Search

BackgroundThe Rey-Osterrieth Complex Figure Test (RCFT) is a neuropsychological test that is widely used to assess visual memory and visuoconstructional deficits in patients with cognitive impairment, including Alzheimer disease (AD). Patients with AD have an increased tendency for exhibiting extraordinary behaviors in the RCFT for selecting the drawing area, organizing the figure, and deciding the order of images, among other activities. However, the conventional scoring system based on pen and paper has a limited ability to reflect these detailed behaviors. ObjectiveThis study aims to establish a scoring system that addresses not only the spatial arrangement of the finished drawing but also the drawing process of patients with AD by using digital pen data. MethodsA digital pen and tablet were used to copy complex figures. The stroke patterns and kinetics of normal controls (NCs) and patients with early-onset AD (EOAD) and late-onset AD (LOAD) were analyzed by comparing the pen tip trajectory, spatial arrangement, and similarity of the finished drawings. ResultsPatients with AD copied the figure in a more fragmented way with a longer pause than NCs (EOAD: P=.045; LOAD: P=.01). Patients with AD showed an increased tendency to draw the figures closer toward the target image in comparison with the NCs (EOAD: P=.005; LOAD: P=.01) Patients with AD showed the lower accuracy than NCs (EOAD: P=.004; LOAD: P=.002). Patients with EOAD and LOAD showed similar but slightly different drawing behaviors, especially in space use and in the initial stage of drawing. ConclusionsThe digitalized complex figure test evaluated copying performance quantitatively and further elucidated the patients’ ongoing process during copying. We believe that this novel approach can be used as a digital biomarker of AD. In addition, the repeatability of the test will delineate the process of executive functions and constructional organization abilities with disease progression.

Responding to threats in the real world demands a sophisticated orchestration of freeze and flight behaviors dynamically modulated by the neural activity. While the medial prefrontal cortex-basolateral amygdala (mPFC-BLA) network is known to play a pivotal role in coordinating these responses, the mechanisms underlying its population dynamics remain vague. As traditional Pavlovian fear conditioning models fall short in encapsulating the breadth of natural escape behaviors, we introduce a novel dataset to bridge this gap, capturing the defensive strategies of mice against a spider robot in a natural-like environment. The adaptive escape behaviors and concurrent mPFC-BLA activity in eight mice were monitored using wireless local field potential (LFP) and video recordings, both individually and in groups. Our data offers a unique avenue to explore the neural dynamics that govern fear- and vigilance-induced threat responses in isolated and social contexts. Supplemented by detailed methodologies and validation, the dataset allows for the analysis of the transient neural oscillatory dynamics, with prospective implications for the fields of neuroscience, robotics, and artificial intelligence. [ABSTRACT FROM AUTHOR]

Although mice are social, multiple animals’ neural activities are rarely explored. To characterise the neural activities during multi-brain interaction, we simultaneously recorded local field potentials (LFP) in the prefrontal cortex of four mice. The social context and locomotive states predominately modulated the entire LFP structure. The power of lower frequency bands—delta to alpha—were correlated with each other and anti-correlated with gamma power. The high-to-low-power ratio (HLR) provided a useful measure to understand LFP changes along the change of behavioural and locomotive states. The HLR during huddled conditions was lower than that during non-huddled conditions, dividing the social context into two. Multi-brain analyses of HLR indicated that the mice in the group displayed high cross-correlation. The mice in the group often showed unilateral precedence of HLR by Granger causality analysis, possibly comprising a hierarchical social structure. Overall, this study shows the importance of the social environment in brain dynamics and emphasises the simultaneous multi-brain recordings in social neuroscience. [ABSTRACT FROM AUTHOR]

Neurovascular coupling is the generic term for changes in cerebral metabolic rate of oxygen (CMRO(2)), cerebral blood flow, and cerebral blood volume related to brain activity. The goal of this paper is to better understand the effects of neurovascular coupling in the visual and motor cortices using frequency-domain near-infrared spectroscopy. Maps of concentration changes in oxyhemoglobin [O(2)Hb], deoxyhemoglobin [HHb], and total hemoglobin of the visual and motor cortices were generated during stimulation using a reversing checkerboard screen and palm-squeezing, respectively. Seven healthy volunteers of 18-37 years of age were included. In the visual cortex the patterns of [O(2)Hb] and [HHb] were strongly linearly correlated (r(2) > 0.8 in 13 of a total of 24 locations). In 20 locations the change in [O(2)Hb] was larger than 0.25 microM. The mean slope of the linear regression between [O(2)Hb] and [HHb] was -3.93 +/- 0.31 (SE). The patterns of the [O(2)Hb] and [HHb] traces over the motor cortex looked different. The [O(2)Hb] reached its maximum change a few seconds before the [HHb] reached its minimum. This was confirmed by the linear regression analysis (r(2) > 0.8 in none of 40 locations). In 20 locations the change in [O(2)Hb] was larger than 0.25 microM. The mean slope of the regression line was -1.76 +/- 0.20, which is significantly higher than that in the motor cortex (P < 0.0000001). Patterns of [O(2)Hb] and [HHb] differ among cortex areas. This implies that the regulation of perfusion in the visual cortex is different from that in the motor cortex. There is evidence that the CMRO(2) increases substantially in the visual cortex, while this is not the case for the motor cortex.

The aim of our study was to explore the possibility of detecting hemodynamic changes in the brain using the phase of the intensity modulated optical signal. To obtain optical signals with the highest possible signal-to-noise ratio, we performed a series of simultaneous NIRS-fMRI measurements, with subsequent correlation of the time courses of both measurements. The cognitive paradigm used arithmetic calculations, with optical signals acquired with sensors placed on the forehead. Measurements were done on seven healthy subjects. In five subjects we demonstrated correlation between the hemodynamic signals obtained using NIRS and BOLD fMRI. In four subjects correlation was found for the hemodynamic signal obtained using the phase of the intensity modulated signal.

The aim of this study was to compare functional cerebral hemodynamic signals obtained simultaneously by near infrared spectroscopy (NIRS) and by functional magnetic resonance imaging (fMRI). The contribution of superficial layers (skin and skull) to the NIRS signal was also assessed. Both methods were used to generate functional maps of the motor cortex area during a periodic sequence of stimulation by finger motion and rest. In all subjects we found a good collocation of the brain activity centers revealed by both methods. We also found a high temporal correlation between the BOLD signal (fMRI) and the deoxy-hemoglobin concentration (NIRS) in the subjects who exhibited low fluctuations in superficial head tissues.

We present high-density EEG datasets of auditory steady-state responses (ASSRs) recorded from the cortex of freely moving mice with or without optogenetic stimulation of basal forebrain parvalbumin (BF-PV) neurons, known as a subcortical hub circuit for the global workspace. The dataset of ASSRs without BF-PV stimulation (dataset 1) contains raw 36-channel EEG epochs of ASSRs elicited by 10, 20, 30, 40, and 50 Hz click trains and time stamps of stimulations. The dataset of ASSRs with BF-PV stimulation (dataset 2) contains raw 36-channel EEG epochs of 40-Hz ASSRs during BF-PV stimulation with latencies of 0, 6.25, 12.5, and 18.75 ms and time stamps of stimulations. We provide the datasets and step-by-step tutorial analysis scripts written in Python, allowing for descriptions of the event-related potentials, spectrograms, and the topography of power. We complement this experimental dataset with simulation results using a time-dependent perturbation on coupled oscillators. This publicly available dataset will be beneficial to the experimental and computational neuroscientists. Measurement(s) functional brain measurement • response to auditory stimulus Technology Type(s) electroencephalography (EEG) Factor Type(s) stimulation frequency • optogenetic stimulation of basal forebrain parvalbumin neurons Sample Characteristic - Organism Mus musculus Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12758720 [ABSTRACT FROM AUTHOR]