Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2022.02.02.478844

A novel hidden Markov approach to studying dynamic functional connectivity states in human neuroimaging

Hussain, S.·Langley, J.·Seitz, A. R.·Peters, M. A. K.·Hu, X.
View original source ↗Export metadata ↓

Abstract

IntroductionHidden Markov models are a popular choice to extract and examine recurring patterns of activity or functional connectivity in neuroimaging data, both in terms of spatial patterns and their temporal progression. Although many diverse hidden Markov models have been applied to neuroimaging data, most have defined states based on activity levels (intensity-based states) rather than patterns of functional connectivity between brain areas (connectivity-based states), which is problematic if we want to understand connectivity dynamics: intensity-based states are unlikely to provide comprehensive information about dynamic connectivity patterns. MethodsWe addressed this problem by introducing a new hidden Markov model that defines states based on full functional connectivity profiles among brain regions. We empirically explored the behavior of this new model in comparison to existing approaches based on intensity-based or summed functional connectivity states using the HCP unrelated 100 functional magnetic resonance imaging "resting state" dataset. ResultsOur full functional connectivity model discovered connectivity states with more distinguishable (i.e., unique and separable from each other) patterns than previous approaches, and recovered simulated connectivity-based states more faithfully than the other models tested. DiscussionThus, if our goal is to extract and interpret connectivity states in neuroimaging data, our new model outperforms previous methods which miss crucial information about the evolution of functional connectivity in the brain. Impact statementHidden Markov models can be used to investigate brain states noninvasively. Previous models "recover" connectivity from intensity-based hidden states, or from connectivity summed across nodes. Here we introduce a novel connectivity-based hidden Markov model and show how it can reveal true connectivity hidden states under minimal assumptions.

Source connections

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hussain, S., Langley, J., Seitz, A. R., Peters, M. A. K., Hu, X.. 2022-02-04. A novel hidden Markov approach to studying dynamic functional connectivity states in human neuroimaging. https://doi.org/10.1101/2022.02.02.478844

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Rab10 and Arhgef7 interact to promote axon extension by linking vesicle supply and exocytosis

The formation of functional neural circuits begins with the extension of a single axon by immature neurons. Axon elongation requires an expansion of the plasma membrane that depends on the polarization of vesicle trafficking and exocytosis. It is not completely understood how the increased transport of vesicles into the growing axon is linked to their subsequent insertion into the plasma membrane. An analysis of early stages of neuronal differentiation indicates that an asymmetry in neurite length precedes axon specification and the accumulation of PPVs in the nascent axon. We show that increasing the transport of vesicles in unpolarized neurons is sufficient to promote the extension of additional axons. This effect indicates that an increased supply of vesicles stimulates their subsequent insertion into the membrane to allow neurite growth. The link between vesicle transport and exocytosis is mediated by the interaction of Rab10 with Arhgef7. Rab10 stimulates Arhgef7 activity on PPVs as a GEF for Cdc42b to promote axon extension through exocyst-dependent exocytosis, suggesting that their interaction links the increased supply of PPVs to their exocytosis.

neuroscience↗
bioRxiv2026

Subcellular Spatial Profiling Unveils Links Between Neuritic Plaques with Selective Tau Vulnerability in Alzheimer's Disease

Alzheimer's disease (AD) features widespread extracellular amyloid-{beta} plaques but selective intraneuronal tau accumulation in cortical layers II/III and V, while layer IV remains relatively resistant. Here, we combined subcellular spatial transcriptomics with co-registered A{beta} and pTau immunohistochemistry to determine why neurons exposed to comparable plaque-associated pathology differ in somatic pTau accumulation. Plaque-associated neuritic stress occurred similarly across layers, but layer IV neurons showed attenuated pTau accumulation and reduced somatic expression of a shared neurite-soma stress program involving DCTN1, CALM3, NORAD, and PIN1. Trajectory analysis within layers identified tau-vulnerable and resistant neuronal states implicating calcium signaling, retrograde transport, and lysosomal function. Notably, astrocytic APOE-neuronal LRP1 co-expression increased significantly along the pTau-vulnerable trajectory, and APOE {epsilon}4 was associated with stronger coupling of local plaque pathology to pTau-associated molecular programs. These findings reveal neuronal programs and neuron-glia interactions underlying selective tau vulnerability and resistance in AD, highlighting potential tau-targeting therapeutics.

neuroscience↗
bioRxiv2026

Widespread oscillatory suppression in autism persists in the absence of psychotropic medication.

Purpose: Investigate the neural oscillatory activity associated with autism spectrum disorder (ASD) using electroencephalographic (EEG) recordings in children and adolescents. Compare the corresponding oscillatory power between ASD patients taking psychotropic medication with a drug-naive group. Methods: ASD diagnosis was done using DSM-IV and CARS (Childhood Autism Rating Scale). EEG recordings employed 22 scalp electrodes (10/20 system). ASD vs. neurotypical groups were matched for sex and age (5 females; average age: ~10 years old). We analyzed the resting state (eyes open) EEG power spectrum during ~2-minute recordings. The multitaper FFT method was used to estimate spectral power for the delta, theta, alpha, beta and gamma frequency bands. We pooled together data from several electrodes to compute comprehensive power spectra and assess the oscillatory behavior of macro brain regions. Results: We observed widespread (~4Hz - 20Hz) oscillatory suppression comprising the theta, alpha and beta frequency bands. The most widespread and robust ASD-associated power suppression was in the high theta and alpha bands. Delta and gamma bands showed no significant power change for any of the brain regions studied. Results from a subgroup of patients (N = 9) who never used psychotropics confirmed these findings, indicating that they are not an artifact of medication. Conclusion: Suppression in theta, alpha and beta oscillations may offer insights into the neurophysiological underpinnings of autism and could be used as an accessible and comprehensive tool in ASD diagnosis, regardless of the patients being or not on medication.

neuroscience↗