Cerebral cortex最新文献

Adolescent depression presented higher risk of suicide than adult depression. However, the neurophysiological mechanisms underlying this phenomenon have not been elucidated. We aimed to identify structural covariance network alterations in depressed adolescents with suicidal behaviors to provide novel neuroimaging evidence for this condition. 64 first-episode, treatment-naïve depressed adolescent patients with suicidal behaviors and 48 healthy controls were enrolled. Nonnegative matrix factorization was used to identify the structural covariance networks. The Kullback-Leibler divergence method was applied to estimate the interregional relationships between the altered brain networks. Correlation analyses were conducted between altered brain networks and clinical characteristics. Patients had lower gray matter volumes in the anterior default mode network (DMN), visual network, sensorimotor network, and right executive control network than healthy controls. Morphological connections were altered in the anterior DMN, visual network, and right executive control network in patients. Correlation analyses revealed negative associations between morphological connections in anterior DMN-visual networks and illness duration in the patient group. This study revealed abnormal gray matter attributes in the anterior DMN, visual network, sensorimotor network, and executive control network in first-episode and treatment-naïve adolescent depression with suicide, which might reflect disease traits and provide essential neurobiological evidence for behavioral disturbances in depression.

Efforts in vision restoration have been focused on a condition called Retinitis Pigmentosa, where photoreceptors in the retina degenerate while the rest of the visual pathway remain mostly intact. Retinal implants that directly stimulate retinal ganglion cells have shown promising but limited results in patients so far. Apart from technical limitations, cross-modal plasticity of visual areas might contribute to this problem. We therefore investigated if the primary visual cortex (V1) of the rd10 mouse model for retinal degeneration became more sensitive to auditory or tactile sensory inputs. After reaching complete blindness confirmed by the lack of optomotor responses, activity in V1 and superior colliculus (SC) was recorded using Neuropixels probes. While we could not find any significant differences in tactile or auditory responses compared to wildtype mice, the local field potential revealed distinct oscillatory events (0.5-6 Hz) in V1 and SC resembling previously observed aberrant activity in the retina of rd10 mice. We therefore propose that aberrant retinal activity is transmitted to higher visual areas where it prevents cross-modal changes. Additionally, our results provide evidence of an intact visual cortex with promising potential for future therapeutic strategies to restore vision.

Physical exercise shows positive effects on cognitive functions such as working memory (WM) for older adults; however, large individual differences in response exist and the underlying mechanisms are not well understood. We tested the hypothesis that exercise-induced changes in cardiorespiratory fitness and leg strength would improve WM-related brain activity, which subsequently would improve WM performance. This study was based on the Umeå HIT study, a randomized controlled trial assessing the effects of watt-controlled supramaximal high-intensity interval training (HIT) versus moderate-intensity training for nonexercising older adults (N = 68). A subsample (n = 43, 66 to 79 years, 56% females) underwent task-based functional magnetic resonance imaging, testing WM. The outcomes of interest were change in WM performance, WM task activation, cardiorespiratory fitness, and leg strength. For WM performance, we found no significant between-group difference in change; however, there was a significant within-group increase for HIT in WM composites. For HIT, changes in leg strength significantly predicted increased right dorsolateral prefrontal cortex activation, which in turn predicted improved in-scanner WM task performance. Cardiorespiratory fitness did not predict WM-related functional change. These results indicate a specific physiological ingredient, namely leg strength gains, that is a potential mechanism in exercise-induced prefrontal activation and WM performance increases.

Understanding the distinct and shared neural mechanisms of generalized anxiety disorder, panic disorder, and social anxiety disorder could help address critical gaps in anxiety disorder diagnosis and treatment. This study aimed to explore common and disorder-specific brain activity and connectivity in patients with generalized anxiety disorder, panic disorder, and social anxiety disorder using resting-state functional magnetic resonance imaging. A total of 127 adults (33 with generalized anxiety disorder, 26 with panic disorder, 36 with social anxiety disorder, and 32 healthy controls) were recruited. We found that all individuals with generalized anxiety disorder, social anxiety disorder, and panic disorder showed abnormal activity in the prefrontal-limbic-cerebellar circuit and default mode network regions. Patients with panic disorder showed unique hypoconnectivity between the default mode network and sensory-motor network, whereas patients with social anxiety disorder showed unique extensive hyperconnectivity between the default mode network and other networks. In addition, increased activity in the left orbital inferior frontal gyrus was associated with depression and anxiety symptom severity, decreased activity in the left superior temporal gyrus was associated with panic symptom severity, and decreased activity in the right fusiform gyrus was correlated with social anxiety symptom severity. These findings provide valuable implications for understanding the neuropathology, diagnosing, and developing targeted therapeutic interventions for different subtypes of anxiety disorders.

Our senses receive a continuous stream of complex information, which we segment into discrete events. Previous research has related such events to neural states: temporally and regionally specific stable patterns of brain activity. The aim of this paper was to investigate whether there was evidence for top-down or bottom-up propagation of neural state boundaries. To do so, we used intracranial measurements with high temporal resolution while subjects were watching a movie. As this is the first study of neural states in intracranial data in the context of event segmentation, we also investigated whether known properties of neural states could be replicated. The neural state boundaries indeed aligned with stimulus features and between brain areas. Importantly, we found evidence for top-down propagation of neural state boundaries at the onsets and offsets of clauses. Interestingly, we did not observe a consistent top-down or bottom-up propagation in general across all timepoints, suggesting that neural state boundaries could propagate in both a top-down and bottom-up manner, with the direction depending on the stimulus input at that moment. Taken together, our findings provide new insights on how neural state boundaries are shared across brain regions and strengthen the foundation of studying neural states in electrophysiology.

The knowledge we have about how the world is structured is known to influence object recognition. One way this is demonstrated is through a congruency effect, where object recognition is faster and more accurate if items are presented in expected scene contexts. However, our understanding of the dynamic neural mechanisms that underlie congruency effects are under-explored. Using magnetoencephalography (MEG), we examine how the congruency between an object and a prior scene results in changes in the oscillatory activity in the brain, which regions underpin this effect, and whether congruency results arise from top-down or bottom-up modulations of connectivity. We observed that prior scene information impacts the processing of visual objects in behavior, neural activity, and connectivity. Processing objects that were incongruent with the prior scene resulted in slower reaction times, increased low frequency activity in the ventral visual pathway, and increased top-down connectivity from the anterior temporal lobe and frontal cortex to the posterior ventral temporal cortex. Our results reveal that the recurrent dynamics within the ventral visual pathway are modulated by the prior knowledge imbued by our surrounding environment, suggesting that the way we recognize objects is fundamentally linked to their context.

To make optimal decisions, intelligent agents must learn latent environmental states from discrete observations. Bayesian frameworks argue that integration of evidence over time allows us to refine our state belief by reducing uncertainty about alternate possibilities. How is this increasing belief precision during learning reflected in the brain? We propose that temporal neural variability should scale with the degree of reduction of uncertainty during learning. In a sample of 47 healthy adults, we found that BOLD signal variability (SDBOLD, as measured across independent learning trials) indeed compressed with successive exposure to decision-related evidence. Crucially, more accurate participants expressed greater SDBOLD compression primarily in default mode network regions, possibly reflecting the increasing precision of their latent state belief during more efficient learning. Further, computational modeling of behavior suggested that more accurate subjects held a more unbiased (flatter) prior belief over possible states that allowed for larger uncertainty reduction during learning, which was directly reflected in SDBOLD changes. Our results provide first evidence that neural variability compresses with increasing belief precision during effective learning, proposing a flexible mechanism for how we come to learn the probabilistic nature of the world around us.

Many of the environments that we navigate through every day are hierarchically organized-they consist of spaces nested within other spaces. How do our mind/brains represent such environments? To address this question, we familiarized participants with a virtual environment consisting of a building within a courtyard, with objects distributed throughout the courtyard and building interior. We then scanned them with functional MRI (fMRI) while they performed a memory task that required them to think about spatial relationships within and across the subspaces. Behavioral responses were less accurate and response times were longer on trials requiring integration across the subspaces compared to trials not requiring integration. FMRI response differences between integration and non-integration trials were observed in scene-responsive and medial temporal lobe brain regions, which were correlated the behavioral integration effects in retrosplenial complex, occipital place area, and hippocampus. Multivoxel pattern analyses provided additional evidence for representations in these brain regions that reflected the hierarchical organization of the environment. These results indicate that people form cognitive maps of nested spaces by dividing them into subspaces and using an active cognitive process to integrate the subspaces. Similar mechanisms might be used to support hierarchical coding in memory more broadly.

How choices are made between rewards is fundamental to understanding the behavior of humans and most other vertebrates. A key factor in the choices is reward-specific satiety, which is the sensory-specific decrease in the reward value of a particular reward when it is consumed to satiety. Another key factor is reward-specific motivation, the increase in the reward value of a reward when it is first provided. Here, we develop the theory based on experimental evidence in humans and other primates, that reward-specific satiety is implemented in orbitofrontal cortex reward value neurons by adaptation in the synapses from visual and taste cortical regions in which the neuronal firing is not influenced by reward-specific satiety. Correspondingly we develop the theory that reward-specific motivation (or incentive motivation) is implemented by shorter-term synaptic facilitation in the same synapses on to orbitofrontal cortex reward value neurons. We complement the theories with an integrate-and-fire neuronal network model of how these reward value computations are performed in the orbitofrontal cortex by synaptic adaptation and synaptic facilitation in the afferent connections to orbitofrontal cortex reward value neurons, to implement a profound influence on behavioral choice that has great adaptive value for humans and many other animals.