Template independent component analysis with spatial priors for accurate subject-level brain network estimation and inference.

Independent component analysis is commonly applied to functional magnetic resonance imaging (fMRI) data to extract independent components (ICs) representing functional brain networks. While ICA produces reliable group-level estimates, single-subject ICA often produces noisy results. Template ICA is a hierarchical ICA model using empirical population priors to produce more reliable subject-level estimates. However, this and other hierarchical ICA models assume unrealistically that subject effects are spatially independent. Here, we propose spatial template ICA (stICA), which incorporates spatial priors into the template ICA framework for greater estimation efficiency. Additionally, the joint posterior distribution can be used to identify brain regions engaged in each network using an excursions set approach. By leveraging spatial dependencies and avoiding massive multiple comparisons, stICA has high power to detect true effects. We derive an efficient expectation-maximization algorithm to obtain maximum likelihood estimates of the model parameters and posterior moments of the latent fields. Based on analysis of simulated data and fMRI data from the Human Connectome Project, we find that stICA produces estimates that are more accurate and reliable than benchmark approaches, and identifies larger and more reliable areas of engagement. The algorithm is computationally tractable, achieving convergence within 12 hours for whole-cortex fMRI analysis.

Rethinking recovery in adolescent concussions: Network-level functional connectivity alterations associated with motor deficits.

Adolescents who are clinically recovered from concussion continue to show subtle motor impairment on neurophysiological and behavioral measures. However, there is limited information on brain-behavior relationships of persistent motor impairment following clinical recovery from concussion. We examined the relationship between subtle motor performance and functional connectivity of the brain in adolescents with a history of concussion, status post-symptom resolution, and subjective return to baseline. Participants included 27 adolescents who were clinically recovered from concussion and 29 never-concussed, typically developing controls (10-17 years); all participants were examined using the Physical and Neurologic Examination of Subtle Signs (PANESS). Functional connectivity between the default mode network (DMN) or dorsal attention network (DAN) and regions of interest within the motor network was assessed using resting-state functional magnetic resonance imaging (rsfMRI). Compared to controls, adolescents clinically recovered from concussion showed greater subtle motor deficits as evaluated by the PANESS and increased connectivity between the DMN and left lateral premotor cortex. DMN to left lateral premotor cortex connectivity was significantly correlated with the total PANESS score, with more atypical connectivity associated with more motor abnormalities. This suggests that altered functional connectivity of the brain may underlie subtle motor deficits in adolescents who have clinically recovered from concussion. More investigation is required to understand the persistence and longer-term clinical relevance of altered functional connectivity and associated subtle motor deficits to inform whether functional connectivity may serve as an important biomarker related to longer-term outcomes after clinical recovery from concussion.

Less is more: balancing noise reduction and data retention in fMRI with data-driven scrubbing.

Functional MRI (fMRI) data may be contaminated by artifacts arising from a myriad of sources, including subject head motion, respiration, heartbeat, scanner drift, and thermal noise. These artifacts cause deviations from common distributional assumptions, introduce spatial and temporal outliers, and reduce the signal-to-noise ratio of the data-all of which can have negative consequences for the accuracy and power of downstream statistical analysis. Scrubbing is a technique for excluding fMRI volumes thought to be contaminated by artifacts and generally comes in two flavors. Motion scrubbing based on subject head motion-derived measures is popular but suffers from a number of drawbacks, among them the need to choose a threshold, a lack of generalizability to multiband acquisitions, and high rates of censoring of individual volumes and entire subjects. Alternatively, data-driven scrubbing methods like DVARS are based on observed noise in the processed fMRI timeseries and may avoid some of these issues. Here we propose "projection scrubbing", a novel data-driven scrubbing method based on a statistical outlier detection framework and strategic dimension reduction, including independent component analysis (ICA), to isolate artifactual variation. We undertake a comprehensive comparison of motion scrubbing with data-driven projection scrubbing and DVARS. We argue that an appropriate metric for the success of scrubbing is maximal data retention subject to reasonable performance on typical benchmarks such as the validity, reliability, and identifiability of functional connectivity. We find that stringent motion scrubbing yields worsened validity, worsened reliability, and produced small improvements to fingerprinting. Meanwhile, data-driven scrubbing methods tend to yield greater improvements to fingerprinting while not generally worsening validity or reliability. Importantly, however, data-driven scrubbing excludes a fraction of the number of volumes or entire sessions compared to motion scrubbing. The ability of data-driven fMRI scrubbing to improve data retention without negatively impacting the quality of downstream analysis has major implications for sample sizes in population neuroscience research.

A Prospective Evaluation of Infant Cerebellar-Cerebral Functional Connectivity in Relation to Behavioral Development in Autism Spectrum Disorder.

Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder diagnosed based on social impairment, restricted interests, and repetitive behaviors. Contemporary theories posit that cerebellar pathology contributes causally to ASD by disrupting error-based learning (EBL) during infancy. The present study represents the first test of this theory in a prospective infant sample, with potential implications for ASD detection. Methods: Data from the Infant Brain Imaging Study (n = 94, 68 male) were used to examine 6-month cerebellar functional connectivity magnetic resonance imaging in relation to later (12/24-month) ASD-associated behaviors and outcomes. Hypothesis-driven univariate analyses and machine learning-based predictive tests examined cerebellar-frontoparietal network (FPN; subserves error signaling in support of EBL) and cerebellar-default mode network (DMN; broadly implicated in ASD) connections. Cerebellar-FPN functional connectivity was used as a proxy for EBL, and cerebellar-DMN functional connectivity provided a comparative foil. Data-driven functional connectivity magnetic resonance imaging enrichment examined brain-wide behavioral associations, with post hoc tests of cerebellar connections. Results: Cerebellar-FPN and cerebellar-DMN connections did not demonstrate associations with ASD. Functional connectivity magnetic resonance imaging enrichment identified 6-month correlates of later ASD-associated behaviors in networks of a priori interest (FPN, DMN), as well as in cingulo-opercular (also implicated in error signaling) and medial visual networks. Post hoc tests did not suggest a role for cerebellar connections. Conclusions: We failed to identify cerebellar functional connectivity-based contributions to ASD. However, we observed prospective correlates of ASD-associated behaviors in networks that support EBL. Future studies may replicate and extend network-level positive results, and tests of the cerebellum may investigate brain-behavior associations at different developmental stages and/or using different neuroimaging modalities.

Group linear non-Gaussian component analysis with applications to neuroimaging.

Independent component analysis (ICA) is an unsupervised learning method popular in functional magnetic resonance imaging (fMRI). Group ICA has been used to search for biomarkers in neurological disorders including autism spectrum disorder and dementia. However, current methods use a principal component analysis (PCA) step that may remove low-variance features. Linear non-Gaussian component analysis (LNGCA) enables simultaneous dimension reduction and feature estimation including low-variance features in single-subject fMRI. A group LNGCA model is proposed to extract group components shared by more than one subject. Unlike group ICA methods, this novel approach also estimates individual (subject-specific) components orthogonal to the group components. To determine the total number of components in each subject, a parametric resampling test is proposed that samples spatially correlated Gaussian noise to match the spatial dependence observed in data. In simulations, estimated group components achieve higher accuracy compared to group ICA. The method is applied to a resting-state fMRI study on autism spectrum disorder in 342 children (252 typically developing, 90 with autism), where the group signals include resting-state networks. The discovered group components appear to exhibit different levels of temporal engagement in autism versus typically developing children, as revealed using group LNGCA. This novel approach to matrix decomposition is a promising direction for feature detection in neuroimaging.

Psilocybin induces spatially constrained alterations in thalamic functional organizaton and connectivity.

BACKGROUND: Classic psychedelics, such as psilocybin and LSD, and other serotonin 2A receptor (5-HT2AR) agonists evoke acute alterations in perception and cognition. Altered thalamocortical connectivity has been hypothesized to underlie these effects, which is supported by some functional MRI (fMRI) studies. These studies have treated the thalamus as a unitary structure, despite known differential 5-HT2AR expression and functional specificity of different intrathalamic nuclei. Independent Component Analysis (ICA) has been previously used to identify reliable group-level functional subdivisions of the thalamus from resting-state fMRI (rsfMRI) data. We build on these efforts with a novel data-maximizing ICA-based approach to examine psilocybin-induced changes in intrathalamic functional organization and thalamocortical connectivity in individual participants. METHODS: Baseline rsfMRI data (n=38) from healthy individuals with a long-term meditation practice was utilized to generate a statistical template of thalamic functional subdivisions. This template was then applied in a novel ICA-based analysis of the acute effects of psilocybin on intra- and extra-thalamic functional organization and connectivity in follow-up scans from a subset of the same individuals (n=18). We examined correlations with subjective reports of drug effect and compared with a previously reported analytic approach (treating the thalamus as a single functional unit). RESULTS: Several intrathalamic components showed significant psilocybin-induced alterations in spatial organization, with effects of psilocybin largely localized to the mediodorsal and pulvinar nuclei. The magnitude of changes in individual participants correlated with reported subjective effects. These components demonstrated predominant decreases in thalamocortical connectivity, largely with visual and default mode networks. Analysis in which the thalamus is treated as a singular unitary structure showed an overall numerical increase in thalamocortical connectivity, consistent with previous literature using this approach, but this increase did not reach statistical significance. CONCLUSIONS: We utilized a novel analytic approach to discover psilocybin-induced changes in intra- and extra-thalamic functional organization and connectivity of intrathalamic nuclei and cortical networks known to express the 5-HT2AR. These changes were not observed using whole-thalamus analyses, suggesting that psilocybin may cause widespread but modest increases in thalamocortical connectivity that are offset by strong focal decreases in functionally relevant intrathalamic nuclei.

Beyond massive univariate tests: Covariance regression reveals complex patterns of functional connectivity related to attention-deficit/hyperactivity disorder, age, sex, and response control.

Background: Studies of brain functional connectivity (FC) typically involve massive univariate tests, performing statistical analysis on each individual connection. In this study we apply a novel whole-matrix regression approach referred to as Covariate Assisted Principal (CAP) regression to identify resting-state FC brain networks associated with attention-deficit/hyperactivity disorder (ADHD) and response control. Methods: Participants included 8-12 year-old children with ADHD (n=115, 29 girls) and typically developing controls (n=102, 35 girls) who completed a resting-state fMRI scan and a go/no-go task (GNG). We modeled three sets of covariates to identify resting-state networks associated with an ADHD diagnosis, sex, and response inhibition (commission errors) and variability (ex-Gaussian parameter tau). Results: The first network includes FC between striatal-cognitive control (CC) network subregions and thalamic-default mode network (DMN) subregions and is positively related to age. The second consists of FC between CC-visual-somatomotor regions and between CC-DMN subregions and is positively associated with response variability in boys with ADHD. The third consists of FC within the DMN and between DMN-CC-visual regions and differs between boys with and without ADHD. The fourth consists of FC between visual-somatomotor regions and between visual-DMN regions and differs between girls and boys with ADHD and is associated with response inhibition and variability in boys with ADHD. Unique networks were also identified in each of the three models suggesting some specificity to the covariates of interest. Conclusions: These findings demonstrate the utility of our novel covariance regression approach to studying functional brain networks relevant for development, behavior, and psychopathology.

Accounting for motion in resting-state fMRI: What part of the spectrum are we characterizing in autism spectrum disorder?

The exclusion of high-motion participants can reduce the impact of motion in functional Magnetic Resonance Imaging (fMRI) data. However, the exclusion of high-motion participants may change the distribution of clinically relevant variables in the study sample, and the resulting sample may not be representative of the population. Our goals are two-fold: 1) to document the biases introduced by common motion exclusion practices in functional connectivity research and 2) to introduce a framework to address these biases by treating excluded scans as a missing data problem. We use a study of autism spectrum disorder in children without an intellectual disability to illustrate the problem and the potential solution. We aggregated data from 545 children (8-13 years old) who participated in resting-state fMRI studies at Kennedy Krieger Institute (173 autistic and 372 typically developing) between 2007 and 2020. We found that autistic children were more likely to be excluded than typically developing children, with 28.5% and 16.1% of autistic and typically developing children excluded, respectively, using a lenient criterion and 81.0% and 60.1% with a stricter criterion. The resulting sample of autistic children with usable data tended to be older, have milder social deficits, better motor control, and higher intellectual ability than the original sample. These measures were also related to functional connectivity strength among children with usable data. This suggests that the generalizability of previous studies reporting naïve analyses (i.e., based only on participants with usable data) may be limited by the selection of older children with less severe clinical profiles because these children are better able to remain still during an rs-fMRI scan. We adapt doubly robust targeted minimum loss based estimation with an ensemble of machine learning algorithms to address these data losses and the resulting biases. The proposed approach selects more edges that differ in functional connectivity between autistic and typically developing children than the naïve approach, supporting this as a promising solution to improve the study of heterogeneous populations in which motion is common.

Edited magnetic resonance spectroscopy in the neonatal brain.

J-difference-edited spectroscopy is a valuable approach for the detection of low-concentration metabolites with magnetic resonance spectroscopy (MRS). Currently, few edited MRS studies are performed in neonates due to suboptimal signal-to-noise ratio, relatively long acquisition times, and vulnerability to motion artifacts. Nonetheless, the technique presents an exciting opportunity in pediatric imaging research to study rapid maturational changes of neurotransmitter systems and other metabolic systems in early postnatal life. Studying these metabolic processes is vital to understanding the widespread and rapid structural and functional changes that occur in the first years of life. The overarching goal of this review is to provide an introduction to edited MRS for neonates, including the current state-of-the-art in editing methods and editable metabolites, as well as to review the current literature applying edited MRS to the neonatal brain. Existing challenges and future opportunities, including the lack of age-specific reference data, are also discussed.

Models of psychedelic drug action: modulation of cortical-subcortical circuits.

Classic psychedelic drugs such as psilocybin and lysergic acid diethylamide (LSD) have recaptured the imagination of both science and popular culture, and may have efficacy in treating a wide range of psychiatric disorders. Human and animal studies of psychedelic drug action in the brain have demonstrated the involvement of the serotonin 2A (5-HT2A) receptor and the cerebral cortex in acute psychedelic drug action, but different models have evolved to try to explain the impact of 5-HT2A activation on neural systems. Two prominent models of psychedelic drug action (the cortico-striatal thalamo-cortical, or CSTC, model and relaxed beliefs under psychedelics, or REBUS, model) have emphasized the role of different subcortical structures as crucial in mediating psychedelic drug effects. We describe these models and discuss gaps in knowledge, inconsistencies in the literature and extensions of both models. We then introduce a third circuit-level model involving the claustrum, a thin strip of grey matter between the insula and the external capsule that densely expresses 5-HT2A receptors (the cortico-claustro-cortical, or CCC, model). In this model, we propose that the claustrum entrains canonical cortical network states, and that psychedelic drugs disrupt 5-HT2A-mediated network coupling between the claustrum and the cortex, leading to attenuation of canonical cortical networks during psychedelic drug effects. Together, these three models may explain many phenomena of the psychedelic experience, and using this framework, future research may help to delineate the functional specificity of each circuit to the action of both serotonergic and non-serotonergic hallucinogens.

Increased integration between default mode and task-relevant networks in children with ADHD is associated with impaired response control.

Default mode network (DMN) dysfunction is theorized to play a role in attention lapses and task errors in children with attention-deficit/hyperactivity disorder (ADHD). In ADHD, the DMN is hyperconnected to task-relevant networks, and both increased functional connectivity and reduced activation are related to poor task performance. The current study extends existing literature by considering interactions between the DMN and task-relevant networks from a brain network perspective and by assessing how these interactions relate to response control. We characterized both static and time-varying functional brain network organization during the resting state in 43 children with ADHD and 43 age-matched typically developing (TD) children. We then related aspects of network integration to go/no-go performance. We calculated participation coefficient (PC), a measure of a region's inter-network connections, for regions of the DMN, canonical cognitive control networks (fronto-parietal, salience/cingulo-opercular), and motor-related networks (somatomotor, subcortical). Mean PC was higher in children with ADHD as compared to TD children, indicating greater integration across networks. Further, higher and less variable PC was related to greater commission error rate in children with ADHD. Together, these results inform our understanding of the role of the DMN and its interactions with task-relevant networks in response control deficits in ADHD.

Which multiband factor should you choose for your resting-state fMRI study?

Multiband acquisition, also called simultaneous multislice, has become a popular technique in resting-state functional connectivity studies. Multiband (MB) acceleration leads to a higher temporal resolution but also leads to spatially heterogeneous noise amplification, suggesting the costs may be greater in areas such as the subcortex. We evaluate MB factors of 2, 3, 4, 6, 8, 9, and 12 with 2 mm isotropic voxels, and additionally 2 mm and 3.3 mm single-band acquisitions, on a 32-channel head coil. Noise amplification was greater in deeper brain regions, including subcortical regions. Correlations were attenuated by noise amplification, which resulted in spatially varying biases that were more severe at higher MB factors. Temporal filtering decreased spatial biases in correlations due to noise amplification, but also tended to decrease effect sizes. In seed-based correlation maps, left-right putamen connectivity and thalamo-motor connectivity were highest in the single-band 3.3 mm protocol. In correlation matrices, MB 4, 6, and 8 had a greater number of significant correlations than the other acquisitions (both with and without temporal filtering). We recommend single-band 3.3 mm for seed-based subcortical analyses, and MB 4 provides a reasonable balance for studies analyzing both seed-based correlation maps and connectivity matrices. In multiband studies including secondary analyses of large-scale datasets, we recommend reporting effect sizes or test statistics instead of correlations. If correlations are reported, temporal filtering (or another method for thermal noise removal) should be used. The Emory Multiband Dataset is available on OpenNeuro.

Neuropsychiatric disease classification using functional connectomics - results of the connectomics in neuroimaging transfer learning challenge.

Large, open-source datasets, such as the Human Connectome Project and the Autism Brain Imaging Data Exchange, have spurred the development of new and increasingly powerful machine learning approaches for brain connectomics. However, one key question remains: are we capturing biologically relevant and generalizable information about the brain, or are we simply overfitting to the data? To answer this, we organized a scientific challenge, the Connectomics in NeuroImaging Transfer Learning Challenge (CNI-TLC), held in conjunction with MICCAI 2019. CNI-TLC included two classification tasks: (1) diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) within a pre-adolescent cohort; and (2) transference of the ADHD model to a related cohort of Autism Spectrum Disorder (ASD) patients with an ADHD comorbidity. In total, 240 resting-state fMRI (rsfMRI) time series averaged according to three standard parcellation atlases, along with clinical diagnosis, were released for training and validation (120 neurotypical controls and 120 ADHD). We also provided Challenge participants with demographic information of age, sex, IQ, and handedness. The second set of 100 subjects (50 neurotypical controls, 25 ADHD, and 25 ASD with ADHD comorbidity) was used for testing. Classification methodologies were submitted in a standardized format as containerized Docker images through ChRIS, an open-source image analysis platform. Utilizing an inclusive approach, we ranked the methods based on 16 metrics: accuracy, area under the curve, F1-score, false discovery rate, false negative rate, false omission rate, false positive rate, geometric mean, informedness, markedness, Matthew's correlation coefficient, negative predictive value, optimized precision, precision, sensitivity, and specificity. The final rank was calculated using the rank product for each participant across all measures. Furthermore, we assessed the calibration curves of each methodology. Five participants submitted their method for evaluation, with one outperforming all other methods in both ADHD and ASD classification. However, further improvements are still needed to reach the clinical translation of functional connectomics. We have kept the CNI-TLC open as a publicly available resource for developing and validating new classification methodologies in the field of connectomics.

A Data Driven Approach Reveals That Anomalous Motor System Connectivity is Associated With the Severity of Core Autism Symptoms.

This study examined whether disruptions in connectivity involving regions critical for learning, planning, and executing movements are relevant to core autism symptoms. Spatially constrained ICA was performed using resting-state fMRI from 419 children (autism spectrum disorder (ASD) = 105; typically developing (TD) = 314) to identify functional motor subdivisions. Comparing the spatial organization of each subdivision between groups, we found voxels that contributed significantly less to the right posterior cerebellar component in children with ASD versus TD (P <0.001). Next, we examined the effect of diagnosis on right posterior cerebellar connectivity with all other motor subdivisions. The model was significant (P = 0.014) revealing that right posterior cerebellar connectivity with bilateral dorsomedial primary motor cortex was, on average, stronger in children with ASD, while right posterior cerebellar connectivity with left-inferior parietal lobule (IPL), bilateral dorsolateral premotor cortex, and supplementary motor area was stronger in TD children (all P ≤0.02). We observed a diagnosis-by-connectivity interaction such that for children with ASD, elevated social-communicative and excessive repetitive-behavior symptom severity were both associated with right posterior cerebellar-left-IPL hypoconnectivity (P ≤0.001). Right posterior cerebellar and left-IPL are strongly implicated in visuomotor processing with dysfunction in this circuit possibly leading to anomalous development of skills, such as motor imitation, that are crucial for effective social-communication. LAY SUMMARY: This study examines whether communication between various brain regions involved in the control of movement are disrupted in children with autism spectrum disorder (ASD). We show communication between the right posterior cerebellum and left IPL, a circuit important for efficient visual-motor integration, is disrupted in children with ASD and associated with the severity of ASD symptoms. These results may explain observations of visual-motor integration impairments in children with ASD that are associated with ASD symptom severity.

Altered Inferior Parietal Functional Connectivity is Correlated with Praxis and Social Skill Performance in Children with Autism Spectrum Disorder.

Children with autism spectrum disorder (ASD) have difficulties perceiving and producing skilled gestures, or praxis. The inferior parietal lobule (IPL) is crucial to praxis acquisition and expression, yet how IPL connectivity contributes to autism-associated impairments in praxis as well as social-communicative skill remains unclear. Using resting-state functional magnetic resonance imaging, we applied independent component analysis to test how IPL connectivity relates to praxis and social-communicative skills in children with and without ASD. Across all children (with/without ASD), praxis positively correlated with connectivity of left posterior-IPL with the left dorsal premotor cortex and with the bilateral posterior/medial parietal cortex. Praxis also correlated with connectivity of right central-IPL connectivity with the left intraparietal sulcus and medial parietal lobe. Further, in children with ASD, poorer praxis and social-communicative skills both correlated with weaker right central-IPL connectivity with the left cerebellum, posterior cingulate, and right dorsal premotor cortex. Our findings suggest that IPL connectivity is linked to praxis development, that contributions arise bilaterally, and that right IPL connectivity is associated with impaired praxis and social-communicative skills in autism. The findings underscore the potential impact of IPL connectivity and impaired skill acquisition on the development of a range of social-communicative and motor functions during childhood, including autism-associated impairments.

Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks using Big Data Population Priors.

Large brain imaging databases contain a wealth of information on brain organization in the populations they target, and on individual variability. While such databases have been used to study group-level features of populations directly, they are currently underutilized as a resource to inform single-subject analysis. Here, we propose leveraging the information contained in large functional magnetic resonance imaging (fMRI) databases by establishing population priors to employ in an empirical Bayesian framework. We focus on estimation of brain networks as source signals in independent component analysis (ICA). We formulate a hierarchical "template" ICA model where source signals-including known population brain networks and subject-specific signals-are represented as latent variables. For estimation, we derive an expectation maximization (EM) algorithm having an explicit solution. However, as this solution is computationally intractable, we also consider an approximate subspace algorithm and a faster two-stage approach. Through extensive simulation studies, we assess performance of both methods and compare with dual regression, a popular but ad-hoc method. The two proposed algorithms have similar performance, and both dramatically outperform dual regression. We also conduct a reliability study utilizing the Human Connectome Project and find that template ICA achieves substantially better performance than dual regression, achieving 75-250% higher intra-subject reliability.

Bridging global and local topology in whole-brain networks using the network statistic jackknife.

Whole-brain network analysis is commonly used to investigate the topology of the brain using a variety of neuroimaging modalities. This approach is notable for its applicability to a large number of domains, such as understanding how brain network organization relates to cognition and behavior and examining disrupted brain network organization in disease. A benefit to this approach is the ability to summarize overall brain network organization with a single metric (e.g., global efficiency). However, important local differences in network structure might exist without any corresponding observable differences in global topology, making a whole-brain analysis strategy unlikely to detect relevant local findings. Conversely, using local network metrics can identify local differences, but are not directly informative of differences in global topology. Here, we propose the network statistic (NS) jackknife framework, a simulated lesioning method that combines the utility of global network analysis strategies with the ability to detect relevant local differences in network structure. We evaluate the NS jackknife framework with a simulation study and an empirical example comparing global efficiency in children with attention-deficit/hyperactivity disorder (ADHD) and typically developing (TD) children. The NS jackknife framework has been implemented in a public, open-source R package, netjack, available at https://cran.r-project.org/package=netjack.

Parsing Heterogeneity in Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder with Individual Connectome Mapping.

Traditional diagnostic systems for neurodevelopmental disorders define diagnostic categories that are heterogeneous in behavior and underlying neurobiological alterations. The goal of this study was to parse heterogeneity in a core executive function (EF), cognitive flexibility, in children with a range of abilities (N = 132; children with autism spectrum disorder, attention-deficit/hyperactivity disorder [ADHD], and typically developing children) using directed functional connectivity profiles derived from resting-state functional magnetic resonance imaging data. Brain regions activated in response to a cognitive flexibility task in adults were used to guide region-of-interest selection to estimate individual connectivity profiles in this study. We expected to find subgroups of children who differed in their network connectivity metrics and symptom measures. Unexpectedly, we did not find a stable or valid subgrouping solution, which suggests that categorical models of the neural substrates of cognitive flexibility in children may be invalid. Exploratory analyses revealed dimensional associations between network connectivity metrics and ADHD symptomatology and EF ability across the entire sample. Results shed light on the validity of conceptualizing the neural substrates of cognitive flexibility categorically in children. Ultimately, this work may provide a foundation for the development of a revised nosology focused on neurobiological substrates as an alternative to traditional symptom-based classification systems.

Investigating functional brain network integrity using a traditional and novel categorical scheme for neurodevelopmental disorders.

BACKGROUND: Current diagnostic systems for neurodevelopmental disorders do not have clear links to underlying neurobiology, limiting their utility in identifying targeted treatments for individuals. Here, we aimed to investigate differences in functional brain network integrity between traditional diagnostic categories (autism spectrum disorder [ASD], attention-deficit/hyperactivity disorder [ADHD], typically developing [TD]) and carefully consider the impact of comorbid ASD and ADHD on functional brain network integrity in a sample adequately powered to detect large effects. We also assess the neurobiological separability of a novel, potential alternative categorical scheme based on behavioral measures of executive function. METHOD: Five-minute resting-state fMRI data were obtained from 168 children (128 boys, 40 girls) with ASD, ADHD, comorbid ASD and ADHD, and TD children. Independent component analysis and dual regression were used to compute within- and between-network functional connectivity metrics at the individual level. RESULTS: No significant group differences in within- or between-network functional connectivity were observed between traditional diagnostic categories (ASD, ADHD, TD) even when stratified by comorbidity (ASD + ADHD, ASD, ADHD, TD). Similarly, subgroups classified by executive functioning levels showed no group differences. CONCLUSIONS: Using clinical diagnosis and behavioral measures of executive function, no differences in functional connectivity were observed among the categories examined. Despite our limited ability to detect small- to medium-sized differences between groups, this work contributes to a growing literature suggesting that traditional diagnostic categories do not define neurobiologically separable groups. Future work is necessary to ascertain the validity of the executive function-based nosology, but current results suggest that nosologies reliant on behavioral data alone may not lead to discovery of neurobiologically distinct categories.