Publications by authors named "Lisa Bonar"

Obsessive-compulsive disorder is a psychiatric disorder characterized by intrusive thoughts and repetitive behaviors. There are two prominent features: Harm Avoidance (HA) and Incompleteness (INC). Previous resting-state studies reported abnormally elevated connectivity between prefrontal cortical (PFC) and subcortical regions (thalamus, striatum) in OCD participants.

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Objective markers of pathophysiological processes underlying lifetime depression and mania/hypomania risk can provide biologically informed targets for novel interventions to help prevent the onset of affective disorders in individuals with subsyndromal symptoms. Greater activity within and functional connectivity (FC) between the central executive network (CEN), supporting emotional regulation (ER) subcomponent processes such as working memory (WM), the default mode network (DMN), supporting self-related information processing, and the salience network (SN), is thought to interfere with cognitive functioning and predispose to depressive disorders. Using an emotional n-back paradigm designed to examine WM and ER capacity, we examined in young adults: (1) relationships among activity and FC in these networks and lifetime depression and mania/hypomania risk; (2) the extent to which these relationships were specific to lifetime depression risk versus lifetime mania/hypomania risk; (3) whether findings in a first, Discovery sample n = 101, 63 female, age = 23.

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Background: Individuals with obsessive-compulsive disorder (OCD) show persistent avoidance behaviors, often in the absence of actual threat. Quality-of-life costs and heterogeneity support the need for novel brain-behavior intervention targets. Informed by mechanistic and anatomical studies of persistent avoidance in rodents and nonhuman primates, our goal was to test whether connections within a hypothesized persistent avoidance-related network predicted OCD-related harm avoidance (HA), a trait measure of persistent avoidance.

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Understanding neurobiological characteristics of cognitive dysfunction in distinct psychiatric disorders remains challenging. In this secondary data analysis, we examined neurobiological differences in brain response during working memory updating among individuals with bipolar disorder (BD), those with unipolar depression (UD), and healthy controls (HC). Individuals between 18-45 years of age with BD (n = 100), UD (n = 109), and HC (n = 172) were scanned using fMRI while performing 0-back (easy) and 2-back (difficult) tasks with letters as the stimuli and happy, fearful, or neutral faces as distractors.

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Diffusion Magnetic Resonance Imaging (dMRI) studies have reported abnormalities in emotion regulation circuits in BD; however, no study has examined the contribution of previous illness on these mechanisms. Using global probabilistic tractography, we aimed to identify neural correlates of previous BD illness and the extent to which these can help predict one-year recurrence of depressive episodes. dMRI data were collected in 70 adults with early-onset BD who were clinically followed for up to 18 years and 39 healthy controls.

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Background: Identifying neural predictors of worsening subthreshold hypomania severity can help identify risk of progression to BD. While diffusion Magnetic Resonance Imaging (dMRI) studies reported white matter microstructural abnormalities in tracts supporting emotional regulation in individuals with BD, it remains unknown whether similar patterns of white matter microstructure predict worsening of subthreshold hypomania severity in non-BD individuals.

Methods: dMRI data were collected in: 81 non-BD individuals recruited across a range of subthreshold depression and hypomania, and followed for six months; and independent samples of 75 BD and 58 healthy individuals.

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Bipolar disorder (BD) is highly heritable. Identifying objective biomarkers reflecting pathophysiological processes predisposing to, versus protecting against BD, can help identify BD risk in offspring of BD parents. We recruited 21 BD participants with a first-degree relative with BD, 25 offspring of BD parents, 27 offspring of comparison parents with non-BD psychiatric disorders, and 32 healthy offspring of healthy parents.

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Background: Behavioral research indicates that caregiver mood disorders and emotional instability in the early months following childbirth are associated with lower positive emotionality and higher negative emotionality in infants, but the neural mechanisms remain understudied.

Methods: Using resting-state functional connectivity as a measure of the functional architecture of the early infant brain, we aimed to determine the extent to which connectivity between the amygdala, a key region supporting emotional learning and perception, and large-scale neural networks mediated the association between caregiver affect and anxiety and early infant negative emotionality and positive emotionality. Two samples of infants (first sample: n = 58; second sample: n = 31) 3 months of age underwent magnetic resonance imaging during natural sleep.

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Affective disorders (AD, including bipolar disorder, BD, and major depressive disorder) are severe recurrent illnesses. Identifying neural markers of processes underlying AD development in at-risk youth can provide objective, "early-warning" signs that may predate onset or worsening of symptoms. Using data (n = 34) from the Bipolar Offspring Study, we examined relationships between neural response in regions supporting executive function, and those supporting self-monitoring, during an emotional n-back task (focusing on the 2-back face distractor versus the 0-back no-face control conditions) and future depressive and hypo/manic symptoms across two groups of youth at familial risk for AD: Offspring of parents with BD (n = 15, age = 14.

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Bipolar disorder (BD) is common and debilitating and confounding effects of depression history on neural activity in BD are unknown. We aimed to dissociate neural activity reflecting past depression-load vs. present symptom severity using the Course and Outcome of Bipolar Youth (COBY), a prospective longitudinal cohort study of pediatric-onset BD.

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Obsessive-Compulsive Disorder (OCD) is characterized by repetitive avoidance behavior which is distressing and associated with marked impairment of everyday life. Recently, paradigms have been designed to explore the hypothesis that avoidance behavior in OCD is consistent with a formal conception of habit. Such studies have involved a devaluation paradigm, in which the value of a previously rewarded cue is altered so that avoidance is no longer necessary.

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Background: Little is known about how early alterations in white matter relate to clinically relevant behaviors such as emotional dysregulation. Thus, our goal was to examine how the white matter structural integrity of key limbic (i.e.

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Obsessive-compulsive disorder (OCD) is characterized by intrusive thoughts and repetitive, compulsive behaviors. While a cortico-striatal-limbic network has been implicated in the pathophysiology of OCD, the neural correlates of this network in OCD are not well understood. In this study, we examined resting state functional connectivity among regions within the cortico-striatal-limbic OCD neural network, including the rostral anterior cingulate cortex, dorsolateral prefrontal cortex, ventrolateral prefrontal cortex, orbitofrontal cortex, ventromedial prefrontal cortex, amygdala, thalamus and caudate, in 44 OCD and 43 healthy participants.

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Article Synopsis
  • Obsessive-compulsive disorder (OCD) is linked to changes in brain connectivity, especially between the cerebellum and cerebral cortex, affecting how these areas interact.
  • A study involving 44 adults with OCD and 43 healthy controls used resting-state fMRI to analyze brain networks, revealing unique connectivity patterns in those with OCD.
  • Results showed that OCD participants had reduced connectivity in the somatomotor network but increased connectivity with the cerebellum and subcortical regions, which correlated with OCD symptom severity, emphasizing the cerebellum's significant role in the disorder.
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A recently developed risk calculator for bipolar disorder (BD) accounts for clinical and parental psychopathology. Yet, it is understood that both familial predisposition and early life adversity contribute to the development of BD. How the interplay between these two factors influence emotion and reward processing networks in youth at risk for BD remains unclear.

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We aimed to identify markers of future affective lability in youth at bipolar disorder risk from the Pittsburgh Bipolar Offspring Study (BIOS) (n = 41, age = 14, SD = 2.30), and validate these predictors in an independent sample from the Longitudinal Assessment of Manic Symptoms study (LAMS) (n = 55, age = 13.7, SD = 1.

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Bipolar disorder (BD) is a serious psychiatric illness with demonstrated abnormalities in reward processing circuitry. Examining this circuitry in youth at familial risk for BD may provide further insight into the underlying mechanisms of BD development. In this study, we compared offspring of bipolar parents (OBP, n = 32), offspring of comparison parents with non-BD psychopathology (OCP, n = 36), and offspring of healthy parents (OHP, n = 39) during a functional magnetic resonance imaging reward processing task.

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Offspring of parents with bipolar disorder (OBP) are at increased risk to develop bipolar disorder (BD). Alterations in resting-state functional connectivity (rsFC) have been identified in OBP; however, replication has been limited and correlation with person-level risk is unknown. A recent study found reduced rsFC between left inferior frontal gyrus (IFG) and clusters in the left insula (LINS), lentiform nucleus (LENT), and midcingulate cortex (MCING) in OBP (Roberts et al.

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The extent to which neural networks underlying emotional behavior in infancy serve as precursors of later behavioral and emotional problems is unclear. Even less is known about caregiving influences on these early brain-behavior relationships. To study brain-emotional behavior relationships in infants, we examined resting-state functional network metrics and infant emotional behavior in the context of early maternal caregiving.

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Background: Early detection of Bipolar Disorder (BD) is critical for targeting interventions to delay or prevent illness onset. Yet, the absence of objective BD biomarkers makes accurately identifying at-risk youth difficult. In this study, we examined how relationships between white matter tract (WMT) structure and activity in emotion processing neural circuitry differentiate youth at risk for BD from youth at risk for other psychiatric disorders.

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Importance: Bipolar disorder (BD) is difficult to distinguish from other psychiatric disorders. Neuroimaging studies can identify objective markers of BD risk.

Objective: To identify neuroimaging measures in emotion processing and regulation neural circuitries and their associations with symptoms specific to youth at risk for BD.

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Background: The DSM-5 separates the diagnostic criteria for mood and behavioral disorders. Both types of disorders share neurocognitive deficits of executive function and reading difficulties in childhood. Children with dyslexia also have executive function deficits, revealing a role of executive function circuitry in reading.

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Mood disorders and behavioral are broad psychiatric diagnostic categories that have different symptoms and neurobiological mechanisms, but share some neurocognitive similarities, one of which is an elevated risk for reading deficit. Our aim was to determine the influence of mood versus behavioral dysregulation on reading ability and neural correlates supporting these skills in youth, using diffusion tensor imaging in 11- to 17-year-old children and youths with mood disorders or behavioral disorders and age-matched healthy controls. The three groups differed only in phonological processing and passage comprehension.

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Difficulty regulating positive mood and energy is a feature that cuts across different pediatric psychiatric disorders. Yet, little is known regarding the neural mechanisms underlying different developmental trajectories of positive mood and energy regulation in youth. Recent studies indicate that machine learning techniques can help elucidate the role of neuroimaging measures in classifying individual subjects by specific symptom trajectory.

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