Attention is a heterogeneous function theoretically divided into different systems. While functional magnetic resonance imaging (fMRI) has extensively characterized their functioning, the role of white matter in cognitive function has gained recent interest due to diffusion-weighted imaging advancements. However, most evidence relies on correlations between white matter properties and behavioral or cognitive measures. This study used a new method that combines the signal from distant voxels of fMRI images using the probability of structural connection given by high-resolution normative tractography. We analyzed three fMRI datasets with a visual perceptual task and three attentional manipulations: phasic alerting, spatial orienting, and executive attention. The phasic alerting network engaged temporal areas and their communication with frontal and parietal regions, with left hemisphere dominance. The orienting network involved bilateral fronto-parietal and midline regions communicating by association tracts and interhemispheric fibers. The executive attention network engaged a broad set of brain regions and white matter tracts connecting them, with a particular involvement of frontal areas and their connections with the rest of the brain. These results partially confirm and extend previous knowledge on the neural substrates of the attentional system, offering a more comprehensive understanding through the integration of structure and function.

Negative urgency (NU), or the tendency to act rashly when stress of negative affect is high, could be the result of an insufficient control of the ventromedial prefrontal cortex (vmPFC) over the striatum, through an impaired dopamine (DA) transmission. Therefore, we investigated in vivo human stress-induced DA release in the vmPFC, its relation with fronto-striatal functional connectivity (FC), and NU in daily life. In total, 12 female healthy participants performed a simultaneous [18F]fallypride PET and fMRI scan during which stress was induced. Regions displaying stress-induced DA release were identified and used to investigate stress-induced changes in fronto-striatal FC. Additionally, participants enrolled in an experience sampling study, reporting on daily life stress and rash actions over a 12-month-long period. Mixed models explored whether stress-induced DA release and FC moderated NU in daily life. Stress led to a lower FC between the vmPFC and dorsal striatum, but a higher FC between the vmPFC and contralateral ventral striatum. Participants with a higher FC between the vmPFC and dorsal striatum displayed more NU in daily life. A higher stress-induced DA release in the vmPFC was related to a higher stress-induced change in FC between the vmPFC and striatum. Participants with a higher DA release in the vmPFC displayed more NU in daily life. In conclusion, Stress could differentially impact fronto-striatal FC whereby the connectivity with the dorsal striatum is especially important for NU in daily life. This could be mediated by a higher, but not a lower, stress-induced DA release in the vmPFC.

BACKGROUND: If brain effective connectivity network modelling (ECN) could be accurately achieved, early diagnosis of neurodegenerative diseases would be possible. It has been observed in the literature that Dynamic Bayesian Network (DBN) based methods are more successful than others. However, DBNs have not been applied easily and tested much due to computational complexity problems in structure learning.
METHODS: This study introduces an advanced method for modelling brain ECNs using improved discrete DBN (Improved- dDBN) which addresses the computational challenges previously limiting DBN application, offering solutions that enable accurate and fast structure modeling.
RESULTS: The practical data and prior sizes needed for the convergence to the globally correct network structure are proved to be much smaller than the theoretical ones using simulated dDBN data. Besides, Hill Climbing is shown to converge to the true structure at a reasonable iteration step size when the appropriate data and prior sizes are used. Finally, importance of data quantization methods are analysed.
METHODS: The Improved-dDBN method performs better and robust, when compared to the existing methods for realistic scenarios such as varying graph complexity, various input conditions, noise cases and non-stationary connections. The data used in these tests is the simulated fMRI BOLD time series proposed in the literature.
CONCLUSIONS: Improved-dDBN is a good candidate to be used on real datasets to accelerate developments in brain ECN modeling and neuroscience. Appropriate data and prior sizes can be identified based on the approach proposed in this study for global and fast convergence.

Object classification has been proposed as a principal objective of the primate ventral visual stream and has been used as an optimization target for deep neural network models (DNNs) of the visual system. However, visual brain areas represent many different types of information, and optimizing for classification of object identity alone does not constrain how other information may be encoded in visual representations. Information about different scene parameters may be discarded altogether (\'invariance\'), represented in non-interfering subspaces of population activity (\'factorization\') or encoded in an entangled fashion. In this work, we provide evidence that factorization is a normative principle of biological visual representations. In the monkey ventral visual hierarchy, we found that factorization of object pose and background information from object identity increased in higher-level regions and strongly contributed to improving object identity decoding performance. We then conducted a large-scale analysis of factorization of individual scene parameters - lighting, background, camera viewpoint, and object pose - in a diverse library of DNN models of the visual system. Models which best matched neural, fMRI, and behavioral data from both monkeys and humans across 12 datasets tended to be those which factorized scene parameters most strongly. Notably, invariance to these parameters was not as consistently associated with matches to neural and behavioral data, suggesting that maintaining non-class information in factorized activity subspaces is often preferred to dropping it altogether. Thus, we propose that factorization of visual scene information is a widely used strategy in brains and DNN models thereof.
When looking at a picture, we can quickly identify a recognizable object, such as an apple, applying a single word label to it. Although extensive neuroscience research has focused on how human and monkey brains achieve this recognition, our understanding of how the brain and brain-like computer models interpret other complex aspects of a visual scene – such as object position and environmental context – remains incomplete. In particular, it was not clear to what extent object recognition comes at the expense of other important scene details. For example, various aspects of the scene might be processed simultaneously. On the other hand, general object recognition may interfere with processing of such details. To investigate this, Lindsey and Issa analyzed 12 monkey and human brain datasets, as well as numerous computer models, to explore how different aspects of a scene are encoded in neurons and how these aspects are represented by computational models. The analysis revealed that preventing effective separation and retention of information about object pose and environmental context worsened object identification in monkey cortex neurons. In addition, the computer models that were the most brain-like could independently preserve the other scene details without interfering with object identification. The findings suggest that human and monkey high level ventral visual processing systems are capable of representing the environment in a more complex way than previously appreciated. In the future, studying more brain activity data could help to identify how rich the encoded information is and how it might support other functions like spatial navigation. This knowledge could help to build computational models that process the information in the same way, potentially improving their understanding of real-world scenes.

Reading fluency, the ability to read quickly and accurately, is a critical marker of successful reading and is notoriously difficult to improve in reading disabled populations. Despite its importance to functional literacy, fluency is a relatively under-studied aspect of reading, and the neural correlates of reading fluency are not well understood. Here, we review the literature of the neural correlates of reading fluency as well as rapid automatized naming (RAN), a task that is robustly related to reading fluency. In a qualitative review of the neuroimaging literature, we evaluated structural and functional MRI studies of reading fluency in readers from a range of skill levels. This was followed by a quantitative activation likelihood estimate (ALE) meta-analysis of fMRI studies of reading speed and RAN measures. We anticipated that reading speed, relative to untimed reading and reading-related tasks, would harness ventral reading pathways that are thought to enable the fast, visual recognition of words. The qualitative review showed that speeded reading taps the entire canonical reading network. The meta-analysis indicated a stronger role of the ventral reading pathway in rapid reading and rapid naming. Both reviews identified regions outside the canonical reading network that contribute to reading fluency, such as the bilateral insula and superior parietal lobule. We suggest that fluent reading engages both domain-specific reading pathways as well as domain-general regions that support overall task performance and discuss future avenues of research to expand our understanding of the neural bases of fluent reading.

The medial prefrontal cortex (mPFC) has been implicated in the pathophysiology of social impairments including social fear. However, the precise subcortical partners that mediate mPFC dysfunction on social fear behaviour have not been identified. Employing a social fear conditioning paradigm, we induced robust social fear in mice and found that the lateral habenula (LHb) neurons and LHb-projecting mPFC neurons are synchronously activated during social fear expression. Moreover, optogenetic inhibition of the mPFC-LHb projection significantly reduced social fear responses. Importantly, consistent with animal studies, we observed an elevated prefrontal-habenular functional connectivity in subclinical individuals with higher social anxiety characterized by heightened social fear. These results unravel a crucial role of the prefrontal-habenular circuitry in social fear regulation and suggest that this pathway could serve as a potential target for the treatment of social fear symptom often observed in many psychiatric disorders.

Traditionally, two fundamentally different theoretical approaches have been used in emotion research to model (human) emotions: discrete emotion theories and dimensional approaches. More recent neurophysiological models like the hierarchical emotion theory suggest that both should be integrated. The aim of this review is to provide neurocognitive evidence for this perspective with a particular focus on experimental studies manipulating anxiety and/or curiosity. We searched for evidence that the neuronal correlates of discrete and dimensional emotional systems are tightly connected. Our review suggests that the ACC (anterior cingulate cortex) responds to both, anxiety, and curiosity. While amygdala activation has been primarily observed for anxiety, at least the NAcc (nucleus accumbens) responds to both, anxiety and curiosity. When these two areas closely collaborate, as indicated by strong connectivity, this may indicate emotion regulation, particularly when the situation is not predictable.

Schizophrenia, as a chronic and persistent disorder, exhibits working memory deficits across various stages of the disorder, yet the neural mechanisms underlying these deficits remain elusive with inconsistent neuroimaging findings. We aimed to compare the brain functional changes of working memory in patients at different stages: clinical high risk, first-episode psychosis, and long-term schizophrenia, using meta-analyses of functional magnetic resonance imaging studies. Following a systematic literature search, 56 whole-brain task-based functional magnetic resonance imaging studies (15 for clinical high risk, 16 for first-episode psychosis, and 25 for long-term schizophrenia) were included. The separate and pooled neurofunctional mechanisms among clinical high risk, first-episode psychosis, and long-term schizophrenia were generated by Seed-based d Mapping toolbox. The clinical high risk and first-episode psychosis groups exhibited overlapping hypoactivation in the right inferior parietal lobule, right middle frontal gyrus, and left superior parietal lobule, indicating key lesion sites in the early phase of schizophrenia. Individuals with first-episode psychosis showed lower activation in left inferior parietal lobule than those with long-term schizophrenia, reflecting a possible recovery process or more neural inefficiency. We concluded that SCZ represent as a continuum in the early stage of illness progression, while the neural bases are inversely changed with the development of illness course to long-term course.

Despite over two decades of neuroimaging research, a unanimous definition of the pattern of structural variation associated with autism spectrum disorder (ASD) has yet to be found. One potential impeding issue could be the sometimes ambiguous use of measurements of variations in gray matter volumes (GMV) or gray matter concentrations (GMC). In fact, while both can be calculated using voxel-based morphometry analysis, these may reflect different underlying pathological mechanisms. We conducted a coordinate-based meta-analysis, keeping apart GMV and GMC studies of subjects with ASD. Results showed distinct and non-overlapping patterns for the two measures. GMV decreases were evident in the cerebellum, while GMC decreases were mainly found in the temporal and frontal regions. GMV increases were found in the parietal, temporal, and frontal brain regions, while GMC increases were observed in the anterior cingulate cortex and middle frontal gyrus. Age-stratified analyses suggested that such variations are dynamic across the ASD lifespan. The present findings emphasize the importance of considering GMV and GMC as distinct yet synergistic indices in autism research.

Alzheimer\'s disease (AD) is a challenging neurodegenerative condition, necessitating early diagnosis and intervention. This research leverages machine learning (ML) and graph theory metrics, derived from resting-state functional magnetic resonance imaging (rs-fMRI) data to predict AD. Using Southwest University Adult Lifespan Dataset (SALD, age 21-76 years) and the Open Access Series of Imaging Studies (OASIS, age 64-95 years) dataset, containing 112 participants, various ML models were developed for the purpose of AD prediction. The study identifies key features for a comprehensive understanding of brain network topology and functional connectivity in AD. Through a 5-fold cross-validation, all models demonstrate substantial predictive capabilities (accuracy in 82-92% range), with the support vector machine model standing out as the best having an accuracy of 92%. Present study suggests that top 13 regions, identified based on most important discriminating features, have lost significant connections with thalamus. The functional connection strengths were consistently declined for substantia nigra, pars reticulata, substantia nigra, pars compacta, and nucleus accumbens among AD subjects as compared to healthy adults and aging individuals. The present finding corroborate with the earlier studies, employing various neuroimagining techniques. This research signifies the translational potential of a comprehensive approach integrating ML, graph theory and rs-fMRI analysis in AD prediction, offering potential biomarker for more accurate diagnostics and early prediction of AD.