Revealing the Selectivity of Neuroanatomical Alteration in Autism Spectrum Disorder via Reverse Inference

Revealing the Multivariate Associations Between Autistic Traits and Principal Functional Connectome

Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition characterized by a spectrum of behavioral and cognitive traits. As the characteristics of ASD are highly heterogeneous across individuals, a dimensional approach that overcomes the limitation of the categorical approach is preferred to reveal the symptomatology of ASD. Previous neuroimaging studies demonstrated strong links between large-scale brain networks and autism phenotypes. However, the existing studies have primarily focused on univariate association analysis, which limits our understanding of autism connectopathy. Using resting-state functional magnetic resonance imaging data from 309 participants (168 individuals with ASD and 141 typically developing controls) across a discovery dataset and two independent validation datasets, we identified multivariate associations between high-dimensional neuroimaging features and diverse phenotypic measures (20 or 7 measures). We generated low-dimensional representations of functional connectivity (i.e., gradients) and assessed their multivariate associations with autism-related phenotypes of social, behavioral, and cognitive problems using sparse canonical correlation analysis (SCCA). We selected three functional gradients that represented the cortical axes of the sensory-transmodal, motor-visual, and multiple demand-rests of the brain. The SCCA revealed multivariate associations between gradients and phenotypic measures, which were noted as linked dimensions. We identified three linked dimensions: the links between (1) the first gradient and social impairment, (2) the second and internalizing/externalizing problems, and (3) the third and metacognitive problems. Our findings were partially replicated in two independent validation datasets, indicating robustness. Multivariate association analysis linking high-dimensional neuroimaging and phenotypic features may offer promising avenues for establishing a dimensional approach to autism diagnosis.

Oxidative and Excitatory Neurotoxic Stresses in CRISPR/Cas9-Induced Kynurenine Aminotransferase Knockout Mice: A Novel Model for Despair-Based Depression and Post-Traumatic Stress Disorder

BACKGROUNDS

Memory and emotion are especially vulnerable to psychiatric disorders such as post-traumatic stress disorder (PTSD), which is linked to disruptions in serotonin (5-HT) metabolism. Over 90% of the 5-HT precursor tryptophan (Trp) is metabolized via the Trp-kynurenine (KYN) metabolic pathway, which generates a variety of bioactive molecules. Dysregulation of KYN metabolism, particularly low levels of kynurenic acid (KYNA), appears to be linked to neuropsychiatric disorders. The majority of KYNA is produced by the aadat (kat2) gene-encoded mitochondrial kynurenine aminotransferase (KAT) isotype 2. Little is known about the consequences of deleting the KYN enzyme gene.

METHODS

In CRISPR/Cas9-induced aadat knockout (kat2-/-) mice, we examined the effects on emotion, memory, motor function, Trp and its metabolite levels, enzyme activities in the plasma and urine of 8-week-old males compared to wild-type mice.

RESULTS

Transgenic mice showed more depressive-like behaviors in the forced swim test, but not in the tail suspension, anxiety, or memory tests. They also had fewer center field and corner entries, shorter walking distances, and fewer jumping counts in the open field test. Plasma metabolite levels are generally consistent with those of urine: antioxidant KYNs, 5-hydroxyindoleacetic acid, and indole-3-acetic acid levels were lower; enzyme activities in KATs, kynureninase, and monoamine oxidase/aldehyde dehydrogenase were lower, but kynurenine 3-monooxygenase was higher; and oxidative stress and excitotoxicity indices were higher. Transgenic mice displayed depression-like behavior in a learned helplessness model, emotional indifference, and motor deficits, coupled with a decrease in KYNA, a shift of Trp metabolism toward the KYN-3-hydroxykynurenine pathway, and a partial decrease in the gut microbial Trp-indole pathway metabolite.

CONCLUSIONS

This is the first evidence that deleting the aadat gene induces depression-like behaviors uniquely linked to experiences of despair, which appear to be associated with excitatory neurotoxic and oxidative stresses. This may lead to the development of a double-hit preclinical model in despair-based depression, a better understanding of these complex conditions, and more effective therapeutic strategies by elucidating the relationship between Trp metabolism and PTSD pathogenesis.

Meta-analytic connectivity perturbation analysis (MACPA): a new method for enhanced precision in fMRI connectivity analysis

Co-activation of distinct brain areas provides a valuable measure of functional interaction, or connectivity, between them. One well-validated way to investigate the co-activation patterns of a precise area is meta-analytic connectivity modeling (MACM), which performs a seed-based meta-analysis on task-based functional magnetic resonance imaging (task-fMRI) data. While MACM stands as a powerful automated tool for constructing robust models of whole-brain human functional connectivity, its inherent limitation lies in its inability to capture the distinct interrelationships among multiple brain regions. Consequently, the connectivity patterns highlighted through MACM capture the direct relationship of the seed region with third brain regions, but also a (less informative) residual relationship between the third regions themselves. As a consequence of this, this technique does not allow to evaluate to what extent the observed connectivity pattern is really associated with the fact that the seed region is activated, or it just reflects spurious co-activations unrelated with it. In order to overcome this methodological gap, we introduce a meta-analytic Bayesian-based method, called meta-analytic connectivity perturbation analysis (MACPA), that allows to identify the unique contribution of a seed region in shaping whole-brain connectivity. We validate our method by analyzing one of the most complex and dynamic structures of the human brain, the amygdala, indicating that MACPA may be especially useful for delineating region-wise co-activation networks.

Gray Matter Volume Correlates of Co-Occurring Depression in Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) involves neurodevelopmental syndromes with significant deficits in communication, motor behaviors, emotional and social comprehension. Often, individuals with ASD exhibit co-occurring depression characterized by a change in mood and diminished interest in previously enjoyable activities. Due to communicative challenges and a lack of appropriate assessments in this cohort, co-occurring depression can often go undiagnosed during routine clinical examinations and, thus, its management neglected. The literature on co-occurring depression in adults with ASD is limited. Therefore, understanding the neural basis of the co-occurring psychopathology of depression in ASD is crucial for identifying brain-based markers for its timely and effective management. Using structural MRI and phenotypic data from the Autism Brain Imaging Data Exchange (ABIDE II) repository, we examined the pattern of relationship regional grey matter volume (rGMV) has with co-occurring depression and autism severity within regions of a priori interest in adults with ASD (n = 44; age = 17-28 years). Further, we performed an exploratory analysis of the rGMV differences between ASD and matched typically developed (TD, n = 39; age = 18-31 years) samples. The severity of co-occurring depression correlated negatively with the rGMV of the right thalamus. Additionally, a significant interaction was evident between the severity of co-occurring depression and core ASD symptoms towards explaining the rGMV in the left cerebellum crus II. The results further the understanding of the neurobiological underpinnings of co-occurring depression in adults with ASD towards exploring neuroimaging-based biomarkers in the same cohort.

Disentangling the role of gray matter volume and concentration in autism spectrum disorder: A meta-analytic investigation of 25 years of voxel-based morphometry research

Despite over two decades of neuroimaging research, a unanimous definition of the pattern of structural variation associated with autism spectrum disorder (ASD) has yet to be found. One potential impeding issue could be the sometimes ambiguous use of measurements of variations in gray matter volume (GMV) or gray matter concentration (GMC). In fact, while both can be calculated using voxel-based morphometry analysis, these may reflect different underlying pathological mechanisms. We conducted a coordinate-based meta-analysis, keeping apart GMV and GMC studies of subjects with ASD. Results showed distinct and non-overlapping patterns for the two measures. GMV decreases were evident in the cerebellum, while GMC decreases were mainly found in the temporal and frontal regions. GMV increases were found in the parietal, temporal, and frontal brain regions, while GMC increases were observed in the anterior cingulate cortex and middle frontal gyrus. Age-stratified analyses suggested that such variations are dynamic across the ASD lifespan. The present findings emphasize the importance of considering GMV and GMC as distinct yet synergistic indices in autism research.

CBMAT: a MATLAB toolbox for data preparation and post hoc analyses in neuroimaging meta-analyses

Coordinate-based meta-analysis (CBMA) is a powerful technique in the field of human brain imaging research. Due to its intense usage, several procedures for data preparation and post hoc analyses have been proposed so far. However, these steps are often performed manually by the researcher, and are therefore potentially prone to error and time-consuming. We hence developed the Coordinate-Based Meta-Analyses Toolbox (CBMAT) to provide a suite of user-friendly and automated MATLAB® functions allowing one to perform all these procedures in a fast, reproducible and reliable way. Besides the description of the code, in the present paper we also provide an annotated example of using CBMAT on a dataset including 34 experiments. CBMAT can therefore substantially improve the way data are handled when performing CBMAs. The code can be downloaded from https://github.com/Jordi-Manuello/CBMAT.git .

Insights into the genetic architecture of cerebellar lobules derived from the UK Biobank

In this work we endeavor to further understand the genetic architecture of the cerebellum by examining the genetic underpinnings of the different cerebellar lob(ul)es, identifying their genetic relation to cortical and subcortical regions, as well as to psychiatric disorders, as well as traces of their evolutionary trajectories. We confirm the moderate heritability of cerebellar volumes, and reveal genetic clustering and variability across their different substructures, which warranted a detailed analysis using this higher structural resolution. We replicated known genetic correlations with several subcortical volumes, and report new cortico-cerebellar genetic correlations, including negative genetic correlations between anterior cerebellar lobules and cingulate, and positive ones between lateral Crus I and lobule VI with cortical measures in the fusiform region. Heritability partitioning for evolutionary annotations highlighted that the vermis of Crus II has depleted heritability in genomic regions of "archaic introgression deserts", but no enrichment/depletion of heritability in any other cerebellar regions. Taken together, these findings reveal novel insights into the genetic underpinnings of the different cerebellar lobules.

Atypical local brain connectivity in pediatric autism spectrum disorder? A coordinate-based meta-analysis of regional homogeneity studies

Despite decades of massive neuroimaging research, the comprehensive characterization of short-range functional connectivity in autism spectrum disorder (ASD) remains a major challenge for scientific advances and clinical translation. From the theoretical point of view, it has been suggested a generalized local over-connectivity that would characterize ASD. This stance is known as the general local over-connectivity theory. However, there is little empirical evidence supporting such hypothesis, especially with regard to pediatric individuals with ASD (age [Formula: see text] 18 years old). To explore this issue, we performed a coordinate-based meta-analysis of regional homogeneity studies to identify significant changes of local connectivity. Our analyses revealed local functional under-connectivity patterns in the bilateral posterior cingulate cortex and superior frontal gyrus (key components of the default mode network) and in the bilateral paracentral lobule (a part of the sensorimotor network). We also performed a functional association analysis of the identified areas, whose dysfunction is clinically consistent with the well-known deficits affecting individuals with ASD. Importantly, we did not find relevant clusters of local hyper-connectivity, which is contrary to the hypothesis that ASD may be characterized by generalized local over-connectivity. If confirmed, our result will provide a valuable insight into the understanding of the complex ASD pathophysiology.

Emerging Translational Research in Neurological and Psychiatric Diseases: From In Vitro to In Vivo Models

Revealing the underlying pathomechanisms of neurological and psychiatric disorders, searching for new biomarkers, and developing novel therapeutics all require translational research [...].