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A neuromarker of sustained attention from whole-brain functional connectivity.

Rosenberg MD, Finn ES, Scheinost D, Papademetris X, Shen X, Constable RT, Chun MM - Nat. Neurosci. (2015)

Bottom Line: To this end, we identified functional brain networks whose strength during a sustained attention task predicted individual differences in performance.Models based on these networks generalized to previously unseen individuals, even predicting performance from resting-state connectivity alone.These results demonstrate that whole-brain functional network strength provides a broadly applicable neuromarker of sustained attention.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Yale University, New Haven, Connecticut, USA.

ABSTRACT
Although attention plays a ubiquitous role in perception and cognition, researchers lack a simple way to measure a person's overall attentional abilities. Because behavioral measures are diverse and difficult to standardize, we pursued a neuromarker of an important aspect of attention, sustained attention, using functional magnetic resonance imaging. To this end, we identified functional brain networks whose strength during a sustained attention task predicted individual differences in performance. Models based on these networks generalized to previously unseen individuals, even predicting performance from resting-state connectivity alone. Furthermore, these same models predicted a clinical measure of attention--symptoms of attention deficit hyperactivity disorder--from resting-state connectivity in an independent sample of children and adolescents. These results demonstrate that whole-brain functional network strength provides a broadly applicable neuromarker of sustained attention.

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Functional connections predicting gradCPT performance and ADHD-RS scores. (A) The 757 edges in the high-attention network (predicting higher d′ values in the gradCPT sample and lower ADHD-RS scores in the ADHD-200 sample) are visualized in orange. The 630 edges in the low-attention network (predicting lower d′ values in the gradCPT sample and higher ADHD-RS scores in the ADHD-200 sample) are visualized in blue. Edges that appear in both the gradCPT and ADHD networks appear in bold. Macroscale regions include prefrontal cortex (PFC), motor cortex (Mot), insula (Ins), parietal (Par), temporal (Tem), occipital (Occ), limbic (including the cingulate cortex, amygdala and hippocampus; Lim), cerebellum (Cer), subcortical (thalamus and striatum; Sub), brainstem (Bsm). (B) Differences in the number of edges between each pair of macroscale regions, calculated by subtracting the number of edges in the low-attention network from the number in the high-attention network. (C) Differences in the number of edges between each pair of canonical networks, calculated by subtracting the number of edges in the low-attention network from the number in the high-attention network. Canonical networks28 include the subcortical-cerebellum (SubC), motor (MT), medial frontal (MF), visual I (VI), visual II (VII), visual association (VA), default mode (DM), and frontoparietal (FP).
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Figure 2: Functional connections predicting gradCPT performance and ADHD-RS scores. (A) The 757 edges in the high-attention network (predicting higher d′ values in the gradCPT sample and lower ADHD-RS scores in the ADHD-200 sample) are visualized in orange. The 630 edges in the low-attention network (predicting lower d′ values in the gradCPT sample and higher ADHD-RS scores in the ADHD-200 sample) are visualized in blue. Edges that appear in both the gradCPT and ADHD networks appear in bold. Macroscale regions include prefrontal cortex (PFC), motor cortex (Mot), insula (Ins), parietal (Par), temporal (Tem), occipital (Occ), limbic (including the cingulate cortex, amygdala and hippocampus; Lim), cerebellum (Cer), subcortical (thalamus and striatum; Sub), brainstem (Bsm). (B) Differences in the number of edges between each pair of macroscale regions, calculated by subtracting the number of edges in the low-attention network from the number in the high-attention network. (C) Differences in the number of edges between each pair of canonical networks, calculated by subtracting the number of edges in the low-attention network from the number in the high-attention network. Canonical networks28 include the subcortical-cerebellum (SubC), motor (MT), medial frontal (MF), visual I (VI), visual II (VII), visual association (VA), default mode (DM), and frontoparietal (FP).

Mentions: As an even stronger test of generalizability, we applied these gradCPT network models to a completely independent validation dataset consisting of resting-state fMRI scans from 113 children and adolescents (age range 8 to 16) with and without ADHD diagnoses. These data were collected at Peking University and provided by the ADHD-200 Consortium25. In this dataset, attentional ability was assessed using the ADHD Rating Scale IV26 (ADHD-RS), a clinical measure of ADHD on which a higher score indicates more frequent symptoms and/or a more severe attention deficit. In order to generalize our network model to this new dataset, we defined a high-attention network as the set of edges that appeared in the positive network of every iteration of the leave-one-out cross-validation described above in the section titled Internal validation: Prediction from task connectivity. A low-attention network was defined in an analogous way with edges whose strength was inversely correlated with d′ (Fig. 2). The high-attention network comprised 757 edges, and the low-attention network, 630 edges. In the full gradCPT sample, we constructed linear models relating high- and low-attention network strength (Sustained Attention Network, or SAN, models) to d′.


A neuromarker of sustained attention from whole-brain functional connectivity.

Rosenberg MD, Finn ES, Scheinost D, Papademetris X, Shen X, Constable RT, Chun MM - Nat. Neurosci. (2015)

Functional connections predicting gradCPT performance and ADHD-RS scores. (A) The 757 edges in the high-attention network (predicting higher d′ values in the gradCPT sample and lower ADHD-RS scores in the ADHD-200 sample) are visualized in orange. The 630 edges in the low-attention network (predicting lower d′ values in the gradCPT sample and higher ADHD-RS scores in the ADHD-200 sample) are visualized in blue. Edges that appear in both the gradCPT and ADHD networks appear in bold. Macroscale regions include prefrontal cortex (PFC), motor cortex (Mot), insula (Ins), parietal (Par), temporal (Tem), occipital (Occ), limbic (including the cingulate cortex, amygdala and hippocampus; Lim), cerebellum (Cer), subcortical (thalamus and striatum; Sub), brainstem (Bsm). (B) Differences in the number of edges between each pair of macroscale regions, calculated by subtracting the number of edges in the low-attention network from the number in the high-attention network. (C) Differences in the number of edges between each pair of canonical networks, calculated by subtracting the number of edges in the low-attention network from the number in the high-attention network. Canonical networks28 include the subcortical-cerebellum (SubC), motor (MT), medial frontal (MF), visual I (VI), visual II (VII), visual association (VA), default mode (DM), and frontoparietal (FP).
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Figure 2: Functional connections predicting gradCPT performance and ADHD-RS scores. (A) The 757 edges in the high-attention network (predicting higher d′ values in the gradCPT sample and lower ADHD-RS scores in the ADHD-200 sample) are visualized in orange. The 630 edges in the low-attention network (predicting lower d′ values in the gradCPT sample and higher ADHD-RS scores in the ADHD-200 sample) are visualized in blue. Edges that appear in both the gradCPT and ADHD networks appear in bold. Macroscale regions include prefrontal cortex (PFC), motor cortex (Mot), insula (Ins), parietal (Par), temporal (Tem), occipital (Occ), limbic (including the cingulate cortex, amygdala and hippocampus; Lim), cerebellum (Cer), subcortical (thalamus and striatum; Sub), brainstem (Bsm). (B) Differences in the number of edges between each pair of macroscale regions, calculated by subtracting the number of edges in the low-attention network from the number in the high-attention network. (C) Differences in the number of edges between each pair of canonical networks, calculated by subtracting the number of edges in the low-attention network from the number in the high-attention network. Canonical networks28 include the subcortical-cerebellum (SubC), motor (MT), medial frontal (MF), visual I (VI), visual II (VII), visual association (VA), default mode (DM), and frontoparietal (FP).
Mentions: As an even stronger test of generalizability, we applied these gradCPT network models to a completely independent validation dataset consisting of resting-state fMRI scans from 113 children and adolescents (age range 8 to 16) with and without ADHD diagnoses. These data were collected at Peking University and provided by the ADHD-200 Consortium25. In this dataset, attentional ability was assessed using the ADHD Rating Scale IV26 (ADHD-RS), a clinical measure of ADHD on which a higher score indicates more frequent symptoms and/or a more severe attention deficit. In order to generalize our network model to this new dataset, we defined a high-attention network as the set of edges that appeared in the positive network of every iteration of the leave-one-out cross-validation described above in the section titled Internal validation: Prediction from task connectivity. A low-attention network was defined in an analogous way with edges whose strength was inversely correlated with d′ (Fig. 2). The high-attention network comprised 757 edges, and the low-attention network, 630 edges. In the full gradCPT sample, we constructed linear models relating high- and low-attention network strength (Sustained Attention Network, or SAN, models) to d′.

Bottom Line: To this end, we identified functional brain networks whose strength during a sustained attention task predicted individual differences in performance.Models based on these networks generalized to previously unseen individuals, even predicting performance from resting-state connectivity alone.These results demonstrate that whole-brain functional network strength provides a broadly applicable neuromarker of sustained attention.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Yale University, New Haven, Connecticut, USA.

ABSTRACT
Although attention plays a ubiquitous role in perception and cognition, researchers lack a simple way to measure a person's overall attentional abilities. Because behavioral measures are diverse and difficult to standardize, we pursued a neuromarker of an important aspect of attention, sustained attention, using functional magnetic resonance imaging. To this end, we identified functional brain networks whose strength during a sustained attention task predicted individual differences in performance. Models based on these networks generalized to previously unseen individuals, even predicting performance from resting-state connectivity alone. Furthermore, these same models predicted a clinical measure of attention--symptoms of attention deficit hyperactivity disorder--from resting-state connectivity in an independent sample of children and adolescents. These results demonstrate that whole-brain functional network strength provides a broadly applicable neuromarker of sustained attention.

Show MeSH
Related in: MedlinePlus