Beyond Massive Univariate Tests: Covariance Regression Reveals Complex Patterns of Functional Connectivity Related to Attention-Deficit/Hyperactivity Disorder, Age, Sex, and Response Control

Differences in Ex-Gaussian Parameters from Response Time Distributions Between Individuals with and Without Attention Deficit/Hyperactivity Disorder: A Meta-analysis

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most prevalent neurodevelopmental disorders in childhood and adolescence. Differences in reaction times (RT) in cognitive tasks have been consistently observed between ADHD and typical participants. Instead of estimating means and standard deviations, fitting non-symmetrical distributions like the ex-Gaussian, characterized by three parameters (µ, σ, and τ), account for the whole RT distributions. A meta-analysis is performed with all the available literature using ex-Gaussian distributions for comparisons between individuals with ADHD and controls. Results show that τ and σ are generally greater for ADHD samples, while µ tends to be larger for typical groups but only for younger ages. Differences in τ are also moderated by ADHD subtypes. τ and σ show, respectively, quadratic and linear relationships with inter-stimulus intervals from Continuous Performance Test and Go/No Go tasks. Furthermore, tasks and cognitive domains influence the three parameters. Interpretations of ex-Gaussian parameters and clinical implications of these findings are also discussed. Fitting ex-Gaussian distributions to RT data is a useful way to explore differences between individuals with ADHD and healthy controls.

ADHD Adults Show Lower Interindividual Similarity in Ex-Gaussian Reaction Time Vectors for Congruent Stimuli Compared to Control Peers

OBJECTIVE

Interindividual similarity refers to how similarly individuals respond when receiving the same stimulus or intervention. In this study, we aimed to examine interindividual similarity in adults with ADHD.

METHOD

We used the cosine similarity index of ex-Gaussian reaction time (RT) vectors of mu, sigma, and tau parameters during a Stroop task.

RESULTS

Our results demonstrate that the ADHD group exhibits a reduced interindividual similarity index in their ex-Gaussian RT vectors for congruent stimuli compared to the healthy control group. Importantly, we did not find significant differences in the interindividual similarity index to incongruent stimuli between both groups, thus suggesting that this reduced index was selective for congruent stimuli.

CONCLUSION

Our findings highlight that ADHD adults exhibit more significant interindividual differences in cognitive functioning when processing congruent stimuli than healthy controls. These results provide new insights into the selective mechanisms underlying ADHD and may contribute to developing new targeted interventions for this disorder.

ADHD-related sex differences in frontal lobe white matter microstructure and associations with response control under conditions of varying cognitive load and motivational contingencies

Children with attention-deficit/hyperactivity disorder (ADHD) demonstrate reduced response inhibition, increased response time variability, and atypical frontal lobe white matter microstructure with emerging evidence of sex differences. This study aims to examine whether frontal lobe white matter microstructure is differentially impacted in ADHD by sex and whether this relates to Go/No-Go (GNG) task performance. Diffusion tensor imaging (DTI) from 187 children (8-12 years), including ADHD (n = 94) and typically developing controls (TD; n = 93). Participants completed three GNG tasks with varying cognitive demands and incentives (standard, cognitive, and motivational). Fractional anisotropy (FA) was examined as an index of white matter microstructure within bilateral frontal lobe regions of interest. Children with ADHD showed reduced FA in primary motor (M1) and supplementary motor area (SMA) regardless of sex. Sex-based dissociation for the effect of diagnosis was observed in medial orbitofrontal cortex (mOFC), with higher FA in girls with ADHD and lower FA in boys with ADHD. Both diagnosis and sex contributed to performance on measures of response inhibition and reaction time (RT) variability, with all children with ADHD demonstrating poorer performance on all GNG tasks, but boys with ADHD demonstrating more impulsivity on standard and motivational behavioral paradigms compared to girls with ADHD. Analyses revealed associations between reduced FA in M1, SMA, and mOFC and increased response inhibition and RT variability with some sex-based differences. These findings provide novel insights regarding the brain basis of ADHD and associated impairments in response inhibition and RT variability, and contribute to our understanding of sexual dimorphic behavioral outcomes.

Regression models for partially localized fMRI connectivity analyses

Background

Brain functional connectivity analysis of resting-state functional magnetic resonance imaging (fMRI) data is typically performed in a standardized template space assuming consistency of connections across subjects. Analysis methods can come in the form of one-edge-at-a-time analyses or dimension reduction/decomposition methods. Common to these approaches is an assumption that brain regions are functionally aligned across subjects; however, it is known that this functional alignment assumption is often violated.

Methods

In this paper, we use subject-level regression models to explain intra-subject variability in connectivity. Covariates can include factors such as geographic distance between two pairs of brain regions, whether the two regions are symmetrically opposite (homotopic), and whether the two regions are members of the same functional network. Additionally, a covariate for each brain region can be included, to account for the possibility that some regions have consistently higher or lower connectivity. This style of analysis allows us to characterize the fraction of variation explained by each type of covariate. Additionally, comparisons across subjects can then be made using the fitted connectivity regression models, offering a more parsimonious alternative to edge-at-a-time approaches.

Results

We apply our approach to Human Connectome Project data on 268 regions of interest (ROIs), grouped into eight functional networks. We find that a high proportion of variation is explained by region covariates and network membership covariates, while geographic distance and homotopy have high relative importance after adjusting for the number of predictors. We also find that the degree of data repeatability using our connectivity regression model-which uses only partial location information about pairs of ROI's-is comparably as high as the repeatability obtained using full location information.

Discussion

While our analysis uses data that have been transformed into a common template-space, we also envision the method being useful in multi-atlas registration settings, where subject data remains in its own geometry and templates are warped instead. These results suggest the tantalizing possibility that fMRI connectivity analysis can be performed in subject-space, using less aggressive registration, such as simple affine transformations, multi-atlas subject-space registration, or perhaps even no registration whatsoever.

Regression models for partially localized fMRI connectivity analyses

Brain functional connectivity analysis of resting-state functional magnetic resonance imaging (fMRI) data is typically performed in a standardized template space assuming consistency of connections across subjects. This can come in the form of one-edge-at-a-time analyses or dimension reduction/decomposition methods. Common to these approaches is the assumption of complete localization (or spatial alignment) of brain regions across subjects. Alternative approaches completely eschew localization assumptions by treating connections as statistically exchangeable (for example, using the density of connectivity between nodes). Yet other approaches, such as hyperalignment, attempt to align subjects on function as well as structure, thereby achieving a different sort of template-based localization. In this paper, we propose the use of simple regression models to characterize connectivity. To that end, we build regression models on subject-level Fisher transformed regional connection matrices using geographic distance, homotopic distance, network labels, and region indicators as covariates to explain variation in connections. While we perform our analysis in template-space in this paper, we envision the method being useful in multi-atlas registration settings, where subject data remains in its own geometry and templates are warped instead. A byproduct of this style of analysis is the ability to characterize the fraction of variation in subject-level connections explained by each type of covariate. Using Human Connectome Project data, we found that network labels and regional characteristics contribute far more than geographic or homotopic relationships (considered non-parametrically). In addition, visual regions had the highest explanatory power (i.e., largest regression coefficients). We also considered subject repeatability and found that the degree of repeatability seen in fully localized models is largely recovered using our proposed subject-level regression models. Further, even fully exchangeable models retain a sizeable amount of repeatability information, despite discarding all localization information. These results suggest the tantalizing possibility that fMRI connectivity analysis can be performed in subject-space, using less aggressive registration, such as simple affine transformations, multi-atlas subject-space registration, or perhaps even no registration whatsoever.