive knowledge is required to identify confounders for estimating total effects in observational settings. The literature is too vast for humans to process. Bias may remain from not adjusting on unknown confounders or erroneously adjusting on a collider or mediator. What is already knownStructured literature-derived knowledge is often useful but is noisy. What this papers addsWe present a biomedical knowledge graph linking literature-derived and ontology-ground knowledge of biochemical processes with definitions of clinical disease. We search the KG to distill a model for estimating the (total) effect of depression on Alzheimers disease from observational data.

systems biology↗