IMPORTANCESevere neuropsychiatric conditions, such as schizophrenia, affect distributed neural computations. One candidate system profoundly altered in chronic schizophrenia involves the thalamocortical networks. It is widely acknowledged that schizophrenia is a neurodevelopmental disorder that likely affects the brain before onset of clinical symptoms. However, no investigation has tested whether thalamocortical connectivity is altered in individuals at risk for psychosis or whether this pattern is more severe in individuals who later develop full-blown illness.OBJECTIVES To determine whether baseline thalamocortical connectivity differs between individuals at clinical high risk for psychosis and healthy controls, whether this pattern is more severe in those who later convert to full-blown illness, and whether magnitude of thalamocortical dysconnectivity is associated with baseline prodromal symptom severity. DESIGN, SETTING, AND PARTICIPANTSIn this multicenter, 2-year follow-up, case-control study, we examined 397 participants aged 12-35 years of age (243 individuals at clinical high risk of psychosis, of whom 21 converted to full-blown illness, and 154 healthy controls). The baseline scan dates were January 15, 2010, to April 30, 2012. MAIN OUTCOMES AND MEASURESWhole-brain thalamic functional connectivity maps were generated using individuals' anatomically defined thalamic seeds, measured using resting-state functional connectivity magnetic resonance imaging.RESULTS Using baseline magnetic resonance images, we identified thalamocortical dysconnectivity in the 243 individuals at clinical high risk for psychosis, which was particularly pronounced in the 21 participants who converted to full-blown illness. The pattern involved widespread hypoconnectivity between the thalamus and prefrontal and cerebellar areas, which was more prominent in those who converted to full-blown illness (t 173 = 3.77, P < .001, Hedge g = 0.88). Conversely, there was marked thalamic hyperconnectivity with sensory motor areas, again most pronounced in those who converted to full-blown illness (t 173 = 2.85, P < .001, Hedge g = 0.66). Both patterns were significantly correlated with concurrent prodromal symptom severity (r = 0.27, P < 3.6 × 10 −8 , Spearman ρ = 0.27, P < 4.75 × 10 −5 , 2-tailed).CONCLUSIONS AND RELEVANCE Thalamic dysconnectivity, resembling that seen in schizophrenia, was evident in individuals at clinical high risk for psychosis and more prominently in those who later converted to psychosis. Dysconnectivity correlated with symptom severity, supporting the idea that thalamic connectivity may have prognostic implications for risk of conversion to full-blown illness.

The effect of life stress on depression is moderated by a repeat length variation in the transcriptional control region of the serotonin transporter gene, which renders carriers of the short variant vulnerable for depression. We investigated the underlying neural mechanisms of these epigenetic processes in individuals with no history of psychopathology by using multimodal magnetic resonance-based imaging (functional, perfusion, and structural), genotyping, and self-reported life stress and rumination. Based on functional MRI and perfusion data, we found support for a model by which life stress interacts with the effect of serotonin transporter genotype on amygdala and hippocampal resting activation, two regions involved in depression and stress. Life stress also differentially affected, as a function of serotonin transporter genotype, functional connectivity of the amygdala and hippocampus with a wide network of other regions, as well as gray matter structural features, and affected individuals' level of rumination. These interactions may constitute a neural mechanism for epigenetic vulnerability toward, or protection against, depression.amygdala ͉ emotion ͉ environment ͉ gene ͉ hippocampus A dverse life events can reveal profound interindividual differences, rousing resilience in some and exposing susceptibility to mood disorders, including depression, in others. Diathesis-stress models have sought to explain these individual differences in terms of genetic predispositions interacting with environmental factors (1). Behavioral genetic studies have supported these models (2), with current work focusing on molecular and neural mechanism that may underlie these associations.Dysfunction of the serotonin (5-hydroxytryptophan, 5-HT) system is implicated in mood disorders, and variation within serotonergic genes has been associated with negative emotional traits such as neuroticism and harm avoidance (3). For example, higher scores in these traits are associated with a common short variant of a repetitive sequence in the transcriptional control region of the 5-HT transporter gene (5-HTT, SERT, SLC6A4), which results in low 5-HT uptake function (4). Two metaanalyses have concluded that presence of the short variant of this repeat (5-HTT-linked polymorphic region, 5-HTTLPR) is associated with higher levels of neuroticism or harm avoidance (5, 6). Although neuroticism itself is a risk factor for depression (7), the link between 5-HTTLPR genotype and depression has been more tenuous, suggesting that 5-HTTLPR genotype does not have a consistent main effect on depression but instead may be moderated through other variables (8).Caspi et al. (9) conducted a 23-year longitudinal study in a large sample of individuals who were genotyped for the 5-HTTLPR. They found that carriers of the 5-HTTLPR short variant showed more depressive symptoms, diagnosed depression, and suicidality as a function of stressful life events than individuals homozygous for the 5-HTTLPR long variant, thus demonstrating a significant gene-by-environment (...

This work examines the influence of changes in baseline activity on the intrinsic functional connectivity fMRI (fc-fMRI) in humans. Baseline brain activity was altered by inducing anesthesia (sevoflurane end-tidal concentration 1%) in human volunteers and fc-fMRI maps between the pre-anesthetized and anesthetized conditions were compared across different brain networks. We particularly focused on low-level sensory areas (primary somatosensory, visual, and auditory cortices), the thalamus, and pain (insula), memory (hippocampus) circuits, and the default mode network (DMN), the latter three to examine higher-order brain regions. The results indicate that, while fc-fMRI patterns did not significantly differ (p b 0.005; 20-voxel cluster threshold) in sensory cortex and in the DMN between the pre-and anesthetized conditions, fc-fMRI in high-order cognitive regions (i.e. memory and pain circuits) was significantly altered by anesthesia. These findings provide further evidence that fc-fMRI reflects intrinsic brain properties, while also demonstrating that 0.5 MAC sevoflurane anesthesia preferentially modulates higher-order connections.

The analysis of spontaneous fluctuations of functional magnetic resonance imaging (fMRI) signals has recently gained attention as a powerful tool for investigating brain circuits in a non-invasive manner. Correlation-based connectivity analysis investigates the correlations of spontaneous fluctuations of the fMRI signal either between a single seed region of interest (ROI) and the rest of the brain or between multiple ROIs. To do this, a priori knowledge is required for defining the ROI(s) and without such knowledge functional connectivity fMRI cannot be used as an exploratory tool for investigating the functional organization of the brain and its modulation under the different conditions. In this work we examine two indices that provide voxel based maps reflecting the intrinsic connectivity contrast (ICC) of individual tissue elements without the need for defining ROIs and hence require no a priori information or assumptions. These voxel based ICC measures can also be used to delineate regions of interest for further functional or network analyses. The indices were applied to the study of sevoflurane anesthesia-induced alterations in intrinsic connectivity. In concordance with previous studies, the results show that sevoflurane affects different brain circuits in a heterogeneous manner. In addition ICC analyses revealed changes in regions not previously identified using conventional ROI connectivity analyses, probably because of an inappropriate choice of the ROI in the earlier studies. This work highlights the importance of such voxel based connectivity methodology.