A spectral sampling algorithm in dynamic causal modelling for resting-state fMRI

Resting-state functional magnetic resonance imaging (rs-fMRI) is widely utilized to study the directed influences among neural populations which were called effective connectivity (EC), and the spectral dynamic causal modelling (spDCM) is the state-of-the-art framework to identify them. However, spDCM used variational Laplace to approximate the posterior density by maximizing the free energy, which might underestimate the variability of posterior density and get locked to the local minima. A spectral sampling algorithm (SS-DCM) was proposed to improve the estimation accuracy of the dynamic causal model for rs-fMRI. In SS-DCM, a naïve Bayesian model was constructed in the spectral domain, which described the probabilistic relationship between the sampled parameters and cross spectra of the observed blood oxygen level-dependent signals, and the parameters were sampled using randomly walked Markov Chain Monto Carlo scheme. The root mean square errors of the estimation of EC and hemodynamic parameters of SS-DCM, spDCM and generalized filter scheme were compared in the synthetic data, and SS-DCM was the most accurate and stable. A comparative evaluation using empirical rs-fMRI data was performed to study the EC pattern of the default mode network and compare the accuracy of classification between typically developed subjects and inattentive attention deficit and hyperactivity disorder patients. The results showed high consistency of positivity and negativity of EC between spDCM and SS-DCM, and SS-DCM also provided higher classification accuracy. It is highlighted that SS-DCM improves the accuracy of the estimation of EC and provides accurate information of discrepancies between diseased and healthy subjects using rs-fMRI.

Changes in both top-down and bottom-up effective connectivity drive visual hallucinations in Parkinson's disease

Visual hallucinations are common in Parkinson's disease and are associated with a poorer quality of life and a higher risk of dementia. An important and influential model that is widely accepted as an explanation for the mechanism of visual hallucinations in Parkinson's disease and other Lewy body diseases is that these arise due to aberrant hierarchical processing, with impaired bottom-up integration of sensory information and overweighting of top-down perceptual priors within the visual system. This hypothesis has been driven by behavioural data and supported indirectly by observations derived from regional activation and correlational measures using neuroimaging. However, until now, there was no evidence from neuroimaging for differences in causal influences between brain regions measured in patients with Parkinson's hallucinations. This is in part because previous resting-state studies focused on functional connectivity, which is inherently undirected in nature and cannot test hypotheses about the directionality of connectivity. Spectral dynamic causal modelling is a Bayesian framework that allows the inference of effective connectivity-defined as the directed (causal) influence that one region exerts on another region-from resting-state functional MRI data. In the current study, we utilize spectral dynamic causal modelling to estimate effective connectivity within the resting-state visual network in our cohort of 15 Parkinson's disease visual hallucinators and 75 Parkinson's disease non-visual hallucinators. We find that visual hallucinators display decreased bottom-up effective connectivity from the lateral geniculate nucleus to primary visual cortex and increased top-down effective connectivity from the left prefrontal cortex to primary visual cortex and the medial thalamus, as compared with non-visual hallucinators. Importantly, we find that the pattern of effective connectivity is predictive of the presence of visual hallucinations and associated with their severity within the hallucinating group. This is the first study to provide evidence, using resting-state effective connectivity, to support a model of aberrant hierarchical predictive processing as the mechanism for visual hallucinations in Parkinson's disease.

Structural and functional underpinnings of precentral abnormalities in amyotrophic lateral sclerosis

BACKGROUND AND PURPOSE

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder characterized by the loss of both upper and lower motor neurons. Studies using various magnetic resonance imaging (MRI) analytical approaches have consistently identified significant precentral abnormalities in ALS, whereas their structural and functional underpinnings remain poorly understood.

METHODS

Using cortical thickness, fractional anisotropy (FA), and effective connectivity, we performed a multimodal MRI study to examine the structural and functional alterations associated with precentral abnormalities in patients with ALS (n = 60) compared with healthy controls (n = 60).

RESULTS

Cortical thickness analysis revealed significant cortical thinning in the right precentral gyrus (PCG), superior frontal gyrus, and superior temporal gyrus in patients with ALS. Tractwise white matter microstructure analyses revealed decreased FA in the tracts connected to the PCG cluster in patients with ALS involving the right corticospinal tract and the middle posterior body of the corpus callosum. Additionally, the cortical thickness of the PCG cluster was found to be positively correlated with FA of the tracts connected to the PCG cluster, suggesting that these two structural features are tightly coupled. Using spectral dynamic causal modelling, effective connectivity analysis among the three regions with cortical thinning revealed decreased self-inhibitory influence in the PCG cluster in patients with ALS, which might be an endophenotypic manifestation of an imbalance in inhibitory and excitatory neurotransmitters in this region.

CONCLUSIONS

The present data shed new light on the structural and functional underpinnings of precentral abnormalities in ALS.