Cerebral cortex最新文献

Whether spontaneous or induced by a tedious task, the transition from a focused mental state to mind wandering is a complex one, possibly involving adjacent mental states and extending over minutes or even hours. This complexity cannot be captured by relying solely on subjective reports of mind wandering. To characterize the transition in a mind-wandering-inducing tone counting task, in addition we collected subjective reports of thought generation along with task performance as a measure of cognitive control and EEG measures, namely auditory probe evoked potentials (AEP) and ongoing 8-12 Hz alpha-band amplitude. We analyzed the cross-correlations between timeseries of these observations to reveal their contributions over time to the occurrence of task-focused and mind-wandering states. Thought generation and cognitive control showed overall a yoked dynamics, in which thought production increased when cognitive control decreased. Prior to mind wandering however, they became decoupled after transient increases in cognitive control-related alpha amplitude. The decoupling allows transitory mental states beyond the unidimensional focused/wandering continuum. Time lags of these effects were on the order of several minutes, with 4-10 min for that of alpha amplitude. We discuss the implications for mind wandering and related mental states, and for mind-wandering prediction applications.

There has been increased interest in understanding the neural substrates of intelligence and several human traits from neuroimaging data. Deep learning can be used to predict different cognitive measures, such as general and fluid intelligence, from different functional magnetic resonance imaging experiments providing information about the main brain areas involved in these predictions. Using neuroimaging and behavioral data from 874 subjects provided by the Human Connectome Project, we predicted various cognitive scores using dynamic functional connectivity derived from language and working memory functional magnetic resonance imaging task states, using a 360-region multimodal atlas. The deep model joins multiscale convolutional and long short-term memory layers and was trained under a 10-fold stratified cross-validation. We removed the confounding effects of gender, age, total brain volume, motion and the multiband reconstruction algorithm using multiple linear regression. We can explain 17.1% and 16% of general intelligence variance for working memory and language tasks, respectively. We showed that task-based dynamic functional connectivity has more predictive power than resting-state dynamic functional connectivity when compared to the literature and that removing confounders significantly reduces the prediction performance. No specific cortical network showed significant relevance in the prediction of general and fluid intelligence, suggesting a spatial homogeneous distribution of the intelligence construct in the brain.

Impaired episodic memory is the primary feature of early Alzheimer's disease (AD), but not all memories are equally affected. Patients with AD and amnestic Mild Cognitive Impairment (aMCI) remember pictures better than words, to a greater extent than healthy elderly. We investigated neural mechanisms for visual object recognition in 30 patients (14 AD, 16 aMCI) and 36 cognitively unimpaired healthy (19 in the "preclinical" stage of AD). Event-related brain potentials (ERPs) were recorded while participants performed a visual object recognition task. Hippocampal occupancy (integrity), amyloid (florbetapir) PET, and neuropsychological measures of verbal & visual memory, executive function were also collected. A right-frontal ERP recognition effect (500-700 ms post-stimulus) was seen in cognitively unimpaired participants only, and significantly correlated with memory and executive function abilities. A later right-posterior negative ERP effect (700-900 ms) correlated with visual memory abilities across participants with low verbal memory ability, and may reflect a compensatory mechanism. A correlation of this retrieval-related negativity with right hippocampal occupancy (r = 0.55), implicates the hippocampus in the engagement of compensatory perceptual retrieval mechanisms. Our results suggest that early AD patients are impaired in goal-directed retrieval processing, but may engage compensatory perceptual mechanisms which rely on hippocampal function.

Humans perceive a pulse, or beat, underlying musical rhythm. Beat strength correlates with activity in the basal ganglia and supplementary motor area, suggesting these regions support beat perception. However, the basal ganglia and supplementary motor area are part of a general rhythm and timing network (regardless of the beat) and may also represent basic rhythmic features (e.g. tempo, number of onsets). To characterize the encoding of beat-related and other basic rhythmic features, we used representational similarity analysis. During functional magnetic resonance imaging, participants heard 12 rhythms-4 strong-beat, 4 weak-beat, and 4 nonbeat. Multi-voxel activity patterns for each rhythm were tested to determine which brain areas were beat-sensitive: those in which activity patterns showed greater dissimilarities between rhythms of different beat strength than between rhythms of similar beat strength. Indeed, putamen and supplementary motor area activity patterns were significantly dissimilar for strong-beat and nonbeat conditions. Next, we tested whether basic rhythmic features or models of beat strength (counterevidence scores) predicted activity patterns. We found again that activity pattern dissimilarity in supplementary motor area and putamen correlated with beat strength models, not basic features. Beat strength models also correlated with activity pattern dissimilarities in the inferior frontal gyrus and inferior parietal lobe, though these regions encoded beat and rhythm simultaneously and were not driven by beat alone.

Neurovascular coupling plays an important role in the progression of Alzheimer's disease. However, it is unclear how ultrasound stimulation modulates neurovascular coupling in Alzheimer's disease. Here, we found that (i) transcranial ultrasound stimulation modulates the time domain and frequency domain characteristics of cerebral blood oxygen metabolism in Alzheimer's disease mice; (ii) transcranial ultrasound stimulation can significantly modulate the relative power of theta and gamma frequency of local field potential in Alzheimer's disease mice; and (iii) transcranial ultrasound stimulation can significantly modulate the neurovascular coupling in time domain and frequency domain induced by forepaw electrical stimulation in Alzheimer's disease mice. It provides a research basis for the clinical application of transcranial ultrasound stimulation in Alzheimer's disease patients.

Parkinson's disease is characterized by multiple neurotransmitter systems beyond the traditional dopaminergic pathway, yet their influence on volumetric alterations is not well comprehended. We included 72 de novo, drug-naïve Parkinson's disease patients and 61 healthy controls. Voxel-wise gray matter volume was evaluated between Parkinson's disease and healthy controls, as well as among Parkinson's disease subgroups categorized by clinical manifestations. The Juspace toolbox was utilized to explore the spatial relationship between gray matter atrophy and neurotransmitter distribution. Parkinson's disease patients exhibited widespread GM atrophy in the cerebral and cerebellar regions, with spatial correlations with various neurotransmitter receptors (FDR-P < 0.05). Cognitively impaired Parkinson's disease patients showed gray matter atrophy in the left middle temporal atrophy, which is associated with serotoninergic, dopaminergic, cholinergic, and glutamatergic receptors (FDR-P < 0.05). Postural and gait disorder patients showed atrophy in the right precuneus, which is correlated with serotoninergic, dopaminergic, gamma-aminobutyric acid, and opioid receptors (FDR-P < 0.05). Patients with anxiety showed atrophy in the right superior orbital frontal region; those with depression showed atrophy in the left lingual and right inferior occipital regions. Both conditions were linked to serotoninergic and dopaminergic receptors (FDR-P < 0.05). Parkinson's disease patients exhibited regional gray matter atrophy with a significant distribution of specific neurotransmitters, which might provide insights into the underlying pathophysiology of clinical manifestations and develop targeted intervention strategies.

Deciphering the rich repertoire of mouse behavior is crucial for understanding the functions of both the healthy and diseased brain. However, the current landscape lacks effective, affordable, and accessible methods for acquiring such data, especially when employing multiple cameras simultaneously. We have developed REVEALS (Rodent Behavior Multi-Camera Laboratory Acquisition), a graphical user interface for acquiring rodent behavioral data via commonly used USB3 cameras. REVEALS allows for user-friendly control of recording from one or multiple cameras simultaneously while streamlining the data acquisition process, enabling researchers to collect and analyze large datasets efficiently. We release this software package as a stand-alone, open-source framework for researchers to use and modify according to their needs. We describe the details of the graphical user interface implementation, including the camera control software and the video recording functionality. We validate results demonstrating the graphical user interface's stability, reliability, and accuracy for capturing rodent behavior using DeepLabCut in various behavioral tasks. REVEALS can be incorporated into existing DeepLabCut, MoSeq, or other custom pipelines to analyze complex behavior. In summary, REVEALS offers an interface for collecting behavioral data from single or multiple perspectives, which, when combined with deep learning algorithms, enables the scientific community to identify and characterize complex behavioral phenotypes.

Error monitoring, which plays a crucial role in shaping adaptive behavior, is influenced by a complex interplay of affective and motivational factors. Understanding these associations often proves challenging due to the intricate nature of these variables. With the aim of addressing previous inconsistencies and methodological gaps, in this study, we utilized network analysis to investigate the relationship between affective and motivational individual differences and error monitoring. We employed six Gaussian Graphical Models on a non-clinical population ($N$ = 236) to examine the conditional dependence between the amplitude of response-related potentials (error-related negativity; correct-related negativity) and 29 self-report measures related to anxiety, depression, obsessive thoughts, compulsive behavior, and motivation while adjusting for covariates: age, handedness, and latency of error-related negativity and correct-related negativity. We then validated our results on an independent sample of 107 participants. Our findings revealed unique associations between error-related negativity amplitudes and specific traits. Notably, more pronounced error-related negativity amplitudes were associated with increased rumination and obsessing, and decreased reward sensitivity. Importantly, in our non-clinical sample, error-related negativity was not directly associated with trait anxiety. These results underscore the nuanced effects of affective and motivational traits on error processing in healthy population.

Visual working memory (VWM) is a core cognitive function wherein visual information is stored and manipulated over short periods. Response errors in VWM tasks arise from the imprecise memory of target items, swaps between targets and nontargets, and random guesses. However, it remains unclear whether these types of errors are underpinned by distinct neural networks. To answer this question, we recruited 80 healthy adults to perform delayed estimation tasks and acquired their resting-state functional magnetic resonance imaging scans. The tasks required participants to reproduce the memorized visual feature along continuous scales, which, combined with mixture distribution modeling, allowed us to estimate the measures of memory precision, swap errors, and random guesses. Intrinsic functional connectivity within and between different networks, identified using a hierarchical clustering approach, was estimated for each participant. Our analyses revealed that higher memory precision was associated with increased connectivity within a frontal-opercular network, as well as between the dorsal attention network and an angular-gyrus-cerebellar network. We also found that coupling between the frontoparietal control network and the cingulo-opercular network contributes to both memory precision and random guesses. Our findings demonstrate that distinct sources of variability in VWM performance are underpinned by different yet partially overlapping intrinsic functional networks.

Although the structure of cortical networks provides the necessary substrate for their neuronal activity, the structure alone does not suffice to understand the activity. Leveraging the increasing availability of human data, we developed a multi-scale, spiking network model of human cortex to investigate the relationship between structure and dynamics. In this model, each area in one hemisphere of the Desikan-Killiany parcellation is represented by a $1,mathrm{mm^{2}}$ column with a layered structure. The model aggregates data across multiple modalities, including electron microscopy, electrophysiology, morphological reconstructions, and diffusion tensor imaging, into a coherent framework. It predicts activity on all scales from the single-neuron spiking activity to the area-level functional connectivity. We compared the model activity with human electrophysiological data and human resting-state functional magnetic resonance imaging (fMRI) data. This comparison reveals that the model can reproduce aspects of both spiking statistics and fMRI correlations if the inter-areal connections are sufficiently strong. Furthermore, we study the propagation of a single-spike perturbation and macroscopic fluctuations through the network. The open-source model serves as an integrative platform for further refinements and future in silico studies of human cortical structure, dynamics, and function.