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The purpose of this study was to evaluate the prognostic value of single-photon emission computed tomography (SPECT)/computed tomography (CT) imaging of angiosome foot perfusion for predicting amputation outcomes in patients with critical limb ischemia (CLI) and diabetes mellitus (DM).Radiotracer imaging can assess microvascular foot perfusion and identify regional perfusion abnormalities in patients with critical limb ischemia CLI and DM, but the relationship between perfusion response to revascularization and subsequent clinical outcomes has not been evaluated.Patients with CLI, DM, and nonhealing foot ulcers (n = 25) were prospectively enrolled for SPECT/CT perfusion imaging of the feet before and after revascularization. CT images were used to segment angiosomes (i.e., 3-dimensional vascular territories) of the foot. Relative changes in radiotracer uptake after revascularization were evaluated within the ulcerated angiosome. Incidence of amputation was assessed at 3 and 12 months after revascularization.SPECT/CT detected a significantly lower microvascular perfusion response for patients who underwent amputation compared with those who remained amputation free at 3 (p = 0.01) and 12 (p = 0.01) months after revascularization. The cutoff percent change in perfusion for predicting amputation at 3 months was 7.55%, and 11.56% at 12 months. The area under the curve based on the amputation outcome was 0.799 at 3 months and 0.833 at 12 months. The probability of amputation-free survival was significantly higher at 3 (p = 0.002) and 12 months (p = 0.03) for high-perfusion responders than low-perfusion responders to revascularization.SPECT/CT imaging detects regional perfusion responses to lower extremity revascularization and provides prognostic value in patients with CLI (Radiotracer-Based Perfusion Imaging of Patients With Peripheral Arterial Disease; NCT03622359).

To determine if three-dimensional whole liver and baseline tumor enhancement features on MRI can serve as staging biomarkers and help predict survival of patients with colorectal cancer liver metastases (CRCLM) more accurately than one-dimensional and non-enhancement-based features. This retrospective study included 88 patients with CRCLM, treated with transarterial chemoembolization or Y90 transarterial radioembolization between 2001 and 2014. Semi-automated segmentations of up to three dominant lesions were performed on pre-treatment MRI to calculate total tumor volume (TTV) and total liver volumes (TLV). Quantitative 3D analysis was performed to calculate enhancing tumor volume (ETV), enhancing tumor burden (ETB, calculated as ETV/TLV), enhancing liver volume (ELV), and enhancing liver burden (ELB, calculated as ELV/TLV). Overall and enhancing tumor diameters were also measured. A modified Kaplan-Meier method was used to determine appropriate cutoff values for each metric. The predictive value of each parameter was assessed by Kaplan-Meier survival curves and univariable and multivariable cox proportional hazard models. All methods except whole liver (ELB, ELV) and one-dimensional/non-enhancement-based methods were independent predictors of survival. Multivariable analysis showed a HR of 2.1 (95% CI 1.3–3.4, p = 0.004) for enhancing tumor diameter, HR 1.7 (95% CI 1.1–2.8, p = 0.04) for TTV, HR 2.3 (95% CI 1.4–3.9, p < 0.001) for ETV, and HR 2.4 (95% CI 1.4–4.0, p = 0.001) for ETB. Tumor enhancement of CRCLM on baseline MRI is strongly associated with patient survival after intra-arterial therapy, suggesting that enhancing tumor volume and enhancing tumor burden are better prognostic indicators than non-enhancement-based and one-dimensional-based markers. • Tumor enhancement of colorectal cancer liver metastases on MRI prior to treatment with intra-arterial therapies is strongly associated with patient survival. • Three-dimensional, enhancement-based imaging biomarkers such as enhancing tumor volume and enhancing tumor burden may serve as the basis of a novel prognostic staging system for patients with liver-dominant colorectal cancer metastases.

Precise cortical brain localization presents an important challenge in the literature. Brain atlases provide data-guided parcellation based on functional and structural brain metrics, and each atlas has its own unique benefits for localization. We offer a parcellation guided by intracranial electroencephalography, a technique which has historically provided pioneering advances in our understanding of brain structure–function relationships. We used a consensus boundary mapping approach combining anatomical designations in Duvernoy’s Atlas of the Human Brain, a widely recognized textbook of human brain anatomy, with the anatomy of the MNI152 template and the magnetic resonance imaging scans of an epilepsy surgery cohort. The Yale Brain Atlas consists of 690 one-square centimeter parcels based around conserved anatomical features and each with a unique identifier to communicate anatomically unambiguous localization. We report on the methodology we used to create the Atlas along with the findings of a neuroimaging study assessing the accuracy and clinical usefulness of cortical localization using the Atlas. We also share our vision for the Atlas as a tool in the clinical and research neurosciences, where it may facilitate precise localization of data on the cortex, accurate description of anatomical locations, and modern data science approaches using standardized brain regions.

Sparsity is a powerful concept to exploit for high-dimensional machine learning and associated representational and computational efficiency. Sparsity is well suited for medical image segmentation. We present a selection of techniques that incorporate sparsity, including strategies based on dictionary learning and deep learning, that are aimed at medical image segmentation and related quantification.

The human cerebral cortex is distinguished by its large size and abundant gyrification, or folding. However, the evolutionary mechanisms that drive cortical size and structure are unknown. Although genes that are essential for cortical developmental expansion have been identified from the genetics of human primary microcephaly (a disorder associated with reduced brain size and intellectual disability) 1 , studies of these genes in mice, which have a smooth cortex that is one thousand times smaller than the cortex of humans, have provided limited insight. Mutations in abnormal spindle-like microcephaly-associated (ASPM), the most common recessive microcephaly gene, reduce cortical volume by at least 50% in humans2–4, but have little effect on the brains of mice5–9; this probably reflects evolutionarily divergent functions of ASPM10,11. Here we used genome editing to create a germline knockout of Aspm in the ferret (Mustela putorius furo), a species with a larger, gyrified cortex and greater neural progenitor cell diversity12–14 than mice, and closer protein sequence homology to the human ASPM protein. Aspm knockout ferrets exhibit severe microcephaly (25–40% decreases in brain weight), reflecting reduced cortical surface area without significant change in cortical thickness, as has been found in human patients3,4, suggesting that loss of ‘cortical units’ has occurred. The cortex of fetal Aspm knockout ferrets displays a very large premature displacement of ventricular radial glial cells to the outer subventricular zone, where many resemble outer radial glia, a subtype of neural progenitor cells that are essentially absent in mice and have been implicated in cerebral cortical expansion in primates12–16. These data suggest an evolutionary mechanism by which ASPM regulates cortical expansion by controlling the affinity of ventricular radial glial cells for the ventricular surface, thus modulating the ratio of ventricular radial glial cells, the most undifferentiated cell type, to outer radial glia, a more differentiated progenitor.

While neuroimaging studies typically collapse data from many subjects, brain functional organization varies between individuals, and characterizing this variability is crucial for relating brain activity to behavioral phenotypes. Rest has become the default state for probing individual differences, chiefly because it is easy to acquire and a supposed neutral backdrop. However, the assumption that rest is the optimal condition for individual differences research is largely untested. In fact, other brain states may afford a better ratio of within- to between-subject variability, facilitating biomarker discovery. Depending on the trait or behavior under study, certain tasks may bring out meaningful idiosyncrasies across subjects, essentially enhancing the individual signal in networks of interest beyond what can be measured at rest. Here, we review theoretical considerations and existing work on how brain state influences individual differences in functional connectivity, present some preliminary analyses of within- and between-subject variability across conditions using data from the Human Connectome Project, and outline questions for future study.

Accurate and robust non-rigid registration of pre-procedure magnetic resonance (MR) imaging to intra-procedure trans-rectal ultrasound (TRUS) is critical for image-guided biopsies of prostate cancer. Prostate cancer is one of the most prevalent forms of cancer and the second leading cause of cancer-related death in men in the United States. TRUS-guided biopsy is the current clinical standard for prostate cancer diagnosis and assessment. State-of-the-art, clinical MR-TRUS image fusion relies upon semi-automated segmentations of the prostate in both the MR and the TRUS images to perform non-rigid surface-based registration of the gland. Segmentation of the prostate in TRUS imaging is itself a challenging task and prone to high variability. These segmentation errors can lead to poor registration and subsequently poor localization of biopsy targets, which may result in false-negative cancer detection. In this paper, we present a non-rigid surface registration approach to MR-TRUS fusion based on a statistical deformation model (SDM) of intra-procedural deformations derived from clinical training data. Synthetic validation experiments quantifying registration volume of interest overlaps of the PI-RADS parcellation standard and tests using clinical landmark data demonstrate that our use of an SDM for registration, with median target registration error of 2.98 mm, is significantly more accurate than the current clinical method. Furthermore, we show that the low-dimensional SDM registration results are robust to segmentation errors that are not uncommon in clinical TRUS data.

Neuroimaging is a fast developing research area where anatomical and functional images of human brains are collected using techniques such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and electroencephalography (EEG). Technical advances and large-scale datasets have allowed for the development of models capable of predicting individual differences in traits and behavior using brain connectivity measures derived from neuroimaging data. Here, we present connectome-based predictive modeling (CPM), a data-driven protocol for developing predictive models of brain-behavior relationships from connectivity data using cross-validation. This protocol includes the following steps: 1) feature selection, 2) feature summarization, 3) model building, and 4) assessment of prediction significance. We also include suggestions for visualizing the most predictive features (i.e., brain connections). The final result should be a generalizable model that takes brain connectivity data as input and generates predictions of behavioral measures in novel subjects, accounting for a significant amount of the variance in these measures. It has been demonstrated that the CPM protocol performs equivalently or better than most of the existing approaches in brain-behavior prediction. However, because CPM focuses on linear modeling and a purely data-driven driven approach, neuroscientists with limited or no experience in machine learning or optimization would find it easy to implement the protocols. Depending on the volume of data to be processed, the protocol can take 10–100 minutes for model building, 1–48 hours for permutation testing, and 10–20 minutes for visualization of results.

To demonstrate the feasibility of simultaneous blood oxygen level-dependent (BOLD) magnetic resonance imaging of calf and foot muscles and investigate age-related changes of BOLD signal changes during ischemia and postocclusive hyperemia in healthy volunteers.In this study, 15 healthy elderly volunteers (mean age: 69.0 +/- 7.4 years) and 15 healthy young volunteers (mean age: 26.1 +/- 3.9 years) were enrolled. In both legs, simultaneous BOLD imaging of calf and foot muscles was performed at 1.5 Tesla. Short-term ischemia and consecutive reactive hyperemia were provoked by a cuff-compression paradigm. T2*-weighted signal time courses were obtained from foot and calf muscles simultaneously. Ischemia was assessed by T2* minimum ischemic value (MIV) and the time to half ischemic minimum (THIM). Reperfusion was assessed by the time to reach T2* half hyperemia peak (THHP). Reactive hyperemia was characterized by hyperemia peak value (HPV), time to peak (TTP), and relative T2* change from end of ischemia to HPV (deltaS). Parameter differences were assessed using a 2-sided Student t test.Dynamic BOLD measurement of foot and calf muscles was techniqually feasible and successful in all volunteers.In comparison, THIM was significantly longer in elderly than in young volunteers for calf (P0.01) muscles (young: 28.9 +/- 3.7 seconds; elderly: 57.8 +/- 31.4 seconds) and foot (P = 0.01) muscles (young: 36.8 +/- 25.5 seconds; elderly: 56.6 +/- 31.7 seconds). MIV relative to baseline T2*-signal was significantly (P0.01) lower in the elderly for calf (young: 96.0% +/- 2.6%; elderly: 91.3% +/- 4.4%) and foot (young: 95.8% +/- 2.5%; elderly: 91.1% +/- 8.2%) muscles. TTP was significantly (calf: P = 0.01; foot: P = 0.02) delayed in the elderly (elderly calf: 103.0 +/- 92.7 seconds and foot: 157.1 +/- 109.9 seconds vs. young calf: 54.8 +/- 42.1 seconds and foot: 95.1 +/- 77.6 seconds). HPV was significantly (calf: P0.01 and foot: P = 0.03) higher in (young calf: 114.1% +/- 7.2% and foot: 105.8% +/- 3.3% vs. elderly calf: 104.0% +/- 2.1% and foot: 103.9% +/- 3.2% seconds) young volunteers.In a muscle-group comparison, no significant differences in THIM and MIV were observed between calf and foot.THHP was significantly (P = 0.02) longer in foot muscles (foot young: 32.5 +/- 29.8 seconds and elderly: 34.1 +/- 25.0 seconds vs. calf young: 16.8 +/- 14.1 seconds and elderly: 23.6 +/- 14.1 seconds) of both age groups. TTP was significantly (P = 0.01 and 0.02) longer in foot muscles (foot young: 95.1 +/- 77.6 seconds and elderly: 157.1 +/- 109.9 seconds vs. calf young: 54.8 +/- 42.1 seconds and elderly: 103.0 +/- 92.7 seconds) of both age groups. HPV was lower (P0.01) in foot muscles of the young (calf: 114.1% +/- 7.2% vs. foot: 105.8 +/- 3.3%).Simultaneous BOLD-imaging of calf and foot muscles is feasible and reveals statistically significant age-related differences during ischemia and postocclusive hyperemia in healthy volunteers.

BACKGROUND: Increasing evidence supports a neurodevelopmental model for bipolar disorder (BD), with adolescence as a critical period in its development. Developmental abnormalities of anterior paralimbic and heteromodal frontal cortices, key structures in emotional regulation processes and central in BD, are implicated. However, few longitudinal studies have been conducted, limiting understanding of trajectory alterations in BD. In this study, we performed longitudinal neuroimaging of adolescents with and without BD and assessed volume changes over time, including changes in tissue overall and within gray and white matter. Larger decreases over time in anterior cortical volumes in the adolescents with BD were hypothesized. Gray matter decreases and white matter increases are typically observed during adolescence in anterior cortices. It was hypothesized that volume decreases over time in BD would reflect alterations in those processes, showing larger gray matter contraction and decreased white matter expansion. METHODS: Two high-resolution magnetic resonance imaging scans were obtained approximately 2 years apart for 35 adolescents with bipolar I disorder (BDI) and 37 healthy adolescents. Differences over time between groups were investigated for volume overall and specifically for gray and white matter. RESULTS: Relative to healthy adolescents, adolescents with BDI showed greater volume contraction over time in a region including insula and orbitofrontal, rostral, and dorsolateral prefrontal cortices (p , .05, corrected), including greater gray matter contraction and decreased white matter expansion over time, in the BD compared with the healthy group. CONCLUSIONS: The findings support neurodevelopmental abnormalities during adolescence in BDI in anterior cortices, including altered developmental trajectories of anterior gray and white matter.

The accurate segmentation of the brain surface in post-surgical computed tomography (CT) images is critical for image-guided neurosurgical procedures in epilepsy patients. Following surgical implantation of intracranial electrodes, surgeons require accurate registration of the post-implantation CT images to the pre-implantation functional and structural magnetic resonance imaging to guide surgical resection of epileptic tissue. One way to perform the registration is via surface matching. The key challenge in this setup is the CT segmentation, where the extraction of the cortical surface is difficult due to the missing parts of the skull and artifacts introduced from the electrodes. In this paper, we present a dictionary learning-based method to segment the brain surface in post-surgical CT images of epilepsy patients following surgical implantation of electrodes. We propose learning a model of locally oriented appearance that captures both the normal tissue and the artifacts found along this brain surface boundary. Utilizing a database of clinical epilepsy imaging data to train and test our approach, we demonstrate that our method using locally oriented image appearance both more accurately extracts the brain surface and better localizes electrodes on the post-operative brain surface compared to standard, non-oriented appearance modeling. In addition, we compare our method to a standard atlas-based segmentation approach and to a U-Net-based deep convolutional neural network segmentation method.

INTRODUCTION: Twin-to-twin transfusion syndrome (TTTS) is a potentially lethal condition that affects pregnancies in which twins share a single placenta. The definitive treatment for TTTS is fetoscopic laser photocoagulation, a procedure in which placental blood vessels are selectively cauterized. Challenges in this procedure include difficulty in quickly identifying placental blood vessels due to the many artifacts in the endoscopic video that the surgeon uses for navigation. We propose using deep-learned segmentations of blood vessels to create masks that can be recombined with the original fetoscopic video frame in such a way that the location of placental blood vessels is discernable at a glance. METHODS: In a process approved by an institutional review board, intraoperative videos were acquired from ten fetoscopic laser photocoagulation surgeries performed at Yale New Haven Hospital. A total of 345 video frames were selected from these videos at regularly spaced time intervals. The video frames were segmented once by an expert human rater (a clinician) and once by a novice, but trained human rater (an undergraduate student). The segmentations were used to train a fully convolutional neural network of 25 layers. RESULTS: The neural network was able to produce segmentations with a high similarity to ground truth segmentations produced by an expert human rater (sensitivity=92.15%±10.69%) and produced segmentations that were significantly more accurate than those produced by a novice human rater (sensitivity=56.87%±21.64%; p < 0.01). CONCLUSION: A convolutional neural network can be trained to segment placental blood vessels with near-human accuracy and can exceed the accuracy of novice human raters. Recombining these segmentations with the original fetoscopic video frames can produced enhanced frames in which blood vessels are easily detectable. This has significant implications for aiding fetoscopic surgeons—especially trainees who are not yet at an expert level.

Although attention plays a ubiquitous role in perception and cognition, researchers lack a simple way to measure a person's overall attentional abilities. Because behavioral measures are diverse and difficult to standardize, we pursued a neuromarker of an important aspect of attention, sustained attention, using functional magnetic resonance imaging. To this end, we identified functional brain networks whose strength during a sustained attention task predicted individual differences in performance. Models based on these networks generalized to previously unseen individuals, even predicting performance from resting-state connectivity alone. Furthermore, these same models predicted a clinical measure of attention--symptoms of attention deficit hyperactivity disorder--from resting-state connectivity in an independent sample of children and adolescents. These results demonstrate that whole-brain functional network strength provides a broadly applicable neuromarker of sustained attention.

Respiratory motion during positron emission tomography (PET)/computed tomography (CT) imaging can cause significant image blurring and underestimation of tracer concentration for both static and dynamic studies. In this paper, with the aim to eliminate both intra-cycle and inter-cycle motions, and apply to dynamic imaging, we developed a non-rigid event-by-event (NR-EBE) respiratory motion-compensated list-mode reconstruction algorithm. The proposed method consists of two components: the first component estimates a continuous non-rigid motion field of the internal organs using the internal–external motion correlation. This continuous motion field is then incorporated into the second component, non-rigid MOLAR (NR-MOLAR) reconstruction algorithm to deform the system matrix to the reference location where the attenuation CT is acquired. The point spread function (PSF) and time-of-flight (TOF) kernels in NR-MOLAR are incorporated in the system matrix calculation, and therefore are also deformed according to motion. We first validated NR-MOLAR using a XCAT phantom with a simulated respiratory motion. NR-EBE motion-compensated image reconstruction using both the components was then validated on three human studies injected with 18F-FPDTBZ and one with 18F-fluorodeoxyglucose (FDG) tracers. The human results were compared with conventional non-rigid motion correction using discrete motion field (NR-discrete, one motion field per gate) and a previously proposed rigid EBE motion-compensated image reconstruction (R-EBE) that was designed to correct for rigid motion on a target lesion/organ. The XCAT results demonstrated that NR-MOLAR incorporating both PSF and TOF kernels effectively corrected for non-rigid motion. The 18F-FPDTBZ studies showed that NR-EBE out-performed NR-Discrete, and yielded comparable results with R-EBE on target organs while yielding superior image quality in other regions. The FDG study showed that NR-EBE clearly improved the visibility of multiple moving lesions in the liver where some of them could not be discerned in other reconstructions, in addition to improving quantification. These results show that NR-EBE motion-compensated image reconstruction appears to be a promising tool for lesion detection and quantification when imaging thoracic and abdominal regions using PET.

Objective: The purpose of this study was to investigate functional connectivity (FC) changes in epileptogenic networks in intractable partial epilepsy obtained from resting-state fMRI by using intrinsic connectivity contrast (ICC), a voxel-based network measure of degree that reflects the number of connections to each voxel. Methods: We measured differences between intrahemispheric- and interhemispheric-ICC (ICC intra−inter ) that could reveal localized connectivity abnormalities in epileptogenic zones while more global network changes would be eliminated when subtracting these values. The ICC intra−inter map was compared with the seizure onset zone (SOZ) based on intracranial EEG (icEEG) recordings in 29 patients with at least 1 year of postsurgical follow-up. Two independent reviewers blindly interpreted the icEEG and fMRI data, and the concordance rates were compared for various clinical factors. Results: Concordance between the icEEG SOZ and ICC intra−inter map was observed in 72.4% (21/29) of the patients, which was higher in patients with good surgical outcome, especially in those patients with temporal lobe epilepsy (TLE) or lateral temporal seizure localization. Concordance was also better in the extratemporal lobe epilepsy than the TLE group. In 85.7% (18/21) of the cases, the ICC intra−inter values were negative in the SOZ, indicating decreased FC within the epileptic hemisphere relative to between hemispheres. Conclusions: Assessing alterations in FC using fMRI-ICC map can help localize the SOZ, which has potential as a noninvasive presurgical diagnostic tool to improve surgical outcome. In addition, the method reveals that, in focal epilepsy, both intrahemispheric- and interhemispheric-FC may be altered, in the presence of both regional as well as global network abnormalities.

We present a novel method for controlling the effects of group differences in motion on functional connectivity studies. Resting-state functional magnetic resonance imaging (rs-fMRI) is a powerful tool that allows for the assessment of whole-brain functional organization across a wide range of clinical populations. However, as highlighted by recent studies, many measures commonly used in rs-fMRI are highly correlated with subject head movement. A source of this problem is that motion itself, and motion correction algorithms, lead to spatial smoothing, which is then variable across the brain and across subjects or groups dependent upon the amount of motion present during scanning. Studies aimed at elucidating differences between populations that have different head-motion characteristics (e.g., patients often move more in the scanner than healthy control subjects) are significantly confounded by these effects. In this work, we propose a solution to this problem, uniform smoothing, which ensures that all subject images in a study have equal effective spatial resolution. We establish that differences in the intrinsic smoothness of images across a group can confound connectivity results and link these differences in smoothness to motion. We demonstrate that eliminating these smoothness differences via our uniform smoothing solution is successful in reducing confounds related to the differences in head motion between subjects.

Real-time functional magnetic resonance imaging (rt-fMRI) has recently gained interest as a possible means to facilitate the learning of certain behaviors. However, rt-fMRI is limited by processing speed and available software, and continued development is needed for rt-fMRI to progress further and become feasible for clinical use. In this work, we present an open-source rt-fMRI system for biofeedback powered by a novel Graphics Processing Unit (GPU) accelerated motion correction strategy as part of the BioImage Suite project ( www.bioimagesuite.org ). Our system contributes to the development of rt-fMRI by presenting a motion correction algorithm that provides an estimate of motion with essentially no processing delay as well as a modular rt-fMRI system design. Using empirical data from rt-fMRI scans, we assessed the quality of motion correction in this new system. The present algorithm performed comparably to standard (non real-time) offline methods and outperformed other real-time methods based on zero order interpolation of motion parameters. The modular approach to the rt-fMRI system allows the system to be flexible to the experiment and feedback design, a valuable feature for many applications. We illustrate the flexibility of the system by describing several of our ongoing studies. Our hope is that continuing development of open-source rt-fMRI algorithms and software will make this new technology more accessible and adaptable, and will thereby accelerate its application in the clinical and cognitive neurosciences.

Objective: The aim of this study was to explore the relationship between global brain activity, changes in whole-brain connectivity, and changes in brain states across subjects using resting-state functional magnetic resonance imaging. Methods: We extended current methods that use a sparse set of coactivation patterns to extract critical time points in global brain activity. Critical activity time points were defined as points where the global signal is greater than one standard deviation above or below the average global signal. Four categories of critical points were defined along dimensions of global signal intensity and trajectory. Voxel-based methods were used to interrogate differences in connectivity between these critical points. Results: Several differences in connectivity were found in functional resting-state networks (RSNs) as a function of global activity. RSNs associated with cognitive functions in frontal, parietal, and subcortical regions exhibited greater whole-brain connectivity during lower global activity states. Meanwhile, RSNs associated with sensory functions exhibited greater whole-brain connectivity during the higher global activity states. Moreover, we present evidence that these results depend in part upon the standard deviation threshold used to define the critical points, suggesting critical points at different thresholds represent unique brain states. Conclusion: Overall, the findings support the hypothesis that the brain oscillates through different states over the course of a resting-state study reflecting differences in RSN connectivity associated with global brain activity. Significance: Increased understanding of brain dynamics may help to elucidate individual differences in behavior and dysfunction.

Recent advances in brain imaging have improved the measure of neural processes related to perceptual, cognitive and affective functions, yet the relation between brain activity and subjective experience remains poorly characterized. In part, it is a challenge to obtain reliable accounts of participant’s experience in such studies. Here we addressed this limitation by utilizing experienced meditators who are expert in introspection. We tested a novel method to link objective and subjective data, using real-time fMRI (rt-fMRI) to provide participants with feedback of their own brain activity during an ongoing task. We provided real-time feedback during a focused attention task from the posterior cingulate cortex, a hub of the default mode network shown to be activated during mind-wandering and deactivated during meditation. In a first experiment, both meditatorsand non-meditators reported significant correspondence between the feedback graph and their subjective experience of focused attention and mind-wandering. When instructed to volitionally decrease the feedback graph, meditators, but not non-meditators, showed significant deactivation of the posterior cingulate cortex. We were able to replicate these results in a separate group of meditators using a novel step-wise rt-fMRI discovery protocol in which participants were not provided with prior knowledge of the expected relationship between their experience and the feedback graph (i.e., focused attention versus mind-wandering). These findings support the feasibility of using rt-fMRI to link objective measures of brain activity with reports of ongoingsubjective experience in cognitive neuroscience research, and demonstrate the generalization of expertise in introspective awareness to novel contexts.

BrainK is a set of automated procedures for characterizing the tissues of the human head from MRI, CT, and photogrammetry images. The tissue segmentation and cortical surface extraction support the primary goal of modeling the propagation of electrical currents through head tissues with a finite difference model (FDM) or finite element model (FEM) created from the BrainK geometries. The electrical head model is necessary for accurate source localization of dense array electroencephalographic (dEEG) measures from head surface electrodes. It is also necessary for accurate targeting of cerebral structures with transcranial current injection from those surface electrodes. BrainK must achieve five major tasks: image segmentation, registration of the MRI, CT, and sensor photogrammetry images, cortical surface reconstruction, dipole tessellation of the cortical surface, and Talairach transformation. We describe the approach to each task, and we compare the accuracies for the key tasks of tissue segmentation and cortical surface extraction in relation to existing research tools (FreeSurfer, FSL, SPM, and BrainVisa). BrainK achieves good accuracy with minimal or no user intervention, it deals well with poor quality MR images and tissue abnormalities, and it provides improved computational efficiency over existing research packages.

Resting-state fMRI (rs-fMRI) holds promise as a clinical tool to characterize and monitor the phenotype of different neurological and psychiatric disorders. The most common analysis approach requires the definition of one or more regions-of-interest (ROIs). However this need for a priori ROI information makes rs-fMRI inadequate to survey functional connectivity differences associated with a range of neurological disorders where the ROI information may not be available. A second problem encountered in fMRI measures of connectivity is the need for an arbitrary correlation threshold to determine whether or not two areas are connected. This is problematic because in many cases the differences in tissue connectivity between disease groups and/or control subjects are threshold dependent. In this work we propose a novel voxel-based contrast mechanism for rs-fMRI, the Intrinsic Connectivity Distribution (ICD), that neither requires a priori information to define a ROI, nor an arbitrary threshold to define a connection. We show the sensitivity of previous methods to the choice of connection thresholds and evaluate ICD using a survey study comparing young adults born prematurely to healthy term control subjects. Functional connectivity differences were found in hypothesized language processing areas in the left temporal-parietal areas. In addition, significant clinically-relevant differences were found between preterm and term control subjects, highlighting the importance of whole brain surveys independent of a priori information.

Fiber tracking in diffusion tensor magnetic resonance images (DTIs) reveals 3-D structural connectivity of the brain conveniently and thus is a viable tool for investigating neural differences. Unfortunately, local noise, image artifacts and numerical tracking errors during integration-based techniques are cumulative. Prematurely terminated fibers and under-sampled fiber bundles result in incomplete reconstruction of white matter fiber tracts and hence incorrect anatomical measurements. Quantitative cross-subject tract analysis, which is critical for abnormality detection, is complicated by inefficient and inaccurate tract reconstruction and normalization from fiber bundles. Because of the above problems, we propose a parcellation method that aims for lower sensitivity to initialization and local orientation error by directly segmenting full white matter tracts (Fasciculography), rather than reconstructing individual curves, from diffusion tensor fields. A fast, robust volumetric, and intrinsically normalized solution is achieved by noise-filtering using a generic parametrized tract model to prevent premature tract termination. At the same time, orientation information reduces the search space, significantly speeding up the tract parcellation process with less human intervention. Detailed comparisons against streamline tracking, shortest-path tracking, and nonrigid registration using synthetic and real DTIs confirmed the superior properties of Fasciculography. Since a normalized tract can be delineated interactively in a just few seconds using the proposed method, accurate high volume tract comparisons become feasible.

Brain surgery faces important challenges when trying to achieve maximum tumor resection while avoiding postoperative neurological deficits.For surgeons to have optimal intraoperative information concerning white matter (WM) anatomy, we developed a platform that allows the intraoperative real-time querying of tractography data sets during frameless stereotactic neuronavigation.Structural magnetic resonance imaging, functional magnetic resonance imaging, and diffusion tensor imaging were performed on 5 patients before they underwent lesion resection using neuronavigation. During the procedure, the tracked surgical tool tip position was transferred from the navigation system to the 3-dimensional Slicer software package, which used this position to seed the WM tracts around the tool tip location, rendering a geometric visualization of these tracts on the preoperative images previously loaded onto the navigation system. The clinical feasibility of this approach was evaluated in 5 cases of lesion resection. In addition, system performance was evaluated by measuring the latency between surgical tool tracking and visualization of the seeded WM tracts.Lesion resection was performed successfully in all 5 patients. The seeded WM tracts close to the lesion and other critical structures, as defined by the functional and structural images, were interactively visualized during the intervention to determine their spatial relationships relative to the lesion and critical cortical areas. Latency between tracking and visualization of tracts was less than a second for a fiducial radius size of 4 to 5 mm.Interactive tractography can provide an intuitive way to inspect critical WM tracts in the vicinity of the surgical region, allowing the surgeon to have increased intraoperative WM information to execute the planned surgical resection.

Real-time three-dimensional (RT3D) echocardiography is a new image acquisition technique that allows instantaneous acquisition of volumetric images for quantitative assessment of cardiac morphology and function. To quantify many important diagnostic parameters, such as ventricular volume, ejection fraction, and cardiac output, an automatic algorithm to delineate the left ventricle (LV) from RT3D echocardiographic images is essential. While a number of efforts have been made towards segmentation of the LV endocardial (ENDO) boundaries, the segmentation of epicardial (EPI) boundaries remains problematic. In this paper, we present a coupled deformable model that addresses this problem. The idea behind our method is that the volume of the myocardium is close to being constant during a cardiac cycle and our model uses this coupling as an important constraint. We employ two surfaces, each driven by the image-derived information that takes into account ultrasound physics by modeling the speckle statistics using the Nakagami distribution while maintaining the coupling. By simultaneously evolving two surfaces, the final segmentation of the myocardium is thus achieved. Results from 80 sets of synthetic data and 286 sets of real canine data were evaluated against the ground truth and against outlines from three independent observers, respectively. We show that results obtained with our incompressibility constraint were more accurate than those obtained without constraint or with a wall thickness constraint, and were comparable to those from manual segmentation.

We used a new theory of the biological basis of the Big Five personality traits to generate hypotheses about the association of each trait with the volume of different brain regions. Controlling for age, sex, and whole-brain volume, results from structural magnetic resonance imaging of 116 healthy adults supported our hypotheses for four of the five traits: Extraversion, Neuroticism, Agreeableness, and Conscientiousness. Extraversion covaried with volume of medial orbitofrontal cortex, a brain region involved in processing reward information. Neuroticism covaried with volume of brain regions associated with threat, punishment, and negative affect. Agreeableness covaried with volume in regions that process information about the intentions and mental states of other individuals. Conscientiousness covaried with volume in lateral prefrontal cortex, a region involved in planning and the voluntary control of behavior. These findings support our biologically based, explanatory model of the Big Five and demonstrate the potential of personality neuroscience (i.e., the systematic study of individual differences in personality using neuroscience methods) as a discipline.

Ictal single photon emission computed tomography (SPECT) is a powerful tool for noninvasive seizure localization, but it has been underutilized because of practical challenges, including difficulty in implementing ictal-interictal SPECT difference analysis. We previously validated a freely available utility for this purpose, ictal-interictal subtraction analysis by statistical parametric mapping (SPM) (ISAS). To further simplify and improve the difference imaging technique, we now compare a new algorithm, ISAS BioImage Suite (see http://spect.yale.edu and http://bioimagesuite.org), to the original ISAS method in 13 patients with known seizure localization. We found that ISAS BioImage Suite was in agreement with the original algorithm in all cases for which ISAS correctly identified a single unambiguous region of seizure onset. We also tested for possible effects of scan-order bias in the control group used for the analysis and found no significant effect on the results. These findings establish a simple, validated and objective method for analyzing ictal-interictal SPECT difference images for use in the care of patients with epilepsy.

Statistical models have shown considerable promise as a basis for segmenting and interpreting cardiac images. While a variety of statistical models have been proposed to improve the segmentation results, most of them are either static models (SMs), which neglect the temporal dynamics of a cardiac sequence, or generic dynamical models (GDMs), which are homogeneous in time and neglect the intersubject variability in cardiac shape and deformation. In this paper, we develop a subject-specific dynamical model (SSDM) that simultaneously handles temporal dynamics (intrasubject variability) and intersubject variability. We also propose a dynamic prediction algorithm that can progressively identify the specific motion patterns of a new cardiac sequence based on the shapes observed in past frames. The incorporation of this SSDM into the segmentation framework is formulated in a recursive Bayesian framework. It starts with a manual segmentation of the first frame, and then segments each frame according to intensity information from the current frame as well as the prediction from past frames. In addition, to reduce error propagation in sequential segmentation, we take into account the periodic nature of cardiac motion and perform segmentation in both forward and backward directions. We perform "leave-one-out" test on 32 canine sequences and 22 human sequences, and compare the experimental results with those from SM, GDM, and active appearance motion model (AAMM). Quantitative analysis of the experimental results shows that SSDM outperforms SM, GDM, and AAMM by having better global and local consistencies with manual segmentation. Moreover, we compare the segmentation results from forward and forward-backward segmentation. Quantitative evaluation shows that forward-backward segmentation suppresses the propagation of segmentation errors.

Sex differences in brain structure have been examined extensively but are not completely understood, especially in relation to possible functional correlates. Our two aims in this study were to investigate sex differences in brain structure, and to investigate a possible relation between orbitofrontal cortex subregions and affective individual differences. We used tensor-based morphometry to estimate local brain volume from MPRAGE images in 117 healthy right-handed adults (58 female), age 18-40 years. We entered estimates of local brain volume as the dependent variable in a GLM, controlling for age, intelligence and whole-brain volume. Men had larger left planum temporale. Women had larger ventromedial prefrontal cortex (vmPFC), right lateral orbitofrontal (rlOFC), cerebellum, and bilateral basal ganglia and nearby white matter. vmPFC but not rlOFC volume covaried with self-reported emotion regulation strategies (reappraisal, suppression), expressivity of positive emotions (but not of negative), strength of emotional impulses, and cognitive but not somatic anxiety. vmPFC volume statistically mediated sex differences in emotion suppression. The results confirm prior reports of sex differences in orbitofrontal cortex structure, and are the first to show that normal variation in vmPFC volume is systematically related to emotion regulation and affective individual differences.

The cerebellar vermis is increasingly implicated in bipolar disorder (BD). In this study, we investigated vermis morphology in BD using a quantitative volumetric analysis.Volumes for total vermis and vermis subregions V1 (lobules I-V), V2 (lobules VI-VII), and V3 (lobules VIII-X) were calculated using high-resolution structural magnetic resonance imaging obtained from 44 individuals with BD (25 females and 19 males) and 43 healthy comparison (HC) subjects (26 females and 17 males). Total vermis volumes were compared between the BD and HC groups. Potential effects of vermis subregions and clinical features were explored.Total vermis volumes were significantly larger in the BD group than in the HC group (p = 0.02). There was a significant group-by-sex interaction (p = 0.02). Total vermis volumes were significantly larger in males with BD than HC males (p = 0.004); vermis volumes did not differ significantly between females with and without BD (p = 0.95). Subregion analyses showed a trend-level interaction between diagnosis and subregion (p = 0.07) in which subregion V1 volumes were significantly larger in BD participants (p = 0.001), with differences primarily driven by males (p = 0.001).Our findings demonstrate increases in cerebellar vermis volumes in males with BD. These findings support the presence of structural alterations in the cerebellar vermis in BD and furthermore the influence of sex on such changes.

A constrained non-rigid registration (CNRR) algorithm for use in prostate image-guided adaptive radiotherapy is presented in a coherent mathematical framework. The registration algorithm is based on a global rigid transformation combined with a series of local injective non-rigid multi-resolution cubic B-spline Free Form Deformation (FFD) transformations. The control points of the FFD are used to non-rigidly constrain the transformation to the prostate, rectum, and bladder. As well, the control points are used to rigidly constrain the transformation to the estimated position of the pelvis, left femur, and right femur. The algorithm was tested with both 3D conformal radiotherapy (3DCRT) and intensity-modulated radiotherapy (IMRT) dose plan data sets. The 3DCRT dose plan set consisted of 10 fan-beam CT (FBCT) treatment-day images acquired from four different patients. The IMRT dose plan set consisted of 32 cone-beam CT (CBCT) treatment-day images acquired from 4 different patients. The CNRR was tested with different combinations of anatomical constraints and each test significantly outperformed both rigid and non-rigid registration at aligning constrained bones and critical organs. The CNRR results were used to adapt the dose plans to account for patient positioning errors as well as inter-day bone motion and intrinsic organ deformation. Each adapted dose plan improved performance by lowering radiation distribution to the rectum and bladder while increasing or maintaining radiation distribution to the prostate.

This paper describes the development and application of a research interface to integrate research image analysis software with a commercial image-guided surgery navigation system. This interface enables bi-directional transfer of data such as images, visualizations and tool positions in real time.We describe both the design and the application programming interface of the research interface, as well as showing the function of an example client program. The resulting interface provides a practical and versatile link for bringing image analysis research techniques into the operating room (OR).We present examples from the successful use of this research interface in both phantom experiments and real neurosurgeries. In particular, we demonstrate that the integrated dual-computer system achieves tool-tracking performance that is comparable to the more typical single-computer scenario.Network interfaces for this type are viable solutions for the integration of commercial image-guided navigation systems and research software.

Previous cross-sectional study of ventral prefrontal cortex (VPFC) implicated progressive volume abnormalities during adolescence in bipolar disorder (BD). In the present study, a within-subject, longitudinal design was implemented to examine brain volume changes during adolescence/young adulthood. We hypothesized that VPFC volume decreases over time would be greater in adolescents/young adults with BD than in healthy comparison adolescents/young adults. Eighteen adolescents/young adults (10 with BD I and 8 healthy comparison participants) underwent two high-resolution magnetic resonance imaging scans over approximately 2 years. Regional volume changes over time were measured. Adolescents/young adults with BD displayed significantly greater volume loss over time, compared to healthy comparison participants, in a region encompassing VPFC and rostral PFC and extending to rostral anterior cingulate cortex (p< .05). Additional areas where volume change differed between groups were observed. While data should be interpreted cautiously due to modest sample size, this study provides preliminary evidence to support the presence of accelerated loss in VPFC and rostral PFC volume in adolescents/young adults with BD. (JINS, 2009,15, 476–481.)

BackgroundConvergent evidence implicates white matter abnormalities in bipolar disorder. The cingulum is an important candidate structure for study in bipolar disorder as it provides substantial white matter connections within the corticolimbic neural system that subserves emotional regulation involved in the disorder.AimsTo test the hypothesis that bipolar disorder is associated with abnormal white matter integrity in the cingulum.MethodFractional anisotropy in the anterior and posterior cingulum was compared between 42 participants with bipolar disorder and 42 healthy participants using diffusion tensor imaging.ResultsFractional anisotropy was significantly decreased in the anterior cingulum in the bipolar disorder group compared with the healthy group (P=0.003); however, fractional anisotropy in the posterior cingulum did not differ significantly between groups.ConclusionsOur findings demonstrate abnormalities in the structural integrity of the anterior cingulum in bipolar disorder. They extend evidence that supports involvement of the neural system comprising the anterior cingulate cortex and its corticolimbic gray matter connection sites in bipolar disorder to implicate abnormalities in the white matter connections within the system provided by the cingulum.

While the Talairach atlas remains the most commonly used system for reporting coordinates in neuroimaging studies, the absence of an actual 3-D image of the original brain used in its construction has severely limited the ability of researchers to automatically map locations from 3-D anatomical MRI images to the atlas. Previous work in this area attempted to circumvent this problem by constructing approximate linear and piecewise-linear mappings between standard brain templates (e.g. the MNI template) and Talairach space. These methods are limited in that they can only account for differences in overall brain size and orientation but cannot correct for the actual shape differences between the MNI template and the Talairach brain. In this paper we describe our work to digitize the Talairach atlas and generate a non-linear mapping between the Talairach atlas and the MNI template that attempts to compensate for the actual differences in shape between the two, resulting in more accurate coordinate transformations. We present examples in this paper and note that the method is available freely online as a Java applet.

Studies of working memory load effects on human EEG power have indicated divergent effects in different frequency bands. Although gamma power typically increases with load, the load dependency of the lower frequency theta and alpha bands is uncertain. We obtained intracranial electroencephalography measurements from 1453 electrode sites in 14 epilepsy patients performing a Sternberg task, in order to characterize the anatomical distribution of load-related changes across the frequency spectrum. Gamma power increases occurred throughout the brain, but were most common in the occipital lobe. In the theta and alpha bands, both increases and decreases were observed, but with different anatomical distributions. Increases in theta and alpha power were most prevalent in frontal midline cortex. Decreases were most commonly observed in occipital cortex, colocalized with increases in the gamma range, but were also detected in lateral frontal and parietal regions. Spatial overlap with group functional magnetic resonance imaging results was minimal except in the precentral gyrus. These findings suggest that power in any given frequency band is not a unitary phenomenon; rather, reactivity in the same frequency band varies in different brain regions, and may relate to the engagement or inhibition of a given area in a cognitive task.

Evaluation of lymphedema and lymph node metastasis in humans has relied primarily on invasive or radioactive modalities. While noninvasive technologies such as magnetic resonance imaging (MRI) offer the potential for true three-dimensional imaging of lymphatic structures, invasive modalities, such as optical fluorescence microscopy, provide higher resolution and clearer delineation of both lymph nodes and lymphatic vessels. Thus, contrast agents that image lymphatic vessels and lymph nodes by both fluorescence and MRI may further enhance our understanding of the structure and function of the lymphatic system. Recent applications of bimodal (fluorescence and MR) contrast agents in mice have not achieved clear visualization of lymphatic vessels and nodes. Here the authors describe the development of a nanoparticulate contrast agent that is taken up by lymphatic vessels to draining lymph nodes and detected by both modalities.A unique nanoparticulate contrast agent composed of a polyamidoamine dendrimer core conjugated to paramagnetic contrast agents and fluorescent probes was synthesized. Anesthetized mice were injected with the nanoparticulates in the hind footpads and imaged by MR and fluorescence microscopy. High resolution MR and fluorescence images were obtained and compared to traditional techniques for lymphatic visualization using Evans blue dye.Lymph nodes and lymphatic vessels were clearly observed by both MRI and fluorescence microscopy using the bimodal nanoparticulate contrast agent. Characteristic tail-lymphatics were also visualized by both modalities. Contrast imaging yielded a higher resolution than the traditional method employing Evans blue dye. MR data correlated with fluorescence and Evans blue dye imaging.A bimodal nanoparticulate contrast agent facilitates the visualization of lymphatic vessels and lymph nodes by both fluorescence microscopy and MRI with strong correlation between the two modalities. This agent may translate to applications such as the assessment of malignancy and lymphedema in humans and the evaluation of lymphatic vessel function and morphology in animal models.

We examined the hypothesis that insulin resistance in skeletal muscle promotes the development of atherogenic dyslipidemia, associated with the metabolic syndrome, by altering the distribution pattern of postprandial energy storage. Following ingestion of two high carbohydrate mixed meals, net muscle glycogen synthesis was reduced by ≈60% in young, lean, insulin-resistant subjects compared with a similar cohort of age–weight–body mass index–activity-matched, insulin-sensitive, control subjects. In contrast, hepatic de novo lipogenesis and hepatic triglyceride synthesis were both increased by >2-fold in the insulin-resistant subjects. These changes were associated with a 60% increase in plasma triglyceride concentrations and an ≈20% reduction in plasma high-density lipoprotein concentrations but no differences in plasma concentrations of TNF-α, IL-6, adiponectin, resistin, retinol binding protein-4, or intraabdominal fat volume. These data demonstrate that insulin resistance in skeletal muscle, due to decreased muscle glycogen synthesis, can promote atherogenic dyslipidemia by changing the pattern of ingested carbohydrate away from skeletal muscle glycogen synthesis into hepatic de novo lipogenesis, resulting in an increase in plasma triglyceride concentrations and a reduction in plasma high-density lipoprotein concentrations. Furthermore, insulin resistance in these subjects was independent of changes in the plasma concentrations of TNF-α, IL-6, high-molecular-weight adiponectin, resistin, retinol binding protein-4, or intraabdominal obesity, suggesting that these factors do not play a primary role in causing insulin resistance in the early stages of the metabolic syndrome.

Functional magnetic resonance imaging (fMRI) studies typically collapse data from many subjects, but brain functional organization varies between individuals. Here we establish that this individual variability is both robust and reliable, using data from the Human Connectome Project to demonstrate that functional connectivity profiles act as a 'fingerprint' that can accurately identify subjects from a large group. Identification was successful across scan sessions and even between task and rest conditions, indicating that an individual's connectivity profile is intrinsic, and can be used to distinguish that individual regardless of how the brain is engaged during imaging. Characteristic connectivity patterns were distributed throughout the brain, but the frontoparietal network emerged as most distinctive. Furthermore, we show that connectivity profiles predict levels of fluid intelligence: the same networks that were most discriminating of individuals were also most predictive of cognitive behavior. Results indicate the potential to draw inferences about single subjects on the basis of functional connectivity fMRI.

This paper presents a dictionary learning-based method to segment the brain surface in post-surgical CT images of epilepsy patients following surgical implantation of electrodes. Using the electrodes identified in the post-implantation CT, surgeons require accurate registration with pre-implantation functional and structural MR imaging to guide surgical resection of epileptic tissue. In this work, we use a surface-based registration method to align the MR and CT brain surfaces. The key challenge here is not the registration, but rather the extraction of the cortical surface from the CT image, which includes missing parts of the skull and artifacts introduced by the electrodes. To segment the brain from these images, we propose learning a model of appearance that captures both the normal tissue and the artifacts found along this brain surface boundary. Using clinical data, we demonstrate that our method both accurately extracts the brain surface and better localizes electrodes than intensity-based rigid and non-rigid registration methods.

Abnormalities in volumes of the amygdala have been reported previously in adolescents and adults with bipolar disorder (BD). Several studies have reported reduced volumes in adolescents with BD; however, both decreases and increases in volumes have been reported in adults with BD. Understanding of potential developmental contributions to these disturbances in morphology of the amygdala has been limited by the absence of longitudinal data in persons with BD. Here we use a within-subject longitudinal design to investigate whether amygdala volume abnormalities persist in adolescents and young adults with BD over a time interval of approximately 2 years.Participants included 18 adolescents and young adults: 10 participants with BD I and 8 healthy comparison participants. Amygdala volumes were measured on high-resolution magnetic resonance imaging scans acquired twice for each subject over intervals of approximately 2 years. Amygdala volumes were the dependent measures in a mixed-model statistical analysis to compare amygdala volumes between groups over time while covarying for total brain volume.Amygdala volumes were significantly smaller in adolescents and young adults with BD compared with healthy participants (p = 0.018). The effect of time was not significant.Although the sample size is modest, this study provides preliminary evidence to support the presence of decreased amygdala volumes in adolescents and young adults with BD that persist during this developmental epoch.

There is a wide interindividual variation in peripheral insulin sensitivity at any given body mass index or percent body fat among obese adolescents with normal glucose tolerance. The goals of this study were to determine whether variability in insulin sensitivity is associated with differences in patterns of lipid partitioning or substrate use under fasting and hyperinsulinemic conditions. We compared 14 obese insulin-resistant adolescents with 14 obese insulin-sensitive controls, pair matched for age, gender, pubertal stage and body composition. Insulin sensitivity was assessed by the hyperinsulinemic-euglycemic clamp, intramyocellular lipid content by (1)H-nuclear magnetic resonance and visceral fat by magnetic resonance imaging. Obese insulin-sensitive subjects had lower intramyocellular (1.64 +/- 0.68 vs.2.26 +/- 0.62% of water peak, P = 0.017) and visceral lipid deposition (45 +/- 23 vs. 77 +/- 52 cm(2), P = 0.04) and a higher level of adiponectin, compared with their obese-resistant counterparts (8.8 +/- 3.6 vs. 6.5 +/- 1.8 mug/dl, P = 0.015). Glycerol fluxes were similar between the two obese groups yet occurred in the face of different concentrations of insulin. Intramyocellular lipid and visceral fat were negatively related to insulin sensitivity. Obese insulin-sensitive adolescents are characterized by lower lipid deposition in the intramyocellular and visceral compartments and greater levels of adiponectin, despite similar degree of adiposity.

The quantitative estimation of regional cardiac deformation from three-dimensional (3-D) image sequences has important clinical implications for the assessment of viability in the heart wall. We present here a generic methodology for estimating soft tissue deformation which integrates image-derived information with biomechanical models, and apply it to the problem of cardiac deformation estimation. The method is image modality independent. The images are segmented interactively and then initial correspondence is established using a shape-tracking approach. A dense motion field is then estimated using a transversely isotropic, linear-elastic model, which accounts for the muscle fiber directions in the left ventricle. The dense motion field is in turn used to calculate the deformation of the heart wall in terms of strain in cardiac specific directions. The strains obtained using this approach in open-chest dogs before and after coronary occlusion, exhibit a high correlation with strains produced in the same animals using implanted markers. Further, they show good agreement with previously published results in the literature. This proposed method provides quantitative regional 3-D estimates of heart deformation.

This magnetic resonance imaging study demonstrates increased lateral ventricle volume (LVV) in adolescents and adults with bipolar disorder (BD) with psychotic symptoms, but not without psychosis, compared to healthy adolescents and adults. This suggests LVV is a morphologic feature associated with psychosis in BD, present by adolescence.

Resting-state functional magnetic resonance image (rs-fMRI) is increasingly used to study functional brain networks. Nevertheless, variability in these networks due to factors such as sex and aging is not fully understood. This study explored sex differences in normal age trajectories of resting-state networks (RSNs) using a novel voxel-wise measure of functional connectivity, the intrinsic connectivity distribution (ICD). Males and females showed differential patterns of changing connectivity in large-scale resting-state networks during normal aging from early adulthood to late middle-age. In some networks, such as the default mode network, males and females both showed decreases in connectivity with age, albeit at different rates. In other networks, such as the fronto-parietal network, males and females showed divergent connectivity trajectories with age. Main effects of sex and age were found in many of the same regions showing sex-related differences in aging. Finally, these sex differences in aging trajectories were robust to choice of preprocessing strategy, such as global signal regression. Our findings resolve some discrepancies in the literature, especially with respect to the trajectory of connectivity in the default mode, which can be explained by our observed interactions between sex and aging. Overall, results indicate that RSNs show different aging trajectories for males and females. Characterizing effects of sex and age on RSNs are critical first steps in understanding the functional organization of the human brain.

Background Functional connectivity analyses of functional magnetic resonance imaging data are a powerful tool for characterizing brain networks and how they are disrupted in neural disorders. However, many such analyses examine only one or a small number of a priori seed regions. Studies that consider the whole brain frequently rely on anatomic atlases to define network nodes, which might result in mixing distinct activation timecourses within a single node. Here, we improve upon previous methods by using a data-driven brain parcellation to compare connectivity profiles of dyslexic (DYS) versus nonimpaired (NI) readers in the first whole-brain functional connectivity analysis of dyslexia. Methods Whole-brain connectivity was assessed in children ( n = 75; 43 NI, 32 DYS) and adult ( n = 104; 64 NI, 40 DYS) readers. Results Compared to NI readers, DYS readers showed divergent connectivity within the visual pathway and between visual association areas and prefrontal attention areas; increased right-hemisphere connectivity; reduced connectivity in the visual word-form area (part of the left fusiform gyrus specialized for printed words); and persistent connectivity to anterior language regions around the inferior frontal gyrus. Conclusions Together, findings suggest that NI readers are better able to integrate visual information and modulate their attention to visual stimuli, allowing them to recognize words on the basis of their visual properties, whereas DYS readers recruit altered reading circuits and rely on laborious phonology-based "sounding out" strategies into adulthood. These results deepen our understanding of the neural basis of dyslexia and highlight the importance of synchrony between diverse brain regions for successful reading.

We present a novel voxel-based connectivity approach for paired functional magnetic resonance imaging (fMRI) data collected under two different conditions labeled the Coupled Intrinsic Connectivity Distribution (coupled-ICD). Our proposed method jointly models both conditions to incorporate additional paired information into the connectivity metric. Voxel-based connectivity holds promise as a clinical tool to characterize a wide range of neurological and psychiatric diseases, and monitor their treatment. As such, examining paired connectivity data such as scans acquired pre- and post-intervention is an important application for connectivity methodologically. When presented with data from paired conditions, conventional voxel-based methods analyze each condition separately. However, summarizing each connection separately can misrepresent patterns of changes in connectivity. We show that commonly used methods can underestimate functional changes and subsequently introduce and evaluate our solution to this problem, the coupled-ICD metric, using two studies: 1) healthy controls scanned awake and under anesthesia, and 2) cocaine-dependent subjects and healthy controls scanned while being presented with relaxing or drug-related imagery cues. The coupled-ICD approach detected differences between paired conditions in similar brain regions as the conventional approaches while also revealing additional changes in regions not identified using conventional voxel-based connectivity analyses. Follow-up seed-based analyses on data independent from the voxel-based results also showed connectivity differences between conditions in regions detected by coupled-ICD. This approach of jointly analyzing paired resting-state scans provides a new and important tool with many applications for clinical and basic neuroscience research.

In this paper, we present a groupwise graph-theory-based parcellation approach to define nodes for network analysis. The application of network-theory-based analysis to extend the utility of functional MRI has recently received increased attention. Such analyses require first and foremost a reasonable definition of a set of nodes as input to the network analysis. To date many applications have used existing atlases based on cytoarchitecture, task-based fMRI activations, or anatomic delineations. A potential pitfall in using such atlases is that the mean timecourse of a node may not represent any of the constituent timecourses if different functional areas are included within a single node. The proposed approach involves a groupwise optimization that ensures functional homogeneity within each subunit and that these definitions are consistent at the group level. Parcellation reproducibility of each subunit is computed across multiple groups of healthy volunteers and is demonstrated to be high. Issues related to the selection of appropriate number of nodes in the brain are considered. Within typical parameters of fMRI resolution, parcellation results are shown for a total of 100, 200, and 300 subunits. Such parcellations may ultimately serve as a functional atlas for fMRI and as such three atlases at the 100-, 200- and 300-parcellation levels derived from 79 healthy normal volunteers are made freely available online along with tools to interface this atlas with SPM, BioImage Suite and other analysis packages.

We present a new strategy to constrain nonrigid registrations of multi-modal images using a low-dimensional statistical deformation model and test this in registering pre-operative and post-operative images from epilepsy patients. For those patients who may undergo surgical resection for treatment, the current gold-standard to identify regions of seizure involves craniotomy and implantation of intracranial electrodes. To guide surgical resection, surgeons utilize pre-op anatomical and functional MR images in conjunction with post-electrode implantation MR and CT images. The electrode positions from the CT image need to be registered to pre-op functional and structural MR images. The post-op MRI serves as an intermediate registration step between the pre-op MR and CT images. In this work, we propose to bypass the post-op MR image registration step and directly register the pre-op MR and post-op CT images using a low-dimensional nonrigid registration that captures the gross deformation after electrode implantation. We learn the nonrigid deformation characteristics from a principal component analysis of a set of training deformations and demonstrate results using clinical data. We show that our technique significantly outperforms both standard rigid and nonrigid intensity-based registration methods in terms of mean and maximum registration error.

Preterm (PT) subjects are at risk for developmental delay, and task-based studies suggest that developmental disorders may be due to alterations in neural connectivity. Since emerging data imply the importance of right cerebellar function for language acquisition in typical development, we hypothesized that PT subjects would have alternate areas of cerebellar connectivity, and that these areas would be responsible for differences in cognitive outcomes between PT subjects and term controls at age 20 years. Nineteen PT and 19 term control young adults were prospectively studied using resting-state functional MRI (fMRI) to create voxel-based contrast maps reflecting the functional connectivity of each tissue element in the grey matter through analysis of the intrinsic connectivity contrast degree (ICC-d). Left cerebellar ICC-d differences between subjects identified a region of interest that was used for subsequent seed-based connectivity analyses. Subjects underwent standardized language testing, and correlations with cognitive outcomes were assessed. There were no differences in gender, hand preference, maternal education, age at study, or Peabody Picture Vocabulary Test (PPVT) scores. Functional connectivity (FcMRI) demonstrated increased tissue connectivity in the biventer, simple and quadrangular lobules of the L cerebellum (p0.05) in PTs compared to term controls; seed-based analyses from these regions demonstrated alterations in connectivity from L cerebellum to both R and L inferior frontal gyri (IFG) in PTs compared to term controls. For PTs but not term controls, there were significant positive correlations between these connections and PPVT scores (R IFG: r=0.555, p=0.01; L IFG: r=0.454, p=0.05), as well as Verbal Comprehension Index (VCI) scores (R IFG: r=0.472, p=0.04). These data suggest the presence of a left cerebellar language circuit in PT subjects at young adulthood. These findings may represent either a delay in maturation or the engagement of alternative neural pathways for language in the developing PT brain.

The spatio-temporal coherence in data plays an important role in echocardiographic segmentation. While learning offline dynamical priors from databases has received considerable attention, these priors may not be suitable for post-infarct patients and children with congenital heart disease. This paper presents a dynamical appearance model (DAM) driven by individual inherent data coherence. It employs multi-scale sparse representation of local appearance, learns online multiscale appearance dictionaries as the image sequence is segmented sequentially, and integrates a spectrum of complementary multiscale appearance information including intensity, multiscale local appearance, and dynamical shape predictions. It overcomes the limitations of database-driven statistical models and applies to a broader range of subjects. Results on 26 4D canine echocardiographic images acquired from both healthy and post-infarct subjects show that our method significantly improves segmentation accuracy and robustness compared to a conventional intensity model and our previous single-scale sparse representation method.