Brain Activity after Intermittent Hypoxic Brain Condition in Rats

Hypoxic brain injury is accompanied by a decrease in various functions. It is also known that obstructive sleep apnea (OSA) can cause hypoxic brain injury. This study aimed to produce a model of an intermittent hypoxic brain condition in rats and determine the activity of the brain according to the duration of hypoxic exposure. Forty male Sprague–Dawley rats were divided into four groups: the control group (n = 10), the 2 h per day hypoxia exposure group (n = 10), the 4 h per day hypoxia exposure group (n = 10), and the 8 h per day hypoxia exposure group (n = 10). All rats were exposed to a hypoxic chamber containing 10% oxygen for five days. Positron emission tomography–computed tomography (PET-CT) brain images were acquired using a preclinical PET-CT scanner to evaluate the activity of brain metabolism. All the rats were subjected to normal conditions. After five days, PET-CT was performed to evaluate the recovery of brain metabolism. Western blot analysis and immunohistochemistry were performed with vascular endothelial growth factor (VEGF) and brain-derived neurotrophic factor (BDNF). The mean SUV was elevated in the 2 h per day and 4 h per day groups, and all brain regions showed increased metabolism except the amygdala on the left side, the auditory cortex on the right side, the frontal association cortex on the right side, the parietal association cortex on the right side, and the somatosensory cortex on the right side immediately after hypoxic exposure. However, there was no difference between 5 days rest after hypoxic exposure and control group. Western blot analysis revealed the most significant immunoreactivity for VEGF in the 2, 4, and 8 h per day groups compared with the control group and quantification of VEGF immunohistochemistry showed more expression in 2 and 4 h per day groups compared with the control group. However, there was no significant difference in immunoreactivity for BDNF among the groups. The duration of exposure to hypoxia may affect the activity of the brain due to angiogenesis after intermittent hypoxic brain conditions in rats.

Behavioral Prioritization Enhances Working Memory Precision and Neural Population Gain

Humans allocate visual working memory (WM) resource according to behavioral relevance, resulting in more precise memories for more important items. Theoretically, items may be maintained by feature-tuned neural populations, where the relative gain of the populations encoding each item determines precision. To test this hypothesis, we compared the amplitudes of delay period activity in the different parts of retinotopic maps representing each of several WM items, predicting the amplitudes would track behavioral priority. Using fMRI, we scanned participants while they remembered the location of multiple items over a WM delay and then reported the location of one probed item using a memory-guided saccade. Importantly, items were not equally probable to be probed (0.6, 0.3, 0.1, 0.0), which was indicated with a precue. We analyzed fMRI activity in 10 visual field maps in occipital, parietal, and frontal cortex known to be important for visual WM. In early visual cortex, but not association cortex, the amplitude of BOLD activation within voxels corresponding to the retinotopic location of visual WM items increased with the priority of the item. Interestingly, these results were contrasted with a common finding that higher-level brain regions had greater delay period activity, demonstrating a dissociation between the absolute amount of activity in a brain area and the activity of different spatially selective populations within it. These results suggest that the distribution of WM resources according to priority sculpts the relative gains of neural populations that encode items, offering a neural mechanism for how prioritization impacts memory precision.

Loneliness is linked to specific subregional alterations in hippocampus-default network co-variation

Social interaction complexity makes humans unique. But in times of social deprivation this strength risks to expose important vulnerabilities. Human social neuroscience studies have placed a premium on the default network (DN). In contrast, hippocampus (HC) subfields have been intensely studied in rodents and monkeys. To bridge these two literatures, we here quantified how DN subregions systematically co-vary with specific HC subfields in the context of subjective social isolation (i.e., loneliness). By co-decomposition using structural brain scans of ~40,000 UK Biobank participants, loneliness was specially linked to midline subregions in the uncovered DN patterns. These association cortex signatures coincided with concomitant HC patterns implicating especially CA1 and molecular layer. These patterns also showed a strong affiliation with the fornix white-matter tract and the nucleus accumbens. In addition, separable signatures of structural HC-DN co-variation had distinct associations with the genetic predisposition for loneliness at the population level.

Toward a Computational Understanding of Neuroaesthetics

Among the most challenging questions in the field of neuroaesthetics concerns how a piece of art comes to be liked in the first place. That is, how can the brain rapidly process a stimulus to form an aesthetic judgment even for stimuli never before encountered? In the article under discussion in this chapter, by leveraging computational methods in combination with behavioral and neuroimaging experiments the authors show that the brain does this by breaking a visual stimulus down into underlying features or attributes. These features are shared across objects, and weights over these features are integrated over to produce aesthetic judgments. This process is structured hierarchically in which elementary statistical properties of an image are combined to generate higher level features which in turn yield aesthetic value. Neuroimaging supports the implementation of this hierarchical integration along a gradient from early to higher order visual cortex extending into association cortex and ultimately converging in the anterior medial prefrontal cortex.

Long-range connections mirror and link microarchitectural and cognitive hierarchies in the human brain

Core features of higher-order cognition are hypothesized to be implemented via distributed cortical networks that are linked via long-range connections. However, these connections are biologically expensive, and it is unknown how the computational advantages long-range connections provide overcome the associated wiring costs. Our study investigated this question by exploring the relationship between long-range functional connections and local cortical microarchitecture. Specifically, our work (i) profiled distant cortical connectivity using resting-state fMRI and cortico-cortical geodesic distance mapping, (ii) assessed how long-range connections reflect local brain microarchitecture, and (iii) studied the microarchitectural similarity of regions connected through long-range connections. Analysis of two independent datasets indicated that sensory and motor areas had more clustered short-range connectivity patterns, while transmodal association cortices, including regions of the default mode network, were characterized by distributed, long-range connections. Confirmatory meta-analysis suggested that this topographical difference mirrored a shift in cognitive function, from perception/action towards emotional and social cognitive processing. Analysis of myelin-sensitive in vivo MRI in the same participants as well as post mortem histology and gene expression established that gradients in functional connectivity distance are paralleled by those present in cortical microarchitecture. Moreover, long-range connections were found to link together spatially remote regions of association cortex with an unexpectedly similar microarchitecture. These findings provide novel insights into how the organization of distributed functional networks in transmodal association cortex contribute to cognition, because they suggest that long-range connections link together distant islands of association cortex with similar microstructural features.

EFFECTS OF A DEEP LEARNING-BASED SMARTPHONE APPLICATION ON SHOULDER ABDUCTION KINEMATICS AND BRAIN ACTIVATION IN ADHESIVE CAPSULITIS: A RANDOMIZED CONTROLLED TRIAL

Patients with adhesive capsulitis (AC) demonstrate limited shoulder movement, often accompanied by pain. Common treatment methods include pain medication, and continuous passive movement (CPM). However, it is sometimes difficult to improve the reduction of pain and movement using a CPM intervention because the patient’s interest is diminished. In this study, we developed an innovative deep learning-based smartphone application (Funrehab exercise game (FEG)) to provide accurate kinematics movement and motivation as well as high-intensity and repetitive movements using deep learning. We compared the effects of CPM and FEG on brain activity and shoulder range of motion in patients with AC. Sixteen patients (males, [Formula: see text]; females, [Formula: see text]; mean age, [Formula: see text] years) with acute AC were randomized into either CPM group or FEG group 4 days/week for 2 weeks. The outcome measures were shoulder abduction kinematics movement and electroencephalography (EEG) brain activity (bilateral prefrontal, bilateral sensorimotor cortex, and somatosensory association cortex) during the intervention. The analysis of variance (ANOVA) test was performed at [Formula: see text], and the analysis demonstrated that FEG showed superior effects on shoulder abduction kinematics and brain [Formula: see text] and [Formula: see text]-wave activations compared to CPM. Our results provide a novel and promising clinical evidence that FEG can more effectively improve neurophysiological EEG data and shoulder abduction movements than CPM in patients with AC.

Neural dynamics of illusory tactile pulling sensations

The sensation of directional forces and their associated sensorimotor commands are inextricably intertwined, complicating the identification of brain circuits responsible for tactile pulling sensations. One hypothesis is that, like tactile frequency discrimination, pulling sensations are generated by early sensory-frontal activity. Alternatively, they may be generated later in the somatosensory association cortex. To dissociate these accounts and uncouple the pulling sensation from unrelated but correlated sensory and motor processing, we combined high-density EEG with an oddball paradigm and asymmetric vibration, which creates an illusory sensation of the hand being directionally pulled. Oddballs that created a pulling sensation in the opposite direction to common stimuli were compared to the same oddballs in the context of neutral common stimuli (symmetric vibration) and to neutral oddballs. Brain responses to having directional pulling expectations violated by directional stimuli were therefore isolated. Contrary to the sensory-frontal account, frontal N140 brain activity was actually larger for neutral than pulling oddballs. Instead, pulling sensations were associated with amplitude and latency modulations of midline P200 and P3b potentials, and specifically, to contralateral parietal lobe activity 280ms post-stimulus. The timing of this activity suggested pulling sensations involve spatial processing, such as tactile remapping between coordinate frames. Source localization showed this activity to be centered on the postcentral sulcus, superior parietal lobule and intraparietal sulcus, suggesting that pulling sensations arise via the processing of body position, tactile orientation and peripersonal space. Our results demonstrate how tactile illusions can uniquely disambiguate parietal contributions to somatosensation by removing unrelated sensory processing.

Functional Orderly Topography of Brain Networks Associated with Gene Expression Heterogeneity

The human cerebral cortex expanded much more relative to non-human primates and rodent in evolution, leading to a functional orderly topography of the brain networks. Here, we show that functional topography may be associated with gene expression heterogeneity in various brain structures. The neocortex exhibits greater gene expression heterogeneity, with lower housekeeping gene proportion, a longer mean path length, less clusters, and a lower degree of ordering of networks, compared to archicortical and subcortical area in human, rhesus macaque, and mouse brains consistently. In particular, the cerebellar cortex displays greater gene expression heterogeneity than cerebellar deep nuclei in the human brain, but not in the mouse brain, corresponding to the emergence of novel functions in the human cerebellar cortex. Moreover, the cortical areas with greater gene expression heterogeneity, primarily located in multimodal association cortex, tend to express genes with higher evolutionary rates and exhibit higher functional connectivity degree measured by resting-state fMRI, implying that such spatial pattern of cortical gene expression may be shaped by evolution and favorable for the specialization of higher cognitive functions. Together, the cross-species imaging genetic findings may provide convergent evidence to support the association between the orderly topography of brain function networks and gene expression.

Parallel systems for social and spatial reasoning within the cortical apex

What is the cognitive and neural architecture of systems for high-level reasoning and memory in humans? We ask this question using deep neuroimaging of individual human brains on various tasks involving reasoning and memory about familiar people, places, and objects. We find that thinking about people and places elicits responses in distinct but parallel networks within high-level association cortex, spanning the frontal, parietal, and temporal lobes. Person- and place-preferring brain regions were systematically yoked across multiple cortical zones. These regions were strongly category-selective, across visual, semantic, and episodic tasks, and were specifically functionally connected to other parts of association cortex with similar category preferences. These results demonstrate that selectivity for content domain is a widespread feature of high-level association cortex in humans. They support a theoretical model in which reasoning about people and places are supported by parallel cognitive and neural mechanisms.