Feature overlap in transdiagnostic connectome-based models of sustained attention and autism symptoms

Abstract

Sustained attention is an important neurobiological process. Difficulties with attention play a key role in neurodevelopmental disorders, such as attention-deficit/hyperactivity disorder (ADHD) and autism. Here, we identified functional connections consistently associated with sustained attention across datasets, participant populations, and fMRI scan types. We interrogated five transdiagnostic, previously published connectome-based models predicting attention and autistic phenotypes. All models were related to sustained attention, including in samples comprising participants with autism. As expected, we observed that models predicting attention phenotypes shared more similar features with each other than models predicting autism symptoms. Interestingly, we observed no statistically significant model similarities when considering factors such as age, functional run type, or diagnosis. This suggests that functional connectivity patterns predicting individual differences in behavior tend to be phenotype-specific, regardless of age or clinical diagnosis. Our results underscore the importance of searching for consistent markers of transdiagnostic sustained attention phenotypes in neurodevelopmental conditions.

Introduction

The ability to pay attention is central to human experience. Whether focusing during conversations, monitoring other drivers on the highway, or searching for groceries in a crowded supermarket aisle, the capacity to sustain attention influences nearly every waking moment. Perhaps in part because of its ubiquity, ‘sustained attention’ is a broad construct with poorly understood brain correlates (Rosenberg et al. 2016). A better characterization of the neurobiology of sustained attention is needed.

Beyond its importance in daily life, sustained attention is important in neurology and psychiatry. Sustained attention is associated with numerous clinical conditions, including dyslexia in youth (Facoetti et al. 2000; Shaywitz and Shaywitz 2008) and delirium in medically ill patients (Echeverría and Schoo, 2022). Difficulties with sustained attention also play an important role in neurodevelopmental disorders, such as attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (hereafter, ‘autism’). It is estimated that approximately 8% of youth worldwide (Ayano et al. 2023) have been diagnosed with ADHD, with that number reaching 10.5% of youth in the United States (Danielson et al. 2024). The prevalence of autism indicates the condition is also pervasive. In the US, approximately 3.2% of eight-year-olds carry an autism diagnosis (Shaw et al. 2025); worldwide, approximately 1 in 127 individuals have been diagnosed (Santomauro et al. 2025).

Although they are distinct diagnoses, there is evidence that ADHD and autism have a number of behavioral similarities. Patients in both conditions tend to have sensory processing issues (Lau-Zhu et al. 2019), executive dysfunction (Corbett et al. 2009), irritability (Brereton et al. 2006), and metacognitive difficulties, such as performance monitoring (Lau-Zhu et al. 2019). There is also a relationship between autism and attention, such that individuals with greater autistic symptoms tend to have more difficulties with attention (Allen and Courchesne 2001; Landry and Parker 2013; Lyall et al. 2017).

Furthermore, there are shared neurobiological correlates between ADHD and autism. Using magnetic resonance imaging (MRI) functional connectivity to measure synchrony between brain regions (Biswal et al. 1995), similarities have been found in the functional networks associated with each condition. For example, heteromodal association cortices—associated with higher-order cognitive functions and comprising networks such as the frontoparietal and default mode networks (Sydnor et al. 2021)—have been shown to consistently mediate both autistic and ADHD phenotypes (Di Martino et al. 2013; Kernbach et al. 2018; Lake et al. 2019; Itahashi et al. 2020).

Despite the many neurobiological similarities between ADHD and autism, the extent to which sustained attention itself is supported by consistent neurobiological patterns is unclear. For instance, sustained attention is exquisitely sensitive to brain state. Factors including caffeine intake (Magalhaes et al. 2021), emotional valence (Tobia et al. 2017), and fatigue (Tagliazucchi and Laufs 2014) have all been shown to alter functional connectivity, as well as performance on attention tasks (Chua et al. 2017; Cooper et al. 2021; Welhaf and Banks 2025). Nevertheless, work in neurotypical subjects suggests there might be a shared functional architecture mediating sustained attention. Across diverse sustained attention tasks spanning different modalities (e.g., auditory and visual attention conditions), commonalities can be found in brain areas, particularly in visual and heteromodal association cortex (Yoo et al. 2022; Jones et al. 2024; Corriveau et al. 2025; Corriveau et al. 2026). The same visual and heteromodal networks tend to be involved when probing resting-state conditions for associations with sustained attention phenotypes (Rosenberg et al. 2016). The consistency in neurotypical participants leads to a question about the functional connections and networks in autism and ADHD: are there consistent functional connectivity features associated with sustained attention in transdiagnostic samples? Or, despite the similarities, are different features underpinning sustained attention in autism and ADHD?

To address this issue, we interrogated data from five previously published models of sustained attention and autism (Figure 1; Table 1) (Rosenberg et al. 2016; Lake et al. 2019; Horien et al. 2023; Corriveau et al. 2025; Horien et al. 2025; Horien et al. 2026). The models were selected because they contained a number of similarities allowing comparisons across studies. First, the same functional brain atlas was used (Shen et al. 2013). Second, all studies used connectome-based predictive modelling (CPM) (Finn et al. 2015; Shen et al. 2017), a machine learning method for deriving brain-behavior models from functional connectomes. Third, all models were related to attention, including in samples comprising autism participants. Specifically, each model was either developed to predict an attention phenotype, shown to generalize to attention phenotypes in external samples, or built from functional data acquired during an attention task.

Figure 1.

Overview of the current paper. A) Study design. The original sample from which each attention model was derived is shown in the oval, with the ovals arranged by increasing age as indicated by the arrow. The fMRI data used to generate the model are shown directly under the dataset name. The Yale youth and ABIDE datasets comprised participants with and without autism (indicated by the purple-to-pink gradient shown around the ovals, with purple indicating a higher proportion of participants with autism). The adult attention sample and avCPT sample comprised neurotypical participants. Autism phenotypes were used to generate models M1 and M2 (SRS total scores and ADOS total calibrated severity scores, respectively). Attention phenotypes were used to generate models M3, M4, and M5 (gradCPT task performance). Note that the same parent sample (the Yale youth sample) was used to generate the M2 and M3 models. B) The overarching goal of the paper is to determine if a core transdiagnostic, sustained attention network could be derived. C) Specifying how each model relates to attention. The leftmost column specifies the model. The second column from the left specifies if the model was generated using in-scanner attention task functional data. The third column from the left specifies if the model was generated by predicting an attention phenotype. The rightmost column specifies if the model generalized to predict attention in an external sample. ABIDE, autism brain imaging data exchange; Attn, attention; avCPT, audio-visual continuous performance task. M, model.

Table 1.

Demographics and modelling parameters of original datasets used to generate predictive models.

	ABIDE (M1)	Yale youth autism (M2)	Youth attention (M3)	avCPT (M4)	Adult attention (M5)
Original publication	(Lake et al. 2019)	(Horien et al. 2026)	(Horien et al. 2023)	(Corriveau et al. 2025)	(Rosenberg et al. 2016)
Number of participants (males)	229 (164)	63 (34)	70 (39)	Visual-relevant: 43 (19) Auditory-relevant: 43 (20)	25 (12)
Number of participants with a neurodevelopmental or psychiatric condition	77 autism	31 total 5 = ADHD 2 = anxiety disorder 20 = autism 4 = BAP	33 total 7 = ADHD 2 = anxiety disorder 20 = autism 3 = BAP 1 = bipolar	-	-
Age in years, mean (st. dev.)	10.45 (1.8)	11.7 (2.8)	11.59 (2.87)	Visual-relevant: 22.40 (4.18) Auditory-relevant: 22.28 (3.66)	22.79 (3.54)
IQ	113.7 (15.1)	107.8 (15.1)	107.23 (15.93)	-	-
fMRI data	Resting-state	Continuous attention task (gradCPT)	Continuous attention task (gradCPT)	Continuous attention task (avCPT)	Continuous attention task (gradCPT)
Amount of fMRI data	One four-minute resting-state run	Two 5:02 gradCPT runs averaged	Two 5:02 gradCPT runs averaged	One ten-minute visual-relevant CPT, one ten-minute auditory-relevant CPT	Three 13-minute 44-second gradCPT runs concatenated
Phenotype predicted in original model	SRS total scores (Constantino et al. 2003)	ADOS total calibrated score (Lord C 2012)	Performance on attention task (d’)	Performance on attention task (d’)	Performance on attention task (d’)
Number of trials in CPT	-	302	302	500	2700
Prediction performance in original sample	Pearson’s r between predicted and observed total SRS scores = 0.32, P-value < 5E-10	Spearman’s rho between predicted and observed total ADOS scores = 0.445, P-value = 0.002	Spearman’s rho between predicted and observed total d’ scores = 0.54, P-value = 0.0001	-	Pearson’s r between predicted and observed total d’ scores = 0.84, P-value = 1.3E-7
Phenotypes successfully predicted in test datasets in original publication	ADHD Rating Scale IV (DuPaul 1998)	Sustained attention task performance (d’) (Rosenberg et al. 2016) SRS scores (Constantino et al. 2003)	Sustained attention task performance (d’) (Rosenberg et al. 2016)	-	ADHD Rating Scale IV (DuPaul 1998)

ABIDE, autism brain imaging data exchange; ADHD, attention-deficit/hyperactivity disorder; BAP, broader autism phenotype; ADOS, Autism Diagnostic Observation Schedule; avCPT, audio-visual continuous performance task; CPM, connectome-based predictive modelling; CPT, continuous performance task; gradCPT, gradual-onset continuous performance task; IQ, intelligence quotient; SRS, social responsiveness scale; TR, repetition time.

To establish the upper bounds of consistency, we leveraged a number of other similarities in the samples. Multiple models were derived using similar attention task scanning conditions. Some of the models were validated on the same external samples (Supplemental Figure 1). Further, we capitalize on the fact that two models from separate publications shared approximately 90% of subjects, but predicted different phenotypes—one predicting attention task performance and the other predicting autism symptoms. We hypothesized that we would find commonalities across all five models. In particular, we predicted the same functional connections and network structure would be observed.

We found that most brain-sustained attention models shared functional connections and received contributions from similar canonical functional networks, although overlap across datasets was not perfect. Models predicting attention phenotypes shared more similar features with each other than models predicting autism symptoms. This finding was exemplified by the fact that two of the only models that did not share connections were from the samples sharing approximately 90% of subjects, but predicted different phenotypes. Surprisingly, we observed no statistically significant model similarities when considering factors like age, functional run type, or diagnosis. In sum, our findings suggest brain-attention models tend to share functional features and that models tend to be phenotype-specific, regardless of age or clinical diagnosis of the source sample.

Methods

Overview of the methodological approach

We have two main goals in this paper. First, we assess the consistency of sustained attention brain-behavior relationships at the edge and network levels. Second, we determine factors influencing brain-sustained attention relationships, such as age, diagnosis, and other demographic variables. We used data from five previously published studies (‘publication preregistration’; Rosenberg et al. 2020). Despite differences in the datasets, all models were related to sustained attention, including those derived from samples comprising participants with autism (Figure 1). A brief description of the original dataset used to generate the model is offered below; see Supplemental Methods and Table 1 for full details.

Description of datasets used to generate predictive models

All datasets were collected in accordance with the institutional review board or research ethics committee at each site. Where appropriate, informed consent was obtained from the parents or guardians of participants. Written assent was obtained from children aged 13–17 years; verbal assent was obtained from participants under the age of 13 years.

Dataset 1, model 1: ABIDE model

The Autism Brain Imaging Data Exchange (ABIDE) (Di Martino et al. 2014; Di Martino et al. 2017) dataset comprised individuals with and without autism (n = 229, 65 females; mean age = 10.45 years, st. dev. = 1.8 years; mean IQ = 113.7, st. dev. = 15.1; 77/229 participants were diagnosed with autism; Table 1). Throughout the text, we refer to this sample as the ‘ABIDE model/sample’. Full exclusion criteria have been described previously (Lake et al. 2019).

Dataset 2, model 2: Yale youth autism model

The second dataset comprised 63 participants (mean age = 11.7 years, st. dev. = 2.8 years; 29 females; mean IQ = 107.8, st. dev. = 15.1); full exclusion criteria are available elsewhere (Horien et al. 2026). Twenty of the participants had autism; 11 other participants had a different psychiatric condition (five with ADHD, two with anxiety disorder, and four were classified as belonging to the broader autism phenotype) (Ingersoll 2010). Hereafter, we refer to this model/sample as the ‘Yale youth autism model/sample.’

Dataset 2, model 3: youth attention model

The second dataset was also used to generate a different model. The sample comprised 70 subjects (mean age = 11.59 years, st. dev. = 2.87 years; 31 females; mean IQ = 107.23, st. dev. = 15.9); full details of the sample have been described elsewhere (Horien et al. 2020; Horien et al. 2023). Twenty of the participants had autism. Thirteen other participants had a different neurodevelopmental condition (seven with ADHD, two with anxiety disorder, one with bipolar disorder, and three were classified as belonging to the broader autism phenotype) (Ingersoll 2010). We refer to this model/sample as the ‘youth attention model/sample,’ but readers should keep in mind the transdiagnostic nature of the dataset. Note that because they were drawn from the same parent studies, models 2 and 3 shared 56 participants.

Dataset 3, model 4: avCPT model

The next dataset has been described elsewhere (Corriveau et al. 2025). The final sample size was n = 43 (n = 24 females in visual condition, n = 23 females in auditory condition); mean age = 22.4 (st. dev. = 4.18). Participants completed an in-scanner 10-minute audio-visual continuous performance task (avCPT; we refer to this model/sample hereafter as the ‘avCPT model/sample’).

Dataset 4, model 5: adult attention model

The last dataset comprised neurotypical adults (hereafter referred to as the ‘adult attention model/sample’; n = 25, 13 females, mean age = 22.8 years, st. dev. = 3.5 years); full details of this sample are described elsewhere (Rosenberg et al. 2016).

Functional imaging conditions: sustained attention tasks and resting-state

Functional imaging data were obtained while participants in the youth attention sample and the Yale youth sample completed the gradCPT in the scanner (Esterman et al. 2013; Rosenberg et al. 2013; Rosenberg et al. 2016). Briefly, participants viewed grayscale pictures of cities and mountains, with images transitioning from one to the next every 1,000 ms. Subjects were instructed to respond with a button press for city scenes and to withhold button presses for mountain scenes. City scenes occurred randomly 90% of the time. Performance was calculated using d’ (sensitivity), the participant’s z-scored hit rate minus z-scored false alarm rate.

Subjects in the adult attention sample (Rosenberg et al. 2016) also performed the gradCPT in the scanner. The same parameters were used as above, except scene transitions took 800 ms. Participants in the avCPT sample completed an audio-visual continuous performance task (avCPT) (Corriveau et al. 2025), with similar parameters as above during an auditory and then a visual condition (see Supplemental Methods for full details). In ABIDE, functional data comprised resting-state scans as described previously (Di Martino et al. 2014; Di Martino et al. 2017).

Processing of functional imaging data

Functional data were processed as previously described (Greene et al. 2018; Horien et al. 2018; Rapuano et al. 2020; Greene et al. 2022; Corriveau et al. 2025) and followed the same general approach. This included alignment of functional data to a shared space, regression of covariates of no interest, including a head motion model, the mean signal from white matter and ventricle masks, and mean global signal (see Supplemental Materials, ‘Preprocessing of imaging data’ for full details of preprocessing).

Connectomes were generated using a 268-node atlas (Shen et al. 2013). The mean time-course of each region of interest (“node”) was calculated, and the Pearson correlation coefficient was calculated between each node pair to achieve a symmetric 268×268 matrix of correlation values representing connections between nodes (“edges”). Nodes were grouped into one of ten canonical functional networks described previously (Noble et al. 2017; Horien et al. 2019). Care was taken in each study to ensure artifacts related to head motion were not driving results (see Supplemental Methods, ‘Minimizing the impact of head motion’ and Supplemental Table 1).

In the avCPT sample, some subjects were missing edges due to incomplete coverage during the functional scan. If any subject was missing an edge, we removed that edge from all participants being compared from all samples; the final connectome size was 32,640 edges and 31,626 edges in the auditory and visual runs, respectively.

Accounting for differences in size of the brain-sustained attention predictive models

Connectome-based predictive modelling (CPM; Supplemental Figure 2) was used to generate the models in the current work. Importantly, each original publication included controls to ensure that variables such as age, sex, head-motion, and other factors were not confounding CPM results (Rosenberg et al. 2016; Lake et al. 2019; Horien et al. 2023; Corriveau et al. 2025; Horien et al. 2026). CPM uses cross-validation to determine the functional connections predictive of a given phenotype, resulting in a summary network model. Based on the edges most positively and negatively correlated with behavior, positive and negative network models are generated, respectively. The summary network model is then used to predict phenotypic outcomes in unseen participants. The summary network model is the output of interest in the present work. Due to the relationship between autism and attention (higher autism symptoms are associated with poorer attention (Allen and Courchesne 2001; Landry and Parker 2013; Lyall et al. 2017)), we considered the networks from ABIDE and the Yale youth autism study in the opposite direction. That is, the positive ABIDE and Yale youth autism networks were considered with the negative networks from the avCPT, adult attention, and youth attention samples, and vice-versa.

Because of differences in modelling parameters in each original study (Supplemental Table 2), each summary network is a different size. To ensure this did not confound results, we re-analyzed the original data used to construct the models in each sample. Edges were correlated with behavior in each sample, ranked according to correlation strength, and the top 1,000 positively and negatively correlated edges were retained for analysis. (This corresponds to the feature selection step in Supplemental Figure 2, box 3, except instead of a feature selection threshold, we retained only the top 1,000 edges.) Note that 2,000 edges are chosen in total, in line with the original networks studied here, as well as previously published CPM networks of other phenotypes (e.g., Cheng et al. 2025). To ensure results were not driven by the use of a specific threshold, we also tested 500 edges in each positive and negative network (1,000 total), as well as 2,500 edges in each network (5,000 edges total).

Statistical overlap of edges in predictive models

To assess the probability of networks containing a statistically significant number of shared edges, we performed 1,000 iterations of generating random networks, ensuring the null networks were the same size as the original network from a given sample (see Supplemental Figure 3A for a schematic of the process). We then compared the number of times the random networks resulted in a greater than or equal number of overlapping edges in the actual predictive networks. In other words, we calculated the overlap of random network models and determined how many edges were shared by models.

We next assessed the overlap of edges among specific models themselves in a pairwise fashion. That is, we considered two predictive models at a time and asked if the overlap among edges was statistically significant. To do so, we used the hypergeometric cumulative density function (CDF) as previously described (Rosenberg et al. 2020; Corriveau et al. 2025). We used the Benjamini-Hochberg procedure (Benjamini 1995) to control for multiple comparisons (20 total, ten comparisons among the positive networks and ten among the negative networks). See Supplemental Methods for a full description of the procedure.

Quantifying the contribution of network pairs to predictive models

To determine the importance of specific networks in the predictive models, we quantified the number of edges between a given network pair, correcting for network size as described previously (Horien et al. 2019; Horien et al. 2023). We calculated null predictive network models for each sample (see Supplemental Methods, as well as Supplemental Figure 3B for a schematic of the process).

Assessing brain-behavior relationships using representational similarity analysis

To assess the importance of different dataset features on the brain-behavior relationships, we used representational similarity analysis (RSA) (Kriegeskorte et al. 2008). In each sample, we correlated each edge in the connectivity matrices from all participants with the behavior of interest, resulting in a 31,626×1 vector per dataset. (The visual run of the avCPT sample was used for our main analysis.) After correlating these vectors across samples to obtain a feature importance similarity matrix, we generated model representational similarity matrices based on mean age of the sample, functional run type (task or rest), behavioral measure (attention score or autism symptom score), and percentage of the sample diagnosed with autism. We z-scored the entries in each representational similarity matrix to obtain standardized betas in a multiple linear regression, allowing us to assess the unique variance in the feature importance similarity matrix explained by each predictor:

$${\mathit{\text{Similarity}}}_{\left\{ij\right\}}={\beta }_0+{\beta }_1{\mathit{\text{AgeDiff}}}_{\left\{ij\right\}}^{\left\{\left(z\right)\right\}}{+\beta }_2{\mathit{\text{RunTypeMatch}}}_{\left\{ij\right\}}^{\left\{\left(z\right)\right\}}{+\beta }_3{\mathit{\text{BehaviorMatch}}}_{\left\{ij\right\}}^{\left\{\left(z\right)\right\}}{+\beta }_4{\mathit{\text{AutismDiff}}}_{\left\{ij\right\}}^{\left\{\left(z\right)\right\}}+{\epsilon }_{\left\{ij\right\}}$$

where Similarity_{ij} is the Pearson correlation between feature importance vectors for datasets i and j; AgeDiff_{ij} is the absolute difference in mean age between datasets; RunTypeMatch_{ij} is a binary indicator of the type of functional data used, either resting-state or task data (1 = same run type, 0 = different); Behavior Match_{ij} is a binary indicator of the type of phenotype predicted, either autistic symptoms or attention task performance (1 = same behavioral measure, 0 = different behavioral measure); AutismDiff_{ij} is the absolute difference in percentage of participants diagnosed with autism between datasets; β₀ represents the intercept; and ε_{ij} is the residual error. To ensure results were robust, we repeated analyses using the auditory run of the avCPT sample.

Code and data availability

Preprocessing of the original datasets was carried out using freely available software: https://medicine.yale.edu/bioimaging/suite/. CPM code used to generate the original predictive models is available here: https://github.com/YaleMRRC/CPM. Template scripts used for preprocessing and analysis of the functional data are available here, as are the node-to-network labels: https://github.com/clhorien/transdiagnostic_attn_overlap/tree/main. The functional parcellation is available here: https://www.nitrc.org/frs/?group_id=51. ABIDE data are available here: https://fcon_1000.projects.nitrc.org/indi/abide/. Data from the avCPT sample are available here: https://osf.io/bt2xy/overview. All other data or materials are available from the authors upon request.

Results

Characteristics of previously published predictive models

We began by assessing basic aspects of the previously published predictive models. Specifically, we analyzed characteristics of the CPM networks derived from each study (i.e., we did not require networks to be the same size at this stage). The original networks comprised a dense collection of edges spanning the entire brain (Supplemental Figures 4–6). Network size and the number of interhemispheric versus intrahemispheric connections were also assessed. Despite using different modelling parameters (Supplemental Table 2), networks were generally the same size and comprised a similar number of connections within and between hemispheres (Supplemental Figure 4; Supplemental Table 3). Functional networks from across the brain contributed to the models, from visual and somatomotor networks to higher-order association networks, including the default mode, frontoparietal, and medial frontal networks (Supplemental Figures 5–6).

Edge overlap in brain-sustained attention predictive models

We next quantified the number of times individual edges were shared across models. Because of differences in network size across samples, network size was kept consistent across samples. Specifically, we assessed the top 1,000 edges most strongly correlated with behavior in each direction (the ‘positive’ and ‘negative’ networks, respectively; recall that we accounted for autism diagnosis in this procedure). In the positive and negative networks, we observed 408 and 423 edges that were shared by two models, respectively (Figure 2A). Both of these values were statistically significant compared to the overlap found in null models (P-value for positive network = 0.00099; P-value for negative network = 0.00099, corrected for multiple comparisons). When assessing whether edges were shared across a greater number of models, 28 edges were shared across three models in the positive networks; 35 edges were shared across three models in the negative networks (P-value for positive network = 0.00099; P-value for negative network = 0.00099, corrected for multiple comparisons). Notably, no edges were shared across four models in the positive networks; only one edge was shared across four models in the negative networks, which was not statistically significant (P-value = 0.167, corrected for multiple comparisons). No edges were shared across five models in either network. To ensure results were consistent, we repeated analyses using a different number of edges. We observed a similar pattern when testing 500 edges in each network (1,000 total) and 2,500 edges in each network (5,000 total; Supplemental Figure 7).

Figure 2.

Quantifying the occurrence of individual edges across models. A) Violin plots show the number of edges shared across models as a red horizontal line in each plot. The grey violin plot represents null results of the number of shared edges obtained from 1,000 permutations, with the black bar in the violin plot indicating the median of the 1,000 permutations. The positive network is shown in the left column; the negative network, in the right column. Above each plot, ‘k’ indicates the number of models in which an edge occurred. P-values are shown at the top of each plot; asterisks (*) indicate statistical significance after multiple comparisons correction. B) Edge overlap. Two matrices are shown: overlap between the top-ranked edges (‘Pos-Pos’, left) and overlap between the bottom-ranked edges (‘Neg-Neg’, right). Each cell corresponds to the number of edges shared between models. The dataset from which the model was obtained is indicated along the rows and columns of each matrix. The colorbar is scaled so that fewer shared edges (in terms of absolute number) are darker colors; more shared edges are lighter colors. An asterisk indicates a statistically significant shared number of edges between the two models after correcting for multiple comparisons. ABIDE, autism brain imaging data exchange. Att, attention; Aut, autism; avCPT, audio-visual continuous performance task; Neg, negative network; Pos, positive network.

Given the shared edges, we next assessed overlap when considering the models pairwise (Figure 2B). In the positive network, we observed 8/10 pairwise comparisons that showed a statistically significant number of shared positive-network edges (all significant comparisons shared >47 edges, all P-values < 0.003, corrected for multiple comparisons). In the negative network, 5/10 pairwise comparisons shared a statistically significant number of edges (all significant comparisons shared >42 edges, all P-values < 0.019, corrected for multiple comparisons). Of note, the two models sharing subjects but predicting different phenotypic domains did not share a statistically significant number of edges in the positive or negative networks (the Yale youth autism model predicting ADOS and the youth attention model predicting gradCPT task performance; positive network shared edges = 32, P-value = 0.43; negative network shared edges = 20, P-value = 0.98), suggesting that shared participants alone did not explain edge overlap.

To ensure findings were robust, we repeated pairwise comparisons with different network sizes. We obtained a similar pattern when testing 500 edges in each network and 2,500 edges in each network (Supplemental Figure 8). Taken together, the data in this section indicate that while we did not observe a core edge set across all samples, most models tended to have overlapping edges when considered pairwise.

Network consistency in brain-sustained attention relationships

The models were next analyzed at the network level, focusing on both within- and between-network pairs. We again assessed the top 1,000 edges correlated positively and the top 1,000 edges correlated negatively with behavior. Because of differences in the size in within- and between-network pairs in the connectomes, we corrected for network size in this procedure (Methods). Network pairs linking regions across the brain were represented (Figure 3A). In the positive network, visual, motor, and default mode networks had the highest fraction of edges across the five samples. In the negative network, visual, default mode, and cerebellar networks had the highest fraction of edges. We obtained similar results when testing network sizes of 500 edges in each network and 2,500 edges in each network (Supplemental Figure 9).

Figure 3.

Neuroanatomy of the attention network model. A) Summary matrix for all five datasets. B) Summary matrix for the datasets predicting autistic phenotypes. C) Summary matrix for the datasets predicting continuous attention task performance. For all plots: Positive network matrices are shown in the top row; negative network matrices, in the bottom row. Each cell in a matrix corresponds to the fraction of edges in a given network pair that were included in the given model, after correcting for network size. The colorbars for each matrix are scaled so that a lower fraction of edges corresponds to darker colors; a higher fraction of edges corresponds to lighter colors. Statistically significant network pairs after multiple comparisons correction are denoted with an asterisk (*). ABIDE, autism brain imaging data exchange; Att, attention; Aut, autism; avCPT, audio-visual continuous performance task; Neg, negative network; Pos, positive network. Network labels: MF, medial frontal; FP, frontoparietal; DM, default mode; MT, motor; VI, visual I; VII, visual II; VA, visual association; CO, cingulo-opercular; SB, subcortical; CB, cerebellum.

Within the individual samples, the location of statistically significant network pairs was assessed after accounting for multiple comparisons (see Supplemental Figure 3B for a schematic of the procedure). No discernible pattern emerged regarding the location of statistically significant network pairs in the individual samples (see Supplemental Figure 10 for the network pairs surviving multiple comparisons correction). Sensorimotor and cerebellar networks were statistically significant, as were higher-order networks, including the default mode and frontoparietal networks. Findings were similar when different-sized networks were used (500 and 2,500 edges; Supplemental Figure 10A-C).

Despite the distributed nature of the models (or perhaps because of it), there was little overlap in terms of which network pairs were statistically significant across the samples as a whole. For example, no network pairs were common across all five samples in the positive or negative networks (that is, none were consistently statistically significant). When we considered overlap among the models predicting autistic phenotypes, we observed three shared network pairs that were consistently statistically significant: one in the positive and two in the negative network (Figure 3B). All three comprised connections involving heteromodal association networks. There were three common network pairs when considering the models predicting continuous attention performance; connections involving visual, motor, and cerebellar networks were shared (Figure 3C).

We also assessed pairwise comparisons of shared network pairs and determined most samples shared at least one statistically significant network. Results were again consistent using the differently sized networks (Supplemental Figure 10D-F). The samples sharing subjects but predicting different phenotypes (Yale youth autism and youth attention) tended not to share network pairs.

To confirm specificity of the positive and negative networks, we flipped the sign of each network and assessed overlap. That is, the positive networks of the autism samples (Yale youth autism and ABIDE) were compared with the negative networks of the attention samples (adult attention, youth attention, and avCPT) and vice-versa. We observed no statistically significant shared network pairs in either case.

The results in this section suggest that sustained attention models have distributed network representations. Although no core set of networks was observed, some degree of overlap was detected when we grouped by phenotype and when we considered the datasets in a pairwise fashion.

Exploring the factors associated with brain-sustained attention relationships

Finally, we conducted a post hoc investigation into which factors were associated with similarities among the models. In the previous sections, we found that although overlap was not absolute, some of the models shared features. A natural follow-up question is whether there are meaningful differences between the datasets—such as demographic and phenotypic factors—that are associated with model similarity. Therefore, we conducted analyses assessing brain-sustained attention relationships using unthresholded data and RSA.

One advance of RSA (Kriegeskorte et al. 2008) is the investigation of whole-brain patterns associated with sustained attention, even those below the surface of what survives significance thresholding used in feature selection. We assessed predictors including the mean age of the sample, functional run type (task or rest), behavioral measure (attention score or autism symptom score), and percentage of participants in the sample with an autism diagnosis. We observed that there were no statistically significant relationships between the predictors and brain-sustained attention similarity vectors (P-values > 0.05; see Table 2 for regression coefficients and confidence intervals).

Table 2.

Results of RSA using unthresholded edge–behavior associations from each study. Shown are regression coefficients from models using z-scored predictors, along with the 95% confidence intervals (CI).

	Regression coefficient	Lower bound 95% CI	Upper bound 95% CI
Age	0.55	−1.61	2.71
Run type	0.026	−0.085	0.14
Behavioral measure	0.069	−0.21	0.35
Autism diagnosis	−0.60	−2.83	1.63

We also examined pairwise correlations among the datasets. The first step in RSA is calculating a similarity matrix of edge-sustained attention vectors across datasets through the use of correlation. We compared the similarity among the samples to that observed from null models. Using the visual run of the avCPT task, 4/10 pairwise dataset comparisons showed positive correlations (Figure 4A; Supplemental Table 4; all Pearson correlations > 0.13; all P-values < 0.005, corrected for multiple comparisons). All of the positive correlations were between samples that had congruent phenotypes (i.e., when both predicted autism symptoms or attention task performance in the original sample). One pairwise comparison showed a statistically significant negative correlation. This was between models with incongruent phenotypes (Yale youth autism sample and adult attention sample; Pearson correlation = −0.14; P-value = 0.005, corrected for multiple comparisons; Figure 4B).

Figure 4.

Relationship of brain-sustained attention across samples A) Histograms showing the observed correlation (in red) between the brain-sustained attention vectors (i.e., the Pearson correlation of the brain-sustained attention vectors). Null values for the histograms are shown in blue. The datasets compared are indicated in the rows and columns beside the histograms. In the upper left of each plot, the Pearson correlation coefficient between the datasets in each row and column is shown, along with the P-value. B) Similarity of edge-behavior vectors across datasets. Each bar represents the Pearson correlation coefficient (y-axis) between the datasets indicated on the x-axis. Behaviors that are both related to attention or autism (‘Congruent phenotypes’) are indicated in salmon color; behaviors that are not related between datasets (‘Incongruent phenotype’), in which one sample comprises an autism phenotype with the other comprising an attention phenotype, are indicated in green. In both plots, an asterisk (*) indicates statistical significance after correction for multiple comparisons. ABIDE, autism brain imaging data exchange dataset; avCPT, audio-visual continuous performance task dataset.

To ensure results were robust, we also tested similarity of the edge-sustained attention vectors when the auditory condition from the avCPT sample was used instead of the visual condition (recall the avCPT sample comprised two separate conditions, one visual condition and one auditory condition). Findings were consistent (Supplemental Table 4). In sum, the factors influencing sustained attention-related brain–behavior relationships are complex, although preliminary analyses suggest that models predicting similar phenotypes may share more similar features.

Discussion

In the present work, we attempted to identify consistent functional connectome-based features associated with sustained attention. Using data from five transdiagnostic, previously published functional connectivity-based brain-behavior predictive models, we found that most sustained attention models contained similar functional connections and resting-state functional networks. As expected, models predicting sustained attention were more closely related to each other than models predicting autism symptoms and vice-versa. Preliminary results using RSA did not reveal a statistically significant relationship between brain-behavior vectors and factors including age and autism diagnosis. In sum, the data suggest that patterns predicting individual differences in sustained attention tend to share some features, but they may be phenotype-specific.

Distributed, transdiagnostic models and the search for a core architecture

We did not identify a core sustained attention network across the five samples. The result is notable given the many similarities among the datasets. It is also notable because the models have generalized in similar (or in many cases the same) datasets. We also reiterate that the two models sharing 90% of subjects did not contain overlapping edges or networks. One interpretation of the lack of overlap is that despite being broadly related to sustained attention, models tend to be specific to the construct they were built to predict. They do not appear to be general across a range of associated phenotypes.

Another explanation for the lack of strict overlap relates to the distributed nature of the models. The models comprised a dense collection of edges spanning the entire brain, with inputs involving nearly every within- and between-network pair across subcortical, cortical, and cerebellar networks. The multivariate nature of the models echoes results in the functional connectivity reliability literature. In particular, individual edges do not tend to have high reliability (Noble et al. 2017), but multivariate measures of reliability tend to be higher (Noble et al. 2019; Marek et al. 2022). In the current study, this might explain why broad, qualitative similarities were observed—most models tended to comprise a high fraction of edges in visual and heteromodal networks. The more similar the phenotypes involved in the brain-behavior prediction, the more related the network similarities become.

Another reason for the lack of consistency might be due to the transdiagnostic samples. Previous studies observed overlap in datasets comprising solely neurotypical participants (Corriveau et al. 2025). The two samples comprising solely neurotypical participants in the present paper shared a statistically significant number of edges and networks at every threshold studied. There are functional connectivity differences that can be reliably used to differentiate autism cases from controls (reviewed in Horien et al. 2022), potentially complicating the search for a core set of features in the present work.

Neurobiological patterns observed in transdiagnostic sustained attention network models

Although perfect overlap was not observed, neurobiological patterns emerged. In the positive network, visual, motor, and default mode networks had the highest fraction of edges in the five samples. In the negative network, visual, default mode, and cerebellar networks had the highest fraction of edges. When we considered overlap among the datasets predicting autistic phenotypes, we observed three shared network pairs. All three comprised connections involving heteromodal association networks. When we assessed commonalities among the datasets predicting attention phenotypes, we observed three shared network pairs, involving visual, motor, and heteromodal networks. One interpretation of these findings is that, given the cognitive demands associated with sustaining attention (Fortenbaugh et al. 2017; Esterman and Rothlein 2019), coordination is required between sensory networks and networks mediating higher-order functions, such as heteromodal networks. In addition, the fact that connections linking visual and motor networks emerged as consistently important in the attention samples is may reflect the in-scanner button presses associated with the gradCPT.

In general, the finding that sensorimotor and heteromodal networks contribute to sustained attention models is reminiscent of the sensorimotor-association (SA) axis (Margulies et al. 2016). Individual differences in the SA axis have previously been linked to differences in complex phenotypes such as intelligence (Nenning et al. 2023) and conditions like autism (Hong et al. 2019; Benkarim et al. 2021; Severino et al. 2025). That we used a fully data-driven approach and still observed SA axis importance speaks to the potential ‘omnipresence’ of the axis in the connectome (Nenning et al. 2023). In addition, our finding that the cerebellum is important in the negative network is intriguing. Previous work (Marek et al. 2018) suggests that the cerebellum may play a key role in mediating the complex interplay between association networks responsible for higher-order functions, like attention. Given the well-known role of the cerebellum in coordinating movement, however, and the button pressing aspect of gradCPT, more work is needed to better understand the role of the cerebellum in sustained attention.

Factors associated with brain-sustained attention relationships

In the RSA, run type, behavioral measure, age, and autism diagnosis were not statistically significant predictors of model similarity. Nevertheless, we did find that when phenotypes were similar, edge-behavior vectors tended to be more similar (Figure 4B) and vice-versa. In other words, if two samples predicted autism phenotypes, or both predicted performance on gradCPT, their brain-behavior vectors tended to be more similar. One interpretation is that while a variable like behavioral measure does not perfectly modulate brain-sustained attention relationships across all of the samples, there are still instances in which two vectors will be similar because they predicted similar phenotypes.

Another possibility for the seemingly discrepant findings regarding the effect of behavioral measure is that there are other confounding factors that were unmodelled. We restricted analyses to demographic features that were common to all five datasets. Future work in prospective studies could more rigorously assess other variables. Given the importance of other sociodemographic factors in brain-behavior models (Greene et al. 2022; Ricard et al. 2023), particular focus could be placed on influences such as socioeconomic status (Yang et al. 2025), air pollution (Kusters et al. 2025), and other environmental exposures (e.g., Lichenstein et al. 2025). Our results indicate that although there appear to be some factors associated with a core sustained attention neurobiology, the relationship is complex and substantial work remains.

Limitations and future directions

There are a number of limitations and future directions to note. We assessed predictive models generated using CPM. Future work could study other modelling methods. In addition, the published models examined here all relied on edge selection to generate binary network masks. We accounted for this by ensuring all networks were of the same size, as well as returning to the original unthresholded data of each study. Nevertheless, there are alternative ways to summarize model weights (Ryali et al. 2012; Smith et al. 2015; He et al. 2020; Ooi et al. 2022; Shafiei et al. 2024) other than the feature selection method described here. In fact, recent work suggests that edges often ignored by feature selection still retain predictive utility (Adkinson et al. 2026). Other groups could investigate the extent to which other feature selection methods are consistent and offer interpretable neurobiological insights.

The samples used in the original studies are somewhat small, particularly when compared with samples such as the Adolescent Brain Cognitive Development (ABCD) (Casey et al. 2018) Study or the UK Biobank (Miller et al. 2016). Assessing whether commonalities can be found from other imaging modalities, such as structural predictive models (Yang et al. 2013), remains an area of further study. There has been growing interest in using precision functional mapping methods (Gordon et al. 2017). Future work could examine consistencies in predictive models generated using longer scan times.

Conclusion

We have characterized neurobiological trends in predictive models of sustained attention in transdiagnostic samples comprising autism and ADHD participants. Functional connectivity patterns associated with sustained attention showed some neurobiological consistency, and exploratory analyses suggested that models predicting similar phenotypes were more similar to one another. Our results underscore the importance of searching for consistent markers of transdiagnostic sustained attention phenotypes.

Highlights.

• We interrogated five previously published, transdiagnostic models of sustained attention and autistic phenotypes

• Functional connectome-based models shared edges and networks

• Phenotype correlated with model similarity; age, functional run type, and diagnosis did not