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Individual variability exists in both brain function and behavioral performance. However, changes in individual variability in brain functional connectivity and capability across adult development and aging have not yet been clearly examined. Based on resting-state functional magnetic resonance imaging data from a large cohort of participants (543 adults, aged 18–88 years), brain functional connectivity was analyzed to characterize the spatial distribution and differences in individual variability across the adult lifespan. Results showed high individual variability in the association cortex over the adult lifespan, whereas individual variability in the primary cortex was comparably lower in the initial stage but increased with age. Individual variability was also negatively correlated with the strength/number of short-, medium-, and long-range functional connections in the brain, with long-range connections playing a more critical role in increasing global individual variability in the aging brain. More importantly, in regard to specific brain regions, individual variability in the motor cortex was significantly correlated with differences in motor capability. Overall, we identified specific patterns of individual variability in brain functional structure during the adult lifespan and demonstrated that functional variability in the brain can reflect behavioral performance. These findings advance our understanding of the underlying principles of the aging brain across the adult lifespan and suggest how to characterize degenerating behavioral capability using imaging biomarkers.

Alzheimer's disease (AD) is associated with disruptions in brain activity and networks. However, there is substantial inconsistency among studies that have investigated functional brain alterations in AD; such contradictions have hindered efforts to elucidate the core disease mechanisms. In this study, we aim to comprehensively characterize AD‐associated functional brain alterations using one of the world's largest resting‐state functional MRI (fMRI) biobank for the disorder. The biobank includes fMRI data from six neuroimaging centers, with a total of 252 AD patients, 221 mild cognitive impairment (MCI) patients and 215 healthy comparison individuals. Meta‐analytic techniques were used to unveil reliable differences in brain function among the three groups. Relative to the healthy comparison group, AD was associated with significantly reduced functional connectivity and local activity in the default‐mode network, basal ganglia and cingulate gyrus, along with increased connectivity or local activity in the prefrontal lobe and hippocampus (p

With the development of neuroimaging and computing technologies, brain research based on in vivo imaging attracts mounting interests from multidisciplinary scientists with a variety of backgrounds, including neuroscience, clinical medicine, psychology, and engineering. Recently, cross-discipline collaborative research has formed new research directions and fields, and by contrasting to other research fields, this is a brilliant and unique feature in the development of brain science. Many imaging techniques for characterizing anatomical structures and detecting functional information in the brain have been populated in both basic neuroscience and clinical applications, including magnetic resonance imaging (MRI), electroencephalograph (EEG), magnetoencephalography (MEG). Meanwhile, brain science research puts higher requirements on advanced imaging techniques with higher temporal-spatial resolutions. Consequently, the data produced poses more challenges in data mining and modeling in deciphering the brain. To better describe the strategy of brain research from multiple spatiotemporal scales, the concept of Brainnetome was proposed. This paper systematically reviewed the current representative techniques of imaging-based brain research and emphasized the applications of multi-modal technologies in translational medicine, which is the research background and connotation of Brainnetome. Finally, we made a prospect for the future development of Brainnetome research.

Scores on intelligence tests are strongly predictive of various important life outcomes. However, the gender discrepancy on intelligence quotient (IQ) prediction using brain imaging variables has not been studied. To this aim, we predicted individual IQ scores for males and females separately using whole-brain functional connectivity (FC). Robust predictions of intellectual capabilities were achieved across three independent data sets (680 subjects) and two intelligence measurements (IQ and fluid intelligence) using the same model within each gender. Interestingly, we found that intelligence of males and females were underpinned by different neurobiological correlates, which are consistent with their respective superiority in cognitive domains (visuospatial vs verbal ability). In addition, the identified FC patterns are uniquely predictive on IQ and its sub-domain scores only within the same gender but neither for the opposite gender nor on the IQ-irrelevant measures such as temperament traits. Moreover, females exhibit significantly higher IQ predictability than males in the discovery cohort. This findings facilitate our understanding of the biological basis of intelligence by demonstrating that intelligence is underpinned by a variety of complex neural mechanisms that engage an interacting network of regions—particularly prefrontal–parietal and basal ganglia—whereas the network pattern differs between genders.

Working memory (WM) is a complex and pivotal cognitive system underlying the performance of many cognitive behaviors. Although individual differences in WM performance have previously been linked to the blood oxygenation level‐dependent (BOLD) response across several large‐scale brain networks, the unique and shared contributions of each large‐scale brain network to efficient WM processes across different cognitive loads remain elusive. Using a WM paradigm and functional magnetic resonance imaging (fMRI) from the Human Connectome Project, we proposed a framework to assess the association and shared‐association strength between imaging biomarkers and behavioral scales. Association strength is the capability of individual brain regions to modulate WM performance and shared‐association strength measures how different regions share the capability of modulating performance. Under higher cognitive load (2‐back), the frontoparietal executive control network (FPN), dorsal attention network (DAN), and salience network showed significant positive activation and positive associations, whereas the default mode network (DMN) showed the opposite pattern, namely, significant deactivation and negative associations. Comparing the different cognitive loads, the DMN and FPN showed predominant associations and globally shared‐associations. When investigating the differences in association from lower to higher cognitive loads, the DAN demonstrated enhanced association strength and globally shared‐associations, which were significantly greater than those of the other networks. This study characterized how brain regions individually and collaboratively support different cognitive loads.

Temperament consists of multi-dimensional traits that affect various domains of human life. Evidence has shown functional connectome-based predictive models are powerful predictors of cognitive abilities. Putatively, individuals' innate temperament traits may be predictable by unique patterns of brain functional connectivity (FC) as well. However, quantitative prediction for multiple temperament traits at the individual level has not yet been studied. Therefore, we were motivated to realize the individualized prediction of four temperament traits (novelty seeking [NS], harm avoidance [HA], reward dependence [RD] and persistence [PS]) using whole-brain FC. Specifically, a multivariate prediction framework integrating feature selection and sparse regression was applied to resting-state fMRI data from 360 college students, resulting in 4 connectome-based predictive models that enabled prediction of temperament scores for unseen subjects in cross-validation. More importantly, predictive models for HA and NS could be successfully generalized to two relevant personality traits for unseen individuals, i.e., neuroticism and extraversion, in an independent dataset. In four temperament trait predictions, brain connectivities that show top contributing power commonly concentrated on the hippocampus, prefrontal cortex, basal ganglia, amygdala, and cingulate gyrus. Finally, across independent datasets and multiple traits, we show person's temperament traits can be reliably predicted using functional connectivity strength within frontal-subcortical circuits, indicating that human social and behavioral performance can be characterized by specific brain connectivity profile.

Background Diffusion tensor imaging (DTI) has been widely used to identify structural integrity and to delineate white matter (WM) degeneration in Alzheimer's disease (AD). However, the validity and replicability of the ability to discriminate AD patients and normal controls (NCs) of WM measures are limited due to the use of small cohorts and diverse image processing methods. As yet, we still do not have a clear idea of whether WM characteristics are biomarkers for AD. Methods We conducted a competition with diffusion measurements along 18 fiber tracts as features extracted via the automated fiber quantification (AFQ) method based on one of the largest worldwide DTI multisite biobanks (862 individuals, consisting of 279 NCs, 318 ADs, and 265 MCIs). After quality control, 825 subjects (276 NCs, 294 ADs, and 255 MCIs) were divided into a public training set (N=700) and a private testing set (N=125). Forty-eight teams submitted 130 solutions that were estimated on the private testing samples. We reported the final results of the top ten models. Results The performance of white matter features in AD classification was stable and generalizable, which indicated the potential of WM to be a biomarker for AD. The best model achieved a prediction accuracy of 82.35% (with a sensitivity of 86.36% and a specificity of 78.05%) on the private testing set. The average accuracy of the top ten solutions was over 80%. Conclusions The results of this competition demonstrated that DTI is a powerful tool to identify AD. A larger dataset and additional independent cohort cross-validation may improve the discriminant performance and generalization power of the classification models, thus revealing more precise disease severity factors associated with AD. For this purpose, we have released this database ( https://github.com/YongLiuLab/AI4AD_AFQ ) to the community, with the expectation of new solutions for the accurate diagnosis of AD.

Although both resting and task-induced functional connectivity (FC) have been used to characterize the human brain and cognitive abilities, the potential of task-induced FCs in individualized prediction for out-of-scanner cognitive traits remains largely unexplored. A recent study Greene etal. (2018) predicted the fluid intelligence scores using FCs derived from rest and multiple task conditions, suggesting that task-induced brain state manipulation improved prediction of individual traits. Here, using a large dataset incorporating fMRI data from rest and 7 distinct task conditions, we replicated the original study by employing a different machine learning approach, and applying the method to predict two reading comprehension-related cognitive measures. Consistent with their findings, we found that task-based machine learning models often outperformed rest-based models. We also observed that combining multi-task fMRI improved prediction performance, yet, integrating the more fMRI conditions can not necessarily ensure better predictions. Compared with rest, the predictive FCs derived from language and working memory tasks were highlighted with more predictive power in predominantly default mode and frontoparietal networks. Moreover, prediction models demonstrated high stability to be generalizable across distinct cognitive states. Together, this replication study highlights the benefit of using task-based FCs to reveal brain-behavior relationships, which may confer more predictive power and promote the detection of individual differences of connectivity patterns underlying relevant cognitive traits, providing strong evidence for the validity and robustness of the original findings.

Background In the past decades, substantial effort has been made to explore the genetic influence on brain structural/functional abnormalities in schizophrenia, as well as cognitive impairments. In this work, we aimed to extend previous studies to explore the internal mediation pathway among genetic factor, brain features and cognitive scores in a large Chinese dataset. Methods Gray matter (GM) volume, fractional amplitude of low-frequency fluctuations (fALFF), and 4522 schizophrenia-susceptible single nucleotide polymorphisms (SNP) from 905 Chinese subjects were jointly analyzed, to investigate the multimodal association. Based on the identified imaging-genetic pattern, correlations with cognition and mediation analysis were then conducted to reveal the potential mediation pathways. Findings One linked imaging-genetic pattern was identified to be group discriminative, which was also associated with working memory performance. Particularly, GM reduction in thalamus, putamen and bilateral temporal gyrus in schizophrenia was associated with fALFF decrease in medial prefrontal cortex, both were also associated with genetic factors enriched in neuron development, synapse organization and axon pathways, highlighting genes including CSMD1, CNTNAP2, DCC, GABBR2 etc. This linked pattern was also replicated in an independent cohort (166 subjects), which although showed certain age and clinical differences with the discovery cohort. A further mediation analysis suggested that GM alterations significantly mediated the association from SNP to fALFF, while fALFF mediated the association from SNP and GM to working memory performance. Interpretation This study has not only verified the impaired imaging-genetic association in schizophrenia, but also initially revealed a potential genetic-brain-cognition mediation pathway, indicating that polygenic risk factors could exert impact on phenotypic measures from brain structure to function, thus could further affect cognition in schizophrenia.

The multiple demand network (MDN) is conceptualized as the core processing system for multitasking. Increasing evidence also provides strong support for the involvement of the MDN in fluid intelligence (gF), that is, the ability to solve new problems. However, the underlying neural mechanisms of declining intelligence in old age are poorly explored, particularly whether maintenance of the functional architecture of the MDN can characterize superior intelligence in successful aging. Here, we used eigenvector centrality (EC) to explore the resting-state functional architecture of the MDN in terms of its communication across the entire brain. We found gF to be negatively associated with age and that the MDN EC competitively mediated age-related decline in gF over the aging lifespan, suggesting that excessive cross-talk from the MDN is deleterious for intelligence. Critically, older individuals with comparable MDN EC as younger individuals exhibited superior gF compared with their age-matched counterparts. Taken together, these data provide support for the maintenance of youth-like functional architecture of the MDN and its implication for superior intelligence in successful aging.

Precision medicine for Alzheimer's disease (AD) necessitates the development of personalized, reproducible, and neuroscientifically interpretable biomarkers, yet despite remarkable advances, few such biomarkers are available. Also, a comprehensive evaluation of the neurobiological basis and generalizability of the end‐to‐end machine learning system should be given the highest priority. For this reason, a deep learning model (3D attention network, 3DAN) that can simultaneously capture candidate imaging biomarkers with an attention mechanism module and advance the diagnosis of AD based on structural magnetic resonance imaging is proposed. The generalizability and reproducibility are evaluated using cross‐validation on in‐house, multicenter (n = 716), and public (n = 1116) databases with an accuracy up to 92%. Significant associations between the classification output and clinical characteristics of AD and mild cognitive impairment (MCI, a middle stage of dementia) groups provide solid neurobiological support for the 3DAN model. The effectiveness of the 3DAN model is further validated by its good performance in predicting the MCI subjects who progress to AD with an accuracy of 72%. Collectively, the findings highlight the potential for structural brain imaging to provide a generalizable, and neuroscientifically interpretable imaging biomarker that can support clinicians in the early diagnosis of AD., This study proposes a deep learning model (3D attention network) to simultaneously capture imaging biomarkers with an attention mechanism module and advance the diagnosis of Alzheimer's disease. The generalizability and reproducibility are cross‐validated on independent databases with an accuracy up to 92%. Significant associations between the classification output and clinical characteristics of patients provide solid neurobiological support for the model.

Multimodal fusion has been regarded as a promising tool to discover covarying patterns of multiple imaging types impaired in brain diseases, such as schizophrenia (SZ). In this article, we aim to investigate the covarying abnormalities underlying SZ in a large Chinese Han population (307 SZs, 298 healthy controls [HCs]). Four types of magnetic resonance imaging (MRI) features, including regional homogeneity (ReHo) from resting-state functional MRI, gray matter volume (GM) from structural MRI, fractional anisotropy (FA) from diffusion MRI, and functional network connectivity (FNC) resulted from group independent component analysis, were jointly analyzed by a data-driven multivariate fusion method. Results suggest that a widely distributed network disruption appears in SZ patients, with synchronous changes in both functional and structural regions, especially the basal ganglia network, salience network (SAN), and the frontoparietal network. Such a multimodal coalteration was also replicated in another independent Chinese sample (40 SZs, 66 HCs). Our results on auditory verbal hallucination (AVH) also provide evidence for the hypothesis that prefrontal hypoactivation and temporal hyperactivation in SZ may lead to failure of executive control and inhibition, which is relevant to AVH. In addition, impaired working memory performance was found associated with GM reduction and FA decrease in SZ in prefrontal and superior temporal area, in both discovery and replication datasets. In summary, by leveraging multiple imaging and clinical information into one framework to observe brain in multiple views, we can integrate multiple inferences about SZ from large-scale population and offer unique perspectives regarding the missing links between the brain function and structure that may not be achieved by separate unimodal analyses.

Introduction Functional magnetic resonance imaging (fMRI) has become very important for noninvasively characterizing BOLD signal fluctuations, which reflect the changes in neuronal firings in the brain. Unlike the activation detection strategy utilized with fMRI, which only emphasizes the synchronicity between the functional nodes (activated regions) and the task design, brain connectivity and network theory are able to decipher the interactive structure across the entire brain. However, little is known about whether and how the activated/less‐activated interactions are associated with the functional changes that occur when the brain changes from the resting state to a task state. What are the key networks that play important roles in the brain state changes? Methods We used the fMRI data from the Human Connectome Project S500 release to examine the changes of network efficiency, interaction strength, and fractional modularity contributions of both the local and global networks, when the subjects change from the resting state to seven different task states. Results We found that, from the resting state to each of seven task states, both the activated and less‐activated regions had significantly changed to be in line with, and comparably contributed to, a global network reconfiguration. We also found that three networks, the default mode network, frontoparietal network, and salience network, dominated the flexible reconfiguration of the brain. Conclusions This study shows quantitatively that contributions from both activated and less‐activated regions enable the global functional network to respond when the brain switches from the resting state to a task state and suggests the necessity of considering large‐scale networks (rather than only activated regions) when investigating brain functions in imaging cognitive neuroscience.

Different cognitively demanding tasks recruit globally distributed but functionally specific networks. However, the configuration of core networks and their reconfiguration patterns across cognitive loads remain unclear, as does whether these patterns are indicators for the performance of cognitive tasks. In this study, we analyzed functional magnetic resonance imaging data of a large cohort of 448 subjects, acquired with the brain at resting state and executing N-back working memory (WM) tasks. We discriminated core networks by functional interaction strength and connection flexibility. Results demonstrated that the frontoparietal network (FPN) and default mode network (DMN) were core networks, but each exhibited different patterns across cognitive loads. The FPN and DMN both showed strengthened internal connections at the low demand state (0-back) compared with the resting state (control level); whereas, from the low (0-back) to high demand state (2-back), some connections to the FPN weakened and were rewired to the DMN (whose connections all remained strong). Of note, more intensive reconfiguration of both the whole brain and core networks (but no other networks) across load levels indicated relatively poor cognitive performance. Collectively these findings indicate that the FPN and DMN have distinct roles and reconfiguration patterns across cognitively demanding loads. This study advances our understanding of the core networks and their reconfiguration patterns across cognitive loads and provides a new feature to evaluate and predict cognitive capability (e.g., WM performance) based on brain networks.

Background Diffusion magnetic resonance imaging (dMRI) techniques are receiving increasing attention due to their ability to characterize the arrangement map of white matter in vivo. However, the existing toolkits for dMRI analysis that have accompanied this surge possess noticeable limitations, such as large installation size, an incomplete pipeline, and a lack of cross-platform support. New method In this work, we developed a light, one-stop, cross-platform solution for dMRI data analysis, called DiffusionKit. It delivers a complete pipeline, including data format conversion, dMRI preprocessing, local reconstruction, white matter fiber tracking, fiber statistical analyses and various visualization schemes. Furthermore, DiffusionKit is a self-contained executable toolkit, without the need to install any other software. Results The DiffusionKit package is implemented in C/C++ and Qt/VTK, is freely available at http://diffusion.brainnetome.org and https://www.nitrc.org/projects/diffusionkit . The website of DiffusionKit includes test data, a complete tutorial and a series of tutorial examples. A mailing list has also been established for update notification and questions and answers. Comparison with existing methods DiffusionKit provides a full-function pipeline for dMRI data analysis, including data processing, modeling and visualization. Additionally, it provides both a graphical user interface (GUI) and command-line functions, which are helpful for batch processing. The standalone installation package has a small size and cross-platform support. Conclusions DiffusionKit provides a complete pipeline with cutting-edge methods for dMRI data analysis, including both a GUI interface and command-line functions. The rich functions for both data analysis and visualization will facilitate and benefit dMRI research.

Helical and circular trajectories are expected to be adopted dominantly for data acquisition in X-ray cone-beam (CB) computed tomography (CT) imaging. However, non-conventional trajectories, such as tilted helical, saddle, circle-circle, and circle-line trajectories, may also find important applications. Therefore, accurate image reconstruction from data acquired with these non-conventional trajectories remains of practical significance. However, the non-conventional trajectories have been studied on a case-per-case basis and little effort has been made to investigate all of these trajectories on a single algorithm. Recently, there has been significant development on image reconstruction for general CB trajectories. In particular, chord-based algorithms have been developed for image reconstruction in CBCT. In this work, we investigate the chord-based algorithms, which can accommodate data containing both longitudinal and transverse truncations to a certain extent, for image reconstruction from data acquired with four different trajectories: the tilted helical, saddle, circle-circle, and circle-line trajectories. The first two represent smooth trajectories, whereas the last two are non-smooth trajectories containing kinks. The significance of the work lies not only in demonstrating the ability of the algorithm for image reconstruction from CB data but also in visualizing the reconstructible regions-of-interest (ROIs) for those general trajectories so that one can setup the scanning parameters with an appropriate guide. (c) 2007 Published by Elsevier B.V.

Abundant study on the hemodynamic response of a brain have brought quite a few advances in technologies of measuring it. The most benefitted is the functional near infrared spectroscope (fNIRS). A variety of devices have been developed for different applications. Because portable fNIRS systems were more competent to measure responses either of special subjects or in natural environment, several kinds of portable fNIRS systems have been reported. However, they all required a computer for receiving data. The extra computer increases the cost of a fNIRS system. What’s more noticeable is the space required to locate the computer even for a portable system. It will discount the portability of the fNIRS system. So we designed a self-contained eight channel fNIRS system, which does not demand a computer to receive data and display data in a monitor. Instead, the system is centered by an ARM core CPU, which takes charge in organizing data and saving data, and then displays data on a touch screen. The system has also been validated by experiments on phantoms and on subjects in tasks.

The default mode network (DMN), which, in the resting state, is in charge of both the brain's intrinsic mentation and its reflexive responses to external stimuli, is recognized as an essential network in the human brain. These two roles of mentation and reflexive response recruit the DMN nodes and other task networks differently. Existing research has revealed that the interactions inside the DMN (between nodes within the DMN) and outside the DMN (between nodes in the DMN and nodes in task networks) have different modes, in terms of both strength and timing. These findings raise interesting questions. For example, are the internal and external interactions of the DMN equally linear or nonlinear? This study examined these interaction patterns using datasets from the Human Connectome Project. A maximal information-based nonparametric exploration statistics strategy was utilized to characterize the full correlations, and the Pearson correlation was used to capture the linear component of the full correlations. We then contrasted the level of linearity/nonlinearity with respect to the internal and external interactions of the DMN. After a brain-wide exploration, we found that the interactions between the DMN and the sensorimotor-related networks (including the sensorimotor, sensory association, and integration areas) showed more nonlinearity, whereas those between the intra-DMN nodes were similarly less nonlinear. These findings may provide a clue for understanding the underlying neuronal principles of the internal and external roles of the DMN.

Modern computed tomography (CT) scanners use cone-beam configurations for increasing volume coverage, improving x-ray-tube utilization, and yielding isotropic spatial resolution. Recently, there have been significant developments in theory and algorithms for exact image reconstruction from cone-beam projections. In particular, algorithms have been proposed for image reconstruction on chords; and advantages over the existing algorithms offered by the chord-based algorithms include the high flexibility of exact image reconstruction for general scanning trajectories and the capability of exact reconstruction of images within a region of interest from truncated data. These chord-based algorithms have been developed only for flat-panel detectors. Many cone-beam CT scanners employ curved detectors for important practical considerations. Therefore, in this work, we have derived chord-based algorithms for a curved detector so that they can be applied to reconstructing images directly from data acquired by use of a CT scanner with a curved detector. We have also conducted preliminary numerical studies to demonstrate and evaluate the reconstruction properties of the derived chord-based algorithms for curved detectors.

Brain functional activity involves complex cellular, metabolic, and vascular chain reactions, making it difficult to comprehend. Electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) have been combined into a multimodal neuroimaging method that captures both electrophysiological and hemodynamic information to explore the spatiotemporal characteristics of brain activity. Because of the significance of visually evoked functional activity in clinical applications, numerous studies have explored the amplitude of the visual evoked potential (VEP) to clarify its relationship with the hemodynamic response. However, relatively few studies have investigated the influence of latency, which has been frequently used to diagnose visual diseases, on the hemodynamic response. Moreover, because the latency and the amplitude of VEPs have different roles in coding visual information, investigating the relationship between latency and the hemodynamic response should be helpful. In this study, checkerboard reversal tasks with graded contrasts were used to evoke visual functional activity. Both EEG and fNIRS were employed to investigate the relationship between neuronal electrophysiological activities and the hemodynamic responses. The VEP amplitudes were linearly correlated with the hemodynamic response, but the VEP latency showed a negative linear correlation with the hemodynamic response.

International audience; Increasing evidence shows that the human brain is a highly self-organized system that shows attributes of smallworldness, hierarchy and modularity. The “connectome” was conceived several years ago to identify the underpinning physical connectivities of brain networks. The need for an integration of multi-spatial and -temporal approaches is becoming apparent. Therefore, the “Brainnetome” (brain-net-ome) project was proposed. Diffusion magnetic resonance imaging (dMRI) is a non-invasive way to study the anatomy of brain networks. Here, we review the principles of dMRI, its methodologies, and some of its clinical applications for the Brainnetome. Future research in this field is discussed.

The Tent-FDK algorithm is one of common approaches to reconstruct images from parallel-fan-beam data, which is obtained by rebinning the cone-beam data acquired with a circular trajectory. Such data rebinning is for saving computational cost as well as the improved quality of image presentation. The rebinning step is in common achieved by using interpolation methods. Different interpolation methods lead to different numerical presentations and noisy textures in the reconstructed images. This presentation systematically evaluates three interpolation methods in rebinning cone-beam data into parallel-fan-beam data in the framework of the Tent-FDK algorithm in Cone-beam CT.

Recently, there has been much progress in algorithm development for image reconstruction in cone-beam computed tomography (CT). Current algorithms, including the chord-based algorithms, now accept minimal data sets for obtaining images on volume regions-of-interest (ROIs) thereby potentially allowing for reduction of X-ray dose in diagnostic CT. As these developments are relatively new, little effort has been directed at investigating the response of the resulting algorithm implementations to physical factors such as data noise. In this paper, we perform an investigation on the noise properties of ROI images reconstructed by using chord-based algorithms for different scanning configurations. We find that, for the cases under study, the chord-based algorithms yield images with comparable quality. Additionally, it is observed that, in many situations, large data sets contain extraneous data that may not reduce the ROI-image variances.

Functional near-infrared spectroscopy (fNIRS) detects hemodynamic responses in the cerebral cortex by transcranial spectroscopy. However, measurements recorded by fNIRS not only consist of the desired hemodynamic response but also consist of a number of physiological noises. Because of these noises, accurately detecting the regions that have an activated hemodynamic response while performing a task is a challenge when analyzing functional activity by fNIRS. In order to better detect the activation, we designed a multiscale analysis based on wavelet coherence. In this method, the experimental paradigm was expressed as a binary signal obtained while either performing or not performing a task. We convolved the signal with the canonical hemodynamic response function to predict a possible response. The wavelet coherence was used to investigate the relationship between the response and the data obtained by fNIRS at each channel. Subsequently, the coherence within a region of interest in the time-frequency domain was summed to evaluate the activation level at each channel. Experiments on both simulated and experimental data demonstrated that the method was effective for detecting activated channels hidden in fNIRS data.

Modern computed tomography (CT) scanners use cone-beam configurations for increasing volume coverage, improving x-ray-tube utilization, and yielding isotropic spatial resolution. Recently, there have been significant developments in theory and algorithms for exact image reconstruction from cone-beam projections. In particular, algorithms have been proposed for image reconstruction on chords; and advantages over the existing algorithms offered by the chord-based algorithms include the high flexibility of exact image reconstruction for general scanning trajectories and the capability of exact reconstruction of images within a region of interest from truncated data. These chord-based algorithms have been developed only for flat-panel detectors. Many cone-beam CT scanners employ curved detectors for important practical considerations. Therefore, in this work, we have derived chord-based algorithms for a curved detector so that they can be applied to reconstructing images directly from data acquired by use of a CT scanner with a curved detector. We have also conducted preliminary numerical studies to demonstrate and evaluate the reconstruction properties of the derived chord-based algorithms for curved detectors.

Helical CT scanners with multi-row detectors have gained wide popularity in clinics. With increasing interest in extending the number of detector rows, the cone angle must be accounted for in image reconstruction algorithms. As the cone angle increases, artifacts in reconstructed images caused by approximate algorithms, such as the widely used FDK-based algorithms, become more of a factor that degrades the image quality. Recently, a novel reconstruction algorithm for helical cone-beam CT (CB-CT) has been proposed, which is referred to as the backprojection-filtration algorithm. This algorithm requires theoretically minimum data to reconstruct a volume image. The original BPF algorithm was presented by assuming a flat-panel detector. However, most current multi-row detectors employed in clinic CT scanners are curved detectors. In this work, we modify the backprojection-filtration (BPF) algorithm to allow for reconstruction from data collected with a curved detector. We perform simulation studies using the Shepp-Logan phantom to evaluate the modified BPF algorithm.

Both flat-panel detectors and cylindrical detectors have been used in CT systems for data acquisition. The cylindrical detector generally offers a sampling of a transverse image plane more uniformly than does a flat-panel detector. However, in the longitudinal dimension, the cylindrical and flat-panel detectors offer similar sampling of the image space. In this work, we investigate a detector of spherical shape, which can yield uniform sampling of the 3D image space because the solid angle subtended by each individual detector bin remains unchanged. We have extended the backprojection-filtration (BPF) algorithm, which we have developed previously for cone-beam CT, to reconstruct images in cone-beam CT with a spherical detector. We also conduct computer-simulation studies to validate the extended BPF algorithm. Quantitative results in these numerical studies indicate that accurate images can be obtained from data acquired with a spherical detector by use of our extended BPF cone-beam algorithms.

Increasing evidence indicates that major depressive disorder (MDD) is usually accompanied by altered white matter in the prefrontal cortex, the parietal lobe and the limbic system. As a behavioral abnormity of MDD, rumination has been believed to be a substantial indicator of the mental state of the depressive state. So far, however, no report that we are aware of has evaluated the relationship between white matter alterations and the ruminative state. In this study, we first explored the altered white matter using a tract-based spatial statistics (TBSS) method based on diffusion tensor imaging of 19 healthy and 16 depressive subjects. We then investigated correlations between the altered white matter microstructure in the identified altered regions and the severity of ruminations measured by the ruminative response scale. Our results demonstrated altered white matter microstructure in circuits connecting the prefrontal lobe, the parietal lobe and the limbic system (p