Dataset Information

Reduced fronto-temporal and limbic connectivity in the 22q11.2 deletion syndrome: vulnerability markers for developing schizophrenia?

ABSTRACT: The 22q11.2 deletion syndrome (22q11DS) is a widely recognized genetic model allowing the study of neuroanatomical biomarkers that underlie the risk for developing schizophrenia. Recent advances in magnetic resonance image analyses enable the examination of structural connectivity integrity, scarcely used in the 22q11DS field. This framework potentially provides evidence for the disconnectivity hypothesis of schizophrenia in this high-risk population. In the present study, we quantify the whole brain white matter connections in 22q11DS using deterministic tractography. Diffusion Tensor Imaging was acquired in 30 affected patients and 30 age- and gender-matched healthy participants. The Human Connectome technique was applied to register white matter streamlines with cortical anatomy. The number of fibers (streamlines) was used as a measure of connectivity for comparison between groups at the global, lobar and regional level. All statistics were corrected for age and gender. Results showed a 10% reduction of the total number of fibers in patients compared to controls. After correcting for this global reduction, preserved connectivity was found within the right frontal and right parietal lobes. The relative increase in the number of fibers was located mainly in the right hemisphere. Conversely, an excessive reduction of connectivity was observed within and between limbic structures. Finally, a disproportionate reduction was shown at the level of fibers connecting the left fronto-temporal regions. We could therefore speculate that the observed disruption to fronto-temporal connectivity in individuals at risk of schizophrenia implies that fronto-temporal disconnectivity, frequently implicated in the pathogenesis of schizophrenia, could precede the onset of symptoms and, as such, constitutes a biomarker of the vulnerability to develop psychosis. On the contrary, connectivity alterations in the limbic lobe play a role in a wide range of psychiatric disorders and therefore seem to be less specific in defining schizophrenia.

SUBMITTER: Ottet MC

PROVIDER: S-EPMC3606218 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Reduced fronto-temporal and limbic connectivity in the 22q11.2 deletion syndrome: vulnerability markers for developing schizophrenia?

Ottet Marie-Christine MC Schaer Marie M Cammoun Leila L Schneider Maude M Debbané Martin M Thiran Jean-Philippe JP Eliez Stephan S

PloS one 20130322 3

The 22q11.2 deletion syndrome (22q11DS) is a widely recognized genetic model allowing the study of neuroanatomical biomarkers that underlie the risk for developing schizophrenia. Recent advances in magnetic resonance image analyses enable the examination of structural connectivity integrity, scarcely used in the 22q11DS field. This framework potentially provides evidence for the disconnectivity hypothesis of schizophrenia in this high-risk population. In the present study, we quantify the whole ...[more]

PMID: 23533586

Similar Datasets

Project description:BackgroundFour of the most consistently replicated variants associated with mood disorder occur in genes important for synaptic function: ANK3 (rs10994336), BDNF (rs6265), CACNA1C (rs1006737), and DGKH (rs1170191).AimsThe present study examined associations between these candidates, mood disorder diagnoses, cognition, and fronto-limbic regions implicated in affect regulation.Methods and materialsParticipants included 128 individuals with bipolar disorder (33% male, Mean age = 38.5), 48 with major depressive disorder (29% male, Mean age = 40.4), and 149 healthy controls (35% male, Mean age = 36.5). Genotypes were determined by 5'-fluorogenic exonuclease assays (TaqMan®). Fronto-limbic volumes were obtained from high resolution brain images using Freesurfer. Chi-square analyses, bivariate correlations, and mediational models examined relationships between genetic variants, mood diagnoses, cognitive measures, and brain volumes.ResultsCarriers of the minor BDNF and ANK3 alleles showed nonsignificant trends toward protective association in controls relative to mood disorder patients (P = 0.047). CACNA1C minor allele carriers had larger bilateral caudate, insula, globus pallidus, frontal pole, and nucleus accumbens volumes (smallest r = 0.13, P = 0.043), and increased IQ (r = 0.18, P < 0.001). CACNA1C associations with brain volumes and IQ were independent; larger fronto-limbic volumes did not mediate increased IQ. Other candidate variants were not significantly associated with diagnoses, cognition, or fronto-limbic volumes.Discussion and conclusionsCACNA1C may be associated with biological systems altered in mood disorder. Increases in fronto-limbic volumes and cognitive ability associated with CACNA1C minor allele genotypes are congruent with findings in healthy samples and may be a marker for increased risk for neuropsychiatric phenotypes. Even larger multimodal studies are needed to quantify the magnitude and specificity of genetic-imaging-cognition-symptom relationships.

Project description:BackgroundThirty-percent of patients with epilepsy are drug-resistant, and might benefit from effective noninvasive therapeutic interventions. Evidence is accumulating on the efficacy of autonomic biofeedback therapy using galvanic skin response (GSR; an index of sympathetic arousal) in treating epileptic seizures. This study aimed to extend previous controlled clinical trials of autonomic biofeedback therapy with a larger homogeneous sample of patients with temporal lobe epilepsy. In addition, we used neuroimaging to characterize neural mechanisms of change in seizure frequency following the therapy.MethodsForty patients with drug-resistant temporal lobe epilepsy (TLE) (age: 18 to 70years old), on stable doses of anti-epileptic medication, were recruited into a controlled and parallel-group trial from three screening centers in the UK. Patients were allocated to either an active intervention group, who received therapy with GSR biofeedback, or a control group, who received treatment as usual. Allocation to the group was informed, in part, by whether patients could travel to attend repeated therapy sessions (non-randomized). Measurement of outcomes was undertaken by an assessor blinded to the patients' group membership. Resting-state functional and structural MRI data were acquired before and after one month of therapy in the therapy group, and before and after a one-month interval in the control group. The percentage change of seizure frequency was the primary outcome measure. The analysis employed an intention-to-treat principle. The secondary outcome was the change in default mode network (DMN) and limbic network functional connectivity tested for effects of therapy. The trial was registered with the National Institute for Health Research (NIHR) portfolio (ID 15967).FindingsData were acquired between May 2014 and October 2016. Twenty participants were assigned to each group. Two patients in the control group dropped out before the second scan, leaving 18 control participants. There was a significant difference in reduction of seizure frequency between the therapy and control groups (p<0.001: Mann Whitney U Test). The seizure frequency in the therapy group was significantly reduced (p<0.001: Wilcoxon Signed Rank Test) following GSR biofeedback, with a mean seizure reduction of 43% (SD=± 32.12, median=-37.26, 95% CI -58.02% to -27.96%). No significant seizure reduction was observed in the control group, with a mean increase in seizure frequency of 31% (SD=±88.27, median=0, 95% CI -12.83% to 74.96%). The effect size of group comparison was 1.14 (95% CI 0.44 to 1.82). 45% of patients in the therapy group showed a seizure reduction of >50%. Neuroimaging analysis revealed that post-therapy seizure reduction was linearly correlated with enhanced functional connectivity between right amygdala and both the orbitofrontal cortex (OFC) and frontal pole (FP).InterpretationOur clinical study provides evidence for autonomic biofeedback therapy as an effective and potent behavioral intervention for patients with drug-resistant epilepsy. This approach is non-pharmacological, non-invasive and seemingly side-effect free.

Project description:ObjectiveIndividuals with 22q11.2 deletion syndrome are known to be at high risk of developing schizophrenia. Previous imaging studies have provided limited data on the relation of schizophrenia expression in 22q11.2 deletion syndrome to specific regional brain volumetric changes. The authors hypothesized that the main structural brain finding associated with schizophrenia expression in 22q11.2 deletion syndrome, as for schizophrenia in the general population, would be gray matter volumetric deficits, especially in the temporal lobes.MethodMR brain images from 29 patients with 22q11.2 deletion syndrome and schizophrenia and 34 comparison subjects with 22q11.2 deletion syndrome and no history of psychosis were analyzed using a voxel-based morphometry method that also yielded volumes for related region-of-interest analyses. The authors compared data from the two groups using an analysis of covariance model correcting for total intracranial volume, age, sex, IQ, and history of congenital cardiac defects. The false discovery rate threshold was set at 0.05 to account for multiple comparisons.ResultsVoxel-based morphometry analyses identified significant gray matter reductions in the left superior temporal gyrus (Brodmann's area 22) in the schizophrenia group. There were no significant between-group differences in white matter or CSF volumes. Region-of-interest analyses showed significant bilateral gray matter volume reductions in the temporal lobes and superior temporal gyri in the schizophrenia group.ConclusionsThe structural brain expression of schizophrenia associated with the highly penetrant 22q11.2 deletion involves lower gray matter volumes in temporal lobe regions. These structural MRI findings in a 22q11.2 deletion syndrome form of schizophrenia are consistent with those from studies involving schizophrenia samples from the general population. The results provide further support for 22q11.2 deletion syndrome as a genetic subtype and as a useful neurodevelopmental model of schizophrenia.

Project description:Background:Electroconvulsive therapy (ECT) is arguably the most effective available treatment for severe depression. Recent studies have used MRI data to predict clinical outcome to ECT and other antidepressant therapies. One challenge facing such studies is selecting from among the many available metrics, which characterize complementary and sometimes non-overlapping aspects of brain function and connectomics. Here, we assessed the ability of aggregated, functional MRI metrics of basal brain activity and connectivity to predict antidepressant response to ECT using machine learning. Methods:A radial support vector machine was trained using arterial spin labeling (ASL) and blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) metrics from n?=?46 (26 female, mean age 42) depressed patients prior to ECT (majority right-unilateral stimulation). Image preprocessing was applied using standard procedures, and metrics included cerebral blood flow in ASL, and regional homogeneity, fractional amplitude of low-frequency modulations, and graph theory metrics (strength, local efficiency, and clustering) in BOLD data. A 5-repeated 5-fold cross-validation procedure with nested feature-selection validated model performance. Linear regressions were applied post hoc to aid interpretation of discriminative features. Results:The range of balanced accuracy in models performing statistically above chance was 58-68%. Here, prediction of non-responders was slightly higher than for responders (maximum performance 74 and 64%, respectively). Several features were consistently selected across cross-validation folds, mostly within frontal and temporal regions. Among these were connectivity strength among: a fronto-parietal network [including left dorsolateral prefrontal cortex (DLPFC)], motor and temporal networks (near ECT electrodes), and/or subgenual anterior cingulate cortex (sgACC). Conclusion:Our data indicate that pattern classification of multimodal fMRI metrics can successfully predict ECT outcome, particularly for individuals who will not respond to treatment. Notably, connectivity with networks highly relevant to ECT and depression were consistently selected as important predictive features. These included the left DLPFC and the sgACC, which are both targets of other neurostimulation therapies for depression, as well as connectivity between motor and right temporal cortices near electrode sites. Future studies that probe additional functional and structural MRI metrics and other patient characteristics may further improve the predictive power of these and similar models.

Dataset Information

Reduced fronto-temporal and limbic connectivity in the 22q11.2 deletion syndrome: vulnerability markers for developing schizophrenia?

Publications

Reduced fronto-temporal and limbic connectivity in the 22q11.2 deletion syndrome: vulnerability markers for developing schizophrenia?

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets