Frontal-Amygdala Connectivity Alterations During Emotion Downregulation in Bipolar I Disorder

Townsend, Jennifer; Torrisi, Salvatore; Lieberman, Matthew D.; Sugar, Catherine A.; Bookheimer, Susan Y.; Altshuler, Lori L.

doi:10.1016/j.biopsych.2012.06.030

Cited by 184 publications

(156 citation statements)

References 83 publications

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“…Taken together with previous findings that connectivity between these regions is abnormal in individuals with already established bipolar disorder (Cerullo et al, 2012;Vizueta et al, 2012;Townsend et al, 2013), these findings suggest that altered right amygdala-ACC and right amygdala-left ventrolateral PFC connectivity in BO may be a risk marker for future bipolar disorder, rather than a protective marker against the development of bipolar disorder, in these youth. Future longitudinal follow-up studies are needed to determine the extent to which this is the case, however.…”

Section: Discussionsupporting

confidence: 76%

“…For example, individuals with bipolar disorder type 1 versus healthy control subjects showed reduced inverse left amygdala-bilateral ventrolateral PFC functional connectivity during emotional downregulation (Townsend et al, 2013), individuals with bipolar disorder type 2 versus healthy control subjects had higher positive right amygdala-left ventrolateral PFC functional connectivity during emotional face processing (Vizueta et al, 2012), and right amygdala-left ventrolateral PFC connectivity increased during mania in individuals with bipolar disorder type 1 (Cerullo et al, 2012). In addition, increased right amygdala-left ventrolateral PFC functional connectivity was reported in anxious versus healthy adolescents during social evaluation anticipation (Guyer et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

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Altered amygdala-prefrontal response to facial emotion in offspring of parents with bipolar disorder

Manelis

Ladouceur

Graur

et al. 2015

Brain

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This study aimed to identify neuroimaging measures associated with risk for, or protection against, bipolar disorder by comparing youth offspring of parents with bipolar disorder versus youth offspring of non-bipolar parents versus offspring of healthy parents in (i) the magnitude of activation within emotional face processing circuitry; and (ii) functional connectivity between this circuitry and frontal emotion regulation regions. The study was conducted at the University of Pittsburgh Medical Centre. Participants included 29 offspring of parents with bipolar disorder (mean age = 13.8 years; 14 females), 29 offspring of non-bipolar parents (mean age = 13.8 years; 12 females) and 23 healthy controls (mean age = 13.7 years; 11 females). Participants were scanned during implicit processing of emerging happy, sad, fearful and angry faces and shapes. The activation analyses revealed greater right amygdala activation to emotional faces versus shapes in offspring of parents with bipolar disorder and offspring of non-bipolar parents than healthy controls. Given that abnormally increased amygdala activation during emotion processing characterized offspring of both patient groups, and that abnormally increased amygdala activation has often been reported in individuals with already developed bipolar disorder and those with major depressive disorder, these neuroimaging findings may represent markers of increased risk for affective disorders in general. The analysis of psychophysiological interaction revealed that offspring of parents with bipolar disorder showed significantly more negative right amygdala-anterior cingulate cortex functional connectivity to emotional faces versus shapes, but significantly more positive right amygdala-left ventrolateral prefrontal cortex functional connectivity to happy faces (all P-values corrected for multiple tests) than offspring of non-bipolar parents and healthy controls. Taken together with findings of increased amygdala-ventrolateral prefrontal cortex functional connectivity, and decreased amygdala-anterior cingulate cortex functional connectivity previously shown in individuals with bipolar disorder, these connectivity patterns in offspring of parents with bipolar disorder may be risk markers for, rather than markers conferring protection against, bipolar disorder in youth. The patterns of activation and functional connectivity remained unchanged after removing medicated participants and those with current psychopathology from analyses. This is the first study to demonstrate that abnormal functional connectivity patterns within face emotion processing circuitry distinguish offspring of parents with bipolar disorder from those of non-bipolar parents and healthy controls.

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Section: Discussionsupporting

confidence: 76%

Section: Discussionmentioning

confidence: 99%

Altered amygdala-prefrontal response to facial emotion in offspring of parents with bipolar disorder

Manelis

Ladouceur

Graur

et al. 2015

Brain

View full text Add to dashboard Cite

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“…An impaired white matter connectivity between frontolimbic circuits and interhemispheric structures has been observed in BD patients [98,99,100]. Studies have also shown deficits in functional connectivity in the brain of bipolar subjects during cognitive tasks and in the resting state.…”

Section: The Phenome Of Bd From the Ion Channel Perspectivementioning

confidence: 99%

Variants in Ion Channel Genes Link Phenotypic Features of Bipolar Illness to Specific Neurobiological Process Domains

2015

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Recent advances in genome-wide association studies are pointing towards a major role for voltage-gated ion channels in neuropsychiatric disorders and, in particular, bipolar disorder (BD). The phenotype of BD is complex, with symptoms during mood episodes and deficits persisting between episodes. We have tried to elucidate the common neurobiological mechanisms associated with ion channel signaling in order to provide a new perspective on the clinical symptoms and possible endophenotypes seen in BD patients. We propose a model in which the multiple variants in genes coding for ion channel proteins would perturb motivational circuits, synaptic plasticity, myelination, hypothalamic-pituitary-adrenal axis function, circadian neuronal rhythms, and energy regulation. These changes in neurobiological mechanisms would manifest in endophenotypes of aberrant reward processing, white matter hyperintensities, deficits in executive function, altered frontolimbic connectivity, increased amygdala activity, increased melatonin suppression, decreased REM latency, and aberrant myo-inositol/ATP shuttling. The endophenotypes result in behaviors of poor impulse control, motivational changes, cognitive deficits, abnormal stress response, sleep disturbances, and energy changes involving different neurobiological process domains. The hypothesis is that these disturbances start with altered neural circuitry during development, following which multiple environmental triggers may disrupt the neuronal excitability balance through an activity-dependent molecular process, resulting in clinical mood episodes.

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“…Some earlier such studies include a look at human visual cortex responses, namely in V1, with presentation of a patterned-flash photic stimulation , imaging of the human primary motor cortex during task activation , generation (Hinke et al, 1993). More recent studies have led to developments in our understanding of many things from group-ICA results of data collected during cinema viewing (Pamilo et al, 2012) to frontalamygdala connectivity alterations during emotion down-regulation in Bipolar I Disorder (Townsend et al, 2012).…”

Section: -Abstractmentioning

confidence: 99%

Evaluation and calibration of functional network modeling methods based on known anatomical connections

et al. 2013

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1-Abstract EnglishRecent studies have identified large scale brain networks based on the spatio-temporal structure of spontaneous fluctuations in resting-state fMRI data. It is expected that functional connectivity based on resting-state data is reflective of-but not identical to the underlying anatomical connectivity.However, which functional connectivity analysis methods reliably predict the network structure remains unclear. Here we tested and compared network connectivity analysis methods by applying them to fMRI restingstate time-series obtained from the human visual cortex. The methods evaluated here are those previously tested against simulated data in Smith et al. (Neuroimage, 2011).To this end, we defined regions within retinotopic visual areas V1, V2, and V3 according to their eccentricity in the visual field, delineating central, intermediate, and peripheral eccentricity regions of interest (ROIs). These ROIs served as nodes in the models we study. We based our evaluation -anatomical connectivity in the monkey visual cortex. For each evaluated method, we computed the fractional rate of detecting connections known to -th percentile of the 4 distribution of interaction magnitudes of those connections not expected to exist.Under optimal conditions, including session duration of 68 minutes, a relatively small network consisting of 9 nodes and artifact-free regression of the global effect, each of the top methods predicted the expected connections with 75%-83% c-sensitivity. Partial Correlation performed best (PCorr; 83%), followed by Regularized Inverse Covariance (ICOV; 79%), Bayesian Network methods (BayesNet; 77%), Correlation (75%), andGeneral Synchronization measures (75%). With decreased session duration, these top methods saw decreases in c-sensitivities, achieving 66%-78% and 60%-70% for 34 and 17 minute sessions, respectively. With a short resting-state fMRI scan of 8.5 minutes (TR = 2s), none of the methods predicted the real network well, with ICOV (53%) and PCorr (51%) performing best. With increased complexity of the network from 9 to 36 nodes, multivariate methods including PCorr and BayesNet saw a decrease in performance. However, this decrease became small when using data from a long (68 minutes) session. Artifact-free regression of the global effect significantly increased the c-sensitivity of all top-performing methods. In an overall evaluation across all tests we performed, PCorr, ICOV and BayesNet set themselves somewhat above all other methods. 5We propose that data-based calibration based on known anatomical connections be integrated into future network studies, in order to maximize sensitivity and reduce false positives. Studies have also demonstrated changes in fMRI-measured brain activity when the subjects are in a state of rest. Importantly, it has been shown that these spontaneous (i.e. without any external stimulation)fluctuations in fMRI signals demonstrate functional connectivity over large parts of the human brain (Xiong et al., 1999). These large parts of the brain correspo...

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Frontal-Amygdala Connectivity Alterations During Emotion Downregulation in Bipolar I Disorder

Cited by 184 publications

References 83 publications

Altered amygdala-prefrontal response to facial emotion in offspring of parents with bipolar disorder

Altered amygdala-prefrontal response to facial emotion in offspring of parents with bipolar disorder

Variants in Ion Channel Genes Link Phenotypic Features of Bipolar Illness to Specific Neurobiological Process Domains

Evaluation and calibration of functional network modeling methods based on known anatomical connections

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