deepPGSegNet: MRI-based pituitary gland segmentation using deep learning.

Introduction: In clinical research on pituitary disorders, pituitary gland (PG) segmentation plays a pivotal role, which impacts the diagnosis and treatment of conditions such as endocrine dysfunctions and visual impairments. Manual segmentation, which is the traditional method, is tedious and susceptible to inter-observer differences. Thus, this study introduces an automated solution, utilizing deep learning, for PG segmentation from magnetic resonance imaging (MRI). Methods: A total of 153 university students were enrolled, and their MRI images were used to build a training dataset and ground truth data through manual segmentation of the PGs. A model was trained employing data augmentation and a three-dimensional U-Net architecture with a five-fold cross-validation. A predefined field of view was applied to highlight the PG region to optimize memory usage. The model's performance was tested on an independent dataset. The model's performance was tested on an independent dataset for evaluating accuracy, precision, recall, and an F1 score. Results and discussion: The model achieved a training accuracy, precision, recall, and an F1 score of 92.7%, 0.87, 0.91, and 0.89, respectively. Moreover, the study explored the relationship between PG morphology and age using the model. The results indicated a significant association between PG volume and midsagittal area with age. These findings suggest that a precise volumetric PG analysis through an automated segmentation can greatly enhance diagnostic accuracy and surveillance of pituitary disorders.

DeepParcellation: A novel deep learning method for robust brain magnetic resonance imaging parcellation in older East Asians.

Accurate parcellation of cortical regions is crucial for distinguishing morphometric changes in aged brains, particularly in degenerative brain diseases. Normal aging and neurodegeneration precipitate brain structural changes, leading to distinct tissue contrast and shape in people aged >60 years. Manual parcellation by trained radiologists can yield a highly accurate outline of the brain; however, analyzing large datasets is laborious and expensive. Alternatively, newly-developed computational models can quickly and accurately conduct brain parcellation, although thus far only for the brains of Caucasian individuals. To develop a computational model for the brain parcellation of older East Asians, we trained magnetic resonance images of dimensions 256 × 256 × 256 on 5,035 brains of older East Asians (Gwangju Alzheimer's and Related Dementia) and 2,535 brains of Caucasians. The novel N-way strategy combining three memory reduction techniques inception blocks, dilated convolutions, and attention gates was adopted for our model to overcome the intrinsic memory requirement problem. Our method proved to be compatible with the commonly used parcellation model for Caucasians and showed higher similarity and robust reliability in older aged and East Asian groups. In addition, several brain regions showing the superiority of the parcellation suggest that DeepParcellation has a great potential for applications in neurodegenerative diseases such as Alzheimer's disease.

Involvement of the intrinsic functional network of the red nucleus in complex behavioral processing.

Previous studies suggested the possibility that the red nucleus (RN) is involved in other cognitive functions than motion per se, even though such functions have yet to be clarified. We investigated the activation of RN during several tasks and its intrinsic functional network associated with social cognition and musical practice. The tasks included finger tapping, n-back, and memory recall tasks. Region of interest for RN was identified through those tasks, anatomical information of RN, and a brain atlas. The intrinsic functional network was identified for RN by an analysis of connectivity between RN and other regions typically involved in seven known resting state functional networks with RN used as the seed region. Association of the RN network with a psychological trait of the interpersonal reactivity index and musical training years revealed subnetworks that included empathy related regions or music practice related regions. These social or highly coordinated motor activity represent the most complex functions ever known to involve the RN, adding further evidence for the multifunctional roles of RN. These discoveries may lead to a new direction of investigations to clarify probable novel roles for RN in high-level human behavior.

Effects of Physiological Signal Removal on Resting-State Functional MRI Metrics.

Resting-state fMRIs (rs-fMRIs) have been widely used for investigation of diverse brain functions, including brain cognition. The rs-fMRI has easily elucidated rs-fMRI metrics, such as the fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC), and degree centrality (DC). To increase the applicability of these metrics, higher reliability is required by reducing confounders that are not related to the functional connectivity signal. Many previous studies already demonstrated the effects of physiological artifact removal from rs-fMRI data, but few have evaluated the effect on rs-fMRI metrics. In this study, we examined the effect of physiological noise correction on the most common rs-fMRI metrics. We calculated the intraclass correlation coefficient of repeated measurements on parcellated brain areas by applying physiological noise correction based on the RETROICOR method. Then, we evaluated the correction effect for five rs-fMRI metrics for the whole brain: FC, fALFF, ReHo, VMHC, and DC. The correction effect depended not only on the brain region, but also on the metric. Among the five metrics, the reliability in terms of the mean value of all ROIs was significantly improved for FC, but it deteriorated for fALFF, with no significant differences for ReHo, VMHC, and DC. Therefore, the decision on whether to perform the physiological correction should be based on the type of metric used.

Brain Plasticity Reflects Specialized Cognitive Development Induced by Musical Training.

Learning a musical instrument requires a long period of training and might induce structural and functional changes in the brain. Previous studies have shown brain plasticity resulting from training with a musical instrument. However, these studies did not distinguish the effects on brain plasticity of specific musical instruments as they examined the brain of musicians who had learned a single musical instrument/genre and did not control for confounding factors, such as common or interactive effects involved in music training. To address this research gap, the present work investigated musicians who had experience with both a piano and a wind instrument, for example, flute, trumpet, clarinet etc. By examining the difference between the 2 musical instruments in the same subject, we avoided the effects common to all musical instruments and the confounding factors. Therefore, we identified several high-tier brain areas displaying a brain plasticity specific to each musical instrument. Our findings show that learning a musical instrument might result in the development of high cognitive functions reflecting the skills/abilities unique to the instrument played.

Measurement of ultra-fast signal progression related to face processing by 7T fMRI.

Given that the brain is a dynamic system, the temporal characteristics of brain function are important. Previous functional magnetic resonance imaging (fMRI) studies have attempted to overcome the limitations of temporal resolution to investigate dynamic states of brain activity. However, finding an fMRI method with sufficient temporal resolution to keep up with the progress of neuronal signals in the brain is challenging. This study aimed to detect between-hemisphere signal progression, occurring on a timescale of tens of milliseconds, in the ventral brain regions involved in face processing. To this end, we devised an inter-stimulus interval (ISI) stimulation scheme and used a 7T MRI system to obtain fMRI signals with a high signal-to-noise ratio. We conducted two experiments: one to measure signal suppression depending on the ISI and another to measure the relationship between the amount of suppression and the ISI. These two experiments enabled us to measure the signal transfer time from a brain region in the ventral visual stream to its counterpart in the opposite hemisphere through the corpus callosum. These findings demonstrate the feasibility of using fMRI to measure ultra-fast signals (tens of milliseconds) and could facilitate the elucidation of further aspects of dynamic brain function.

Correlation between Changes in the Transverse Relaxation Time and Electromyographic Measurements of the Superficial Masseter and Temporal Muscles.

We analyzed the correlations between the T2 shift and integrated electromyographic (iEMG) values in the masseter and temporal muscles. Six healthy adults engaged in a clenching task over two durations at various bite forces. We evaluated the mean T2 shift per voxel and assessed their correlations with iEMG using a linear mixed model. The regression coefficients were different for each muscle type, similar for the left and right sides, and decreased upon doubling duration.

Selected Topics Relating to Functional MRI Study of the Brain.

The authors selected some interesting current topics among many in the field of functional MRI (fMRI) of the brain. The selection was based on authours' immediate interests in exploring these aspects further; the topics are presented and discussed along with their perspectives. If progress can be made in these areas, it would be very advantageous to the field of brain research. The topics are (I) Detectable MRI signals in response to functional activity of the brain, including the current status of neurocurrent MRI; (II) Vascular-dependent and vascular-independent MRI signals, leading to the distinction of functional and structural MRI; (III) Functional specificity and functional connectivity of local sites, including differences between task-fMRI and resting state fMRI; (IV) Functional networks: an example of application to assessing the vocational aptitude test by fMRI; (V) Neural oscillation relevant to the formation of fMRI signals and of networks; (VI) Upgrading fMRI to "information-content-reflecting" fMRI, discussed as one of the prospects of near-future fMRI.

Estimation of vocational aptitudes using functional brain networks.

The success of human life in modern society is highly dependent on occupation. Therefore, it is very important for people to identify and develop a career plan that best suits their aptitude. Traditional test batteries for vocational aptitudes are not oriented to measure developmental changes in job suitability because repeated measurements can introduce bias as the content of the test batteries is learned. In this study, we attempted to objectively assess vocational aptitudes by measuring functional brain networks and identified functional brain networks that intrinsically represented vocational aptitudes for 19 job divisions in a General Aptitude Test Battery. In addition, we derived classifiers based on these networks to predict the aptitudes of our test participants for each job division. Our results suggest that the measurement of brain function can indeed yield an objective evaluation of vocational aptitudes; this technique will enable a person to follow changes in one's job suitability with additional training or learning, paving a new way to advise people on career development.

A Set of Functional Brain Networks for the Comprehensive Evaluation of Human Characteristics.

Many human characteristics must be evaluated to comprehensively understand an individual, and measurements of the corresponding cognition/behavior are required. Brain imaging by functional MRI (fMRI) has been widely used to examine brain function related to human cognition/behavior. However, few aspects of cognition/behavior of individuals or experimental groups can be examined through task-based fMRI. Recently, resting state fMRI (rs-fMRI) signals have been shown to represent functional infrastructure in the brain that is highly involved in processing information related to cognition/behavior. Using rs-fMRI may allow diverse information about the brain through a single MRI scan to be obtained, as rs-fMRI does not require stimulus tasks. In this study, we attempted to identify a set of functional networks representing cognition/behavior that are related to a wide variety of human characteristics and to evaluate these characteristics using rs-fMRI data. If possible, these findings would support the potential of rs-fMRI to provide diverse information about the brain. We used resting-state fMRI and a set of 130 psychometric parameters that cover most human characteristics, including those related to intelligence and emotional quotients and social ability/skill. We identified 163 brain regions by VBM analysis using regression analysis with 130 psychometric parameters. Next, using a 163 × 163 correlation matrix, we identified functional networks related to 111 of the 130 psychometric parameters. Finally, we made an 8-class support vector machine classifiers corresponding to these 111 functional networks. Our results demonstrate that rs-fMRI signals contain intrinsic information about brain function related to cognition/behaviors and that this set of 111 networks/classifiers can be used to comprehensively evaluate human characteristics.

Estimation of physiological sources of nonlinearity in blood oxygenation level-dependent contrast signals.

Blood oxygenation level-dependent (BOLD) contrast appears through a variation in the transverse relaxation rate of magnetic resonance signals induced by neurovascular coupling and is known to have nonlinear characteristics along echo time (TE) due to the intra-vasculature. However, the physiological causes of this nonlinearity are unclear. We attempted to estimate the physiological information related to the nonlinearity of BOLD signals by using a two-compartment model. For this purpose, we used a multi-echo gradient-echo echo-planar imaging sequence and developed a computational method to estimate the physiological information from the TE-dependent BOLD signals. The results showed that the average chemical exchange time in the intra-vasculature varied during stimulation, which might be the essential source of the nonlinearity.

Attitude and practice of physical activity and social problem-solving ability among university students.

BACKGROUND: Effective social problem-solving abilities can contribute to decreased risk of poor mental health. In addition, physical activity has a favorable effect on mental health. These previous studies suggest that physical activity and social problem-solving ability can interact by helping to sustain mental health. The present study aimed to determine the association between attitude and practice of physical activity and social problem-solving ability among university students. METHODS: Information on physical activity and social problem-solving was collected using a self-administered questionnaire. We analyzed data from 185 students who participated in the questionnaire surveys and psychological tests. Social problem-solving as measured by the Social Problem-Solving Inventory-Revised (SPSI-R) (median score 10.85) was the dependent variable. Multiple logistic regression analysis was employed to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) for higher SPSI-R according to physical activity categories. RESULTS: The multiple logistic regression analysis indicated that the ORs (95% CI) in reference to participants who said they never considered exercising were 2.08 (0.69-6.93), 1.62 (0.55-5.26), 2.78 (0.86-9.77), and 6.23 (1.81-23.97) for participants who did not exercise but intended to start, tried to exercise but did not, exercised but not regularly, and exercised regularly, respectively. This finding suggested that positive linear association between physical activity and social problem-solving ability (p value for linear trend < 0.01). CONCLUSIONS: The present findings suggest that regular physical activity or intention to start physical activity may be an effective strategy to improve social problem-solving ability.

Steady-state and dynamic network modes for perceptual expectation.

Perceptual expectation can attenuate repetition suppression, the stimulus-induced neuronal response generated by repeated stimulation, suggesting that repetition suppression is a top-down modulatory phenomenon. However, it is still unclear which high-level brain areas are involved and how they interact with low-level brain areas. Further, the temporal range over which perceptual expectation can effectively attenuate repetition suppression effects remains unclear. To elucidate the details of this top-down modulatory process, we used two short and long inter-stimulus intervals for a perceptual expectation paradigm of paired stimulation. We found that top-down modulation enhanced the response to the unexpected stimulus when repetition suppression was weak and that the effect disappeared at 1,000 ms prior to stimulus exposure. The high-level areas involved in this process included the left inferior frontal gyrus (IFG_L) and left parietal lobule (IPL_L). We also found two systems providing modulatory input to the right fusiform face area (FFA_R): one from IFG_L and the other from IPL_L. Most importantly, we identified two states of networks through which perceptual expectation modulates sensory responses: one is a dynamic state and the other is a steady state. Our results provide the first functional magnetic resonance imaging (fMRI) evidence of temporally nested networks in brain processing.

Structural and functional plasticity specific to musical training with wind instruments.

Numerous neuroimaging studies have shown structural and functional changes resulting from musical training. Among these studies, changes in primary sensory areas are mostly related to motor functions. In this study, we looked for some similar functional and structural changes in other functional modalities, such as somatosensory function, by examining the effects of musical training with wind instruments. We found significant changes in two aspects of neuroplasticity, cortical thickness, and resting-state neuronal networks. A group of subjects with several years of continuous musical training and who are currently playing in university wind ensembles showed differences in cortical thickness in lip- and tongue-related brain areas vs. non-music playing subjects. Cortical thickness in lip-related brain areas was significantly thicker and that in tongue-related areas was significantly thinner in the music playing group compared with that in the non-music playing group. Association analysis of lip-related areas in the music playing group showed that the increase in cortical thickness was caused by musical training. In addition, seed-based correlation analysis showed differential activation in the precentral gyrus and supplementary motor areas (SMA) between the music and non-music playing groups. These results suggest that high-intensity training with specific musical instruments could induce structural changes in related anatomical areas and could also generate a new functional neuronal network in the brain.

Evaluation of the Contribution of Signals Originating from Large Blood Vessels to Signals of Functionally Specific Brain Areas.

The fusiform face area (FFA) is known to play a pivotal role in face processing. The FFA is located in the ventral region, at the base of the brain, through which large blood vessels run. The location of the FFA via functional MRI (fMRI) may be influenced by these large blood vessels. Responses of large blood vessels may not exactly correspond to neuronal activity in a target area, because they may be diluted and influenced by inflow effects. In this study, we investigated the effects of large blood vessels in the FFA, that is, whether the FFA includes large blood vessels and/or whether inflow signals contribute to fMRI signals of the FFA. For this purpose, we used susceptibility-weighted imaging (SWI) sequences to visualize large blood vessels and dual-echo gradient-echo echo-planar imaging (GE-EPI) to measure inflow effects. These results showed that the location and response signals of the FFA were not influenced by large blood vessels or inflow effects, although large blood vessels were located near the FFA. Therefore, the data from the FFA obtained by individual analysis were robust to large blood vessels but leaving a warning that the data obtained by group analysis may be prone to large blood vessels.

Fast Imaging Technique for fMRI: Consecutive Multishot Echo Planar Imaging Accelerated with GRAPPA Technique.

This study was to evaluate the proposed consecutive multishot echo planar imaging (cmsEPI) combined with a parallel imaging technique in terms of signal-to-noise ratio (SNR) and acceleration for a functional imaging study. We developed cmsEPI sequence using both consecutively acquired multishot EPI segments and variable flip angles to minimize the delay between segments and to maximize the SNR, respectively. We also combined cmsEPI with the generalized autocalibrating partially parallel acquisitions (GRAPPA) method. Temporal SNRs were measured at different acceleration factors and number of segments for functional sensitivity evaluation. We also examined the geometric distortions, which inherently occurred in EPI sequence. The practical acceleration factors, R = 2 or R = 3, of the proposed technique improved the temporal SNR by maximally 18% in phantom test and by averagely 8.2% in in vivo experiment, compared to cmsEPI without parallel imaging. The data collection time was decreased in inverse proportion to the acceleration factor as well. The improved temporal SNR resulted in better statistical power when evaluated on the functional response of the brain. In this study, we demonstrated that the combination of cmsEPI with the parallel imaging technique could provide the improved functional sensitivity for functional imaging study, compensating for the lower SNR by cmsEPI.

Microscopic functional specificity can be predicted from fMRI signals in ventral visual areas.

Functional areas specialized for recognition can be activated by a non-preferred stimulus as well as a preferred stimulus. The functional magnetic resonance imaging signals detected in response to different stimuli in an area may have the same or different amplitudes. However, it is uncertain whether the responses originate from the same neuronal populations or heterogeneous ones. To address this concern, we propose a novel method that uses multi-echo echo-planar imaging sequences to evaluate changes in the transverse relaxation profile caused by stimulation. According to this method, the areas related with visual recognition, i.e. fusiform face area and parahippocampal place area, have different transverse relaxation profiles to preferred and non-preferred stimuli, which can be considered as reflecting a difference in neuronal population processing stimuli in those areas. The proposed method can be useful for probing the microscopic functional specificity of brain areas.

Intermixed structure of voxels with different hemispheric characteristics in the fusiform face area.

The fusiform face area, a high-level visual area, is pivotal in processing facial information. This area receives inputs from the left and right visual fields unlike the primary visual area, which only receives inputs from its contralateral visual field. Response of the fusiform face area to ipsilateral stimulation depends on the signals crossing over at the corpus callosum. We investigated the distribution of voxel-wise activation to determine whether ipsilateral-dominant voxels exist in the fusiform face area using high spatial resolution functional MRI at 7 T. We further examined the possible functional differences between ipsilateral-dominant and contralateral-dominant voxels. By unilateral visual field stimulation, we detected ipsilateral-dominant voxels in the right fusiform face area. Their distribution was spatially heterogeneous. We tested upright and inverted facial stimulation confined to unilateral visual fields and found that these ipsilateral-dominant voxels had a different functional nature from contralateral-dominant voxels.

Involvement of low-level visual areas in hemispheric superiority for face processing.

Previous studies on laterality in face processing have indicated superiority of the right hemisphere in discriminating and recognizing faces; however, the reasons for this feature are poorly understood. We employed functional MRI (fMRI) to elucidate the origin of this feature and used a paired-stimulus paradigm in which face pairs were presented unilaterally at the left or right visual hemifield of the participants. Each face in a pair was at a different position in the visual field. Refractory suppression in the fMRI response was observed bilaterally at the fusiform face area (FFA) for the same face pairs when pictures were presented in the left visual hemifield. In contrast, suppression was observed bilaterally at the FFA for the different as well as for the same face pairs when pictures were presented in the right visual hemifield. This pattern indicated inferior discrimination ability for paired stimuli presented to the right visual hemifield. These observations, along with changes in bilaterally interlocked responses at the FFA, suggest that low-level visual areas, and not high-level face areas, are strongly associated with the superiority of the right hemisphere in face processing.

An fMRI study of neuronal interactions in face-selective areas of the brain.

Functionally activated sites usually have multiple specificities where coactivation is induced by inputs with different informational content. Using typical voxel-based functional mapping (univariate analysis), it is possible to show the presence of coactivation; however, it is not possible to distinguish whether coactivation occurs by independent or interactive processing units unless measurements are made at an extremely high spatial resolution. To address this problem, we have utilized the phenomenon of refractory suppression observable when two stimuli were given with a temporal separation of approximately 100 ms. This phenomenon is known to be noticeable in primary sensory areas. In this study, refractory suppression was investigated to determine its possible applications in higher functional sites. Two visual stimuli were presented in different hemifields, using a paradigm in which the first of the paired stimuli (face pictures) was placed in the left visual hemifield and the second stimulus was placed in the right hemifield. In this manner, the primary visual areas (V1) of the left and right hemispheres were independently activated. Along with independent activation in both V1, refractory suppression was indeed observed in higher functional areas, including the occipital and fusiform face areas in both hemispheres.