The Factorial Structure of the Tower Test From the Delis-Kaplan Executive Function System: A Confirmatory Factor Analysis Study.

The Tower of London (TOL) is a well-known, widely used task that captures executive function abilities. We examined the factorial structure and discriminant validity of three measures extracted from the Delis-Kaplan Executive Function System (D-KEFS) version of the TOL, namely the D-KEFS Tower Test, in 270 individuals from a publicly available release of the Enhanced Nathan Kline Institute-Rockland sample. Confirmatory factor analyses revealed a multidimensional three-factor solution of the measures extracted from the D-KEFS Tower Test; first-move-time, excess moves, and rule violations. This model was better than the unidimensional model, the two-factor model, the bifactor model and the model that included the total achievement scores. These results support the discriminant validity of the three latent factors, over their distinct relations to the total achievement score. The best fitting model was gender-invariant and age-variant. Overall, the multidimensionality of the measures extracted from the D-KEFS Tower Test reflects the need to use multiple metrics from this version of TOL to capture executive functions instead of a single score.

mTOR-related synaptic pathology causes autism spectrum disorder-associated functional hyperconnectivity.

Postmortem studies have revealed increased density of excitatory synapses in the brains of individuals with autism spectrum disorder (ASD), with a putative link to aberrant mTOR-dependent synaptic pruning. ASD is also characterized by atypical macroscale functional connectivity as measured with resting-state fMRI (rsfMRI). These observations raise the question of whether excess of synapses causes aberrant functional connectivity in ASD. Using rsfMRI, electrophysiology and in silico modelling in Tsc2 haploinsufficient mice, we show that mTOR-dependent increased spine density is associated with ASD -like stereotypies and cortico-striatal hyperconnectivity. These deficits are completely rescued by pharmacological inhibition of mTOR. Notably, we further demonstrate that children with idiopathic ASD exhibit analogous cortical-striatal hyperconnectivity, and document that this connectivity fingerprint is enriched for ASD-dysregulated genes interacting with mTOR or Tsc2. Finally, we show that the identified transcriptomic signature is predominantly expressed in a subset of children with autism, thereby defining a segregable autism subtype. Our findings causally link mTOR-related synaptic pathology to large-scale network aberrations, revealing a unifying multi-scale framework that mechanistically reconciles developmental synaptopathy and functional hyperconnectivity in autism.

Imbalanced social-communicative and restricted repetitive behavior subtypes of autism spectrum disorder exhibit different neural circuitry.

Social-communication (SC) and restricted repetitive behaviors (RRB) are autism diagnostic symptom domains. SC and RRB severity can markedly differ within and between individuals and may be underpinned by different neural circuitry and genetic mechanisms. Modeling SC-RRB balance could help identify how neural circuitry and genetic mechanisms map onto such phenotypic heterogeneity. Here, we developed a phenotypic stratification model that makes highly accurate (97-99%) out-of-sample SC = RRB, SC > RRB, and RRB > SC subtype predictions. Applying this model to resting state fMRI data from the EU-AIMS LEAP dataset (n = 509), we find that while the phenotypic subtypes share many commonalities in terms of intrinsic functional connectivity, they also show replicable differences within some networks compared to a typically-developing group (TD). Specifically, the somatomotor network is hypoconnected with perisylvian circuitry in SC > RRB and visual association circuitry in SC = RRB. The SC = RRB subtype show hyperconnectivity between medial motor and anterior salience circuitry. Genes that are highly expressed within these networks show a differential enrichment pattern with known autism-associated genes, indicating that such circuits are affected by differing autism-associated genomic mechanisms. These results suggest that SC-RRB imbalance subtypes share many commonalities, but also express subtle differences in functional neural circuitry and the genomic underpinnings behind such circuitry.

A normative modelling approach reveals age-atypical cortical thickness in a subgroup of males with autism spectrum disorder.

Understanding heterogeneity is an important goal on the path to precision medicine for autism spectrum disorders (ASD). We examined how cortical thickness (CT) in ASD can be parameterized as an individualized metric of atypicality relative to typically-developing (TD) age-related norms. Across a large sample (n = 870 per group) and wide age range (5-40 years), we applied normative modelling resulting in individualized whole-brain maps of age-related CT atypicality in ASD and isolating a small subgroup with highly age-atypical CT. Age-normed CT scores also highlights on-average differentiation, and associations with behavioural symptomatology that is separate from insights gleaned from traditional case-control approaches. This work showcases an individualized approach for understanding ASD heterogeneity that could potentially further prioritize work on a subset of individuals with cortical pathophysiology represented in age-related CT atypicality. Only a small subset of ASD individuals are actually highly atypical relative to age-norms. driving small on-average case-control differences.

Intrinsic excitation-inhibition imbalance affects medial prefrontal cortex differently in autistic men versus women.

Excitation-inhibition (E:I) imbalance is theorized as an important pathophysiological mechanism in autism. Autism affects males more frequently than females and sex-related mechanisms (e.g., X-linked genes, androgen hormones) can influence E:I balance. This suggests that E:I imbalance may affect autism differently in males versus females. With a combination of in-silico modeling and in-vivo chemogenetic manipulations in mice, we first show that a time-series metric estimated from fMRI BOLD signal, the Hurst exponent (H), can be an index for underlying change in the synaptic E:I ratio. In autism we find that H is reduced, indicating increased excitation, in the medial prefrontal cortex (MPFC) of autistic males but not females. Increasingly intact MPFC H is also associated with heightened ability to behaviorally camouflage social-communicative difficulties, but only in autistic females. This work suggests that H in BOLD can index synaptic E:I ratio and that E:I imbalance affects autistic males and females differently.

Autism is a condition that is usually diagnosed early in life that affects how a person communicates and socializes, and is often characterized by repetitive behaviors. One key theory of autism is that it reflects an imbalance in levels of excitation and inhibition in the brain. Excitatory signals are those that make other brain cells more likely to become active; inhibitory signals have the opposite effect. In non-autistic individuals, inhibitory activity outweighs excitatory activity. In people with autism, by contrast, an increase in excitatory activity is believed to produce an imbalance in excitation and inhibition. Most of the evidence to support this excitation-inhibition imbalance theory has come from studies of rare mutations that cause autism. Many of these mutations occur on the sex chromosomes or are influenced by androgen hormones (hormones that usually play a role on typically male traits). However, most people with autism do not possess these particular mutations. It was thus unclear whether the theory could apply to everyone with autism or, for example, whether it may better apply to specific groups of individuals based on their sex or gender. This is especially important given that about four times as many men and boys compared to women and girls are diagnosed with autism. Trakoshis, Martínez-Cañada et al. have now found a way to ask whether any imbalance in excitation and inhibition in the brain occurs differently in men and women. Using computer modeling, they identified a signal in brain scans that corresponds to an imbalance of excitation and inhibition. After showing that the technique works to identify real increases in excitation in the brain scans of mice, Trakoshis, Martínez-Cañada et al. looked for this signal, or biomarker, in brain scans of people with and without autism. All the people in the study identified with the gender that matched the sex they were assigned at birth. The results revealed differences between the men and women with autism. Men with autism showed an imbalance in excitation and inhibition in specific 'social brain' regions including the medial prefrontal cortex, but women with autism did not. Notably, many of these brain regions are strongly affected by androgen hormones. Previous studies have found that women with autism are sometimes better at hiding or 'camouflaging' their difficulties when socializing or communicating than men with autism. Trakoshis, Martínez-Cañada et al. showed that the better a woman was at camouflaging her autism, the more her brain activity in this region resembled that of non-autistic women. Excitation-inhibition imbalance may thus affect specific brain regions involved in socializing and communication more in men who have autism than in women with the condition. Balanced excitation and inhibition in these brain areas may enable some women with autism to camouflage their difficulties socializing or communicating. Being able to detect imbalances in activity using standard brain imaging could be useful for clinical trials. Future studies could use this biomarker to monitor responses to drug treatments that aim to adjust the balance between excitation and inhibition.