A Critical Appraisal of Neuroimaging Studies of Bipolar Disorder: Toward a New Conceptualization of Underlying Neural Circuitry and a Road Map for Future Research
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OBJECTIVE: In this critical review, the authors appraise neuroimaging findings in bipolar disorder in emotion-processing, emotion-regulation, and reward-processing neural circuitry in order to synthesize the current knowledge of the neural underpinnings of bipolar disorder and provide a neuroimaging research road map for future studies. METHOD: The authors examined findings from all major studies in bipolar disorder that used functional MRI, volumetric analysis, diffusion imaging, and resting-state techniques, integrating findings to provide a better understanding of larger-scale neural circuitry abnormalities in bipolar disorder. RESULTS: Bipolar disorder can be conceptualized, in neural circuitry terms, as parallel dysfunction in prefrontal cortical (especially ventrolateral prefrontal cortical)-hippocampal-amygdala emotion-processing and emotion-regulation circuits bilaterally, together with an "overactive" left-sided ventral striatal-ventrolateral and orbitofrontal cortical reward-processing circuitry, resulting in characteristic behavioral abnormalities associated with bipolar disorder: emotional lability, emotional dysregulation, and heightened reward sensitivity. A potential structural basis for these functional abnormalities is gray matter volume decreases in the prefrontal and temporal cortices, the amygdala, and the hippocampus and fractional anisotropy decreases in white matter tracts connecting prefrontal and subcortical regions. CONCLUSIONS: Neuroimaging studies of bipolar disorder clearly demonstrate abnormalities in neural circuits supporting emotion processing, emotion regulation, and reward processing, although there are several limitations to these studies. Future neuroimaging research in bipolar disorder should include studies adopting dimensional approaches; larger studies examining neurodevelopmental trajectories in youths with bipolar disorder or at risk for bipolar disorder; multimodal neuroimaging studies using integrated systems approaches; and studies using pattern recognition approaches to provide clinically useful individual-level data. Such studies will help identify clinically relevant biomarkers to guide diagnosis and treatment decision making for individuals with bipolar disorder.
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In predictive coding terms, the connectivity differences in bipolar disorder might reflect inconsistent application of top-down constraints on emotional inference: sometimes too much (mania) and sometimes too little (depression).
“In predictive coding terms, this might reflect inconsistent application of top-down constraints on emotional inference: sometimes too much (leading to an inflated, unfettered mood in mania) and sometimes too little (leading to depressive pessimism) (69, 70).”
From Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders (2026) · cites this paper as [69]
The section on neuroimaging research needed reports differing patterns of amygdala functional connectivity during mania and depression. The paper does not interpret those differences in predictive-coding terms or describe top-down constraints as excessive in mania and insufficient in depression; those are the citing authors’ proposed interpretation. (LLM, conf=0.82)
Experimental — accuracy not yet measuredIn depression, the precision might crash for positive priors, leading to hypo-learning and negativity.
“In depression (the other pole), the precision might crash for positive priors, leading to the state described earlier of hypo-learning and negativity (68, 69).”
From Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders (2026) · cites this paper as [69]
The Introduction’s sections on positive emotional stimuli and reward-processing circuitry discuss heightened responses in bipolar disorder, but do not address precision of positive priors, hypo-learning, or negativity in depression. Searches for those terms and for reduced reward learning in depression found no passage establishing the proposed mechanism. (LLM, conf=0.75)
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A Voxel-Based Diffusion Tensor Imaging Study of White Matter in Bipolar Disorder
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Self-citationElevated left mid-frontal cortical activity prospectively predicts conversion to bipolar I disorder.
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Neurodevelopmental pathways in bipolar disorder
Kloiber, S., Rosenblat, J. D., Husain, M. I., Ortiz, A., Berk, M., Quevedo, J., Vieta, E., Maes, M., Birmaher, B., Soares, J. C., et al. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.02.005 · Cited by 122 · see citing works
Advances in microstructural diffusion neuroimaging for psychiatric disorders
Pasternak, O., Kelly, S., Sydnor, V. J., & Shenton, M. E. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.04.051 · Cited by 116 · see citing works
Differential brain network activity across mood states in bipolar disorder
Brady Jr., R. O., Tandon, N., Masters, G. A., Margolis, A., Cohen, B. M., Keshavan, M., & Öngür, D. (2017) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.09.041 · Cited by 115 · see citing works
Serafini, G., Pompili, M., Borgwardt, S., Houenou, J., Geoffroy, P. A., Jardri, R., Girardi, P., & Amore, M. (2014) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-014-0614-z · Cited by 94 · see citing works
Costi, S., Morris, L. S., Collins, A., Fernandez, N. F., Patel, M., Xie, H., Kim-Schulze, S., Stern, E. R., Collins, K. A., Cathomas, F., et al. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01668-1 · Cited by 87 · see citing works
Kozicky, J., McGirr, A., Bond, D. J., Gonzalez, M., Silveira, L. E., Keramatian, K., Torres, I. J., Lam, R. W., & Yatham, L. N. (2016) · Bipolar Disorders · doi:10.1111/bdi.12437 · Cited by 83 · see citing works
Cyprien, F., de Champfleur, N. M., Deverdun, J., Olié, E., Le Bars, E., Bonafé, A., Mura, T., Jollant, F., Courtet, P., & Artero, S. (2016) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.07.026 · Cited by 50 · see citing works
Reduced lateral orbitofrontal cortex volume and suicide behavior in youth with bipolar disorder
Huber, R. S., Subramaniam, P., Kondo, D. G., Shi, X., Renshaw, P. F., & Yurgelun‐Todd, D. A. (2019) · Bipolar Disorders · doi:10.1111/bdi.12729 · Cited by 46 · see citing works
Shaw, A. D., Sumner, R. L., & Berndt, L. C. S. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1713833 · Cited by 9 · see citing works
“In depression (the other pole), the precision might crash for positive priors, leading to the state described earlier of hypo-learning and negativity (68, 69).”
- Authors
- Phillips, M. L., & Swartz, H. A.
- Affiliations
- University of Pittsburgh (US)
- Venue
- American Journal of Psychiatry
- Published
- 2014-08
- Volume
- 171
- Issue
- 8
- Pages
- 829-843
- ISSN
- 0002-953X (ISSN-L)
- 1535-7228
- PMC
- PMC4119497
- OpenAlex
- W2124979499
- Cited by
- 636
- References
- 157
- Citations by year
- 2026: 49 · 2025: 36 · 2024: 35 · 2023: 41 · 2022: 50 · 2021: 74 · 2020: 60 · 2019: 55 · 2018: 70 · 2017: 54 · 2016: 51 · 2015: 43 · 2014: 17
- FWCI
- 7.52
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- American Journal of Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- Funders
- National Institutes of Health (5r01mh076971-05)
- National Institute of Mental Health (R01-MH076971, R01MH060952, R01MH0076971, R01MH084831, R01MH083647, U01MH092221, R01MH073953)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Bipolar Disorder and Treatment
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
- Keywords
- bipolar disorder, neural circuits, emotional processing, emotion regulation, reward processing, ventrolateral prefrontal cortex, hippocampus, amygdala, ventral striatum, orbitofrontal cortex, gray matter volume, fractional anisotropy, white matter tracts, emotional lability, emotional dysregulation, reward sensitivity, biomarkers
- MeSH terms
- Bipolar Disorder, Brain, Emotions, Humans, Neural Pathways, Reward, Neuroimaging
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