Momentary level of slow default mode network activity is associated with distinct propagation and connectivity patterns in the anesthetized mouse cortex.

Complex spatiotemporal changes of slow spontaneous activity occur in the form of propagating waves in the cortex, leading to the transient formation of a specific activation topography, followed by a transition in the topography. The topographies resemble the stimulation-induced activation patterns and the underlying structural projections, suggesting that they contain motifs of task-related activation. However, little is known about how propagation-mediated transitions between topographies are structured in terms of functional connectivity. Therefore, we investigated whether specific topographies or regions are associated with transitions involving long-range connections and hub modulation. We hypothesized that the activity level of the default mode network (DMN) at a given topography would affect the pattern of upcoming transitions, since high activity levels of the DMN are a distinct feature of the brain at rest. Using mesoscale voltage-sensitive dye imaging in the cortex of lightly anesthetized mice, we revealed that momentary levels of DMN activity are associated with distinct patterns of activity propagation and functional connectivity. High levels of DMN activity led to activity propagation across secondary and association cortices, increasing the centrality of a main hub region, whereas low-level activity led to global, diffuse, yet efficient changes in functional connectivity. Furthermore, low levels of activity resulted in increased long-range connectivity between frontal and posterior regions of the cortex. Our results indicate that DMN activity is associated with functional connectivity and wave propagation patterns, raising the possibility that the DMN may be involved in the modulation of long-range information processing associated with upcoming transitions. NEW & NOTEWORTHY Using voltage-sensitive dye imaging with high spatiotemporal resolution, we have revealed that increased DMN activity is associated with activity propagation to secondary/association cortices, whereas decreased activity is associated with stronger long-range frontal-posterior connections in the mouse cortex. Hub metric and global functional connectivity parameters were accompanied by activity level changes. These results indicate that the DMN may aid in modulating the structure of transitions.

Magnetoencephalographic study of event-related fields and cortical oscillatory changes during cutaneous warmth processing.

Thermoreception is an important cutaneous sense, which plays a role in the maintenance of our body temperature and in the detection of potential noxious heat stimulation. In this study, we investigated event-related fields (ERFs) and neural oscillatory activities, which were modulated by warmth stimulation. We developed a warmth stimulator that could elicit a warmth sensation, without pain or tactile sensation, by using a deep-penetrating 980-nm diode laser. The index finger of each participant (n = 24) was irradiated with the laser warmth stimulus, and the cortical responses were measured using magnetoencephalography (MEG). The ERFs and oscillatory responses had late latencies (∼1.3 s and 1.0-1.5 s for ERFs and oscillatory responses, respectively), which could be explained by a slow conduction velocity of warmth-specific C-fibers. Cortical sources of warmth-related ERFs were seen in the bilateral primary and secondary somatosensory cortices (SI and SII), posterior part of the anterior cingulate cortex (pACC), ipsilateral primary motor, and premotor cortex. Thus, we suggested that SI, SII, and pACC play a role in processing the warmth sensation. Time-frequency analysis demonstrated the suppression of the alpha (8-13 Hz) and beta (18-23 Hz) band power in the bilateral sensorimotor cortex. We proposed that the suppressions in alpha and beta band power are involved in the automatic response to the input of warmth stimulation and sensorimotor interactions. The delta band power (1-4 Hz) increased in the frontal, temporal, and cingulate cortices. The power changes in delta band might be related with the attentional processes during the warmth stimulation.

Digital mammography dataset for breast cancer diagnosis research (DMID) with breast mass segmentation analysis.

Purpose:In the last two decades, computer-aided detection and diagnosis (CAD) systems have been created to help radiologists discover and diagnose lesions observed on breast imaging tests. These systems can serve as a second opinion tool for the radiologist. However, developing algorithms for identifying and diagnosing breast lesions relies heavily on mammographic datasets. Many existing databases do not consider all the needs necessary for research and study, such as mammographic masks, radiology reports, breast composition, etc. This paper aims to introduce and describe a new mammographic database. Methods:The proposed dataset comprises mammograms with several lesions, such as masses, calcifications, architectural distortions, and asymmetries. In addition, a radiologist report is provided, describing the details of the breast, such as breast density, description of abnormality present, condition of the skin, nipple and pectoral muscles, etc., for each mammogram. Results:We present results of commonly used segmentation framework trained on our proposed dataset. We used information regarding the class of abnormalities (benign or malignant) and breast tissue density provided with each mammogram to analyze the segmentation model's performance concerning these parameters. Conclusion:The presented dataset provides diverse mammogram images to develop and train models for breast cancer diagnosis applications.

Dynamic pattern decoding of source-reconstructed MEG or EEG data: Perspective of multivariate pattern analysis and signal leakage.

Recently, an increasing number of studies have employed multivariate pattern analysis (MVPA) rather than univariate analysis for the dynamic pattern decoding of event-related responses recorded with a MEG/EEG sensor. The use of the MVPA approach for source-reconstructed MEG/EEG data is uncommon. For these data, we need to consider the source orientation information and the signal leakage among brain regions. In the present study, we evaluate the perspective of the MVPA approach in the context of source orientation information and signal leakage in source-reconstructed MEG data. We perform face vs. tool object category decoding (FvsT-OCD) of event-related responses from single or multiple voxels from a brain region using a univariate analysis approach and/or the MVPA approach. We also propose and perform symmetric signal leakage correction of source-reconstructed data using an independent component analysis-based approach. FvsT-OCD using single voxel information shows higher sensitivity for the MVPA approach than univariate analysis, as the MVPA approach efficiently utilizes information on all three dipole orientations and is less affected by inter-subject variability. The MVPA approach shows higher sensitivity for FvsT-OCD when considering information from multiple voxels than for a single voxel in a brain region. This finding suggests that the MVPA approach captures the latent multivoxel distributed pattern. However, the results may be partly or entirely attributable to signal leakage between brain regions, as the sensitivity is substantially reduced after signal leakage correction. A consideration of signal leakage is therefore essential during the evaluation of MVPA outcomes.

Approximate Subject Specific Pseudo MRI from an Available MRI Dataset for MEG Source Imaging.

Computation of headmodel and sourcemodel from the subject's MRI scan is an essential step for source localization of magnetoencephalography (MEG) (or EEG) sensor signals. In the absence of a real MRI scan, pseudo MRI (i.e., associated headmodel and sourcemodel) is often approximated from an available standard MRI template or pool of MRI scans considering the subject's digitized head surface. In the present study, we approximated two types of pseudo MRI (i.e., associated headmodel and sourcemodel) using an available pool of MRI scans with the focus on MEG source imaging. The first was the first rank pseudo MRI; that is, the MRI scan in the dataset having the lowest objective registration error (ORE) after being registered (rigid body transformation with isotropic scaling) to the subject's digitized head surface. The second was the averaged rank pseudo MRI that is generated by averaging of headmodels and sourcemodels from multiple MRI scans respectively, after being registered to the subject's digitized head surface. Subject level analysis showed that the mean upper bound of source location error for the approximated sourcemodel in reference to the real one was 10 ± 3 mm for the averaged rank pseudo MRI, which was significantly lower than the first rank pseudo MRI approach. Functional group source response in the brain to visual stimulation in the form of event-related power (ERP) at the time latency of peak amplitude showed noticeably identical source distribution for first rank pseudo MRI, averaged rank pseudo MRI, and real MRI. The source localization error for functional peak response was significantly lower for averaged rank pseudo MRI compared to first rank pseudo MRI. We conclude that it is feasible to use approximated pseudo MRI, particularly the averaged rank pseudo MRI, as a substitute for real MRI without losing the generality of the functional group source response.

Modality-specific spectral dynamics in response to visual and tactile sequential shape information processing tasks: An MEG study using multivariate pattern classification analysis.

Brain regions that respond to more than one sensory modality are characterized as multisensory regions. Studies on the processing of shape or object information have revealed recruitment of the lateral occipital cortex, posterior parietal cortex, and other regions regardless of input sensory modalities. However, it remains unknown whether such regions show similar (modality-invariant) or different (modality-specific) neural oscillatory dynamics, as recorded using magnetoencephalography (MEG), in response to identical shape information processing tasks delivered to different sensory modalities. Modality-invariant or modality-specific neural oscillatory dynamics indirectly suggest modality-independent or modality-dependent participation of particular brain regions, respectively. Therefore, this study investigated the modality-specificity of neural oscillatory dynamics in the form of spectral power modulation patterns in response to visual and tactile sequential shape-processing tasks that are well-matched in terms of speed and content between the sensory modalities. Task-related changes in spectral power modulation and differences in spectral power modulation between sensory modalities were investigated at source-space (voxel) level, using a multivariate pattern classification (MVPC) approach. Additionally, whole analyses were extended from the voxel level to the independent-component level to take account of signal leakage effects caused by inverse solution. The modality-specific spectral dynamics in multisensory and higher-order brain regions, such as the lateral occipital cortex, posterior parietal cortex, inferior temporal cortex, and other brain regions, showed task-related modulation in response to both sensory modalities. This suggests modality-dependency of such brain regions on the input sensory modality for sequential shape-information processing.

Evaluation of Phase-Amplitude Coupling in Resting State Magnetoencephalographic Signals: Effect of Surrogates and Evaluation Approach.

Phase-amplitude coupling (PAC) plays an important role in neural communication and computation. Interestingly, recent studies have indicated the presence of ubiquitous PAC phenomenon even during the resting state. Despite the importance of PAC phenomenon, estimation of significant physiological PAC is challenging because of the lack of appropriate surrogate measures to control false positives caused by non-physiological PAC. Therefore, in the present study, we evaluated PAC phenomenon during resting-state magnetoencephalography (MEG) signal and considered various surrogate measures and computational approaches widely used in the literature in addition to proposing new ones. We evaluated PAC phenomenon over the entire length of the MEG signal and for multiple shorter time segments. The results indicate that the extent of PAC phenomenon mainly depends on the surrogate measures and PAC computational methods used, as well as the evaluation approach. After a careful and critical evaluation, we found that resting-state MEG signals failed to exhibit ubiquitous PAC phenomenon, contrary to what has been suggested previously.

Sensory modality-specific spatio-temporal dynamics in response to counting tasks.

From perception to behavior, the human brain processes information in a flexible and abstract manner independent of an input sensory modality. However, the mechanism of such multisensory neural information processing in the brain remains under debate. Relatedly, studies often aim to investigate whether certain brain regions behave in a modality-specific manner or invariantly. Previous studies regarding multisensory information processing have commonly reported only on the activation of brain regions in response to unimodal or multimodal sensory stimuli. However, less attention has been given to the modality effect on the dynamics of such regions, which could advance our understanding of neuronal information processing. In this study, we investigated whether brain regions show modality-specific or invariant high-temporal dynamics. Electrocardiogram (EEG) was recorded from healthy, normal subjects during beep-, flash- and click-counting tasks, which corresponded to auditory, visual and tactile modalities, respectively. EEG dynamics regarding event-related spectral perturbations (ERSP) in ICA time-series data were compared across the sensory modalities using a multivariate pattern analysis. We found modality-specific EEG dynamics in the prefrontal cortex, whereas we found modality-specific and cross-modal dynamics in the early visual cortex.

Nearest-neighbor classifier as a tool for classification of protein families.

Knowledge about protein function is essential in understanding the biological processes. A specific class or family of protein shares common structural and chemical properties amongst its member sequences. The set of properties that display its unique characteristics for clearly classifying a protein sequence into its corresponding protein family needs to be studied. Our study of these important properties conducted on four major classes of proteins namely Globins, Homeoboxes, Heat Shock proteins (HSP) and Kinase have shown that frequency of twenty naturally occurring amino acids, hydrophobic content of protein, molecular weight of protein, isoelectric point of protein, secondary structure composition of amino acid residues as helices, coils and sheets and the composition of helices, coils and sheets in the secondary structure topology plays a significant role in correctly classifying the protein into its corresponding class or family as indicated by the overall efficiency of Nearest Neighbor Classifier as 84.92%.