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A suite of zinc porphyrin-cored polymeric nanoparticles were synthesized and characterized to study secondary coordination sphere interactions. We show that with proper design these materials catalyze the reaction of cyanide with N,N-dimethylformamide.

The authors sought to identify brain regions whose frequency-specific, orthogonalized resting-state EEG power envelope connectivity differs between combat veterans with posttraumatic stress disorder (PTSD) and healthy combat-exposed veterans, and to determine the behavioral correlates of connectomic differences.The authors first conducted a connectivity method validation study in healthy control subjects (N=36). They then conducted a two-site case-control study of veterans with and without PTSD who were deployed to Iraq and/or Afghanistan. Healthy individuals (N=95) and those meeting full or subthreshold criteria for PTSD (N=106) underwent 64-channel resting EEG (eyes open and closed), which was then source-localized and orthogonalized to mitigate effects of volume conduction. Correlation coefficients between band-limited source-space power envelopes of different regions of interest were then calculated and corrected for multiple comparisons. Post hoc correlations of connectomic abnormalities with clinical features and performance on cognitive tasks were conducted to investigate the relevance of the dysconnectivity findings.Seventy-four brain region connections were significantly reduced in PTSD (all in the eyes-open condition and predominantly using the theta carrier frequency). Underconnectivity of the orbital and anterior middle frontal gyri were most prominent. Performance differences in the digit span task mapped onto connectivity between 25 of the 74 brain region pairs, including within-network connections in the dorsal attention, frontoparietal control, and ventral attention networks.Robust PTSD-related abnormalities were evident in theta-band source-space orthogonalized power envelope connectivity, which furthermore related to cognitive deficits in these patients. These findings establish a clinically relevant connectomic profile of PTSD using a tool that facilitates the lower-cost clinical translation of network connectivity research.

A mechanistic understanding of the pathology of psychiatric disorders has been hampered by extensive heterogeneity in biology, symptoms, and behavior within diagnostic categories that are defined subjectively. We investigated whether leveraging individual differences in information-processing impairments in patients with post-traumatic stress disorder (PTSD) could reveal phenotypes within the disorder. We found that a subgroup of patients with PTSD from two independent cohorts displayed both aberrant functional connectivity within the ventral attention network (VAN) as revealed by functional magnetic resonance imaging (fMRI) neuroimaging and impaired verbal memory on a word list learning task. This combined phenotype was not associated with differences in symptoms or comorbidities, but nonetheless could be used to predict a poor response to psychotherapy, the best-validated treatment for PTSD. Using concurrent focal noninvasive transcranial magnetic stimulation and electroencephalography, we then identified alterations in neural signal flow in the VAN that were evoked by direct stimulation of that network. These alterations were associated with individual differences in functional fMRI connectivity within the VAN. Our findings define specific neurobiological mechanisms in a subgroup of patients with PTSD that could contribute to the poor response to psychotherapy.

Background Despite cognitive function impairment in depression, its relationship to treatment outcome is not well understood. Here, we examined whether pretreatment activation of cortical circuitry during test of cognitive functions predicts outcomes for three commonly used antidepressants. Methods Eighty medication-free outpatients with major depression and 34 matched healthy controls were included as participants in the International Study to Predict Optimized Treatment in Depression (iSPOT-D) trial. During functional magnetic resonance imaging, participants completed three tasks that assessed core domains of cognitive functions: response inhibition (Go/NoGo), selective attention (oddball), and selective working memory updating (1-back). Participants were randomized to 1 of 3 arms: escitalopram, sertraline (serotonin-specific reuptake inhibitors [SSRI]), or venlafaxine-extended release (serotonin and norepinephrine reuptake inhibitor [SNRI]) therapy. Functional magnetic resonance imaging scans were repeated after 8 weeks of treatment, and remission was assessed using the Hamilton Rating Scale for Depression. Results Dorsolateral prefrontal cortex activation during inhibitory “no go” responses was a general predictor of remission, with remitters having the same pretreatment activation as control participants and nonremitters hypoactivating relative to controls. Posttreatment dorsolateral prefrontal cortex activation was reduced in both remitters and controls but not in nonremitters. By contrast, inferior parietal activation differentially predicted remission between SSRI and SNRI medications, with SSRI remitters showing greater pretreatment activation than SSRI nonremitters and the SNRI group showing the opposite pattern. Conclusions Intact activation in the frontoparietal network during response inhibition, a core cognitive function, predicts remission with antidepressant treatment, particularly for SSRIs, and may be a potential substrate of the clinical effect of treatment.

BACKGROUND: Childhood maltreatment (CM) history has been associated with poor treatment response in major depressive disorder (MDD), but the mechanisms underlying this relationship remain opaque. Dysfunction in the neural circuits for executive cognition is a putative neurobiological consequence of CM that may contribute importantly to adverse clinical outcomes. We used behavioral and neuroimaging measures of executive functioning to assess their contribution to the relationship between CM and antidepressant response in MDD patients. METHODS: Ninety eight medication-free MDD outpatients participating in the International Study to Predict Optimized Treatment in Depression were assessed at baseline on behavioral neurocognitive measures and functional magnetic resonance imaging during tasks probing working memory (continuous performance task, CPT) and inhibition (Go/No-go). Seventy seven patients completed 8 weeks of antidepressant treatment. Baseline behavioral and neuroimaging measures were assessed in relation to CM (history of childhood physical, sexual, and/or emotional abuse) and posttreatment depression outcomes. RESULTS: Patients with maltreatment exhibited decreased modulation of right dorsolateral prefrontal cortex (DLPFC) activity during working memory updating on the CPT, and a corresponding impairment in CPT behavioral performance outside the scanner. No between-group differences were found for imaging or behavior on the Go/No-go test of inhibition. Greater DLPFC activity during CPT significantly predicted posttreatment symptom improvement in patients without maltreatment, whereas the relationship between DLPFC activity and symptom change was nonsignificant, and in the opposite direction, in patients with maltreatment. CONCLUSIONS: The effect of CM on prefrontal circuitry involved in executive function is a potential predictor of antidepressant outcomes. Language: en

Background There is increasing interest in using neurobiological measures to inform psychiatric nosology. It is unclear at the present time whether anxiety and depression are neurobiologically distinct or similar processes. It is also unknown if the best way to examine these disorders neurobiologically is by contrasting categorical definitions or by examining symptom dimensions. Methods A cross-sectional neuroimaging study was conducted of patients with generalized anxiety disorder (GAD), major depressive disorder (MDD), comorbid GAD and MDD (GAD/MDD), or neither GAD nor MDD (control subjects). There were 90 participants, all medication-free (17 GAD, 12 MDD, 23 GAD/MDD, and 38 control subjects). Diagnosis/category and dimensions/symptoms were assessed to determine the best fit for neurobiological data. Symptoms included general distress, common to anxiety and depression, and anxiety-specific (anxious arousal) or depression-specific (anhedonia) symptoms. Low-frequency (.008–.1 Hz) signal amplitude and functional connectivity analyses of resting-state functional magnetic resonance imaging data focused on a priori cortical and subcortical regions of interest. Results Support was found for effects of diagnosis above and beyond effects related to symptom levels as well as for effects of symptom levels above and beyond effects of diagnostic categories. The specific dimensional factors of general distress and anxious arousal as well as a diagnosis of MDD explained unique proportions of variance in signal amplitude or functional connectivity. Conclusions Using resting-state functional magnetic resonance imaging, our data show that a single conceptual model alone (i.e., categorical diagnoses or symptom dimensions) provides an incomplete mapping of psychopathology to neurobiology. Instead, the data support an additive model that best captures abnormal neural patterns in patients with anxiety and depression.

The automation of segmentation of medical images is an active research area. However, there has been criticism of the standard of evaluation of methods. We have comprehensively evaluated four novel methods of automatically segmenting subcortical structures using volumetric, spatial overlap and distance-based measures. Two of the methods are atlas-based - classifier fusion and labelling (CFL) and expectation-maximisation segmentation using a dynamic brain atlas (EMS), and two model-based - profile active appearance models (PAM) and Bayesian appearance models (BAM). Each method was applied to the segmentation of 18 subcortical structures in 270 subjects from a diverse pool varying in age, disease, sex and image acquisition parameters. Our results showed that all four methods perform on par with recently published methods. CFL performed significantly better than the other three methods according to all three classes of metrics. © 2008 Springer-Verlag Berlin Heidelberg.

Morphometric analysis and anatomical correspondence across MR images is important in understanding neurological diseases as well as brain function. By registering shape models to unseen data, we will be able to segment the brain into its sub-cortical regions. A Bayesian cost function was derived for this purpose and serves to minimize the residuals to a planar intensity model. The aim of this paper is to explore the properties and justify the use of the cost function. In addition to apure residual term (similar to correlation ratio) there are three additional terms, one of which is a growth term. We show the benefit of incorporating an additional growth term into a purely residual cost function. The growth term minimizes the size of the structure in areas of high residual variance. We further show the cost function's dependence on the local intensity contrast estimate for a given structure. © Springer-Verlag Berlin Heidelberg 2006.

Our motivation is to develop an automated technique for the segmentation of sub-cortical human brain structures from MR images. To this purpose, models of shape-and-appearance are constructed and fit to new image data. The statistical models are trained from 317 manually labelled T1-weighted MR images. Shape is modelled using a surface-based point distribution model (PDM) such that the shape space is constrained to the linear combination of the mean shape and eigenvectors of the vertex coordinates. In addition, to model intensity at the structural boundary, intensities are sampled along the surface normal from the underlying image. We propose a novel Bayesian appearance model whereby the relationship between shape and intensity are modelled via the conditional distribution of intensity given shape. Our fully probabilistic approach eliminates the need for arbitrary weightings between shape and intensity as well as for tuning parameters that specify the relative contribution between the use of shape constraints and intensity information. Leave-one-out cross-validation is used to validate the model and fitting for 17 structures.The PDM for shape requires surface parameterizations of the volumetric, manual labels such that vertices retain a one-to-one correspondence across the training subjects. Surface parameterizations with correspondence are generated through the use of deformable models under constraints that embed the correspondence criterion within the deformation process. A novel force that favours equal-area triangles throughout the mesh is introduced. The force adds stability to the mesh such that minimal smoothing or within-surface motion is required.The use of the PDM for segmentation across a series of subjects results in a set surfaces that retain point correspondence. The correspondence facilitates landmark-based shape analysis. Amongst other metrics, vertex-wise multivariate statistics and discriminant analysis are used to investigate local and global size and shape differences between groups. The model is fit, and shape analysis is applied to two clinical datasets.

BACKGROUND AND PURPOSE: Voxel-based analysis has suggested that deep gray matter rather than cortical regions is initially affected in adult Niemann-Pick type C. We sought to examine a range of deep gray matter structures in adults with NPC and relate these to clinical variables. MATERIALS AND METHODS: Ten adult patients with NPC (18–49 years of age) were compared with 27 age- and sex-matched controls, and subcortical structures were automatically segmented from normalized T1-weighted MR images. Absolute volumes (in cubic millimeters) were generated for a range of deep gray matter structures and were compared between groups and correlated with illness variables. RESULTS: Most structures were smaller in patients with NPC compared with controls. The thalamus, hippocampus, and striatum showed the greatest and most significant reductions, and left hippocampal volume correlated with symptom score and cognition. Vertex analysis of the thalamus, hippocampus, and caudate implicated regions involved in memory, executive function, and motor control. CONCLUSIONS: Thalamic and hippocampal reductions may underpin the memory and executive deficits seen in adult NPC. Volume losses in other subcortical regions may also be involved in the characteristic range of motor, psychiatric, and cognitive deficits seen in the disease.

Background Research in chronic alcoholics on memory, decision making, learning, stress and reward circuitry has increasingly highlighted the importance of subcortical brain structures. In addition, epidemiological studies have established the pervasiveness of co-occurring psychiatric diagnoses in alcoholism. Subcortical structures have been implicated in externalizing pathology, including alcohol dependence, and in dysregulated stress and reward circuitry in anxiety and mood disorders and alcohol dependence. Most studies have focused on active or recently detoxified alcoholics, while subcortical structures in long-term abstinent alcoholics (LTAA) have remained relatively uninvestigated.

Alzheimer's disease (AD) is associated with neuronal loss not only in the hippocampus and amygdala but also in the thalamus. Anterodorsal, centromedial, and pulvinar nuclei are the main sites of degeneration in AD. Here we combined shape analysis and diffusion tensor imaging (DTI) tractography to study degeneration in AD in the thalamus and its connections. Structural and diffusion tensor MRI scans were obtained from 16 AD patients and 22 demographically similar healthy Volunteers. The thalamus, hippocampus, and amygdala were automatically segmented using our locally developed algorithm, and group comparisons were carried out for each Surface vertex. We also employed Probabilistic diffusion tractography to obtain connectivity measures between individual thalamic voxels and hippocampus/amygdala voxels and to segment the internal medullary lamina (IML). Shape analysis showed significant bilateral regional atrophy in the dorsal-medial part of the thalamus in AD patients compared to controls. Probabilistic tractography demonstrated that these regions are mainly connected with the hippocampus, temporal, and prefrontal cortex. Intrathalamic FA comparisons showed reductions in the anterodorsal region of thalamus. Intrathalamic tractography from this region revealed that the IML was significantly smaller in AD patients than in controls. We suggest that these changes can be attributed to the degeneration of the anterodorsal and intralaminar nuclei, respectively. In addition, based on previous neuropathological reports, ventral and dorsal-medial shape change in the thalamus in AD patients is likely to be driven by IML atrophy. This combined shape and connectivity analysis provides MRI evidence of regional thalamic degeneration in AD. (c) 2009 Elsevier Inc. All rights reserved.

The automation of segmentation of subcortical structures in the brain is an active research area. We have comprehensively evaluated four novel methods of fully automated segmentation of subcortical structures using volumetric, spatial overlap and distance-based measures. Two methods are atlas-based - classifier fusion and labelling (CFL) and expectation-maximisation segmentation using a brain atlas (EMS), and two incorporate statistical models of shape and appearance - profile active appearance models (PAM) and Bayesian appearance models (BAM). Each method was applied to the segmentation of 18 subcortical structures in 270 subjects from a diverse pool varying in age, disease, sex and image acquisition parameters. Our results showed that all four methods perform on par with recently published methods. CFL performed better than the others according to all three classes of metrics. In summary over all structures, the ranking by the Dice coefficient was CFL, BAM, joint EMS and PAM. The Hausdorff distance ranked the methods as CFL, joint PAM and BAM, EMS, whilst percentage absolute volumetric difference ranked them as joint CFL and PAM, joint BAM and EMS. Furthermore, as we had four methods of performing segmentation, we investigated whether the results obtained by each method were more similar to each other than to the manual segmentations using Williams' Index. Reassuringly, the Williams' Index was close to 1 for most subjects (mean=1.02, sd=0.05), indicating better agreement of each method with the gold standard than with the other methods. However, 2% of cases (mainly amygdala and nucleus accumbens) had values outside 3 standard deviations of the mean.

A way to modulate reward-seeking Which brain regions are causally involved in reward-related behavior? Ferenczi et al. combined focal, cell type-specific, optogenetic manipulations with brain imaging, behavioral testing, and in vivo electrophysiology (see the Perspective by Robbins). Stimulation of midbrain dopamine neurons increased activity in a brain region called the striatum and was correlated with reward-seeking across individual animals. However, elevated excitability of an area called the medial prefrontal cortex reduced both striatal responses to the stimulation of dopamine neurons and the behavioral drive to seek the stimulation of dopamine neurons. Finally, modulating the excitability of medial prefrontal cortex pyramidal neurons drove changes in neural circuit synchrony, as well as corresponding anhedonic behavior. These observations resemble imaging and clinical phenotypes observed in human depression, addiction, and schizophrenia. Science , this issue p. 10.1126/science.aac9698 ; see also p. 10.1126/science.aad9698

BACKGROUND AND PURPOSE: As yet, there are no in vivo data on tissue water changes and associated morphometric changes involved in the osmo-adaptation of normal brains. Our aim was to evaluate osmoadaptive responses of the healthy human brain to osmotic challenges of de- and rehydration by serial measurements of brain volume, tissue fluid, and metabolites. MATERIALS AND METHODS: Serial T1-weighted and 1H-MR spectroscopy data were acquired in 15 healthy individuals at normohydration, on 12 hours of dehydration, and during 1 hour of oral rehydration. Osmotic challenges were monitored by serum measures, including osmolality and hematocrit. MR imaging data were analyzed by using FreeSurfer and LCModel. RESULTS: On dehydration, serum osmolality increased by 0.67% and brain tissue fluid decreased by 1.63%, on average. MR imaging morphometry demonstrated corresponding decreases of cortical thickness and volumes of the whole brain, cortex, white matter, and hypothalamus/thalamus. These changes reversed during rehydration. Continuous fluid ingestion of 1 L of water for 1 hour within the scanner lowered serum osmolality by 0.96% and increased brain tissue fluid by 0.43%, on average. Concomitantly, cortical thickness and volumes of the whole brain, cortex, white matter, and hypothalamus/thalamus increased. Changes in brain tissue fluid were related to volume changes of the whole brain, the white matter, and hypothalamus/thalamus. Only volume changes of the hypothalamus/thalamus significantly correlated with serum osmolality. CONCLUSIONS: This is the first study simultaneously evaluating changes in brain tissue fluid, metabolites, volume, and cortical thickness. Our results reflect cellular volume regulatory mechanisms at a macroscopic level and emphasize that it is essential to control for hydration levels in studies on brain morphometry and metabolism in order to avoid confounding the findings.

Childhood maltreatment (CM) history has been associated with poor treatment response in major depressive disorder (MDD), but the mechanisms underlying this relationship remain opaque. Dysfunction in the neural circuits for executive cognition is a putative neurobiological consequence of CM that may contribute importantly to adverse clinical outcomes. We used behavioral and neuroimaging measures of executive functioning to assess their contribution to the relationship between CM and antidepressant response in MDD patients.Ninety eight medication-free MDD outpatients participating in the International Study to Predict Optimized Treatment in Depression were assessed at baseline on behavioral neurocognitive measures and functional magnetic resonance imaging during tasks probing working memory (continuous performance task, CPT) and inhibition (Go/No-go). Seventy seven patients completed 8 weeks of antidepressant treatment. Baseline behavioral and neuroimaging measures were assessed in relation to CM (history of childhood physical, sexual, and/or emotional abuse) and posttreatment depression outcomes.Patients with maltreatment exhibited decreased modulation of right dorsolateral prefrontal cortex (DLPFC) activity during working memory updating on the CPT, and a corresponding impairment in CPT behavioral performance outside the scanner. No between-group differences were found for imaging or behavior on the Go/No-go test of inhibition. Greater DLPFC activity during CPT significantly predicted posttreatment symptom improvement in patients without maltreatment, whereas the relationship between DLPFC activity and symptom change was nonsignificant, and in the opposite direction, in patients with maltreatment.The effect of CM on prefrontal circuitry involved in executive function is a potential predictor of antidepressant outcomes.

Depression involves impairments in a range of cognitive and emotional capacities. It is unknown whether these functions can inform medication choice when considered as a composite predictive biomarker. We tested whether behavioral tests, grounded in the neurobiology of cognitive and emotional functions, predict outcome with common antidepressants. Medication-free outpatients with nonpsychotic major depressive disorder (N=1008; 665 completers) were assessed before treatment using 13 computerized tests of psychomotor, executive, memory–attention, processing speed, inhibitory, and emotional functions. Matched healthy controls (N=336) provided a normative reference sample for test performance. Depressed participants were then randomized to escitalopram, sertraline, or venlafaxine–extended release, and were assessed using the 16-item Quick Inventory of Depressive Symptomatology (QIDS-SR16) and the 17-item Hamilton Rating Scale for Depression. Given the heterogeneity of depression, analyses were furthermore stratified by pretreatment performance. We then used pattern classification with cross-validation to determine individual patient-level composite predictive biomarkers of antidepressant outcome based on test performance. A subgroup of depressed participants (approximately one-quarter of patients) were found to be impaired across most cognitive tests relative to the healthy norm, from which they could be discriminated with 91% accuracy. These patients with generally impaired cognitive task performance had poorer treatment outcomes. For this impaired subgroup, task performance furthermore predicted remission on the QIDS-SR16 at 72% accuracy specifically following treatment with escitalopram but not the other medications. Therefore, tests of cognitive and emotional functions can form a clinically meaningful composite biomarker that may help drive general treatment outcome prediction for optimal treatment selection in depression, particularly for escitalopram.

Cross-sectional age effects in normal control volunteers were investigated using magnetic resonance imaging in the following eight subcortical structures: lateral ventricles, thalamus, caudate, putamen, pallidum, hippocampus, amygdala and nucleus accumbens. Two hundred and twenty-six control subjects, ranging in age from 19 to 85 years, were scanned on a 1.5 T GE system (n = 184) or a 3.0 T Siemens system (n = 42). Volumes of subcortical structures, adjusted for cranium size, were estimated using FSL's FIRST software, which is fully automated. Significant age effects were found for all volumes when the entire age range was analyzed; however, the older subjects (60–85 years of age) showed a stronger correlation between age and structural volume for the ventricles, hippocampus, amygdala and accumbens than middle-aged (35–60 years of age) subjects. Middle-aged subjects were studied at both sites, and age effects in these groups were comparable, despite differences in magnet strength and acquisition systems. This agreement lends support to the validity of the image-analysis tools and procedures used in the present study.

Automatic segmentation of subcortical structures in human brain MR images is an important but difficult task due to poor and variable intensity contrast. Clear, well-defined intensity features are absent in many places along typical structure boundaries and so extra information is required to achieve successful segmentation. A method is proposed here that uses manually labelled image data to provide anatomical training information. It utilises the principles of the Active Shape and Appearance Models but places them within a Bayesian framework, allowing probabilistic relationships between shape and intensity to be fully exploited. The model is trained for 15 different subcortical structures using 336 manually-labelled T1-weighted MR images. Using the Bayesian approach, conditional probabilities can be calculated easily and efficiently, avoiding technical problems of ill-conditioned covariance matrices, even with weak priors, and eliminating the need for fitting extra empirical scaling parameters, as is required in standard Active Appearance Models. Furthermore, differences in boundary vertex locations provide a direct, purely local measure of geometric change in structure between groups that, unlike voxel-based morphometry, is not dependent on tissue classification methods or arbitrary smoothing. In this paper the fully-automated segmentation method is presented and assessed both quantitatively, using Leave-One-Out testing on the 336 training images, and qualitatively, using an independent clinical dataset involving Alzheimer's disease. Median Dice overlaps between 0.7 and 0.9 are obtained with this method, which is comparable or better than other automated methods. An implementation of this method, called FIRST, is currently distributed with the freely-available FSL package.

Typically in neuroimaging we are looking to extract some pertinent information from imperfect, noisy images of the brain. This might be the inference of percent changes in blood flow in perfusion FMRI data, segmentation of subcortical structures from structural MRI, or inference of the probability of an anatomical connection between an area of cortex and a subthalamic nucleus using diffusion MRI. In this article we will describe how Bayesian techniques have made a significant impact in tackling problems such as these, particularly in regards to the analysis tools in the FMRIB Software Library (FSL). We shall see how Bayes provides a framework within which we can attempt to infer on models of neuroimaging data, while allowing us to incorporate our prior belief about the brain and the neuroimaging equipment in the form of biophysically informed or regularising priors. It allows us to extract probabilistic information from the data, and to probabilistically combine information from multiple modalities. Bayes can also be used to not only compare and select between models of different complexity, but also to infer on data using committees of models. Finally, we mention some analysis scenarios where Bayesian methods are impractical, and briefly discuss some practical approaches that we have taken in these cases.

A variety of different methods of finding correspondences across sets of images to build statistical shape models have been proposed, each of which is likely to result in a different model. When dealing with large datasets (particularly in 3D), it is difficult to evaluate the quality of the resulting models. However, if the different methods are successfully modelling the true underlying shape variation, the resulting models should be similar. If two different techniques lead to similar models, it suggests that they are indeed approximating the true shape change. In this paper we explore a method of comparing statistical shape models by evaluating the Bhattacharya overlap between their implied shape distributions. We apply the technique to investigate the similarity of three models of the same 3D dataset constructed using different methods.