Atypical Dynamic Functional Network Connectivity State Engagement during Social-Emotional Processing in Schizophrenia and Autism

Studying time-resolved functional connectivity via communication theory: on the complementary nature of phase synchronization and sliding window Pearson correlation

Time-resolved functional connectivity (trFC) assesses the time-resolved coupling between brain regions using functional magnetic resonance imaging (fMRI) data. This study aims to compare two techniques used to estimate trFC, to investigate their similarities and differences when applied to fMRI data. These techniques are the sliding window Pearson correlation (SWPC), an amplitude-based approach, and phase synchronization (PS), a phase-based technique. To accomplish our objective, we used resting-state fMRI data from the Human Connectome Project (HCP) with 827 subjects (repetition time: 0.7s) and the Function Biomedical Informatics Research Network (fBIRN) with 311 subjects (repetition time: 2s), which included 151 schizophrenia patients and 160 controls. Our simulations reveal distinct strengths in two connectivity methods: SWPC captures high-magnitude, low-frequency connectivity, while PS detects low-magnitude, high-frequency connectivity. Stronger correlations between SWPC and PS align with pronounced fMRI oscillations. For fMRI data, higher correlations between SWPC and PS occur with matched frequencies and smaller SWPC window sizes (~30s), but larger windows (~88s) sacrifice clinically relevant information. Both methods identify a schizophrenia-associated brain network state but show different patterns: SWPC highlights low anti-correlations between visual, subcortical, auditory, and sensory-motor networks, while PS shows reduced positive synchronization among these networks. In sum, our findings underscore the complementary nature of SWPC and PS, elucidating their respective strengths and limitations without implying the superiority of one over the other.

Identifying Diagnostic Biomarkers for Autism Spectrum Disorder From Higher-order Interactions Using the PED Algorithm

In the field of neuroimaging, more studies of abnormalities in brain regions of the autism spectrum disorder (ASD) usually focused on two brain regions connected, and less on abnormalities of higher-order interactions of brain regions. To explore the complex relationships of brain regions, we used the partial entropy decomposition (PED) algorithm to capture higher-order interactions by computing the higher-order dependencies of all three brain regions (triads). We proposed a method for examining the effect of individual brain regions on triads based on the PED and surrogate tests. The key triads were discovered by analyzing the effects. Further, the hypergraph modularity maximization algorithm revealed the higher-order brain structures, of which the link between right thalamus and left thalamus in ASD was more loose compared with the typical control (TC). Redundant key triad (left cerebellum crus 1 and left precuneus and right inferior occipital gyrus) exhibited a discernible attenuation in interaction in ASD, while the synergistic key triad (right cerebellum crus 1 and left postcentral gyrus and left lingual gyrus) indicated a notable decline. The results of classification model further confirmed the potential of the key triads as diagnostic biomarkers.

Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain's intrinsic connectivity and capture dynamic changes in the brain. In this study, the hidden Markov model (HMM) and dynamic graph (DG) theory are used to study the spatial-temporal characteristics and dynamics of brain networks based on dynamic functional connectivity (DFC). By using HMM, we identified three typical brain states for ASD and healthy control (HC). Furthermore, we explored the correlation between HMM time-varying properties and clinical autism scale scores. Differences in brain topological characteristics and dynamics between ASD and HC were compared by DG analysis. The experimental results indicate that ASD is more inclined to enter a strongly connected HMM brain state, leading to the isolation of brain networks and alterations in the topological characteristics of brain networks, such as default mode network (DMN), ventral attention network (VAN), and visual network (VN). This work suggests that using different data-driven methods based on DFC to study brain network dynamics would have better information complementarity, which can provide a new direction for the extraction of neuro-biomarkers in the early diagnosis of ASD.

Dynamic functional connectivity reveals hyper-connected pattern and abnormal variability in freezing of gait of Parkinson's disease

BACKGROUND

Freezing of gait (FOG) is an intractable and paroxysmal gait disorder that seriously affects the quality of life of Parkinson's disease (PD) patients. Emerging studies have reported abnormal brain activity of distributed networks in FOG patients, whereas ignoring the intrinsic dynamic fluctuations of functional connectivity. The purpose of this study was to examine the dynamic functional network connectivity (dFNC) of PD-FOG.

METHODS

In total, 52 PD patients with FOG (PD-FOG), 73 without FOG (PD-NFOG) and 38 healthy controls (HCs) received resting state functional magnetic resonance imaging (rs-fMRI). Sliding window method, k-means clustering and graph theory analysis were employed to retrieve dynamic characteristics of PD-FOG. Partial correlation analysis was conducted to verify whether the dFNC was related to freezing gait severity.

RESULTS

Seven brain networks were identified and configured into seven states. Compared to PD-NFOG, significant spatial pattern was identified for state 2 in freezers, showing increased functional coupling between default mode network (DMN) and basal ganglia network (BG), as a concrete manifestation of increased precuneus-caudate coupling. The mean dwell time and fractional window of state 2 had a positive correlation with FOG severity. Furthermore, PD-FOG group exhibited lower variance in nodal efficiency of independent components (IC) 7 (left precuneus).

CONCLUSIONS

Our study suggested that aberrant coupling of precuneus-caudate and disrupted variability of precuneus efficiency might be associated to the neural mechanisms of FOG.

Intrinsic neural timescales in autism spectrum disorder and schizophrenia. A replication and direct comparison study

Intrinsic neural timescales (INT) reflect the duration for which brain areas store information. A posterior-anterior hierarchy of increasingly longer INT has been revealed in both typically developed individuals (TD), as well as persons diagnosed with autism spectrum disorder (ASD) and schizophrenia (SZ), though INT are, overall, shorter in both patient groups. In the present study, we aimed to replicate previously reported group differences by comparing INT of TD to ASD and SZ. We partially replicated the previously reported result, showing reduced INT in the left lateral occipital gyrus and the right post-central gyrus in SZ compared to TD. We also directly compared the INT of the two patient groups and found that these same two areas show significantly reduced INT in SZ compared to ASD. Previously reported correlations between INT and symptom severity were not replicated in the current project. Our findings serve to circumscribe the brain areas that can potentially play a determinant role in observed sensory peculiarities in ASD and SZ.

Assessment and correlates of autistic symptoms in Schizophrenia Spectrum Disorders measured with the PANSS Autism Severity Score: A systematic review

Schizophrenia Spectrum Disorders (SSD) and Autism Spectrum Disorders (ASD) are considered separate entities, but the two spectra share important similarities, and the study of these areas of overlap represents a field of growing scientific interest. The PANSS Autism Score (PAUSS) was recently developed specifically to assess autistic symptoms in people living with SSD reliably and quickly. The aims of the present systematic review were to provide a comprehensive assessment of the use of the PAUSS scale in available literature and to systematically analyze cognitive, functional and neurobiological correlates of autistic symptoms measured with this instrument in SSD. The systematic literature search included three electronic databases (PubMed, Scopus and PsycINFO) as well as a manual search in Google Scholar and in reference lists of included papers. Screening and extraction were conducted by at least two independent reviewers. Out of 213 identified records, 22 articles referring to 15 original studies were included in the systematic review. Studies were conducted in several different countries by independent groups, showing consistent scientific interest in the use of the scale; most works focused on cognitive and functional correlates of ASD symptoms, but some also considered neurobiological features. Results of included studies showed that autistic symptoms in people with SSD are consistently associated with worse cognitive performance, especially in the social cognition domain, and with worse psychosocial functioning. However, the presence of autistic symptoms appears to also have a protective role, particularly on functioning, in subjects with more severe psychotic symptoms. Further exploring the impact of autistic symptoms could be of significant scientific and clinical interest, allowing the development of tailored interventions to improve treatment for people living with SSDs.