An Electroencephalography Connectomic Profile of Posttraumatic Stress Disorder.

OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Using fMRI connectivity to define a treatment-resistant form of post-traumatic stress disorder.

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.

Prefrontal cortical regulation of brainwide circuit dynamics and reward-related behavior.

Motivation for reward drives adaptive behaviors, whereas impairment of reward perception and experience (anhedonia) can contribute to psychiatric diseases, including depression and schizophrenia. We sought to test the hypothesis that the medial prefrontal cortex (mPFC) controls interactions among specific subcortical regions that govern hedonic responses. By using optogenetic functional magnetic resonance imaging to locally manipulate but globally visualize neural activity in rats, we found that dopamine neuron stimulation drives striatal activity, whereas locally increased mPFC excitability reduces this striatal response and inhibits the behavioral drive for dopaminergic stimulation. This chronic mPFC overactivity also stably suppresses natural reward-motivated behaviors and induces specific new brainwide functional interactions, which predict the degree of anhedonia in individuals. These findings describe a mechanism by which mPFC modulates expression of reward-seeking behavior, by regulating the dynamical interactions between specific distant subcortical regions.

Frontoparietal Activation During Response Inhibition Predicts Remission to Antidepressants in Patients With Major Depression.

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.

COGNITION-CHILDHOOD MALTREATMENT INTERACTIONS IN THE PREDICTION OF ANTIDEPRESSANT OUTCOMES IN MAJOR DEPRESSIVE DISORDER PATIENTS: RESULTS FROM THE iSPOT-D TRIAL.

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.

Neurobiological signatures of anxiety and depression in resting-state functional magnetic resonance imaging.

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.

A cognitive-emotional biomarker for predicting remission with antidepressant medications: a report from the iSPOT-D trial.

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.

Age effect on subcortical structures in healthy adults.

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.

A Bayesian model of shape and appearance for subcortical brain segmentation.

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.

Subcortical volumes in long-term abstinent alcoholics: associations with psychiatric comorbidity.

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. METHODS: Structural MRI was used to compare volumes of 8 subcortical structures (lateral ventricles, thalamus, caudate, putamen, pallidum, hippocampus, amygdala, and nucleus accumbens) in 24 female and 28 male LTAA (mean abstinence=6.3 years, mean age= 46.6 years) and 23 female and 25 male nonalcoholic controls (NAC) (mean age=45.6 years) to explore relations between subcortical brain volumes and alcohol use measures in LTAA and relations between subcortical volumes and psychiatric diagnoses and symptom counts in LTAA and NAC. RESULTS: We found minimal differences between LTAA and NAC in subcortical volumes. However, in LTAA, but not NAC, volumes of targeted subcortical structures were smaller in individuals with versus without comorbid lifetime or current psychiatric diagnoses, independent of lifetime alcohol consumption. CONCLUSIONS: Our finding of minimal differences in subcortical volumes between LTAA and NAC is consistent with LTAA never having had volume deficits in these regions. However, given that imaging studies have frequently reported smaller subcortical volumes in active and recently detoxified alcoholics compared to controls, our results are also consistent with the recovery of subcortical volumes with sustained abstinence. The finding of persistent smaller subcortical volumes in LTAA, but not NAC, with comorbid psychiatric diagnoses, suggests that the smaller volumes are a result of the combined effects of chronic alcohol dependence and psychiatric morbidity and suggests that a comorbid psychiatric disorder (even if not current) interferes with the recovery of subcortical volumes.

Combining shape and connectivity analysis: an MRI study of thalamic degeneration in Alzheimer's disease.

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.

An evaluation of four automatic methods of segmenting the subcortical structures in the brain.

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.

Bayesian analysis of neuroimaging data in FSL.

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.