Extinction of conditioned fear in adolescents and adults: A human fmri study

Little is known about the neural correlates of fear learning in adolescents, a population at increased risk for anxiety disorders. Healthy adolescents (mean age 16.26) and adults (mean age 29.85) completed a fear learning paradigm across two stages during functional magnetic resonance imaging (fMRI). Stage 1 involved conditioning and extinction, and stage 2 involved extinction recall, re-conditioning, followed by re-extinction. During extinction recall, we observed a higher skin conductance response to the CS+ relative to CS− in adolescents compared to adults, which was accompanied by a reduction in dorsolateral prefrontal cortex (dlPFC) activity. Relative to adults, adolescents also had significantly reduced activation in the ventromedial PFC, dlPFC, posterior cingulate cortex (PCC), and temporoparietal junction (TPJ) during extinction recall compared to late extinction. Age differences in PCC activation between late extinction and late conditioning were also observed. These results show for the first time that healthy adolescent humans show different behavioral responses, and dampened PFC activity during short-term extinction recall compared to healthy adults. We also identify the PCC and TPJ as novel regions that may be associated with impaired extinction in adolescents. Also, while adults showed significant correlations between differential SCR and BOLD activity in some brain regions during late extinction and recall, adolescents did not show any significant correlations. This study highlights adolescent-specific neural correlates of extinction, which may explain the peak in prevalence of anxiety disorders during adolescence

Extinction of Conditioned Fear in Adolescents and Adults: A Human fMRI Study

Little is known about the neural correlates of fear learning in adolescents, a population at increased risk for anxiety disorders. Healthy adolescents (mean age 16.26) and adults (mean age 29.85) completed a fear learning paradigm across two stages during functional magnetic resonance imaging (fMRI). Stage 1 involved conditioning and extinction, and stage 2 involved extinction recall, re-conditioning, followed by re-extinction. During extinction recall, we observed a higher skin conductance response to the CS+ relative to CS− in adolescents compared to adults, which was accompanied by a reduction in dorsolateral prefrontal cortex (dlPFC) activity. Relative to adults, adolescents also had significantly reduced activation in the ventromedial PFC, dlPFC, posterior cingulate cortex (PCC), and temporoparietal junction (TPJ) during extinction recall compared to late extinction. Age differences in PCC activation between late extinction and late conditioning were also observed. These results show for the first time that healthy adolescent humans show different behavioral responses, and dampened PFC activity during short-term extinction recall compared to healthy adults. We also identify the PCC and TPJ as novel regions that may be associated with impaired extinction in adolescents. Also, while adults showed significant correlations between differential SCR and BOLD activity in some brain regions during late extinction and recall, adolescents did not show any significant correlations. This study highlights adolescent-specific neural correlates of extinction, which may explain the peak in prevalence of anxiety disorders during adolescence

Spatio-temporal dynamics of resting-state brain networks improve single-subject prediction of schizophrenia diagnosis

Correlation in functional MRI activity between spatially separated brain regions can fluctuate dynamically when an individual is at rest. These dynamics are typically characterized temporally by measuring fluctuations in functional connectivity between brain regions that remain fixed in space over time. Here, dynamics in functional connectivity were characterized in both time and space. Temporal dynamics were mapped with sliding‐window correlation, while spatial dynamics were characterized by enabling network regions to vary in size (shrink/grow) over time according to the functional connectivity profile of their constituent voxels. These temporal and spatial dynamics were evaluated as biomarkers to distinguish schizophrenia patients from controls, and compared to current biomarkers based on static measures of resting‐state functional connectivity. Support vector machine classifiers were trained using: (a) static, (b) dynamic in time, (c) dynamic in space, and (d) dynamic in time and space characterizations of functional connectivity within canonical resting‐state brain networks. Classifiers trained on functional connectivity dynamics mapped over both space and time predicted diagnostic status with accuracy exceeding 91%, whereas utilizing only spatial or temporal dynamics alone yielded lower classification accuracies. Static measures of functional connectivity yielded the lowest accuracy (79.5%). Compared to healthy comparison individuals, schizophrenia patients generally exhibited functional connectivity that was reduced in strength and more variable. Robustness was established with replication in an independent dataset. The utility of biomarkers based on temporal and spatial functional connectivity dynamics suggests that resting‐state dynamics are not trivially attributable to sampling variability and head motion

Brain network dynamics in schizophrenia: reduced dynamism of the default mode network

Complex human behavior emerges from dynamic patterns of neural activity that transiently synchronize between distributed brain networks. This study aims to model the dynamics of neural activity in individuals with schizophrenia and to investigate whether the attributes of these dynamics associate with the disorder's behavioral and cognitive deficits. A hidden Markov model (HMM) was inferred from resting-state functional magnetic resonance imaging (fMRI) data that was temporally concatenated across individuals with schizophrenia (n = 41) and healthy comparison individuals (n = 41). Under the HMM, fluctuations in fMRI activity within 14 canonical resting-state networks were described using a repertoire of 12 brain states. The proportion of time spent in each state and the mean length of visits to each state were compared between groups, and canonical correlation analysis was used to test for associations between these state descriptors and symptom severity. Individuals with schizophrenia activated default mode and executive networks for a significantly shorter proportion of the 8-min acquisition than healthy comparison individuals. While the default mode was activated less frequently in schizophrenia, the duration of each activation was on average 4-5 s longer than the comparison group. Severity of positive symptoms was associated with a longer proportion of time spent in states characterized by inactive default mode and executive networks, together with heightened activity in sensory networks. Furthermore, classifiers trained on the state descriptors predicted individual diagnostic status with an accuracy of 76-85%

Functional brain networks in treatment-resistant schizophrenia

Introduction Up to 20% of individuals with schizophrenia show minimal or no response to medication and are considered to have ‘treatment-resistant’ schizophrenia (TRS). Unlike early and established schizophrenia, few studies have investigated resting-state functional connectivity (rs-FC) in TRS. Here, we test for disruptions in FC and altered efficiency of functional brain networks in a well-characterized cohort of TRS patients. Methods Resting-state functional magnetic resonance imaging was used to investigate functional brain networks in 42 TRS participants prescribed clozapine (30 males, mean age = 41.3(10)) and 42 healthy controls (24 males, mean age = 38.4(10)). Graph analysis was used to characterize between-group differences in local and global efficiency of functional brain network organization as well as the strength of FC. Results Global brain FC was reduced in TRS patients (p = 0.0001). Relative to controls, 3.4% of all functional connections showed reduced strength in TRS (p < 0.001), predominantly involving fronto-temporal, fronto-occipital and temporo-occipital connections. Global efficiency was reduced in TRS (p = 0.0015), whereas local efficiency was increased (p = 0.0042). Conclusions TRS is associated with widespread reductions in rs-FC and altered network topology. Increased local functional network efficiency coupled with decreased global efficiency suggests that hub-to-hub connections are preferentially affected in TRS. These findings further our understanding of the neurobiological impairments in TRS

Linking cortical and connectional pathology in schizophrenia

Schizophrenia is associated with cortical thickness (CT) deficits and breakdown in white matter microstructure. Whether these pathological processes are related remains unclear. We used multimodal neuroimaging to investigate the relationship between regional cortical thinning and breakdown in adjacent infracortical white matter as a function of age and illness duration. Structural magnetic resonance and diffusion images were acquired in 218 schizophrenia patients and 167 age-matched healthy controls to map CT and fractional anisotropy in regionally adjacent infracortical white matter at various cortical depths. We found a robust and reproducible relationship between thickness and anisotropy deficits, which were inversely correlated across cortical regions (r = -.5, P .05). Frontal pathology contributed most to this pattern, with cortical thinning in patients compared to controls at all ages (P < .05); in contrast to initially elevated frontal white matter anisotropy in patients at 30 years, followed by rapid white matter decline with age (rate of annual decline; patients: 0.0012, controls 0.0006, P < .001). Our findings point to pathological dependencies between gray and white matter in a large sample of schizophrenia patients. We argue that elevated frontal anisotropy reflects regionally-specific, compensatory responses to cortical thinning, which are eventually overwhelmed with increasing illness duration

Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictors

In a machine learning setting, this study aims to compare the prognostic utility of connectomic, brain structural, and clinical/demographic predictors of individual change in symptom severity in individuals with schizophrenia. Symptom severity at baseline and 1-year follow-up was assessed in 30 individuals with a schizophrenia-spectrum disorder using the Brief Psychiatric Rating Scale. Structural and functional neuroimaging was acquired in all individuals at baseline. Machine learning classifiers were trained to predict whether individuals improved or worsened with respect to positive, negative, and overall symptom severity. Classifiers were trained using various combinations of predictors, including regional cortical thickness and gray matter volume, static and dynamic resting-state connectivity, and/or baseline clinical and demographic variables. Relative change in overall symptom severity between baseline and 1-year follow-up varied markedly among individuals (interquartile range: 55%). Dynamic resting-state connectivity measured within the default-mode network was the most accurate single predictor of change in positive (accuracy: 87%), negative (83%), and overall symptom severity (77%) at follow-up. Incorporating predictors based on regional cortical thickness, gray matter volume, and baseline clinical variables did not markedly improve prediction accuracy and the prognostic utility of these predictors in isolation was moderate

Risk and resilience brain networks in treatment-resistant schizophrenia

Background Genes, molecules and neural circuits that are associated with, or confer risk to developing schizophrenia have been studied and mapped. It is hypothesized that certain neural systems may counterbalance familial risk of schizophrenia, and thus confer resilience to developing the disorder. This study sought to identify resting-state functional brain connectivity (rs-FC) representing putative risk or resilience endophenotypes in schizophrenia. Methods Resting-state functional magnetic resonance imaging (rs-fMRI) was performed in 42 individuals with treatment resistant schizophrenia (TRS), 16 unaffected first-degree family members (UFM) and 42 healthy controls. Whole-brain rs-FC networks were mapped for each individual and analysed graph theoretically to identify network markers associated with schizophrenia risk or resilience. Results The ~ 900 functional connections showing between-group differences were operationalized as conferring: i) resilience, ii) risk, or iii) precipitating risk and/or illness effects. Approximately 95% of connections belonged to the latter two categories, with substantially fewer connections associated with resilience. Schizophrenia risk primarily involved reduced frontal and occipital rs-FC, with patients showing additional reduced frontal and temporal rs-FC. Functional brain networks were characterized by greater local efficiency in UFM, compared to TRS and controls. Conclusions TRS and UFM share frontal and occipital rs-FC deficits, representing a ‘risk’ endophenotype. Additional reductions in frontal and temporal rs-FC appear to be associated with risk that precipitates psychosis in vulnerable individuals, or may be due to other illness-related effects, such as medication. Functional brain networks are more topologically resilient in UFM compared to TRS, which may protect UFM from psychosis onset despite familial liability

An fMRI study of theory of mind in individuals with first episode psychosis

Theory of mind (ToM), the ability to infer one's own and others’ mental states, is the social cognitive process shown to have the greatest impact on functional outcome in schizophrenia. It is not yet known if neural abnormalities underlying ToM present early, during the first episode of psychosis (FEP). Fourteen FEP participants and twenty-two healthy control participants, aged 15–25, were included in analyses. All participants had a 3T magnetic resonance imaging scan and completed a block-design picture-story attribution-of-intentions ToM fMRI task, and completed a battery of behavioral social cognitive measures including a ToM task. General linear model analyses were carried out. Post-hoc regression analyses were conducted to explore whether aberrant ToM-related activation in FEP participants was associated with symptomatology and global social and occupational functioning. FEP participants, when compared to healthy controls, had significantly less activity in the right temporoparietal junction, right orbitofrontal cortex and left middle prefrontal/inferior frontal cortex, when making social attributions. Aberrant ToM-related activation in the right temporoparietal junction was associated with severity of overall psychopathology, but not functional outcome. Specific regions of the social brain network, associated with ToM, are dysfunctional in young people with FEP. Future research should determine whether alteration of normal brain functioning in relation to ToM occurs before or during illness onset