Pattern similarity in the frontoparietal control network reflects an "off-veridical" template that optimizes target-match decisions during visual search

Theories of attention hypothesize the existence of an "attentional" or "target" template that contains task-relevant information in memory when searching for an object. The target template contributes to visual search by directing visual attention towards potential targets and serving as a decisional boundary for target identification. However, debate still exists regarding how template information is stored in the human brain. Here, we conducted a pattern-based fMRI study to assess how template information is encoded to optimize target-match decisions during visual search. To ensure that match decisions reflect visual search demands, we used a visual search paradigm in which all distractors were linearly separable but highly similar to the target and were known to shift the target representation away from the distractor features (Yu & Geng, 2019). In a separate match-to-sample probe task, we measured the target representation used for match decisions across two resting state networks that have long been hypothesized to maintain and control target information: the frontoparietal control network (FPCN) and the visual network (VisN). Our results showed that lateral prefrontal cortex in FPCN maintained the context-dependent "off-veridical" template; in contrast, VisN encoded a veridical copy of the target feature during match decisions. By using behavioral drift diffusion modeling, we verified that the decision criterion during visual search and the probe task relied on a common biased target template. Taken together, our results suggest that sensory-veridical information is transformed in lateral prefrontal cortex into an adaptive code of target-relevant information that optimizes decision processes during visual search.

Resting-state based prediction of task-related activation in patients with disorders of consciousness

ABSTRACTBackgroundAssessment of the level of awareness of people with disorders of consciousness (DOC) is clinically challenging, motivating several studies to combine brain imaging with machine learning to improve this process. While this work has shown promise, it has limited clinical utility, as misdiagnosis of DOC patients is relatively high. As machine learning algorithms rely on accurately labelled data, any error in diagnosis will be learned by the algorithm, resulting in an equally limited diagnostic tool. The goal of the present study is to overcome this problem by stratifying patients, not by diagnosis, but by their capacity to perform volitional tasks during functional magnetic resonance imaging (fMRI) scanning.MethodsA total of 71 patients were assessed for inclusion. They were excluded for the final analysis if they had large focal brain damage, excessive head motion during scanning, or suboptimal MRI preprocessing. Patients underwent both resting-state and task-based fMRI scanning. Univariate fMRI analysis was performed to determine if an individual patient had brain activity consistent with having retained volitional capacity (VC). Differences in resting brain network connectivity between patients with VC and patients without volitional capacity (non-VC) were measured. Connectivity data was then entered as input to a deep learning framework. We used a deep graph convolutional neural network (DGCNN) on connectivity data to identify a specific brain network that most significantly differentiates patients.FindingsWe included 30 patients in our final analysis. Univariate analysis revealed that 13 patients displayed signs of VC, while 17 did not. We found that resting-state connectivity between frontoparietal control and salience network was significantly different between VC and non-VC patients (T(28) = 3.347, p = 0.0023, Bonferroni corrected p = 0.042). Furthermore, we found that using frontoparietal control network connectivity as input to the DGCNN resulted in the best classification performance (test accuracy = 0.85; ROC AUC = 0.92).InterpretationWe found that the DGCNN performed best at discriminating between patients with VC when using only the frontoparietal control network as input to the model. The use of this deep learning method is a significant advance since its inherent flexibility permits the inclusion of both whole-brain and network-specific properties as input, allowing us to classify patients as either having or not having VC. This inclusion of multi-scale inputs (e.g. whole-brain and network-level) facilitates model interpretability and increases our understanding of the neurobiology of DOC. The results propose that the integrity of frontoparietal control network, a brain network well known to play a key role in executive functions and cognitive control, is essential for volitional capacity preservation in patients with DOC. The study also lays groundwork for development of a biomarker to aid in the diagnosis of DOC patients.RESEARCH IN CONTEXTEvidence before this studyDisorders of consciousness (DOC) are a group of severe brain disorders characterised by damage to the neural systems underlying wakefulness and awareness. DOC are often caused by traumatic brain injury, hypoxia, or neurodegenerative diseases. The motor and cognitive impairments in DOC patients make providing an accurate diagnosis very challenging. Diagnosis is primarily made at the bedside by assessing a patient’s response to motor commands.

Memory of Others' Disclosures Is Consolidated during Rest and Associated with Providing Support: Neural and Linguistic Evidence

Social scientists have documented the power of being heard: Disclosing emotional experiences to others promotes mental and physical health. Yet, far less is known about how listeners digest the sensitive information people share with them. We combined brain imaging and text analysis methods with a naturalistic emotional disclosure paradigm to assess how listeners form memories of others' disclosures. Neural and linguistic evidence support the hypothesis that listeners consolidate memories for others' disclosures during rest after listening and that their ability to do so facilitates subsequently providing the speakers with support. In Study 1, brain imaging methods showed that functional connectivity between the dorsomedial subsystem of the default network and frontoparietal control network increased during rest after listening to others' disclosures and predicted subsequent memory for their experiences. Moreover, graph analytic methods demonstrated that the left anterior temporal lobe may function as a connector hub between these two networks when consolidating memory for disclosures. In Study 2, linguistic analyses revealed other-focused thought increased during rest after listening to others' disclosures and predicted not only memory for the information disclosed but also whether listeners supported the speakers the next day. Collectively, these findings point to the important role of memory consolidation during rest in helping listeners respond supportively to others' disclosures. In our increasingly busy lives, pausing to briefly rest may not only help us care for ourselves but also help us care for others.

Distinct neural networks associated with obsession and delusion: a connectome-wide association study

Background Obsession and delusion are theoretically distinct from each other in terms of reality testing. Despite such phenomenological distinction, no extant studies have examined the identification of common and distinct neural correlates of obsession and delusion by employing biologically grounded methods. Here, we investigated dimensional effects of obsession and delusion spanning across the traditional diagnostic boundaries reflected upon the resting-state functional connectivity (RSFC) using connectome-wide association studies (CWAS). Methods Our study sample comprised of 96 patients with obsessive–compulsive disorder, 75 patients with schizophrenia, and 65 healthy controls. A connectome-wide analysis was conducted to examine the relationship between obsession and delusion severity and RFSC using multivariate distance-based matrix regression. Results Obsession was associated with the supplementary motor area, precentral gyrus, and superior parietal lobule, while delusion was associated with the precuneus. Follow-up seed-based RSFC and modularity analyses revealed that obsession was related to aberrant inter-network connectivity strength. Additional inter-network analyses demonstrated the association between obsession severity and inter-network connectivity between the frontoparietal control network and the dorsal attention network. Conclusions Our CWAS study based on the Research Domain Criteria (RDoC) provides novel evidence for the circuit-level functional dysconnectivity associated with obsession and delusion severity across diagnostic boundaries. Further refinement and accumulation of biomarkers from studies embedded within the RDoC framework would provide useful information in treating individuals who have some obsession or delusion symptoms but cannot be identified by the category of clinical symptoms alone.

Memory of others’ disclosures is consolidated during rest and associated with providing support: neural and linguistic evidence

Social scientists have documented the power of being heard: Disclosing emotional experiences to others promotes mental and physical health. Yet, far less is known about how listeners digest the sensitive information people share with them. We combined brain imaging and text analysis methods with a naturalistic emotional disclosure paradigm to assess how listeners form memories of others’ disclosures. Neural and linguistic evidence support the hypothesis that listeners consolidate memories for others’ disclosures during rest after listening and that their ability to do so facilitates subsequently providing the speakers with support. In Study 1, brain imaging methods showed that functional connectivity between the dorsomedial subsystem (dMPFC) of the default network and frontoparietal control network (FPCN) increased during rest after listening to others’ disclosures and predicted subsequent memory for their experiences. Moreover, graph analytic methods demonstrated that the left anterior temporal lobe (ATL) may function as a connector hub between these two networks when consolidating memory for disclosures. In Study 2, linguistic analyses revealed other-focused thought increased during rest after listening to others’ disclosures, and predicted not only memory for the information disclosed, but also whether listeners supported the speakers the next day. Collectively, these findings point to the important role of memory consolidation during rest in helping listeners respond supportively to others’ disclosures. In our increasingly busy lives, pausing to briefly rest may not only help us care for ourselves, but also help us care for others.