Dynamic Transitions Between Brain States Predict Auditory Attentional Fluctuations

Functional brain networks predicting sustained attention are not specific to perceptual modality

Sustained attention is essential for daily life and can be directed to information from different perceptual modalities, including audition and vision. Recently, cognitive neuroscience has aimed to identify neural predictors of behavior that generalize across datasets. Prior work has shown strong generalization of models trained to predict individual differences in sustained attention performance from patterns of fMRI functional connectivity. However, it is an open question whether predictions of sustained attention are specific to the perceptual modality in which they are trained. In the current study, we test whether connectome-based models predict performance on attention tasks performed in different modalities. We show first that a predefined network trained to predict adults' visual sustained attention performance generalizes to predict auditory sustained attention performance in three independent datasets (N 1 = 29, N 2 = 60, N 3 = 17). Next, we train new network models to predict performance on visual and auditory attention tasks separately. We find that functional networks are largely modality general, with both model-unique and shared model features predicting sustained attention performance in independent datasets regardless of task modality. Results support the supposition that visual and auditory sustained attention rely on shared neural mechanisms and demonstrate robust generalizability of whole-brain functional network models of sustained attention.

Striatal GABA levels correlate with risk sensitivity in monetary loss

Background

Decision-making under risk is a common challenge. It is known that risk-taking behavior varies between contexts of reward and punishment, yet the mechanisms underlying this asymmetry in risk sensitivity remain unclear.

Methods

This study used a monetary task to investigate neurochemical mechanisms and brain dynamics underpinning risk sensitivity. Twenty-eight participants engaged in a task requiring selection of visual stimuli to maximize monetary gains and minimize monetary losses. We modeled participant trial-and-error processes using reinforcement learning.

Results

Participants with higher subjective utility parameters showed risk preference in the gain domain (r = -0.59) and risk avoidance in the loss domain (r = -0.77). Magnetic resonance spectroscopy (MRS) revealed that risk avoidance in the loss domain was associated with γ-aminobutyric acid (GABA) levels in the ventral striatum (r = -0.42), but not in the insula (r = -0.15). Using functional magnetic resonance imaging (fMRI), we tested whether risk-sensitive brain dynamics contribute to participant risky choices. Energy landscape analyses demonstrated that higher switching rates between brain states, including the striatum and insula, were correlated with risk avoidance in the loss domain (r = -0.59), a relationship not observed in the gain domain (r = -0.02).

Conclusions

These findings from MRS and fMRI suggest that distinct mechanisms are involved in gain/loss decision making, mediated by subcortical neurometabolite levels and brain dynamic transitions.

Edge-Based General Linear Models Capture Moment-to-Moment Fluctuations in Attention

Although we must prioritize the processing of task-relevant information to navigate life, our ability to do so fluctuates across time. Previous work has identified fMRI functional connectivity (FC) networks that predict an individual's ability to sustain attention and vary with attentional state from 1 min to the next. However, traditional dynamic FC approaches typically lack the temporal precision to capture moment-to-moment network fluctuations. Recently, researchers have "unfurled" traditional FC matrices in "edge cofluctuation time series" which measure timepoint-by-timepoint cofluctuations between regions. Here we apply event-based and parametric fMRI analyses to edge time series to capture moment-to-moment fluctuations in networks related to attention. In two independent fMRI datasets examining young adults of both sexes in which participants performed a sustained attention task, we identified a reliable set of edges that rapidly deflects in response to rare task events. Another set of edges varies with continuous fluctuations in attention and overlaps with a previously defined set of edges associated with individual differences in sustained attention. Demonstrating that edge-based analyses are not simply redundant with traditional regions-of-interest-based approaches, up to one-third of reliably deflected edges were not predicted from univariate activity patterns alone. These results reveal the large potential in combining traditional fMRI analyses with edge time series to identify rapid reconfigurations in networks across the brain.

Aberrant brain dynamics of large-scale functional networks across schizophrenia and mood disorder

INTRODUCTION

The dynamics of large-scale networks, which are known as distributed sets of functionally synchronized brain regions and include the visual network (VIN), somatomotor network (SMN), dorsal attention network (DAN), salience network (SAN), limbic network (LIN), frontoparietal network (FPN), and default mode network (DMN), play important roles in emotional and cognitive processes in humans. Although disruptions in these large-scale networks are considered critical for the pathophysiological mechanisms of psychiatric disorders, their role in psychiatric disorders remains unknown. We aimed to elucidate the aberrant dynamics across large-scale networks in patients with schizophrenia (SZ) and mood disorders.

METHODS

We performed energy-landscape analysis to investigate the aberrant brain dynamics of seven large-scale networks across 50 healthy controls (HCs), 36 patients with SZ, and 42 patients with major depressive disorder (MDD) recruited at Wakayama Medical University. We identified major patterns of brain activity using energy-landscape analysis and estimated their duration, occurrence, and ease of transition.

RESULTS

We identified four major brain activity patterns that were characterized by the activation patterns of the DMN and VIN (state 1, DMN (-) VIN (-); state 2, DMN (+) VIN (+); state 3, DMN (-) VIN (+); and state 4, DMN (+) VIN (-)). The duration of state 1 and the occurrence of states 1 and 2 were shorter in the SZ group than in HCs and the MDD group, and the duration of state 3 was longer in the SZ group. The ease of transition between states 3 and 4 was larger in the SZ group than in the HCs and the MDD group. The ease of transition from state 3 to state 4 was negatively associated with verbal fluency in patients with SZ. The current study showed that the brain dynamics was more disrupted in SZ than in MDD.

CONCLUSIONS

Energy-landscape analysis revealed aberrant brain dynamics across large-scale networks between SZ and MDD and their associations with cognitive abilities in SZ, which cannot be captured by conventional functional connectivity analyses. These results provide new insights into the pathophysiological mechanisms underlying SZ and mood disorders.

Low-frequency variability in theta activity modulates the attention-fluctuation across task and resting states

Sustained attention is not constant but fluctuates influencing our task performance. Albeit intensive investigations, it remains unclear whether the attention-fluctuation during tasks is derived from its spontaneous fluctuation in the resting state. Here, we addressed this issue by investigating the attention-fluctuation in both task and resting states, through the EEG measurement of theta-variability. We found significant rest-task modulation of theta-variability, i.e., reduced theta-variability in the task state compared to the resting state. This task and rest modulation was manifested in the low-frequency of theta-variability (<0.1 Hz). Furthermore, the low-frequency theta-variability exhibited a significant rest-task correlation, however, only the low-frequency theta-variability in the task state but not in the resting state was correlated with the behavioral performance. These findings shed light on the low-frequency feature of attention-fluctuation, and advanced our understanding of sustained attention by suggesting that the theta-variability in low-frequencies was relevant to attention level in task state.

Prefrontal GABA and glutamate-glutamine levels affect sustained attention

Attention levels fluctuate during the course of daily activities. However, factors underlying sustained attention are still unknown. We investigated mechanisms of sustained attention using psychological, neuroimaging, and neurochemical approaches. Participants were scanned with functional magnetic resonance imaging (fMRI) while performing gradual-onset, continuous performance tasks (gradCPTs). In gradCPTs, narrations or visual scenes gradually changed from one to the next. Participants pressed a button for frequent Go trials as quickly as possible and withheld responses to infrequent No-go trials. Performance was better for the visual gradCPT than for the auditory gradCPT, but the 2 were correlated. The dorsal attention network was activated during intermittent responses, regardless of sensory modality. Reaction-time variability of gradCPTs was correlated with signal changes (SCs) in the left fronto-parietal regions. We also used magnetic resonance spectroscopy (MRS) to measure levels of glutamate-glutamine (Glx) and γ-aminobutyric acid (GABA) in the left prefrontal cortex (PFC). Glx levels were associated with performance under undemanding situations, whereas GABA levels were related to performance under demanding situations. Combined fMRI-MRS results demonstrated that SCs of the left PFC were positively correlated with neurometabolite levels. These findings suggest that a neural balance between excitation and inhibition is involved in attentional fluctuations and brain dynamics.

Edge-based general linear models capture high-frequency fluctuations in attention

Although we must prioritize the processing of task-relevant information to navigate life, our ability to do so fluctuates across time. Previous work has identified fMRI functional connectivity (FC) networks that predict an individual's ability to sustain attention and vary with attentional state from one minute to the next. However, traditional dynamic FC approaches typically lack the temporal precision to capture moment-by-moment network fluctuations. Recently, researchers have 'unfurled' traditional FC matrices in 'edge cofluctuation time series' which measure time point-by-time point cofluctuations between regions. Here we apply event-based and parametric fMRI analyses to edge time series to capture high-frequency fluctuations in networks related to attention. In two independent fMRI datasets in which participants performed a sustained attention task, we identified a reliable set of edges that rapidly deflects in response to rare task events. Another set of edges varies with continuous fluctuations in attention and overlaps with a previously defined set of edges associated with individual differences in sustained attention. Demonstrating that edge-based analyses are not simply redundant with traditional regions-of-interest based approaches, up to one-third of reliably deflected edges were not predicted from univariate activity patterns alone. These results reveal the large potential in combining traditional fMRI analyses with edge time series to identify rapid reconfigurations in networks across the brain.