Feasibility of real-time motion management with helical tomotherapy.

PURPOSE: This study investigates the potential application of image-based motion tracking and real-time motion correction to a helical tomotherapy system. METHODS: A kV x-ray imaging system was added to a helical tomotherapy system, mounted 90 degrees offset from the MV treatment beam, and an optical camera system was mounted above the foot of the couch. This experimental system tracks target motion by acquiring an x-ray image every few seconds during gantry rotation. For respiratory (periodic) motion, software correlates internal target positions visible in the x-ray images with marker positions detected continuously by the camera, and generates an internal-external correlation model to continuously determine the target position in three-dimensions (3D). Motion correction is performed by continuously updating jaw positions and MLC leaf patterns to reshape (effectively re-pointing) the treatment beam to follow the 3D target motion. For motion due to processes other than respiration (e.g., digestion), no correlation model is used - instead, target tracking is achieved with the periodically acquired x-ray images, without correlating with a continuous camera signal. RESULTS: The system's ability to correct for respiratory motion was demonstrated using a helical treatment plan delivered to a small (10 mm diameter) target. The phantom was moved following a breathing trace with an amplitude of 15 mm. Film measurements of delivered dose without motion, with motion, and with motion correction were acquired. Without motion correction, dose differences within the target of up to 30% were observed. With motion correction enabled, dose differences in the moving target were less than 2%. Nonrespiratory system performance was demonstrated using a helical treatment plan for a 55 mm diameter target following a prostate motion trace with up to 14 mm of motion. Without motion correction, dose differences up to 16% and shifts of greater than 5 mm were observed. Motion correction reduced these to less than a 6% dose difference and shifts of less than 2 mm. CONCLUSIONS: Real-time motion tracking and correction is technically feasible on a helical tomotherapy system. In one experiment, dose differences due to respiratory motion were greatly reduced. Dose differences due to nonrespiratory motion were also reduced, although not as much as in the respiratory case due to less frequent tracking updates. In both cases, beam-on time was not increased by motion correction, since the system tracks and corrects for motion simultaneously with treatment delivery.

Concurrent segmentation of the prostate on MRI and CT via linked statistical shape models for radiotherapy planning.

PURPOSE: Prostate gland segmentation is a critical step in prostate radiotherapy planning, where dose plans are typically formulated on CT. Pretreatment MRI is now beginning to be acquired at several medical centers. Delineation of the prostate on MRI is acknowledged as being significantly simpler to perform, compared to delineation on CT. In this work, the authors present a novel framework for building a linked statistical shape model (LSSM), a statistical shape model (SSM) that links the shape variation of a structure of interest (SOI) across multiple imaging modalities. This framework is particularly relevant in scenarios where accurate boundary delineations of the SOI on one of the modalities may not be readily available, or difficult to obtain, for training a SSM. In this work the authors apply the LSSM in the context of multimodal prostate segmentation for radiotherapy planning, where the prostate is concurrently segmented on MRI and CT. METHODS: The framework comprises a number of logically connected steps. The first step utilizes multimodal registration of MRI and CT to map 2D boundary delineations of the prostate from MRI onto corresponding CT images, for a set of training studies. Hence, the scheme obviates the need for expert delineations of the gland on CT for explicitly constructing a SSM for prostate segmentation on CT. The delineations of the prostate gland on MRI and CT allows for 3D reconstruction of the prostate shape which facilitates the building of the LSSM. In order to perform concurrent prostate MRI and CT segmentation using the LSSM, the authors employ a region-based level set approach where the authors deform the evolving prostate boundary to simultaneously fit to MRI and CT images in which voxels are classified to be either part of the prostate or outside the prostate. The classification is facilitated by using a combination of MRI-CT probabilistic spatial atlases and a random forest classifier, driven by gradient and Haar features. RESULTS: The authors acquire a total of 20 MRI-CT patient studies and use the leave-one-out strategy to train and evaluate four different LSSMs. First, a fusion-based LSSM (fLSSM) is built using expert ground truth delineations of the prostate on MRI alone, where the ground truth for the gland on CT is obtained via coregistration of the corresponding MRI and CT slices. The authors compare the fLSSM against another LSSM (xLSSM), where expert delineations of the gland on both MRI and CT are employed in the model building; xLSSM representing the idealized LSSM. The authors also compare the fLSSM against an exclusive CT-based SSM (ctSSM), built from expert delineations of the gland on CT alone. In addition, two LSSMs trained using trainee delineations (tLSSM) on CT are compared with the fLSSM. The results indicate that the xLSSM, tLSSMs, and the fLSSM perform equivalently, all of them out-performing the ctSSM. CONCLUSIONS: The fLSSM provides an accurate alternative to SSMs that require careful expert delineations of the SOI that may be difficult or laborious to obtain. Additionally, the fLSSM has the added benefit of providing concurrent segmentations of the SOI on multiple imaging modalities.

Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 Tesla endorectal, in vivo T2-weighted MR imagery.

PURPOSE: To identify and evaluate textural quantitative imaging signatures (QISes) for tumors occurring within the central gland (CG) and peripheral zone (PZ) of the prostate, respectively, as seen on in vivo 3 Tesla (T) endorectal T2-weighted (T2w) MRI. MATERIALS AND METHODS: This study used 22 preoperative prostate MRI data sets (16 PZ, 6 CG) acquired from men with confirmed prostate cancer (CaP) and scheduled for radical prostatectomy (RP). The prostate region-of-interest (ROI) was automatically delineated on T2w MRI, following which it was corrected for intensity-based acquisition artifacts. An expert pathologist manually delineated the dominant tumor regions on ex vivo sectioned and stained RP specimens as well as identified each of the studies as either a CG or PZ CaP. A nonlinear registration scheme was used to spatially align and then map CaP extent from the ex vivo RP sections onto the corresponding MRI slices. A total of 110 texture features were then extracted on a per-voxel basis from all T2w MRI data sets. An information theoretic feature selection procedure was then applied to identify QISes comprising T2w MRI textural features specific to CG and PZ CaP, respectively. The QISes for CG and PZ CaP were evaluated by means of Quadratic Discriminant Analysis (QDA) on a per-voxel basis against the ground truth for CaP on T2w MRI, mapped from corresponding histology. RESULTS: The QDA classifier yielded an area under the Receiver Operating characteristic curve of 0.86 for the CG CaP studies, and 0.73 for the PZ CaP studies over 25 runs of randomized three-fold cross-validation. By comparison, the accuracy of the QDA classifier was significantly lower when (a) using all 110 texture features (with no feature selection applied), as well as (b) a randomly selected combination of texture features. CONCLUSION: CG and PZ prostate cancers have significantly differing textural quantitative imaging signatures on T2w endorectal in vivo MRI.

Elastic registration of multimodal prostate MRI and histology via multiattribute combined mutual information.

PURPOSE: By performing registration of preoperative multiprotocol in vivo magnetic resonance (MR) images of the prostate with corresponding whole-mount histology (WMH) sections from postoperative radical prostatectomy specimens, an accurate estimate of the spatial extent of prostate cancer (CaP) on in vivo MR imaging (MRI) can be retrospectively established. This could allow for definition of quantitative image-based disease signatures and lead to development of classifiers for disease detection on multiprotocol in vivo MRI. Automated registration of MR and WMH images of the prostate is complicated by dissimilar image intensities, acquisition artifacts, and nonlinear shape differences. METHODS: The authors present a method for automated elastic registration of multiprotocol in vivo MRI and WMH sections of the prostate. The method, multiattribute combined mutual information (MACMI), leverages all available multiprotocol image data to drive image registration using a multivariate formulation of mutual information. RESULTS: Elastic registration using the multivariate MI formulation is demonstrated for 150 corresponding sets of prostate images from 25 patient studies with T2-weighted and dynamic-contrast enhanced MRI and 85 image sets from 15 studies with an additional functional apparent diffusion coefficient MRI series. Qualitative results of MACMI evaluation via visual inspection suggest that an accurate delineation of CaP extent on MRI is obtained. Results of quantitative evaluation on 150 clinical and 20 synthetic image sets indicate improved registration accuracy using MACMI compared to conventional pairwise mutual information-based approaches. CONCLUSIONS: The authors' approach to the registration of in vivo multiprotocol MRI and ex vivo WMH of the prostate using MACMI is unique, in that (1) information from all available image protocols is utilized to drive the registration with histology, (2) no additional, intermediate ex vivo radiology or gross histology images need be obtained in addition to the routinely acquired in vivo MRI series, and (3) no corresponding anatomical landmarks are required to be identified manually or automatically on the images.

HistoStitcher(©): an interactive program for accurate and rapid reconstruction of digitized whole histological sections from tissue fragments.

We present an interactive program called HistoStitcher(©) for accurate and rapid reassembly of histology fragments into a pseudo-whole digitized histological section. HistoStitcher(©) provides both an intuitive graphical interface to assist the operator in performing the stitch of adjacent histology fragments by selecting pairs of anatomical landmarks, and a set of computational routines for determining and applying an optimal linear transformation to generate the stitched image. Reconstruction of whole histological sections from images of slides containing smaller fragments is required in applications where preparation of whole sections of large tissue specimens is not feasible or efficient, and such whole mounts are required to facilitate (a) disease annotation and (b) image registration with radiological images. Unlike manual reassembly of image fragments in a general purpose image editing program (such as Photoshop), HistoStitcher(©) provides memory efficient operation on high resolution digitized histology images and a highly flexible stitching process capable of producing more accurate results in less time. Further, by parameterizing the series of transformations determined by the stitching process, the stitching parameters can be saved, loaded at a later time, refined, or reapplied to multi-resolution scans, or quickly transmitted to another site. In this paper, we describe in detail the design of HistoStitcher(©) and the mathematical routines used for calculating the optimal image transformation, and demonstrate its operation for stitching high resolution histology quadrants of a prostate specimen to form a digitally reassembled whole histology section, for 8 different patient studies. To evaluate stitching quality, a 6 point scoring scheme, which assesses the alignment and continuity of anatomical structures important for disease annotation, is employed by three independent expert pathologists. For 6 studies compared with this scheme, reconstructed sections generated via HistoStitcher(©) scored higher than reconstructions generated by an expert pathologist using Photoshop.

Determining histology-MRI slice correspondences for defining MRI-based disease signatures of prostate cancer.

Mapping the spatial disease extent in a certain anatomical organ/tissue from histology images to radiological images is important in defining the disease signature in the radiological images. One such scenario is in the context of men with prostate cancer who have had pre-operative magnetic resonance imaging (MRI) before radical prostatectomy. For these cases, the prostate cancer extent from ex vivo whole-mount histology is to be mapped to in vivo MRI. The need for determining radiology-image-based disease signatures is important for (a) training radiologist residents and (b) for constructing an MRI-based computer aided diagnosis (CAD) system for disease detection in vivo. However, a prerequisite for this data mapping is the determination of slice correspondences (i.e. indices of each pair of corresponding image slices) between histological and magnetic resonance images. The explicit determination of such slice correspondences is especially indispensable when an accurate 3D reconstruction of the histological volume cannot be achieved because of (a) the limited tissue slices with unknown inter-slice spacing, and (b) obvious histological image artifacts (tissue loss or distortion). In the clinic practice, the histology-MRI slice correspondences are often determined visually by experienced radiologists and pathologists working in unison, but this procedure is laborious and time-consuming. We present an iterative method to automatically determine slice correspondence between images from histology and MRI via a group-wise comparison scheme, followed by 2D and 3D registration. The image slice correspondences obtained using our method were compared with the ground truth correspondences determined via consensus of multiple experts over a total of 23 patient studies. In most instances, the results of our method were very close to the results obtained via visual inspection by these experts.

Spatially weighted mutual information (SWMI) for registration of digitally reconstructed ex vivo whole mount histology and in vivo prostate MRI.

In this work, we present a scheme for the registration of digitally reconstructed whole mount histology (WMH) to pre-operative in vivo multiprotocol prostate MR imagery (T2w and DCE) using spatially weighted mutual information (SWMI). Spatial alignment of ex vivo histological sections to pre-operative in vivo MRI for prostate cancer (CaP) patients undergoing radical prostatectomy is a necessary first step in the discovery of quantitative multiprotocol MRI signatures for CaP. This may be done by spatially mapping delineated extent of disease on ex vivo histopathology onto pre-operative in vivo MRI via image registration. Apart from the challenges in spatially registering multi-modal data (histology and MRI) on account of (a) modality specific differences, (b) deformation due to the endorectal coil and tissue loss on histology, another complication is that the ex vivo histological sections, in the lab, are usually obtained as quadrants. This means they need to be reconstituted as a pseudo-whole mount histologic section (WMHS) prior to registration with MRI. An additional challenge is that most registration techniques rely on availability of the pre-segmented prostate capsule on T2w MRI. The novel contribution of this paper is that it leverages a spatially weighted mutual information (SWMI) scheme to automatically register and map CaP extent from WMHS onto pre-operative, multiprotocol MRI. The SWMI scheme obviates the need for pre-segmentation of the prostate capsule on MRI. Additionally, we leverage a program developed by our group, Histostitcher©, for interactive stitching of individual histology quadrants to digitally reconstruct the pseudo WMHS. Our registration methodology comprises the following main steps, (1) affine registration of T2w and DCE MRI, (2) affine registration of stitched WMHS to multiprotocol T2w and DCE MRI, and (3) multimodal image registration of WMHS to multiprotocol T2w and DCE MRI using SWMI. We quantitatively and qualitatively evaluated all aspects of our methodology in the multimodal registration of a total of 7 corresponding histology and MRI sections from 2 different patients. For the 7 studies, we obtained an average Hausdorff distance of 1.85 mm, mean absolute distance of 0.99 mm, RMS of 1.65 mm, and DICE of 0.83, when comparing the capsular alignment on MRI to histology.

Enhanced Multi-Protocol Analysis via Intelligent Supervised Embedding (EMPrAvISE): Detecting Prostate Cancer on Multi-Parametric MRI.

Currently, there is significant interest in developing methods for quantitative integration of multi-parametric (structural, functional) imaging data with the objective of building automated meta-classifiers to improve disease detection, diagnosis, and prognosis. Such techniques are required to address the differences in dimensionalities and scales of individual protocols, while deriving an integrated multi-parametric data representation which best captures all disease-pertinent information available. In this paper, we present a scheme called Enhanced Multi-Protocol Analysis via Intelligent Supervised Embedding (EMPrAvISE); a powerful, generalizable framework applicable to a variety of domains for multi-parametric data representation and fusion. Our scheme utilizes an ensemble of embeddings (via dimensionality reduction, DR); thereby exploiting the variance amongst multiple uncorrelated embeddings in a manner similar to ensemble classifier schemes (e.g. Bagging, Boosting). We apply this framework to the problem of prostate cancer (CaP) detection on 12 3 Tesla pre-operative in vivo multi-parametric (T2-weighted, Dynamic Contrast Enhanced, and Diffusion-weighted) magnetic resonance imaging (MRI) studies, in turn comprising a total of 39 2D planar MR images. We first align the different imaging protocols via automated image registration, followed by quantification of image attributes from individual protocols. Multiple embeddings are generated from the resultant high-dimensional feature space which are then combined intelligently to yield a single stable solution. Our scheme is employed in conjunction with graph embedding (for DR) and probabilistic boosting trees (PBTs) to detect CaP on multi-parametric MRI. Finally, a probabilistic pairwise Markov Random Field algorithm is used to apply spatial constraints to the result of the PBT classifier, yielding a per-voxel classification of CaP presence. Per-voxel evaluation of detection results against ground truth for CaP extent on MRI (obtained by spatially registering pre-operative MRI with available whole-mount histological specimens) reveals that EMPrAvISE yields a statistically significant improvement (AUC=0.77) over classifiers constructed from individual protocols (AUC=0.62, 0.62, 0.65, for T2w, DCE, DWI respectively) as well as one trained using multi-parametric feature concatenation (AUC=0.67).

CADOnc©: An Integrated Toolkit For Evaluating Radiation Therapy Related Changes In The Prostate Using Multiparametric MRI.

The use of multi-parametric Magnetic Resonance Imaging (T2-weighted, MR Spectroscopy (MRS), Diffusion-weighted (DWI)) has recently shown great promise for diagnosing and staging prostate cancer (CaP) in vivo. Such imaging has also been utilized for evaluating the early effects of radiotherapy (RT) (e.g. intensity-modulated radiation therapy (IMRT), proton beam therapy, brachytherapy) in the prostate with the overarching goal being to successfully predict short- and long-term patient outcome. Qualitative examination of post-RT changes in the prostate using MRI is subject to high inter- and intra-observer variability. Consequently, there is a clear need for quantitative image segmentation, registration, and classification tools for assessing RT changes via multi-parametric MRI to identify (a) residual disease, and (b) new foci of cancer (local recurrence) within the prostate. In this paper, we present a computerized image segmentation, registration, and classification toolkit called CADOnc©, and leverage it for evaluating (a) spatial extent of disease pre-RT, and (b) post-RT related changes within the prostate. We demonstrate the applicability of CADOnc© in studying IMRTrelated changes using a cohort of 7 multi-parametric (T2w, MRS, DWI) prostate MRI patient datasets. First, the different MRI protocols from pre- and post-IMRT MRI scans are affinely registered (accounting for gland shrinkage), followed by automated segmentation of the prostate capsule using an active shape model. A number of feature extraction schemes are then applied to extract multiple textural, metabolic, and functional MRI attributes on a per-voxel basis. An AUC of 0.7132 was achieved for automated detection of CaP on pre-IMRT MRI (via integration of T2w, DWI, MRS features); evaluated on a per-voxel basis against radiologist-derived annotations. CADOnc© also successfully identified a total of 40 out of 46 areas where disease-related changes (both absence and recurrence) occurred post-IMRT, based on changes in the expression of quantitative MR imaging biomarkers. CADOnc© thus provides an integrated platform of quantitative analysis tools to evaluate treatment response in vivo, based on multi-parametric MRI data.

Integrating Structural and Functional Imaging for Computer Assisted Detection of Prostate Cancer on Multi-Protocol In Vivo 3 Tesla MRI.

Screening and detection of prostate cancer (CaP) currently lacks an image-based protocol which is reflected in the high false negative rates currently associated with blinded sextant biopsies. Multi-protocol magnetic resonance imaging (MRI) offers high resolution functional and structural data about internal body structures (such as the prostate). In this paper we present a novel comprehensive computer-aided scheme for CaP detection from high resolution in vivo multi-protocol MRI by integrating functional and structural information obtained via dynamic-contrast enhanced (DCE) and T2-weighted (T2-w) MRI, respectively. Our scheme is fully-automated and comprises (a) prostate segmentation, (b) multimodal image registration, and (c) data representation and multi-classifier modules for information fusion. Following prostate boundary segmentation via an improved active shape model, the DCE/T2-w protocols and the T2-w/ex vivo histological prostatectomy specimens are brought into alignment via a deformable, multi-attribute registration scheme. T2-w/histology alignment allows for the mapping of true CaP extent onto the in vivo MRI, which is used for training and evaluation of a multi-protocol MRI CaP classifier. The meta-classifier used is a random forest constructed by bagging multiple decision tree classifiers, each trained individually on T2-w structural, textural and DCE functional attributes. 3-fold classifier cross validation was performed using a set of 18 images derived from 6 patient datasets on a per-pixel basis. Our results show that the results of CaP detection obtained from integration of T2-w structural textural data and DCE functional data (area under the ROC curve of 0.815) significantly outperforms detection based on either of the individual modalities (0.704 (T2-w) and 0.682 (DCE)). It was also found that a meta-classifier trained directly on integrated T2-w and DCE data (data-level integration) significantly outperformed a decision-level meta-classifier, constructed by combining the classifier outputs from the individual T2-w and DCE channels.

Multi-attribute non-initializing texture reconstruction based active shape model (MANTRA).

In this paper we present MANTRA (Multi-Attribute, Non-Initializing, Texture Reconstruction Based Active Shape Model) which incorporates a number of features that improve on the the popular Active Shape Model (ASM) algorithm. MANTRA has the following advantages over the traditional ASM model. (1) It does not rely on image intensity information alone, as it incorporates multiple statistical texture features for boundary detection. (2) Unlike traditional ASMs, MANTRA finds the border by maximizing a higher dimensional version of mutual information (MI) called combined MI (CMI), which is estimated from kNN entropic graphs. The use of CMI helps to overcome limitations of the Mahalanobis distance, and allows multiple texture features to be intelligently combined. (3) MANTRA does not rely on the mean pixel intensity values to find the border; instead, it reconstructs potential image patches, and the image patch with the best reconstruction based on CMI is considered the object border. Our algorithm was quantitatively evaluated against expert ground truth on almost 230 clinical images (128 1.5 Tesla (T) T2 weighted in vivo prostate magnetic resonance (MR) images, 78 dynamic contrast enhanced breast MR images, and 21 3T in vivo T1-weighted prostate MR images) via 6 different quantitative metrics. Results from the more difficult prostate segmentation task (in which a second expert only had a 0.850 mean overlap with the first expert) show that the traditional ASM method had a mean overlap of 0.668, while the MANTRA model had a mean overlap of 0.840.

A comprehensive segmentation, registration, and cancer detection scheme on 3 Tesla in vivo prostate DCE-MRI.

Recently, high resolution 3 Tesla (T) Dynamic Contrast-Enhanced MRI (DCE-MRI) of the prostate has emerged as a promising modality for detecting prostate cancer (CaP). Computer-aided diagnosis (CAD) schemes for DCE-MRI data have thus far been primarily developed for breast cancer and typically involve model fitting of dynamic intensity changes as a function of contrast agent uptake by the lesion. Comparatively there is relatively little work in developing CAD schemes for prostate DCE-MRI. In this paper, we present a novel unsupervised detection scheme for CaP from 3 T DCE-MRI which comprises 3 distinct steps. First, a multi-attribute active shape model is used to automatically segment the prostate boundary from 3 T in vivo MR imagery. A robust multimodal registration scheme is then used to non-linearly align corresponding whole mount histological and DCE-MRI sections from prostatectomy specimens to determine the spatial extent of CaP. Non-linear dimensionality reduction schemes such as locally linear embedding (LLE) have been previously shown to be useful in projecting such high dimensional biomedical data into a lower dimensional subspace while preserving the non-linear geometry of the data manifold. DCE-MRI data is embedded via LLE and then classified via unsupervised consensus clustering to identify distinct classes. Quantitative evaluation on 21 histology-MRI slice pairs against registered CaP ground truth estimates yielded a maximum CaP detection accuracy of 77.20% while the popular three time point (3TP) scheme yielded an accuracy of 67.37%.

Nonlinear neurobiological probability weighting functions for aversive outcomes.

While mainstream economic models assume that individuals treat probabilities objectively, many people tend to overestimate the likelihood of improbable events and underestimate the likelihood of probable events. However, a biological account for why probabilities would be treated this way does not yet exist. While undergoing fMRI, we presented individuals with a series of lotteries, defined by the voltage of an impending cutaneous electric shock and the probability with which the shock would be received. During the prospect phase, neural activity that tracked the probability of the expected outcome was observed in a circumscribed network of brain regions that included the anterior cingulate, visual, parietal, and temporal cortices. Most of these regions displayed responses to probabilities consistent with nonlinear probability weighting. The neural responses to passive lotteries predicted 79% of subsequent decisions when individuals were offered choices between different lotteries, and exceeded that predicted by behavior alone near the indifference point.

Neurobiological substrates of dread.

Given the choice of waiting for an adverse outcome or getting it over with quickly, many people choose the latter. Theoretical models of decision-making have assumed that this occurs because there is a cost to waiting-i.e., dread. Using functional magnetic resonance imaging, we measured the neural responses to waiting for a cutaneous electric shock. Some individuals dreaded the outcome so much that, when given a choice, they preferred to receive more voltage rather than wait. Even when no decision was required, these extreme dreaders were distinguishable from those who dreaded mildly by the rate of increase of neural activity in the posterior elements of the cortical pain matrix. This suggests that dread derives, in part, from the attention devoted to the expected physical response and not simply from fear or anxiety. Although these differences were observed during a passive waiting procedure, they correlated with individual behavior in a subsequent choice paradigm, providing evidence for a neurobiological link between the experienced disutility of dread and subsequent decisions about unpleasant outcomes.

Human striatal activation reflects degree of stimulus saliency.

Salient stimuli are characterized by their capability to perturb and seize available cognitive resources. Although the striatum and its dopaminergic inputs respond to a variety of stimuli categorically defined as salient, including rewards, the relationship between striatal activity and saliency is not well understood. Specifically, it is unclear if the striatum responds in an all-or-none fashion to salient events or instead responds in a graded fashion to the degree of saliency associated with an event. Using functional magnetic resonance imaging, we measured activity in the brains of 20 participants performing a visual classification task in which they identified single digits as odd or even numbers. An auditory tone preceded each number, which was occasionally, and unexpectedly, substituted by a novel sound. The novel sounds varied in their ability to interrupt and reallocate cognitive resources (i.e., their saliency) as measured by a delay in reaction time to immediately subsequent numerical task-stimuli. The present findings demonstrate that striatal activity increases proportionally to the degree to which an unexpected novel sound interferes with the current cognitive focus, even in the absence of reward. These results suggest that activity in the human striatum reflects the level of saliency associated with a stimulus, perhaps providing a signal to reallocate limited resources to important events.

Neurobiological correlates of social conformity and independence during mental rotation.

BACKGROUND: When individual judgment conflicts with a group, the individual will often conform his judgment to that of the group. Conformity might arise at an executive level of decision making, or it might arise because the social setting alters the individual's perception of the world. METHODS: We used functional magnetic resonance imaging and a task of mental rotation in the context of peer pressure to investigate the neural basis of individualistic and conforming behavior in the face of wrong information. RESULTS: Conformity was associated with functional changes in an occipital-parietal network, especially when the wrong information originated from other people. Independence was associated with increased amygdala and caudate activity, findings consistent with the assumptions of social norm theory about the behavioral saliency of standing alone. CONCLUSIONS: These findings provide the first biological evidence for the involvement of perceptual and emotional processes during social conformity.

Human striatal responses to monetary reward depend on saliency.

While the striatum has been implicated in reward processing, an alternative view contends that the striatum processes salient events in general. Using fMRI, we investigated human striatal responses to monetary reward while modulating the saliency surrounding its receipt. Money was maximally salient when its receipt depended on a correct response (active) and minimally salient when its receipt was completely independent of the task (passive). The saliency manipulation was confirmed by skin conductance responses and subjective ratings of the stimuli. Significant caudate and nucleus accumbens activations occurred following the active compared to passive money. Such activations were attributed to saliency rather than the motor requirement associated with the active money because striatal activations were not observed when the money was replaced by inconsequential, nonrewarding stimuli. The present study provides evidence that the striatum's role in reward processing is dependent on the saliency associated with reward, rather than value or hedonic feelings.