PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Psychiatr Res. Author manuscript; available in PMC 2009 June 9.
Published in final edited form as:
PMCID: PMC2693401
NIHMSID: NIHMS113701

Corticolimbic metabolic dysregulation in euthymic older adults with bipolar disorder

Abstract

The corticolimbic dysregulation hypothesis of bipolar disorder suggests that depressive symptoms are related to dysregulation of components of an anterior paralimbic network (anterior cingulate, anterior temporal cortex, dorsolateral prefrontal cortex, parahippocampal gyrus, and amygdala) with excessive anterior limbic activity accompanied by diminished prefrontal activity. In younger patients, such abnormalities tend to resolve with remission of depression, but it remains to be established whether the same is true for older patients. This was a cross-sectional, between-subjects design conducted with 16 euthymic, medicated patients with bipolar disorder (10 type I, six type II) and 11 age-matched healthy controls. All participants were over age 50. Our main outcome measures were relative rates of cerebral metabolism derived from a resting 18flourodeoxyglucose positron emission tomography scan in specified regions of interest in the corticolimbic network. Resting metabolic rates in bipolar patients were significantly greater than in controls in bilateral amygdalae, bilateral parahippocampal gyri, and right anterior temporal cortex (BA 20, 38); they were significantly lower in bipolar patients than in controls in the bilateral dorsolateral prefrontal cortices (BA 9, 10, 46). The evidence of corticolimbic dysregulation observed is consistent with the hypothesis that bipolar disorder entails progressive, pernicious neurobiological disruptions that may eventually persist during euthymia. Persistent corticolimbic dysregulation may be related to residual affective, behavioral, and cognitive symptoms in older patients with bipolar disorder, even when not experiencing syndromal mood disturbance.

Keywords: Bipolar disorder, Bipolar depression, Tomography, Emission-computed, PET, Prefrontal, Limbic, Hippocampus

1. Introduction

Patients with bipolar disorder spend approximately half of the time in euthymic states, during which not struggling with syndromal affective symptoms, still experience cognitive (Dias et al., 2008; Gildengers et al., 2007, 2004) and functional (Gildengers et al., 2007; Depp et al., 2005; Gupta et al., 2007) deficits. Neurobiological and neurocognitive changes during manic and depressed episodes of bipolar disorder are better established than those during euthymia. Most functional neuroimaging studies suggest that abnormalities during affective episodes tend to attenuate or resolve during euthymia, although a few studies have demonstrated differences between euthymic bipolar patients and healthy controls while engaged in various cognitive tasks (Strakowski et al., 2005a, 2004; Wessa et al., 2007; Deckersbach et al., 2005; Lagopoulos et al., 2007; Lagopoulos and Malhi, 2007; Adler et al., 2004; Monks et al., 2004). However, neuroimaging studies demonstrating resting state differences in euthymic bipolar disorder patients are generally lacking. Knowledge of abnormalities during euthymia – especially related to resting state neuroimaging findings in bipolar disorder patients – may advance our understanding of underlying neurobiological changes.

The corticolimbic dysregulation theory of depression (Seminowicz et al., 2004; Mayberg et al., 1999; Mayberg, 1997) posits increased ventral paralimbic activity in conjunction with decreased dorsal cortical activity as a mechanism of depression. This explanation overlaps with integrative literature reviews (Adler et al., 2006; Strakowski et al., 2005b; Ketter et al., 2002), which have advanced the hypothesis that corticolimbic network dysregulation can account for the expression of bipolar disorder in part because of decreased prefrontal modulation of overactive limbic structures. Fig. 1 depicts the corticolimbic network (Strakowski et al., 2005b) with areas of altered metabolism during affective episodes. As Strakowski et al. (2005b) suggested, affect may be related to hypothalamic function, whereas behavior may be related to activity in circuits terminating in the thalamus. Cognition may be related to activity in a pathway involving the anterior cingulate cortex (ACC). Interestingly, many of the structures associated with affective and behavioral alterations in bipolar disorder (including the dorsolateral, ventrolateral, medial orbitofrontal, and subgenual cortices, as well as the hippocampal complex) are also associated with specific neurocognitive functions (Adler et al., 2006). This suggests that corticolimbic dysregulation may provide a parsimonious explanation of the neural substrates involved in diverse aspects of bipolar disorder.

Fig. 1
Hypothesized corticolimbic dysregulation in bipolar disorders. Areas associated with increased activation during affective episodes in bipolar disorder are shaded red and those with decreased activation are shaded blue. Note that color-coding of regions ...

18Flourodeoxyglucose positron emission tomography (FDG-PET) provides a measure of cerebral metabolic changes that could complement other evidence supporting the corticolimbic dysregulation hypothesis. Though studies of patients with bipolar disorder demonstrating differences in activation during cognitive tasks are informative, it is unclear whether these differences are task-specific and fail to occur at rest. Arguably, group differences observed in the resting state represent a more generalized finding for this disorder. Furthermore, studying patients in the euthymic state aids in understanding the neurobiology of trait characteristics not influenced by abnormal mood states. For example, it is possible that corticolimbic dysregulation arises only when patients are in altered mood states, with a return to normal during euthymia. Thus, the study of the euthymic state is vital for understanding trait, as opposed to state, neurobiological aspects of bipolar disorder.

Older adults with bipolar disorder may be an opportune sub-population in which to demonstrate corticolimbic dysregulation at rest during euthymia. Older adults with bipolar disorder may be more likely to have residual neurobiological abnormalities as well as age-related cognitive changes. Thus, older adults could provide a population that allows for increased sensitivity to detect cerebral metabolic changes during euthymia. We hypothesized that in euthymic older adults with bipolar disorder we would find differences in resting cerebral metabolism in areas with prominent corticolimbic dysregulation during depression.

2. Methods

2.1. Participants

The Stanford University Administrative Panel on Human Subjects approved this study, and all participants provided verbal and written informed consent prior to enrollment.

2.1.1. Bipolar disorder patients

Participants included 16 outpatients diagnosed with bipolar disorder (10 type I, 6 type II) who were recruited from the community through advertisements. All patients were at least age 50 at the time of testing and were clinically evaluated to meet DSM-IV criteria for Bipolar Disorder and to be currently euthymic. One patient was medication-free and the remaining patients were taking standard pharmacotherapy including lithium (n = 5), lamotrigine (n = 5), divalproex (n = 1), antidepressants (n = 6), and atypical antipsychotics (n = 7). With respect to medication combinations, six were taking one medication, five were taking two medications, and three were taking three medications. Five patients had a history of substance use disorders (alcohol, cocaine, and polysubstance dependence) that had been in remission an average of 12.4 years (SD = 9.7). Demographic information for the bipolar disorder patients is provided in Table 1.

Table 1
Demographic variables

2.1.2. Healthy controls

The 11 participants in the healthy control group were volunteers recruited from the community through advertisements. The control participants did not meet DSM-IV criteria for any affective, psychotic, or substance use disorder nor were they taking any psychiatric medications. Demographic information for healthy controls is also provided in Table 1.

2.1.3. Exclusion criteria

Participants were excluded from the study if they had any of the following conditions: seizure disorder; progressive neurological disorder; any implanted metal; significant unstable concurrent medical illness; other major Axis I diagnoses; a syndromal affective episode within 30 days of the study; administration benzodiazepines, stimulants, and steroids; hormone replacement therapy; electroconvulsive or light therapy; administration of any investigational drug in the 30 days prior to screening; alcohol abuse within the two weeks prior to screening; drug abuse less than six months prior to screening; pregnancy or breastfeeding; or incapacity to consent to study procedures. Participants were excluded if they had a score on the Young Mania Rating Scale (YMRS) (Young et al., 1978) or the Montgomery–Åsberg depression rating scale (MADRS) (Montgomery and Åsberg, 1979) greater than 10.

2.2. Image acquisition and processing

PET scans were acquired on a Siemens Exact 921 scanner, which yields 69 tomographic slices with an in-plane full-width half-maximum resolution of 7 mm, and an axial resolution (slice thickness) of 5 mm at the center of the gantry. Subjects fasted for at least 6 h prior to the scan to increase FDG uptake by the brain and reduce intrascan blood glucose variability. Intrascan head movement was restricted by use of foam headrests and light restraints placed upon the chin and forehead. Subjects’ eyes were covered, and the PET gantry was aligned to the canthomeatal line. A 68Galium rotating pin source was used to obtain a 20-minute transmission scan to correct for photon attenuation of emission data. Subjects were instructed to relax and to avoid any active mental processes, aside from passively attending to their current emotional and sensory experiences prior to and during the first 30 min after the intravenous injection of 10 mCi FDG. Emission data were obtained for 30 min following radioisotope uptake.

Image processing was performed using Statistical Parametric Mapping (SPM5, Update 826) software (www.fil.ion.ucl.ac.uk/spm/). All images were stereotactically normalized to the Montreal Neurological Institute (MNI) template provided with SPM5 through a 12-parameter affine transformation and nonlinear warping with basis functions (Ashburner and Friston, 1999). Whole brain metabolic activity was calculated for each image as the mean of all voxels greater than the background threshold of 1/8 of the mean of the image space. Each image was subjected to proportionate scaling to normalize values across images. Gray matter was thresholded at 80% of each subject's mean whole brain metabolic activity. The normalized images had 2 mm isotropic voxels and were smoothed with a 12 mm Gaussian kernel.

For the primary analyses, we generated region of interest (ROI) masks using Wake Forest University (WFU) PickAtlas (Maldjian et al., 2003) to provide a standardized approach for spatially normalized images. We used the regional BA descriptions provided by Adler et al. (2006) to provide a priori definitions of our ROI. In this way, we created masks for amygdala, parahippocampal gyrus, anterior temporal cortex (BA 20 and 38), subgenual prefrontal cortex (SGPFC; BA 25), cerebellar vermis, medial orbitofrontal cortex (BA 11), ventrolateral prefrontal cortex (BA 10 and 47), dorsolateral prefrontal cortex (DLPFC; BA 9, 10, 46), anterior cingulate (ACC; BA 24 and 32), thalamus, and hypothalamus. With the exception of the cerebellar vermis, which crossed the midline, separate masks were made bilaterally for ROI's. Because our FDG-PET images had undergone Gaussian smoothing, we applied a dilation factor of one in 3D mode to the ROI's in PickAtlas. The masks were then used to extract the ROI from the individual scans using MarsBar (Brett et al., 2002). A between-groups t-test was performed on the average values of the voxels in each ROI using SPM5.

In order to assess any other group differences not captured by the a priori specified ROI analyses and to confirm the results of the primary analysis, we performed a secondary exploratory analysis in which the whole brain was assessed on a voxel-by-voxel basis. An intensity threshold of P < .005 (uncorrected) and a spatial extent threshold of 50 voxels were applied. As in our primary analyses, all images were processed using SPM5 for preprocessing and group level statistics. For localization, the MNI coordinates were converted to Talairach space (Talairach and Tournoux, 1988) using WFU PickAtlas.

3. Results

The results of the primary ROI analyses are provided in Table 2. We found significantly greater cerebral metabolism in the bipolar group relative to healthy controls bilaterally in the amygdala (left: P < .05; right: P < .01), the right anterior temporal cortex (P < .05), and bilateral parahippocampal gyri (P < .01). The difference in metabolism in the cerebellar vermis fell short of statistical significance (P = .06). All regions with significantly greater cerebral metabolism in bipolar patients were within limbic and paralimbic structures; no prefrontal regions exhibited statistically significant greater metabolism in the bipolar group compared to the control group.

Table 2
ROI analysis of cerebral metabolism in patients with bipolar disorder compared to healthy controls

The greater limbic metabolism in patients with bipolar disorder compared to healthy controls was complemented by lower metabolism in bilateral dorsolateral prefrontal cortices (left: P < .05; right: P < .01). Differences in metabolism in the bilateral ventromedial prefrontal cortices (P = .06) and subgenual prefrontal cortices (left: P = .10; right: P > .10) did not reach statistical significance. Table 2 also includes the effect sizes averaged across bilateral regions in terms of Cohen's d (Cohen, 1977). Based on this measure, all of the statistically significant effect sizes would be classified as large (d > 0.5).

Next, we conducted secondary analyses in which we generated statistical parametric maps of the group differences. This was performed as the prescribed ROI's may have excluded some areas of significance. Given the novelty of the findings, we felt it important to provide as comprehensive a picture as possible of the differences. The results of the SPM analysis comparing bipolar patients and healthy controls are depicted in Fig. 2 and the regions of significant hypo- and hypermetabolism are provided in Table 3. Areas of relatively greater or lesser metabolism in this secondary voxel-based analysis confirmed the findings of the primary analyses, and provide a graphic depiction of the regional extent of actual differences between the bipolar and control groups. The areas of metabolic differences shown in Fig. 2 suggest that the areas of differential metabolism extend beyond the regions assessed in the primary ROI analyses. For example, greater metabolism in bilateral parahippocampal gyri extended into the hippocampus bilaterally.

Fig. 2
Corticolimbic dysregulation in euthymic older adults with bipolar disorders. Axial slices with statistical parametric map of regions of relative hypermetabolism (in red) and hypometabolism (in blue) in euthymic older adults with bipolar disorders compared ...
Table 3
Regions with statistically significant differences in cerebral metabolism in bipolar disorder patients relative to healthy controls

4. Discussion

Our findings provide evidence of resting corticolimbic dysregulation of cerebral metabolism in euthymic older adults with bipolar disorder. Specifically, we found greater limbic and lesser cortical metabolism in bipolar patients compared to healthy controls. Our finding of corticolimbic dysregulation in euthymic older adults with bipolar disorder complements previous research that has found such differences during affective episodes in younger adults. It may be that advancing age, the accumulated damage from affective episodes, or a combination thereof, results in illness progression such that cerebral metabolism and clinical (e.g., cognitive) abnormalities encountered during affective episodes in younger adults persist between episodes in older adults (Savard et al., 1980).

In addition, our evidence of differential resting state metabolism involves regions that have been found to have metabolic or blood flow abnormalities when bipolar patients are experiencing manic or depressive episodes. For example, cerebral blood flow studies have found increased activation in the ACC and decreased activation in prefrontal cortex in manic patients while engaging in attention (Benabarre et al., 2005) or decision-making (Rubinsztein et al., 2001) tasks. In addition, SGPFC (Drevets et al., 1997) and left ACC (Blumberg et al., 2000) metabolism appear to increase during mania. In healthy control subjects, the anterior cingulate cortex (ACC) has been associated with performance monitoring (MacDonald et al., 2000) and contribute importantly to motivated behavior (Mega and Cummings, 1994).

The corticolimbic dysregulation hypothesis is supported by neuroanatomical evidence of subcortical tracts between the regions involved (Schmahmann and Pandya, 2006), as well as a variety of many neuroimaging findings. Supporting evidence is largely derived from neuroimaging activation studies (usually involving a cognitive task) during depressive or manic episodes with a notable absence of studies at rest and/or during euthymia. The general rationale in activation studies is that associations between abnormal performance and particular cerebral regions in patients indicate involvement of regions implicated in the pathophysiology of bipolar disorder. While such a rationale is no doubt applicable with respect to the task studied during a given affective state, one may question the extent to which such findings generalize to euthymic states or provide information about bipolar traits, as task demands may influence regional activation differences (Strakowski et al., 2005b). Thus, methodological differences may contribute to the nature of findings. One aspect of many neuroimaging studies that may be overlooked is that many studies use an active task (e.g., tests of memory, attention, etc.) in conjunction with neuroimaging. Our findings are noteworthy because all bipolar patients were euthymic and differences between the bipolar and control groups cannot be attributed to interactions with task demands, because the FDG-PET scans were performed at rest.

The functional significance of our findings of corticolimbic dysregulation has been detailed in other reports. For example, parahippocampal hypermetabolism and dorsolateral prefrontal hypermetabolism appear to be associated with deficits in delayed cued verbal recall in older patients (Brooks et al., 2007). Sustained attention deficits appear to be related to hypometabolism in the SGPFC and medial orbital frontal cortex as well as ventrolateral prefrontal hypometabolism (Hoblyn et al., 2007). Thus, key areas of the corticolimbic network appear to be associated with performance of important neurocognitive tasks in euthymic older adults with bipolar disorder, which highlights the clinical significance of the baseline differences reported in this paper.

Interestingly, in our sample we did not find statistically significant differences in metabolism in the SGPFC in bipolar disorder patients, as reported for younger depressed bipolar patients compared to healthy controls (Drevets et al., 1997). However, in previous work from our laboratory, subgenual prefrontal metabolism in depressed younger bipolar patients was not significantly different from that of healthy controls, despite being related to attention (Brooks et al., 2006). In the present study, however, there was a nonsignificant trend for increased left subgenual prefrontal metabolism in the bipolar group, which suggests we may not have had sufficient statistical power to detect the difference or that our rather strict definition of the SGPFC ROI was such that it did not overlap sufficiently with a region of increased metabolism in that area. It is also possible that metabolic rates in this region do not differ between euthymic older bipolar patients and healthy controls, possibly reflecting age- or mood state-related functional reorganization of corticolimbic circuits (Krüger et al., 2005).

There are several limitations of this study. First, our patients, though euthymic, were on various medications, which may have had an effect on the observed metabolic findings. However, because medications may normalize cerebral metabolism (Kennedy et al., 2001) and likely do so in varying ways, the medicated status of our patients presumably would have reduced, rather than enhanced, metabolic differences between patients and controls. A recent analysis suggested that medications may have no detectable effects in functional imaging studies of bipolar disorder (Phillips et al., 2008), although we cannot definitively rule out such effects in the current study.

Another potential limitation of our study is that we used only patients over the age of 50, which limits the generalizability of our findings to younger populations. The lack of inclusion of younger subjects in this study prevents us from drawing conclusions about interactions between age and cerebral metabolic changes in bipolar disorder, which is an important topic for future study. Although the use of a resting scan avoided the interference of the demands of an active task, the resting paradigm did not account for variations in subjects’ “resting” mental processes. The limited sample size and age range of our study does not afford us the ability to evaluate reliably correlations of metabolism and illness duration across different cerebral regions. In addition, our sample size may not permit us sufficient power to detect differences in other regions, such as the ventrolateral prefrontal cortex and cerebellar vermis, which might also have been affected by the inclusion of both bipolar disorder type I and type II patients.

Nevertheless, these findings could serve as the basis for future investigations of the relations between corticolimbic dysregulation and clinical parameters in patients with bipolar disorder. Longitudinal work is necessary to explore possible interactions between age and illness progression and the resultant effects on level of functioning. Such work is vital to understanding the deficits patients experience during both euthymia and affective episodes.

Acknowledgements

This research was supported in part by the Medical Research Service of the Veterans Affairs Palo Alto Health Care System (JB, AR), and by the Department of Veterans Affairs Sierra-Pacific Mental Illness Research, Education, and Clinical Center (AR).

Funding source

This research was supported in part by the Medical Research Service of the Veterans Affairs Palo Alto Health Care System (JB, AR), and by the Department of Veterans Affairs Sierra-Pacific Mental Illness Research, Education, and Clinical Center (AR).

The Veterans Affairs Health Care System had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.

Footnotes

Financial disclosure

Dr. Rosen and Ms. Woodard declare no conflicts of interest. Dr. Brooks is on the speaker's bureau of Eli Lilly and Company, Bristol-Myers Squibb, and AstraZeneca. Dr. Hoblyn is on the speaker's bureau for Eli Lilly and Company, Pfizer, AstraZeneca, and Bristol-Myers Squibb.

Here is Dr. Ketter's complete conflict of interest disclosure: Affiliation/Financial Interest, Name of Organization, Grant/Research Support, Abbott Laboratories, Inc., AstraZeneca Pharmaceuticals LP, Bristol-Myers Squibb Company, Eisai Inc., Elan Pharmaceuticals, Inc., Eli Lilly and Company, GlaxoSmithKline, Janssen Pharmaceutica Products, LP, Novartis Pharmaceuticals Corporation, Pfizer Inc., Repligen Corporation, Shire Pharmaceuticals Group plc., Solvay Pharmaceuticals, Inc., Wyeth Pharmaceuticals, Consultant, Abbott Laboratories, Inc, AstraZeneca Pharmaceuticals LP, Bristol-Myers Squibb Company, Cephalon Inc., Corcept Therapeutics, Elan Pharmaceuticals, Inc., Eli Lilly and Company, Forest Laboratories, Inc., GlaxoSmithKline, Janssen Pharmaceutica Products, LP, Jazz Pharmaceuticals, Inc, Merck & Co., Inc., Novartis Pharmaceuticals Corporation, Pfizer Inc., Repligen Corporation, Shire Pharmaceuticals Group plc., Solvay Pharmaceuticals, Inc., UCB Pharmaceuticals, Wyeth Pharmaceuticals, Lecture Honoraria, Abbott Laboratories, Inc, AstraZeneca Pharmaceuticals LP, Bristol-Myers Squibb Company, Eli Lilly and Company, GlaxoSmithKline, Janssen Pharmaceutica Products, LP, Novartis Pharmaceuticals Corporation, Pfizer Inc., Shire Pharmaceuticals Group plc., Employee (Nzeera Ketter, MD, Spouse), Johnson & Johnson.

Contributors

Dr. Brooks contributed to study design, data analysis, and preparation of the paper. Dr. Hoblyn contributed to study design and preparation of the paper. Ms. Woodard assisted in data collection and preparation of the paper. Dr. Rosen assisted with data analysis and preparation of the paper. Dr. Ketter contributed to study design and preparation of the paper.

References

  • Adler CM, Holland SK, Schmithorst V, Tuchfarber MJ, Strakowski SM. Changes in neuronal activation in patients with bipolar disorder during performance of a working memory task. Bipolar Disorders. 2004;6:540–9. [PubMed]
  • Adler CM, DelBello MP, Strakowski SM. Brain network dysfunction in bipolar disorder. CNS Spectrums. 2006;11:312–20. [PubMed]
  • Ashburner J, Friston KJ. Nonlinear spatial normalization using basis functions. Human Brain Mapping. 1999;7:254–66. [PubMed]
  • Benabarre A, Vieta E, Martínez-Arán A, Garcia-Garcia M, Martín F, Lomeña F, et al. Neuropsychological disturbances and cerebral blood flow in bipolar disorder. The Australian and New Zealand Journal of Psychiatry. 2005;39:227–34. [PubMed]
  • Blumberg HP, Stern E, Martinez D, Ricketts S, de Asis J, White T, et al. Increased anterior cingulate and caudate activity in bipolar mania. Biological Psychiatry. 2000;48:1045–52. [PubMed]
  • Brett M, Anton J-L, Valabregue R, Poline J-B. Region of interest analysis using an SPM toolbox.. Presented at the 8th international conference on functional mapping of the brain; Sendai, Japan. June 2, 2002.
  • Brooks JO, III, Wang PW, Strong C, Sachs N, Hoblyn JC, Fenn R, et al. Preliminary evidence of differential relations between prefrontal cortex metabolism and sustained attention in depressed adults with bipolar disorder and healthy controls. Bipolar Disorders. 2006;8:248–54. [PubMed]
  • Brooks JO, III, Hoblyn JC, Woodard SA, Rosen AC, Krasnykh O, Ketter TA, et al. Relations between delayed memory and cerebral metabolism in older euthymic adults with bipolar disorder.. Presented at the annual convention of the society of biological psychiatry; San Diego, CA. May, 2007.
  • Cohen J. Statistical power analysis for the behavioral sciences. Academic Press; New York: 1977.
  • Deckersbach T, Dougherty DD, Savage C, McMurrich S, Fischman AJ, Nierenberg A, et al. Impaired recruitment of the dorsolateral prefrontal cortex and hippocampus during encoding in bipolar disorder. Biological Psychiatry. 2005;59:138–46. [PubMed]
  • Depp CA, Lindamer LA, Folsom DP, Gilmer T, Hough RL, Garcia P, et al. Differences in clinical features and mental health service use in bipolar disorder across the lifespan. American Journal of Geriatric Psychiatry. 2005;13:290–8. [PubMed]
  • Dias VV, Brissos S, Carita AI. Clinical and neurocognitive correlates of insight in patients with bipolar I disorder in remission. Acta Psychiatrica Scandinavica. 2008;117:28–34. [PubMed]
  • Drevets WC, Price JL, Simpson JR, Todd RD, Reich T, Vannier M, et al. Subgenual prefrontal cortex abnormalities in mood disorders. Nature. 1997;386:824–7. [PubMed]
  • Gildengers AG, Butters MA, Seligman K, McShea M, Miller MD, Mulsant BH, et al. Cognitive functioning in late-life bipolar disorder. American Journal of Psychiatry. 2004;161:736–8. [PubMed]
  • Gildengers AG, Butters MA, Chisholm D, Rogers JC, Holm MB, Bhalla RK, et al. Cognitive functioning and instrumental activities of daily living in late-life bipolar disorder. American Journal of Geriatric Psychiatry. 2007;15:174–9. [PubMed]
  • Gupta S, Steinmeyer CH, Lockwood K, Lentz B, Schultz K. Comparison of older patients with bipolar disorder and schizophrenia/schizoaffective disorder. American Journal of Geriatric Psychiatry. 2007;15:627–33. [PubMed]
  • Hoblyn JC, Brooks JO, III, Rosen AC, Woodard S, Krasnykh O, Ketter TA, et al. Cerebral metabolic correlates of attention in older euthymic adults with bipolar disorder.. Presented at the annual convention of the society of biological psychiatry; San Diego, CA. May, 2007.
  • Kennedy SH, Evans KR, Krüger S, Mayberg HS, Meyer JH, McCann S, et al. Changes in regional brain glucose metabolism measured with positron emission tomography after paroxetine treatment of major depression. American Journal of Psychiatry. 2001;158:899–905. [PubMed]
  • Ketter TA, Wang PW, Dieckmann NF, Lembke A, Becker OV, Camilleri C. Brain Imaging in Affective Disorders. Marcel Dekker; New York: 2002. Brain anatomic circuits and the pathophysiology of affective disorders. pp. 70–118.
  • Krüger S, Trevor Young L, Bräunig P. Pharmacotherapy of bipolar mixed states. Bipolar Disorders. 2005;7:205–15. [PubMed]
  • Lagopoulos J, Malhi GS. A functional magnetic resonance imaging study of emotional stroop in euthymic bipolar disorder. Neuroreport. 2007;18:1583–7. [PubMed]
  • Lagopoulos J, Ivanovski B, Malhi GS. An event-related functional MRI study of working memory in euthymic bipolar disorder. Journal of Psychiatry and Neuroscience. 2007;32:174–84. [PMC free article] [PubMed]
  • MacDonald AW, Cohen JD, Stenger VA, Carter CS. Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science. 2000;288:1835–8. [PubMed]
  • Maldjian JA, Laurienti PJ, Kraft RA, Burdette JH. An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage. 2003;19:1233–9. WFU Pickatlas, version 2.4. [PubMed]
  • Mayberg HS. Limbic-cortical dysregulation: a proposed model of depression. Journal of Neuropsychiatry and Clinical Neuroscience. 1997;9:471–81. [PubMed]
  • Mayberg HS, Liotti M, Brannan SK, McGinnis S, Mahurin RK, Jerabek PA, et al. Reciprocal limbic-cortical function and negative mood: converging PET findings in depression and normal sadness. American Journal of Psychiatry. 1999;156:675–82. [PubMed]
  • Mega MS, Cummings JL. Frontal-subcortical circuits and neuropsychiatric disorders. Journal of Neuropsychiatry and Clinical Neuroscience. 1994;6:358–70. [PubMed]
  • Monks PJ, Thompson JM, Bullmore ET, Suckling J, Brammer MJ, Williams SC, et al. A functional MRI study of working memory task in euthymic bipolar disorder: evidence for task-specific dysfunction. Bipolar Disorders. 2004;6:550–64. [PubMed]
  • Montgomery SA, Åsberg M. A new depression scale designed to be sensitive to change. British Journal of Psychiatry. 1979;134:382–9. [PubMed]
  • Phillips ML, Travis MJ, Fagiolini A, Kupfer DJ. Medication effects in neuroimaging studies of bipolar disorder. American Journal of Psychiatry. 2008;165:313–20. [PMC free article] [PubMed]
  • Rubinsztein JS, Fletcher PC, Rogers RD, Ho LW, Aigbirhio FI, Paykel ES, et al. Decision-making in mania: a PET study. Brain. 2001;124:2550–63. [PubMed]
  • Savard RJ, Rey AC, Post RM. Halstead–Reitan Category Test in bipolar and unipolar affective disorders. Relationship to age and phase of illness. Journal of Nervous and Mental Disease. 1980;168:297–304. [PubMed]
  • Schmahmann JD, Pandya DN. Fiber pathways of the brain. Oxford University Press; New York: 2006.
  • Seminowicz DA, Mayberg HS, McIntosh AR, Goldapple K, Kennedy S, Segal Z, et al. Limbic-frontal circuitry in major depression: a path modeling metanalysis. Neuroimage. 2004;22:409–18. [PubMed]
  • Strakowski SM, Adler CM, Holland SK, Mills N, DelBello MP. A preliminary FMRI study of sustained attention in euthymic, unmedicated bipolar disorder. Neuropsychopharmacology. 2004;29:1734–40. [PubMed]
  • Strakowski SM, Adler CM, Holland SK, Mills NP, Delbello MP, Eliassen JC. Abnormal FMRI brain activation in euthymic bipolar disorder patients during a counting stroop interference task. American Journal of Psychiatry. 2005a;162:1697–705. [PubMed]
  • Strakowski SM, Delbello MP, Adler CM. The functional neuroanatomy of bipolar disorder: a review of neuroimaging findings. Mol Psychiatry. 2005b;10:105–16. [PubMed]
  • Talairach J, Tournoux P. Co-planar stereotactic atlas of the human brain. Theime Medical Publishers, Inc.; New York: 1988.
  • Wessa M, Houenou J, Paillère-Martinot ML, Berthoz S, Artiges E, Leboyer M, et al. Fronto-striatal overactivation in euthymic bipolar patients during an emotional go/nogo task. American Journal of Psychiatry. 2007;164:638–46. [PubMed]
  • Young RC, Biggs JT, Ziegler VE, Meyer DA. A rating scale for mania: reliability, validity and sensitivity. British Journal of Psychiatry. 1978;133:429–35. [PubMed]