The Neurogenetics of Functional Connectivity Alterations in Autism: Insights From Subtyping in 657 Individuals

Age-dependent cortical overconnectivity in Shank3 mice is reversed by anesthesia

Growing evidence points to brain network dysfunction as a central neurobiological basis for autism spectrum disorders (ASDs). As a result, studies on Functional Connectivity (FC) have become pivotal for understanding the large-scale network alterations associated with ASD. Despite ASD being a neurodevelopmental disorder, and FC being significantly influenced by the brain state, existing FC studies in mouse models predominantly focus on adult subjects under anesthesia. The differential impact of anesthesia and age on cortical functional networks in ASD subjects remains unexplored. To fill this gap, we conducted a longitudinal evaluation of FC across three brain states and three ages in the Shank3b mouse model of autism. We utilized wide-field calcium imaging to monitor cortical activity in Shank3b+/- and Shank3b+/+ mice from late development (P45) through adulthood (P90), and isoflurane anesthesia to manipulate the brain state. Our findings reveal that network hyperconnectivity, emerging from the barrel-field cortices during the juvenile stage, progressively expands to encompass the entire dorsal cortex in adult Shank3b+/- mice. Notably, the severity of FC imbalance is highly dependent on the brain state: global network alterations are more pronounced in the awake state and are strongly reduced under anesthesia. These results underscore the crucial role of anesthesia in detecting autism-related FC alterations and identify a significant network of early cortical dysfunction associated with autism. This network represents a potential target for non-invasive translational treatments.

Changes in degree centrality and its associated genes: A longitudinal study of patients with schizophrenia undergoing pharmacological treatment

BACKGROUND

The role of degree centrality (DC) in schizophrenia (SCZ), its trajectory following pharmacological treatment, and its potential as a prognostic biomarker and genetic mechanism remain unclear.

METHODS

We recruited 51 healthy controls (HC) and 56 patients with SCZ. Additionally, the SCZ patients underwent three months of antipsychotic medication treatment. We collected resting-state functional magnetic resonance imaging data, clinical variables, and conducted analyses using support vector machines, support vector regression, and gene expression correlation analysis.

RESULTS

Our study revealed that SCZ patients had generally reduced DC values in the cerebral cortex compared to HC at baseline, with increased DC values observed in the left occipital gyrus. After three months of treatment, SCZ patients exhibited a significant decrease in DC values in the left fusiform gyrus and an increase in the left inferior parietal gyrus. Variations in DC values in SCZ patients were associated with multiple genes, primarily enriched in molecular functions.

CONCLUSION

Changes in DC values in the right inferior occipital/fusiform gyrus and right calcarine/middle occipital gyrus may serve as neuroimaging markers to differentiate between HC and SCZ patients. Additionally, DC values in the left middle/postcentral gyrus could be used to predict treatment outcomes. Transcriptome-neuroimaging spatial correlation analysis provides valuable insights into the neurobiological mechanisms underlying SCZ pathology.

Individualized resting-state functional connectivity abnormalities unveil two major depressive disorder subtypes with contrasting abnormal patterns of abnormality

Modern neuroimaging research has recognized that major depressive disorder (MDD) is a connectome disorder, characterized by altered functional connectivity across large-scale brain networks. However, the clinical heterogeneity, likely stemming from diverse neurobiological disturbances, complicates findings from standard group comparison methods. This variability has driven the search for MDD subtypes using objective neuroimaging markers. In this study, we sought to identify potential MDD subtypes from subject-level abnormalities in functional connectivity, leveraging a large multi-site dataset of resting-state MRI from 1276 MDD patients and 1104 matched healthy controls. Subject-level extreme functional connections, determined by comparing against normative ranges derived from healthy controls using tolerance intervals, were used to identify biological subtypes of MDD. We identified a set of extreme functional connections that were predominantly between the visual network and the frontoparietal network, the default mode network and the ventral attention network, with the key regions in the anterior cingulate cortex, bilateral orbitofrontal cortex, and supramarginal gyrus. In MDD patients, these extreme functional connections were linked to age of onset and reward-related processes. Using these features, we identified two subtypes with distinct patterns of functional connectivity abnormalities compared to healthy controls (p < 0.05, Bonferroni correction). When considering all patients together, no significant differences were found. These subtypes significantly enhanced case-control discriminability and showed strong internal discriminability between subtypes. Furthermore, the subtypes were reproducible across varying parameters, study sites, and in untreated patients. Our findings provide new insights into the taxonomy and have potential implications for both diagnosis and treatment of MDD.

High neural noise in autism: A hypothesis currently at the nexus of explanatory power

Autism is a neurodevelopmental difference associated with specific autistic experiences and characteristics. Early models such as Weak Central Coherence and Enhanced Perceptual Functioning have tried to capture complex autistic behaviours in a single framework, however, these models lacked a neurobiological explanation. Conversely, current neurobiological theories of autism at the cellular and network levels suggest excitation/inhibition imbalances lead to high neural noise (or, a 'noisy brain') but lack a thorough explanation of how autistic behaviours occur. Critically, around 15 years ago, it was proposed that high neural noise in autism produced a stochastic resonance (SR) effect, a phenomenon where optimal amounts of noise improve signal quality. High neural noise can thus capture both the enhanced (through SR) and reduced performance observed in autistic individuals during certain tasks. Here, we provide a review and perspective that positions the "high neural noise" hypothesis in autism as best placed to provide research direction and impetus. Emphasis is placed on evidence for SR in autism, as this promising prediction has not yet been reviewed in the literature. Using this updated approach towards autism, we can explain a spectrum of autistic experiences all through a neurobiological lens. This approach can further aid in developing specific support or services for autism.

A Genetic Bridge Between Medicine and Neurodiversity for Autism

Autism represents a large spectrum of diverse individuals with varying underlying genetic architectures and needs. For some individuals, a single de novo or ultrarare genetic variant has a large effect on the intensity of specific dimensions of the phenotype, while, for others, a combination of thousands of variants commonly found in the general population are involved. The variants with large impact are found in up to 30% of autistic individuals presenting with intellectual disability, significant speech delay, motor delay, and/or seizures. The common variants are shared with those found in individuals with attention-deficit/hyperactivity disorder, major depressive disorders, greater educational attainment, and higher cognitive performance, suggesting overlapping genetic architectures. The genetic variants modulate the function of chromatin remodeling and synaptic proteins that influence the connectivity of neuronal circuits and, in interaction with the environment of each individual, the subsequent cognitive and personal trajectory of the child. Overall, this genetic heterogeneity mirrors the phenotypic diversity of autistic individuals and provides a helpful bridge between biomedical and neurodiversity perspectives. We propose that participative and multidisciplinary research should use this information to understand better the assessment, treatments, and accommodations that individuals with autism and families need.

Three autism subtypes based on single-subject gray matter network revealed by semi-supervised machine learning

Autism spectrum disorder (ASD) is a heterogeneous, early-onset neurodevelopmental condition characterized by persistent impairments in social interaction and communication. This study aims to delineate ASD subtypes based on individual gray matter brain networks and provide new insights from a graph theory perspective. In this study, we extracted and normalized single-subject gray matter networks and calculated each network's topological properties. The heterogeneity through discriminative analysis (HYDRA) method was utilized to subtype all patients based on network properties. Next, we explored the differences among ASD subtypes in terms of network properties and clinical measures. Our investigation identified three distinct ASD subtypes. In the case-control study, these subtypes exhibited significant differences, particularly in the precentral gyrus, lingual gyrus, and middle frontal gyrus. In the case analysis, significant differences in global and nodal properties were observed between any two subtypes. Clinically, subtype 1 showed lower VIQ and PIQ compared to subtype 3, but exhibited higher scores in ADOS-Communication and ADOS-Total compared to subtype 2. The results highlight the distinct brain network properties and behaviors among different subtypes of male patients with ASD, providing valuable insights into the neural mechanisms underlying ASD heterogeneity.

A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence

Emotion perception is essential to affective and cognitive development which involves distributed brain circuits. Emotion identification skills emerge in infancy and continue to develop throughout childhood and adolescence. Understanding the development of the brain's emotion circuitry may help us explain the emotional changes during adolescence. In this work, we aim to deepen our understanding of emotion-related functional connectivity (FC) from association to causation. We proposed a Bayesian incorporated linear non-Gaussian acyclic model (BiLiNGAM), which incorporated association model into the estimation pipeline. Simulation results indicated stable and accurate performance over various settings, especially when the sample size was small. We used fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) to validate the approach. It included 855 individuals aged 8-22 years who were divided into five different adolescent stages. Our network analysis revealed the development of emotion-related intra- and intermodular connectivity and pinpointed several emotion-related hubs. We further categorized the hubs into two types: in-hubs and out-hubs, as the center of receiving and distributing information, respectively. In addition, several unique developmental hub structures and group-specific patterns were discovered. Our findings help provide a directed FC template of brain network organization underlying emotion processing during adolescence.

Exploring the landscape of adult autism research in psychology: a bibliometric and network analysis

The global prevalence of autism spectrum disorder (ASD) is increasing. ASD manifests with persistent social communication and interaction challenges, limited interests, and repetitive behaviors. As the scientific literature on ASD in adults varies greatly, mapping the recent global research becomes valuable for enhancing comprehension of this subject. This study aims to map recent global scientific publications on ASD in adults. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, bibliometrics, and network analyses, we assessed 850 articles indexed in the Web of Science Core Collection between 2013 and 2022 assigned to the research area of psychology. Findings indicate an annual average growth of 11.69%. Key keywords include Emotion, Anxiety, and Depression, with Anxiety, Depression, and Mental Health as central nodes in the network. Rehabilitation, Behavioral Sciences, and Psychiatry frequently co-occur, and Psychology, Psychiatry, and 'Neurosciences and Neurology' are central nodes in the network of research areas. The United States of America and the United Kingdom lead in publications, with the United Kingdom being the most central country in the network. King's College London and the University of California are the main research organizations, with King's College London as the central node in the network. The American Psychiatric Association's DSM-5-TR was the most cited reference in the period. This comprehensive analysis contributes to understanding the landscape of ASD research in adults, providing insights for future research and fostering collaborations.

Cerebellar functional connectivity and its associated genes: A longitudinal study in drug-naive patients with obsessive-compulsive disorder

The role of cerebellar-cerebral functional connectivity (CC-FC) in obsessive-compulsive disorder (OCD), its trajectory post-pharmacotherapy, and its potential as a prognostic biomarker and genetic mechanism remain uncertain. To address these gaps, this study included 37 drug-naive OCD patients and 37 healthy controls (HCs). Participants underwent baseline functional magnetic resonance imaging (fMRI), followed by four weeks of paroxetine treatment for patients with OCD, and another fMRI scan post-treatment. We examined seed-based CC-FC differences between the patients and HCs, and pre- and post-treatment patients. Support vector regression (SVR) based on CC-FC was performed to predict treatment response. Correlation analysis explored associations between CC-FC and clinical features, as well as gene profiles. Compared to HCs, drug-naive OCD patients exhibited reduced CC-FC in executive, affective-limbic, and sensorimotor networks, with specific genetic profiles associated with altered CC-FC. Gene enrichment analyses highlighted the involvement of these genes in various biological processes, molecular functions, and pathways. Post-treatment, the patients showed partial clinical improvement and partial restoration of the previously decreased CC-FC. Abnormal CC-FC at baseline correlated negatively with compulsions severity and social functional impairment, while changes in CC-FC correlated with cognitive function changes post-treatment. CC-FC emerged as a potential predictor of symptom severity in patients following paroxetine treatment. This longitudinal resting-state fMRI study underscores the crucial role of CC-FC in the neuropsychological mechanisms of OCD and its pharmacological treatment. Transcriptome-neuroimaging spatial correlation analyses provide insight into the neurobiological mechanisms underlying OCD pathology. Furthermore, SVR analyses hold promise for advancing precision medicine approaches in treating patients with OCD.

Transcriptional patterns of amygdala functional connectivity in first-episode, drug-naïve major depressive disorder

Previous research has established associations between amygdala functional connectivity abnormalities and major depressive disorder (MDD). However, inconsistencies persist due to limited sample sizes and poorly elucidated transcriptional patterns. In this study, we aimed to address these gaps by analyzing a multicenter magnetic resonance imaging (MRI) dataset consisting of 210 first-episode, drug-naïve MDD patients and 363 age- and sex-matched healthy controls (HC). Using Pearson correlation analysis, we established individualized amygdala functional connectivity patterns based on the Automated Anatomical Labeling (AAL) atlas. Subsequently, machine learning techniques were employed to evaluate the diagnostic utility of amygdala functional connectivity for identifying MDD at the individual level. Additionally, we investigated the spatial correlation between MDD-related amygdala functional connectivity alterations and gene expression through Pearson correlation analysis. Our findings revealed reduced functional connectivity between the amygdala and specific brain regions, such as frontal, orbital, and temporal regions, in MDD patients compared to HC. Importantly, amygdala functional connectivity exhibited robust discriminatory capability for characterizing MDD at the individual level. Furthermore, we observed spatial correlations between MDD-related amygdala functional connectivity alterations and genes enriched for metal ion transport and modulation of chemical synaptic transmission. These results underscore the significance of amygdala functional connectivity alterations in MDD and suggest potential neurobiological mechanisms and markers for these alterations.

Open datasets and code for multi-scale relations on structure, function and neuro-genetics in the human brain

The human brain is an extremely complex network of structural and functional connections that operate at multiple spatial and temporal scales. Investigating the relationship between these multi-scale connections is critical to advancing our comprehension of brain function and disorders. However, accurately predicting structural connectivity from its functional counterpart remains a challenging pursuit. One of the major impediments is the lack of public repositories that integrate structural and functional networks at diverse resolutions, in conjunction with modular transcriptomic profiles, which are essential for comprehensive biological interpretation. To mitigate this limitation, our contribution encompasses the provision of an open-access dataset consisting of derivative matrices of functional and structural connectivity across multiple scales, accompanied by code that facilitates the investigation of their interrelations. We also provide additional resources focused on neuro-genetic associations of module-level network metrics, which present promising opportunities to further advance research in the field of network neuroscience, particularly concerning brain disorders.