Biology subjects

Behjat, H. H.

Publications and source records attributed to Behjat, H. H..

5 recordsLinked to original sources

bioRxiv2025

Patient-specific functional brain architecture explains cortical patterns of tau PET in Alzheimer's disease

The spatial distribution of tau pathology, a key correlate of neurodegeneration and cognitive decline in Alzheimer's disease (AD), varies markedly across individuals. While tau is thought to spread along brain networks, the role of inter-individual variability in accounting for these patterns remains underexplored. Using resting-state fMRI and tau-PET from 805 BioFINDER participants across the AD continuum, with replication in ADNI (n=361) and A4 (n=336), we studied whether subject-specific functional connectivity (FC) profiles enhance the characterization of tau deposition patterns. A hybrid approach integrating individual and group-average FC explained individual tau-PET topographies better than either FC representation alone, particularly in symptomatic individuals and at finer spatial resolutions. Hybrid FC also better captured individual tau topographies than canonical tau-PET maps derived from cohort-level data. These effects were specific to tau and not similarly observed for {beta}-amyloid, and the explanatory advantage of FC-based models increased with spatial granularity. Furthermore, baseline hybrid FC explained follow-up tau-PET topography better than template FC, suggesting that individualized baseline connectivity contains information about future tau-PET progression. The main FC model-comparison findings replicated in ADNI and A4. Collectively, these findings show that individual functional brain architecture is associated with heterogeneity in tau-PET topography. While not establishing a causal propagation mechanism, our findings are consistent with network-spread models. This work advances the methodological characterization of tau-PET heterogeneity in AD and highlights functional connectivity as a potentially informative marker of individual tau-PET trajectories.

neuroscience↗

bioRxiv2025

Age and Alzheimer's disease affect functional connectivity along separate axes of functional brain organization

Aging and Alzheimers disease (AD) are accompanied by alterations to large-scale communication patterns in the brain, which can be tracked in vivo using functional connectivity (FC). The location, direction and relevance of these changes remain widely debated, though they are rarely studied in the context of whole-cortex communication dynamics. In two independent cohorts (BioFINDER-2, N=973; ADNI, N=129), we show that FC changes associated with aging and AD are strongly aligned with separate fundamental axes of hierarchical brain communication. Early accumulation of AD pathology and subsequent cognitive decline are both linked to functional change along the sensory-association axis. Meanwhile, age-related functional changes occur along the representation-executive axis consistently throughout the adult lifespan. These findings together suggest AD and aging both alter major but orthogonal functional pathways in the brain. More broadly, our findings position whole-brain connectivity dynamics as a unifying framework for interpreting functional changes across the adult lifespan.

neuroscience↗

bioRxiv2025

Small-world scale-free brain graphs from EEG

Developing individualized spatial models that capture the complex dynamics of multi-electrode EEG data is essential for accurately decoding global neural activity. A widely used approach is network modeling, where electrodes are represented as nodes. A key challenge lies in defining the network edges and weights, as precise connectivity estimation is critical for enhancing neural characterization and extracting discriminative features, such as those needed for task decoding. Traditional EEG-derived brain graphs often fail to capture biologically grounded organizational principles such as small-world structure and heavy-tailed (scale-free) connectivity patterns. To address this gap, we introduce a framework for inferring subject-specific EEG-based brain graphs that explicitly designed to exhibit small-world and scale-free properties. Our approach begins by computing phase-locking values (PLV) between EEG channel pairs to build a backbone graph, which is then refined into an individualized small-world and scale-free network. To reduce computational complexity while preserving subject-specific characteristics, we apply Kron reduction to the resulting graph. Using two public EEG datasets, we evaluate the proposed method on motor imagery (MI) decoding and brain fingerprinting tasks. Our approach improves MI classification accuracy by 4-7% compared to conventional PLV, small-world, and scale-free graph models, and enhances differential identifiability in fingerprinting by 8-20% across six canonical frequency bands. These gains were statistically significant in both applications. Moreover, integrating graph signal processing features derived from our constructed graphs with classical EEG features further boosts performance. Overall, our findings highlight the potential of the proposed graph construction framework to enhance EEG analysis. By jointly capturing local segregation, global integration, and hub-driven hierarchical organization, the method strengthens downstream decoding and identification tasks, with promising implications for a wide range of applications in cognitive neuroscience and brain-computer interface research.

neuroscience↗

bioRxiv2025

Hemispheric Asymmetry of Tau Pathology is Related to Asymmetric Amyloid Deposition in Alzheimer's Disease

The distribution of tau pathology in Alzheimers disease (AD) shows remarkable inter-individual heterogeneity, including hemispheric asymmetry. However, the factors driving this asymmetry remain poorly understood. We explored whether tau asymmetry is linked to i) reduced inter-hemispheric brain connectivity (potentially restricting tau spread), or ii) asymmetry in amyloid-beta (A{beta}) distribution (indicating greater hemisphere-specific vulnerability to AD pathology). 452 participants from the Swedish BioFINDER-2 cohort with evidence of both A{beta} pathology (CSF A{beta}42/40 or neocortical A{beta}-PET) and tau pathology (temporal tau-PET), were categorised as left asymmetric (n=102), symmetric (n=306), or right asymmetric (n=44) based on temporal lobe tau-PET uptake distribution. Edge-wise inter-hemispheric functional (RSfMRI; n=318) and structural connectivity (dMRI; n=352) patterns were examined but no differences in inter-hemispheric functional or structural connectivity were found between groups. However, a strong association was observed between tau and A{beta} laterality patterns based on PET uptake (n=233; {beta}=0.632, p<0.001), which was replicated in three independent cohorts (n=234; {beta}=0.535, p<0.001). In a longitudinal A{beta}-positive sample, baseline A{beta} asymmetry predicted the progression of tau laterality over time (n=289; {beta}=0.025, p=0.028). These findings suggest that tau asymmetry is not associated with a weaker inter-hemispheric connectivity but might reflect hemispheric differences in vulnerability to A{beta} pathology, underscoring the role of regional vulnerability in determining the distribution of AD pathology.

neuroscience↗

bioRxiv2025

Frequency-Specific Resting-State MEG Network Characteristics of Tinnitus Patients Revealed by Graph Learning

Tinnitus, the perception of sound without an external source, affects a significant portion of the population, yet its impact on brain communication diagram known as the functional connectome, remains limited. Traditional functional connectivity (FC) methods, such as Pearson correlation, phase lag index and coherence rely on pairwise comparisons and are therefore limited in providing a holistic encoding of FC. Here, we employ an alternative approach to estimate the entire connectivity structure by analyzing all time-courses simultaneously. This approach is robust even for short-duration recordings, facilitating faster functional connectome identification and real-time applications. Using resting-state MEG recordings from controls and individuals with tinnitus, we demonstrated that the learned connectomes outperform correlation-based connectomes in fingerprinting, that is, identifying an individual from test-retest acquisitions. Group-level analysis revealed distinct altered FC in tinnitus across multiple frequency bands, affecting the default mode, auditory, visual, and salience networks, suggesting a reorganization of these large-scale networks beyond auditory areas. Our study reveals that tinnitus presents highly individualized and heterogeneous whole-brain connectome profiles, highlighting the need to focus on individual variability rather than group-level differences to gain a more nuanced understanding of tinnitus. Personalized FC could enable patient-specific tinnitus models, optimizing treatment strategies for individualized care.

neuroscience↗