Publications

OBJECTIVE: The interpretability of results in psychiatric neuroimaging is significantly limited by an overreliance on correlational relationships. Purely correlational studies cannot alone determine whether behavior-imaging relationships are causal to illness, functionally compensatory processes, or purely epiphenomena. Negative symptoms (e.g., anhedonia, amotivation, and expressive deficits) are refractory to current medications and are among the foremost causes of disability in schizophrenia. The authors used a two-step approach in identifying and then empirically testing a brain network model of schizophrenia symptoms.

METHODS: In the first cohort (N=44), a data-driven resting-state functional connectivity analysis was used to identify a network with connectivity that corresponds to negative symptom severity. In the second cohort (N=11), this network connectivity was modulated with 5 days of twice-daily transcranial magnetic stimulation (TMS) to the cerebellar midline.

RESULTS: A breakdown of connectivity in a specific dorsolateral prefrontal cortex-to-cerebellum network directly corresponded to negative symptom severity. Restoration of network connectivity with TMS corresponded to amelioration of negative symptoms, showing a statistically significant strong relationship of negative symptom change in response to functional connectivity change.

CONCLUSIONS: These results demonstrate that a connectivity breakdown between the cerebellum and the right dorsolateral prefrontal cortex is associated with negative symptom severity and that correction of this breakdown ameliorates negative symptom severity, supporting a novel network hypothesis for medication-refractory negative symptoms and suggesting that network manipulation may establish causal relationships between network markers and clinical phenomena.

Background:

Neuroimaging of psychiatric disease is challenged by the difficulty of establishing the causal role of neuroimaging abnormalities. Lesions that cause mania present a unique opportunity to understand how brain network disruption may cause mania in both lesions and in bipolar disorder.

Methods:

A literature search revealed 23 case reports with imaged lesions that caused mania in patients without history of bipolar disorder. We traced these lesions and examined resting-state functional Magnetic Resonance Imaging (rsfMRI) connectivity to these lesions and control lesions to find networks that would be disrupted specifically by mania-causing lesions. The results were then used as regions-of-interest to examine rsfMRI connectivity in patients with bipolar disorder (n=16) who underwent imaging longitudinally across states of both mania and euthymia alongside a cohort of healthy participants scanned longitudinally. We then sought to replicate these results in independent cohorts of manic (n=26) and euthymic (n=21) participants with bipolar disorder.

Results:

Mania-inducing lesions overlap significantly in network connectivity. Mania-causing lesions selectively disrupt networks that include orbitofrontal cortex, dorsolateral prefrontal cortex, and temporal lobes. In bipolar disorder, the manic state was reflected in strong, significant, and specific disruption in network communication between these regions and regions implicated in bipolar pathophysiology: the amygdala and ventro-lateral prefrontal cortex.

Limitations:

There was heterogeneity in the clinical characterization of mania causing lesions.

Conclusions:

Lesions causing mania demonstrate shared and specific network disruptions. These disruptions are also observed in bipolar mania and suggest a convergence of multiple disorders on shared circuit dysfunction to cause mania.

Background Social cognitive ability is a significant determinant of functional outcome and deficits in social cognition are a disabling symptom of psychotic disorders. The neurobiological underpinnings of social cognition are not well understood, hampering our ability to ameliorate these deficits.

Objective Using ‘resting-state’ fMRI (functional magnetic resonance imaging) and a trans-diagnostic, data-driven analytic strategy, we sought to identify the brain network basis of emotional intelligence, a key domain of social cognition.

Methods Study participants included 60 participants with a diagnosis of schizophrenia or schizoaffective disorder and 46 healthy comparison participants. All participants underwent a resting-state fMRI scan. Emotional Intelligence was measured using the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). A connectome-wide analysis of brain connectivity examined how each individual brain voxel’s connectivity correlated with emotional intelligence using multivariate distance matrix regression (MDMR).

Results We identified a region in the left superior parietal lobule (SPL) where individual network topology predicted emotional intelligence. Specifically, the association of this region with the Default Mode Network predicted higher emotional intelligence and association with the Dorsal Attention Network predicted lower emotional intelligence. This correlation was observed in both schizophrenia and healthy comparison participants.

Conclusion Previous studies have demonstrated individual variance in brain network topology but the cognitive or behavioral relevance of these differences was undetermined. We observe that the left SPL, a region of high individual variance at the cytoarchitectonic level, also demonstrates individual variance in its association with large scale brain networks and that network topology predicts emotional intelligence.

PURPOSE: Rosenblat and McIntyre (Acta Psychiatr Scand. 2015;132: 180-191) propose that immune disorders are important mediators between bipolar disorders and medical comorbidities. Rosenblat et al (Bipolar Disord. 2016;18:89-101) present a meta-analysis showing that adjunctive anti-inflammatory agents could evoke moderate antidepressant responses in bipolar disorders. We propose using the anti-inflammatory drug colchicine to improve the long-term safety and efficacy of lithium treatment for bipolar disorders.

METHODS: This report is based on searches of the PubMed and Web of Science databases.

RESULTS: Bipolar disorders are associated with significant medical comorbidities such as hypertension, overweight/obesity, diabetes mellitus, metabolic syndrome, and arteriosclerosis, accompanied by enhanced release of pro-inflammatory markers during changes in mood state. During lithium therapy, granulocyte-colony stimulating factor, CD34+ hematopoietic stem/progenitor cells, and neutrophil elastase enter the circulation with activated neutrophils to promote the extravascular migration of activated neutrophils and enhance tissue inflammation. Concurrent treatment with lithium and low-dose colchicine could facilitate the responsiveness of bipolar patients to lithium by reducing leukocyte tissue emigration, the release of neutrophil elastase, and the release of leukocyte pro-inflammatory cytokines such as IL-1β that are regulated by the NLRP3 inflammasome assembly complex.

CONCLUSIONS: Concurrent therapy with lithium and low-dose colchicine could reduce complications involving leukocyte-mediated inflammatory states in bipolar patients and promote patient acceptance and responsiveness to lithium therapy.

Background

Using resting-state functional magnetic resonance imaging (rsfMRI), we previously compared cohorts of bipolar I subjects in a manic state to those in a euthymic state to identify mood state-specific patterns of cortico-amygdala connectivity. Our results suggested that mania is reflected in the disruption of emotion regulation circuits. We sought to replicate this finding in a group of subjects with bipolar disorder imaged longitudinally across states of mania and euthymia

Methods

We divided our subjects into three groups: 26 subjects imaged in a manic state, 21 subjects imaged in a euthymic state, and 10 subjects imaged longitudinally across both mood states. We measured differences in amygdala connectivity between the mania and euthymia cohorts. We then used these regions of altered connectivity to examine connectivity in the longitudinal bipolar group using a within-subjects design.

Results

Our findings in the mania vs euthymia cohort comparison were replicated in the longitudinal analysis. Bipolar mania was differentiated from euthymia by decreased connectivity between the amygdala and pre-genual anterior cingulate cortex. Mania was also characterized by increased connectivity between amygdala and the supplemental motor area, a region normally anti-correlated to the amygdala in emotion regulation tasks.

Limitations

Stringent controls for movement effects limited the number of subjects in the longitudinal sample.

Conclusions

In this first report of rsfMRI conducted longitudinally across mood states, we find that previously observed between-group differences in amygdala connectivity are also found longitudinally within subjects. These results suggest resting state cortico-amygdala connectivity is a biomarker of mood state in bipolar disorder.

Background

This study aimed to identify how the activity of large-scale brain networks differs between mood states in bipolar disorder. The authors measured spontaneous brain activity in subjects with bipolar disorder in mania and euthymia and compared these states to a healthy comparison population.

Methods

23 subjects with bipolar disorder type I in a manic episode, 24 euthymic bipolar I subjects, and 23 matched healthy comparison (HC) subjects underwent resting state fMRI scans. Using an existing parcellation of the whole brain, we measured functional connectivity between brain regions and identified significant differences between groups.

Results

In unbiased whole-brain analyses, functional connectivity between parietal, occipital, and frontal nodes within the dorsal attention network (DAN) were significantly greater in mania than euthymia or HC subjects. In the default mode network (DMN), connectivity between dorsal frontal nodes and the rest of the DMN differentiated both mood state and diagnosis.

Limitations

The bipolar groups were separate cohorts rather than subjects imaged longitudinally across mood states.

Conclusions

Bipolar mood states are associated with highly significant alterations in connectivity in two large-scale brain networks. These same networks also differentiate bipolar mania and euthymia from a HC population. State related changes in DAN and DMN connectivity suggest a circuit based pathology underlying cognitive dysfunction as well as activity / reactivity in bipolar mania. Altered activities in neural networks may be biomarkers of bipolar disorder diagnosis and mood state that are accessible to neuromodulation are promising novel targets for scientific investigation and possible clinical intervention.

Depression is a major public health problem and a common cause of disability. To help physicians choose among available treatment options, the American College of Physicians recently issued a guideline titled "Nonpharmacologic Versus Pharmacologic Treatment of Adult Patients with Major Depressive Disorder." The evidence review done for the guideline found no statistically significant difference in the efficacy of second-generation antidepressants (SGAs) versus most other treatments for this disorder. However, rates of adverse events and discontinuation were generally higher in patients treated with SGAs. This Beyond the Guidelines reviews the guideline and includes a discussion between 2 experts on how they would apply it to a 64-year-old man with depression who is reluctant to begin medication. They review the data on which the guideline is based, discuss the limitations of applying the data to real-world settings, review how they would incorporate patient preferences when making treatment decisions, and outline options for patients in whom first-line therapy has failed.

BACKGROUND: The adaptability of the human brain to the constantly changing environment is reduced in patients with psychotic disorders, leading to impaired cognitive functions. Brain signal complexity, which may reflect adaptability, can be readily quantified via resting-state functional magnetic resonance imaging (fMRI) signals. We hypothesized that resting-state brain signal complexity is altered in psychotic disorders, and is correlated with cognitive impairment.

METHODS: We assessed 156 healthy controls (HC) and 330 probands, including 125 patients with psychotic bipolar disorder (BP), 107 patients with schizophrenia (SZ), 98 patients with schizoaffective disorder (SAD) and 230 of their unaffected first-degree relatives (76 BPR, 79 SADR, and 75 SZR) from four sites of the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) consortium. Using multi-scale entropy analysis, we determined whether patients and/or relatives had pathologic differences in complexity of resting-state fMRI signals toward regularity (reduced entropy in all time scales), or toward uncorrelated randomness (increased entropy in fine time scales that decays as the time scale increases) and how these complexity differences might be associated with cognitive impairment.

RESULTS: Compared to HC subjects, proband groups showed either decreased complexity toward regularity or toward randomness. SZ probands showed decreased complexity toward regular signal in hypothalamus, and BP probands in left inferior occipital, right precentral and left superior parietal regions, whereas no brain region with decreased complexity toward regularity was found in SAD probands. All proband groups showed significantly increased brain signal randomness in dorsal and ventral prefrontal cortex (PFC), and unaffected relatives showed no complexity differences in PFC regions. SZ had the largest area of involvement in both dorsal and ventral PFC. BP and SAD probands shared increased brain signal randomness in ventral medial PFC, BP and SZ probands shared increased brain signal randomness in ventral lateral PFC, whereas SAD and SZ probands shared increased brain signal randomness in dorsal medial PFC. Only SZ showed increased brain signal randomness in dorsal lateral PFC. The increased brain signal randomness in dorsal or ventral PFC was weakly associated with reduced cognitive performance in psychotic probands.

CONCLUSION: These observations support the loss of brain complexity hypothesis in psychotic probands. Furthermore, we found significant differences as well as overlaps of pathologic brain signal complexity between psychotic probands by DSM diagnoses, thus suggesting a biological approach to categorizing psychosis based on functional neuroimaging data.

Background

Existing models of the pathophysiology of bipolar disorder posit disruption in neural circuits of emotion regulation and reward processing. However, few fMRI studies have compared regional brain activity and connectivity in different mood states in bipolar disorder to determine if manic symptomatology is reflected in specific circuit abnormalities. The purpose of this study was to test the hypothesis that bipolar mania is associated with altered connectivity between cortical regions thought to regulate subcortical structures such as the amygdala and striatum.

Methods

28 subjects with bipolar disorder in a manic state, 24 different bipolar subjects in a euthymic state, and 23 matched healthy comparison subjects underwent resting state fMRI scans. Several cortical and sub-cortical structures implicated in the pathogenesis of bipolar disorder were selected for study. We conducted a whole-brain analysis of functional connectivity of these regions.

Results

Bipolar mania was differentiated from euthymia by decreased functional connectivity between the amygdala and anterior cingulate cortex (ACC). Mania was also characterized by increased connectivity between amygdala and dorsal frontal cortical structures that are normally anti-correlated in emotion regulation tasks.

Limitations

Both groups of bipolar subjects were prescribed medications. The study was not longitudinal in design.

Conclusions

Compared to bipolar subjects in a euthymic state, subjects in the manic state demonstrate disrupted functional connectivity between brain regions involved in the regulation of emotion and the amygdala. This disruption of activity in neural circuits involved in emotion may underlie the emotional dysregulation inherent to a bipolar manic episode.