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Cingulate-mediated depressive symptoms in neurologic disease and therapeutics
Handbook of Clinical Neurology, Vol. 166 (3rd series) Cingulate Cortex B.A. Vogt, Editor https://doi.org/10.1016/B978-0-444-64196-0.00021-2 Copyright © 2019 Elsevier B.V. All rights reserved
Chapter 21
Cingulate-mediated depressive symptoms in neurologic disease and therapeutics PATRICIO RIVA-POSSE1, PAUL E. HOLTZHEIMER2, AND HELEN S. MAYBERG3* Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
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Departments of Psychiatry and Surgery, Geisel School of Medicine at Dartmouth, Dartmouth Hitchcock Medical Center, Lebanon, NH, United States 3
Departments of Neurology, Neurosurgery, Psychiatry, and Neuroscience, Center of Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, United States
Abstract The depressive syndrome includes a number of symptoms that are clinically diverse. Research in the past decades has consistently demonstrated that the cingulate cortex plays an essential role in these manifestations. With anatomic studies initially showing volumetric changes, followed by the insights that functional imaging and physiology contributed to neuroscience and psychiatry, the distinct areas of the cingulate subdivisions were seen to have unique contributions. The subcallosal cingulate, with its functional responsivity to mood states and to antidepressant therapies, has been identified as a central node within the mood regulation network. In this chapter, detailed descriptions of the anatomic and functional changes that are seen in depression will be discussed. Finally, a focus on the development of deep brain stimulation in the subcallosal cingulate area will be used to emphasize the conceptualization of a network model with the cingulate as a hub, where engagement of remote areas of the depression network is needed to treat depression.
OVERVIEW Patients with depressive disorders share a number of symptoms, but there are many possible combinations of symptoms that result in the same syndrome. The presence of depressed mood and/or anhedonia is required, but the disruption of cognition, motor function, and homeostatic processes such as sleep, appetite, libido, and drive and motivation can be present in various manners, making the disorder a highly heterogeneous one. Therefore, it manifests as a multidimensional disorder (Zimmerman et al., 2015). Some patients may suffer from severe insomnia, flattened affective response, and appetite loss, while others have profound hypersomnia,
increased mood reactivity, and significantly increased appetite; still other patients within each of these groups may have varying levels of disturbance of cognition, psychomotor function, and libido and varying levels of anxiety. Additionally, antidepressant treatments fail to achieve full response in many patients. The first line of antidepressant medications, modulating the monoaminergic system (serotonin, dopamine, norepinephrine), have an efficacy of around 60%, leaving a significant number of this population having an inadequate response. Evidence-based psychotherapies (such as cognitive behavioral therapy and interpersonal psychotherapy) are clearly effective in many, but not
*Correspondence to: Helen S. Mayberg, MD, Center of Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, 1000 10th Avenue G10-47, New York, NY, 10019, United States. Tel: +1-212-523-8276, Fax: +1-212-523-8331, E-mail: helen. [email protected]
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all, depressed patients. Electroconvulsive therapy (ECT) is the most effective acute treatment for depression, but it fails in up to 20%–30% of patients (Haq et al., 2015). The past decades’ efforts in neurobiology of depression have tried to understand better this clinical variability of the diagnosis and dramatic differences in symptom presentations and clinical response. Unfortunately, efforts to date have been largely unsuccessful in translating neurobiologic findings into personalized treatment options with higher efficacy. Specific dysfunctions in the functional and structural connectivity of circuits that manage emotional, cognitive, and other mood-related functions define unique biotypes of depression and anxiety. A neural circuit approach to depression may generate different depression biotypes, unlike traditional diagnostic categories, which are likely to overlap, interact, or co-occur in individual patients (Williams, 2016). This chapter will review the basis for the role of the cingulate cortex in the neurobiology of depression, with the subgenual anterior cingulate cortex (sACC) as a critical node in neural networks involved in negative mood regulation and antidepressant treatment response. Focusing specifically on the cingulate cortex, it will describe the anatomic and functional studies supporting the position of ACC in depression. Emphasizing the nodal approach, the sACC in its relations to other regions in the brain is proposed as a central player in the circuits involved in mood and emotion regulation.
IS CINGULATE CORTEX THE ORIGIN OF DEPRESSIVE SYMPTOMS? As discussed earlier, the depressive syndrome is polymorphous and not explained uniformly by the involvement or lack of function of a single region, area, or network. In other disorders, such as the parkinsonian syndrome, a large component of it can be explained by single lesions to the extrapyramidal system. Interestingly, even the parkinsonian model of disease has evolved in recent years beyond the simplistic explanation of an extrapyramidal liaison model, with the understanding of other pathways, neurotransmitters, and disease mechanisms that exceed the previously dopaminergic deficit model (Barone, 2010). Research that in the last few decades has moved beyond the lesion model has highlighted the importance of the cingulate cortex in depression. Neuroimaging techniques introduced the possibility of identifying the role of the anterior cingulate cortex in the pathophysiology of depression. This region has been identified to
have a leading position and it has been verified through imaging data (both structural and functional) as well as though physiology. Imaging data suggest that certain subdivisions of the ACC play critical roles in the neurobiology of depression. Further, the importance of the sACC as a critical node in mood regulation networks involved in negative mood and treatment response has been confirmed many times. Over the last two to three decades, significant technical advances in neuroimaging have allowed investigators to define multiple aspects of brain structure, function, and neurochemical processes to a degree that was previously impossible. Data from these investigations have been used to further develop and redefine existing neurobiologic models of depression. The last few years have allowed for research of the neurobiology of depression in vivo. This has led to advances in the study of brain function in depressed states. The ability to directly investigate brain function in depressed patients has been greatly advanced by developments in neuroimaging and neurophysiology. Imaging studies have shown that the functioning of the ACC (and especially the sACC) is key in the neurobiology of the disease. The role of the ACC does not fit classic “lesion-deficit” expectations, and it is a primary dynamic modulator within a larger, multicomponent mood regulation system. The importance of this shift in thinking about the biology of depression cannot be overemphasized. This new framework changes the understanding of antidepressant treatments from interventions designed to correct a deficit to interventions that modulate function within a dynamic, dysfunctional system. This change in the paradigm provides a better understanding of the structure and function of this system providing the basis for optimizing the use of currently available treatments and, importantly, for the development of novel interventions that more directly target dysfunctional components.
Anatomic characteristics of the cingulate cortex in depression Volumetric imaging studies are well established in the study of depression and have been used to help identify the brain regions that are most likely involved in the pathophysiology of the disease. A large number of imaging studies using structural magnetic resonance imaging (sMRI) have investigated structural brain changes associated with major depressive disorder (MDD). Data from a systematic review and meta-analysis of 40 studies indicate that reduction of gray matter volume in subjects with MDD is consistently observed in cortical
CINGULATE-MEDIATED DEPRESSIVE SYMPTOMS (prefrontal and ACC) as well as hippocampus and subcortical (caudate and putamen) regions compared to healthy individuals without MDD (Schur et al., 2016). A large meta-analysis by the ENIGMA group of 20 cohorts using harmonized protocols (2148 MDD patients and 7957 healthy controls) indicated that cortical thinning occurs in subjects with MDD, possibly in a way impacted by development, across a variety of structures including the orbitofrontal cortex, insula, and the ACC and posterior cingulate cortex (PCC; Schmaal et al., 2017). Volume differences between depressed and nondepressed subjects have been reported for the prefrontal cortex, hippocampus, amygdalae, and various basal ganglia structures (Sheline, 2003). Even considering for genetic and gender variances, the sACC volume in depressed patients is smaller than that in control subjects. The sACC is significantly reduced in volume, compared with controls, in patients with familial depression, patients with familial mood disorder have smaller sACC compared with controls, and mood disorder patients without a family history do not have this volume reduction (Drevets et al., 1997; Hirayasu et al., 1999). Finally, in a group of elderly depressed patients, significantly reduced ACC volume was found compared with controls—the volume of interest included both ACC and anterior midcingulate cortex (aMCC) regions; a number of other inferior frontal regions also showed volume reduction in these older depressed patients (Ballmaier et al., 2004). Additionally, gray matter volumes both before and after antidepressant treatment may be related to the prognosis of the illness. For example, thicker aMCC at baseline is associated with greater symptom improvement over follow-up (Phillips et al., 2015). The relationship between structural brain changes and illness burden, which may indicate changes in cortical volume, is a state-related rather than trait-related marker and thus may be reversible upon treatment or remission. In a study comparing volume changes in patients with depression, patients whose depression remitted during a 3-year follow-up period had less volume decline than nonremitted patients in the left hippocampus, left ACC, left dorsomedial prefrontal cortex, and bilaterally in the dorsolateral prefrontal cortex (Frodl et al., 2008). Volumetric reductions of the sACC, but also of the hippocampus, basal ganglia, and orbitofrontal cortex are consistently found in patients with depression, with more persistent and chronic forms of the illness being associated with greater impact on regional brain volumes (Lorenzetti et al., 2009). Volumetric studies in depression suffer from a number of limitations. While they provide important information on the structure of the brain, they only indirectly inform
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on potential functional differences. Further, techniques for defining and measuring regions of interest differ among studies. Also, most studies included phenomenologically heterogeneous patients. For example, many studies combined patients with unipolar and bipolar depression as well as patients with and without familial history of mood disorder. Given their phenomenological overlap, it is indeed likely that unipolar and bipolar depression share some pathophysiologic features; however, it is also likely that these two illnesses have important biologic differences. Several studies have suggested that smaller ACC volumes are associated with bipolar disorder in general, although the specific regions of the ACC investigated have differed among studies (Lochhead et al., 2004; McDonald et al., 2004; Sassi et al., 2004; Wilke et al., 2004). Further, it is likely that familial mood disorders may be biologically distinct from nonfamilial disorders. The presence or absence of psychosis may also be a confounder. Gender and other genetic factors may influence volumetric findings, especially in the ACC; however, these factors were not carefully controlled for in the majority of investigations. Stage of illness and treatment history may also impact brain structures in mood disorders (Sheline, 2003; Sassi et al., 2004). Finally, age and comorbid diseases (e.g., hypertension, smoking, anxiety disorders, and substance abuse) may affect the volume of brain structures. Despite these limitations, several studies have found significant structural abnormalities in the ACC (especially the sACC) in patients with depression and other mood disorders. Data suggest that gender, genetics, and treatment history may impact these structural changes. Volume reductions in the sACC may be primarily related to loss of glial cells; however, it is unclear whether such loss is due to a reduction in astrocytes or oligodendrocytes, although data suggest oligodendrocyte abnormalities in depression (Hamidi et al., 2004; Aston et al., 2005). If loss of oligodendrocytes in the ACC is confirmed, this would suggest abnormalities in the ability to properly myelinate axons resulting in abnormal neural connectivity and interaction of sACC with other parts of the neural network involved in mood regulation. Pezawas et al. (2005) showed that, in healthy subjects, the volume of the sACC was smaller in subjects carrying the allele for the short version (s-allele) of the gene for the promoter region of the serotonin transporter (5-HTTLPR) compared with subjects without this allele. This group also found that subjects carrying the s-allele had functional “decoupling” of activity in the ACC from activity in the amygdala. Carrying the 5-HTTLPR s-allele has been associated with trait
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anxiety and has been shown to increase the likelihood of developing depression in response to stressful experiences (Lesch et al., 1996; Caspi et al., 2003; Kendler et al., 2005).
FUNCTIONAL ABNORMALITIES IN CINGULATE CORTEX AND THEIR RELATION TO DEPRESSIVE SYMPTOMATOLOGY Within the cingulate cortex, the subgenual region is a particular area of interest as several studies show a dysfunctional state in depression. The sACC is a critical brain region in emotion processing and the pathogenesis of mood disorders. Its metabolism is increased during depressive states (Kennedy et al., 2001; Drevets et al., 2002). The metabolism in this region is also positively correlated with depression and anxiety severity (Osuch et al., 2000). A sadness induction experiment in healthy controls has shown increases in blood flow in the sACC (Liotti et al., 2000). Patients who respond appropriately to antidepressant treatment have a decline in metabolism from an abnormal elevation toward normality (Mayberg et al., 2000). While the local manifestations of depressive symptoms are more and more evident, in the last decade there has been an increased interest in the description of dysfunctional networks as an explanatory model for cognitive and affective pathologies in depression. These connectivity alterations in MDD are related primarily to three major brain networks: the default mode network (DMN), the salience network (SN), and the central executive network (CEN). Each of these networks has critical nodes involving cingulate regions (Raichle et al., 2001). Resting-state sACC and thalamic functional connectivity with the DMN are significantly greater in depressed subjects. Within depressed subjects, the length of the current depressive episode is positively correlated with functional connectivity in the sACC (Greicius et al., 2007). Meta-analytic findings show reliably increased functional connectivity between the DMN and sACC, predicting levels of depressive rumination (Dutta et al., 2014; Hamilton et al., 2015). The affective-salience circuit is critically involved in the direction of motivated behavior in response to the presence of perceived threats or rewards. Meta-analytic data support the finding of heightened connectivity and hyperactivity of the amygdala as well as hyperactivity of the MCC and anterior insula following exposure to negative stimuli in patients with MDD (Hamilton et al., 2012). Resting-state and task-based functional imaging data from the Prediction of Remission to Individual and Combined Treatments (PReDICT) study indicate that functional connectivity of the sACC was differentially associated with outcomes of remission and
treatment failure in patients with depression (Dunlop et al., 2017). In this study, the resting-state functional connectivity of the SCC to three regions (left anterior ventrolateral prefrontal cortex/insula, the dorsal midbrain, and the left ventromedial prefrontal cortex) was differentially associated with outcomes of remission and treatment failure to cognitive behavioral therapy (CBT) and antidepressant medication. Data using extended electroencephalography from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) trial indicate that increased right ACC theta band activity (Pizzagalli et al., 2018) and right ACC–right anterior insula connectivity (Whitton et al., 2019) are incrementally predictive of antidepressant treatment response. In summary, it can be argued that there is extensive evidence of network dysfunction or disrupted network organization as a neurobiologic cause for or consequence of MDD.
MULTINODAL NETWORK MODEL OF MOOD REGULATION The conceptualization of mood disorders as network models has grown in the last decades and the understanding of distinct but closely related processes in the depressive syndrome has increased. Cognitive and sensorimotor processing are associated with prefrontal, parietal, midcingulate, and posterior cingulate cortices. The PCC in particular represents a functional hub of the default mode network (DMN; Raichle et al., 2001). Major depression is associated with altered static functional connectivity in various brain networks, particularly the DMN (Wise et al., 2017). Cognitive processing of emotional stimuli (as well as emotional processing of cognitive stimuli) is most associated with medial frontal and orbitofrontal cortices and pregenual ACC (pACC). Other aspects of cognitive-emotional processing are associated with amygdala, basal ganglia, thalamic, and midbrain structures. Ventral structures that include the sACC, insula, hypothalamus, and brainstem nuclei are central to autonomic, circadian, homeostatic, and drive processes. Following on this, specific behavioral or emotional states involve the integrated activity of all of these regions. Therefore, dysfunction of these networks may occur rarely because of an abnormality within a single region, but mostly due to abnormal functional connectivity of these nodes. As discussed earlier, lesion models in depression are not truly representative of the disease, but the group dysfunction may provide a better explanation. Subdivisions of the cingulate cortex are also functionally segregated: the MCC is involved mostly in cognitive processing and the sACC in emotional and autonomic experiences, with the MCC involved in mediating the
CINGULATE-MEDIATED DEPRESSIVE SYMPTOMS interaction of the first two. The pACC and MCC regions are involved in cognitive processes, pACC is involved in overt cognitive-emotional processing, and the sACC is involved in autonomic, circadian/drive processes. Functional imaging shows that sACC activity is linked to negative mood and sadness (in both healthy controls as well as patients with depression) (Phan et al., 2002). The many different depressive types may then reflect unique manifestations of specific cingulate subdivisions and their interactions with other regions of the mood network. A recent study using functional magnetic resonance imaging (fMRI) in a large multisite sample (n ¼ 1188) described that patients with depression could be subdivided into four distinct biotypes defined by distinct patterns of dysfunctional connectivity in limbic and frontostriatal networks. This clustering enabled the development of diagnostic classifiers (biomarkers) with high (82%–93%) sensitivity and specificity for depression subtypes. While these biotypes did not allow for differences recognizable only by clinical features, they were able to be associated with selective response to antidepressant treatments (i.e., repetitive transcranial magnetic stimulation; rTMS) (Drysdale et al., 2017). The use of resting-state fMRI has also been shown to indicate that connectivity changes in the network is predictive of response to rTMS, with nonresponders displaying hyperconnectivity in ACC to ventromedial prefrontal cortex, PCC to precuneus, MCC, and insula, which are hub regions of the DMN and salience networks (Ge et al., 2017). It is then possible to hypothesize that treatments with different primary mechanisms of action likely act at different points within the circuit. For example, certain “first-line” treatments, such as serotonergic antidepressant medications and CBT, may have different primary sites of action within the network (frontal cortex for CBT and midbrain-subcortical regions for medications) but rely on intact connections between various regions of the circuit and the ability of these connected regions to respond appropriately (i.e., changes in midbrainsubcortical regions with medications must be able to result in downstream functional changes in frontal cortex; vice versa for CBT). Similarly, poor adaptive capacity within the network may underlie lack of response to common treatments and explain why progressively more aggressive treatments (such as ECT and surgery) are needed to ameliorate symptoms. This model was proposed and confirmed using structural equation modeling (SEM) of data from prior combined imaging-treatment studies (Seminowicz et al., 2004). Based on SEM analyses, independent groups of depressed patients can be characterized by a neural network model that includes sACC area 25 and pregenual area 24; orbital (BA 11), medial (BA 10), and dorsolateral prefrontal cortex (BA 9); and the anterior thalamus
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and hippocampus. Variability in the model across different patient groups was related to differences in the strength of interactions between area 25 and other model constituents, with differences across groups best explained by treatment response outcomes rather than any symptom, severity, or classification variable. Patients who responded to medications were distinguished by a dominant area 25–BA 9–hippocampal connectivity pattern, while CBT responders where defined by an area 25–area 24–BA 11–BA 10 connectivity pattern. Importantly, medication nonresponders showed a distinct third connectivity pattern involving area 25, area 24, and ventral-subcortical regions (BA 11, anterior thalamus), without significant involvement of either prefrontal or medial frontal regions. These different studies confirm the idea that no unique brain region is responsible for the illness state—rather, it is the interplay both structurally and functionally of all brain regions that then lead to diverse manifestations of the same syndrome (whether with clinical characteristics, suggestive of treatment decisions or predictive of outcomes). Subgroups of depressed patients (defined by differences in treatment response and nonresponse, rather than specific symptoms or other illness features), manifest subtle but distinct differences in the functional integrity of some but not all paths within a more general mood regulation network. The functional connectivity of subdivisions of the ACC plays a critical role in these networks.
THERAPEUTIC IMPLICATIONS OF sACC INVOLVEMENT IN DEPRESSION: DEEP BRAIN STIMULATION Based on the modeling approaches described earlier in the chapter, it was determined that the sACC (Area 25) was overactive and had abnormal connectivity with other brain regions involved in mood regulation in patients with treatment-resistant depression. It was then hypothesized that high-frequency deep brain stimulation (DBS) of this region in such patients might help correct these abnormalities and restore mood regulation. In a small pilot study of this approach in six severely treatment– resistant depressed patients (five had failed ECT), four patients responded at 6 months, with three in remission (Mayberg et al., 2005). The initial cohort of 6 patients was increased to 20 patients, who were implanted and monitored for antidepressant effects of chronic stimulation. One month after surgery, 35% of patients met the criteria for response, with 10% of patients in remission. Six months after surgery, 60% of patients were responders and 35% met the criteria for remission, benefits that were largely maintained at 12 months (Lozano et al., 2008). Notably, the antidepressant effects of DBS were observed to be maintained. The long-term follow-up of
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this cohort of patients showed sustained reduction in the mood rating scales. After 1 year of DBS, 62.5% of patients were responders (50% decline in the Hamilton Depression Rating Scale from baseline), and the response rate after 2 years was 46%, with 75% response rate after 3 years of stimulation. These patients were considered to have the highest level of treatment resistance, having been in the current depressive episode for almost 7 years, having profound occupational impairment (only 10% were employed), and having tried and relapsed after electroconvulsive therapy. DBS did not only cause a symptoms reduction that was remarkable and sustained, but functional impairment in the areas of physical health and social functioning progressively also improved up to the last follow-up visit (Kennedy et al., 2011). No significant stimulation-related adverse events were reported during this follow-up, although two patients died by suicide during depressive relapses. The initial enthusiasm led the field to attempt to replicate these findings in different centers. Gradually, a number of single- and multicenter studies were published, demonstrating similar degrees of efficacy in open-label designs. Seventeen patients (seven of them with bipolar 2 disorder) were implanted at Emory University. Holtzheimer et al. (2012) included not only participants with unipolar depression but also patients with bipolar 2 disorder (seven patients). The severity of these participants was similar and the response rates were 41% at 6 months and 92% after 2 years of stimulation (12 of the 17 patients having reached the latter time point by the time of publication). In the last few years, other centers have described similar outcomes, with many case series (Lozano et al., 2012; Puigdemont et al., 2012; Merkl et al., 2013). Puigdemont et al. reported response and remission rates of 87.5% and 37.5%, respectively, in eight patients with severe TRD at 6 months. These dramatic improvements were sustained after 12 months with 62.5% response and 50% remission rates. Another group in Germany implanted six subjects and explored acute effects of stimulation but also described long-term antidepressant effects, with two of the six patients in remission of depression after 6 months of stimulation. Importantly, they did not describe side effects secondary to high-voltage stimulation (Merkl et al., 2013). An early meta-analysis described 6-month pooled response rates of 53.9% and 39.9% at 12 months (Berlim et al., 2014). There was an additional multicenter study conducted in three different sites in Canada that implanted 21 patients (Lozano et al., 2012). Forty-eight percent of patients were responders at 6 months. This study corroborated the results of previous reports showing that the outcome of SCG DBS was replicable. An industry-sponsored, multicenter study was conducted with the initial intention of
recruiting 200 patients in the United States. The study was halted after a futility analysis determined that the likelihood of it achieving its primary outcome was low (Holtzheimer et al., 2017). At the time of the interruption, 90 patients had been implanted in the sACC. The study involved randomizations to either active (n ¼ 60) or sham (n ¼ 30) stimulation for the initial 24 weeks of the study. The primary outcome was frequency of response (defined as a 40% or greater reduction in depression severity from baseline) averaged over months 4–6 of the double-blind phase. Both groups showed improvement, but there was no statistically significant difference in response during the doubleblind, sham-controlled phase (12 [20%] patients in the stimulation group vs five [17%] patients in the control group). Notably, the response rates improved in the long-term follow-up phase with results that doubled the initial response rates at 18, 24, and 30 months; half of the patients who were in the study showed a positive effect of chronic DBS. The reasons for failure in studies of this kind are multiple and can many times be attributed to patient selection and study design, among other factors (Schlaepfer, 2015). In the sACC, the variations in anatomy and its white matter connections are not evident in standard neuroimaging sequences (Hamani et al., 2009). The difference between responders and nonresponders to DBS is not evidenced by the anatomic coordinates that were used to guide surgical implantation. Instead, effective DBS in the sACC appears to be related to the network involvement and the white matter fibers that not only are related to the local cingulate effects but the connections to other regions involved the “depression network.” This was better understood when the imaging analysis was done using diffusion tensor imaging tractography. Riva-Posse et al. (2014) did a retrospective analysis of the initial 17 subjects who had received open-label stimulation at their center. The authors described that all responders shared a common tractography map. Using models that calculate volume of stimulation with individualized stimulation parameters, each contact had a unique set of white matter fibers. When all the responders were compiled, a clear pattern showed fibers that connected the sACC to the rest of the ACC (via the cingulum bundle), bilateral medial frontal cortices (through the forceps minor), subcortical nuclei, and the thalamus (via uncinate fasciculus and frontostriatal fibers). Once the pattern was identified in the retrospective analysis, a prospective testing of this hypothesis was conducted. Eleven subjects were implanted in the sACC using target selection for the DBS that was based on the connectivity map that was present in responders (Riva-Posse et al., 2018) (Fig. 21.1). This approach resulted in a significant increase in the response rate, with 8/11 (73%) of patients
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findings in recent years point toward cingulate cortex as a primary dynamic modulator within a larger, multicomponent mood regulation system. Its relations to different frontal and parietal regions, insula, and subcortical nuclei, and the individual network contributions to the complex syndromal presentations seen in clinical practice emphasize the continued need for cingulate research.
REFERENCES
Fig. 21.1. Structural connectivity-based target selection. (A) Individualized deterministic tractography target selection in one subject: optimal target location within sACC region with modeled stimulation impacting necessary fiber bundles for effective DBS. Blue sphere represents the estimated volume of activated tissue with standard stimulation settings. The deterministic tractography shows fibers traveling through that sphere, with cable models in the cingulum bundle, the forceps minor, the uncinate fasciculus, and frontostriatal fibers. (B and C) Postimplantation computerized tomography (CT) merged on presurgical MRI showing sagittal (B) and coronal (C) views of the location of the DBS lead in the subcallosal cingulate. CB, cingulum bundle; FM, forceps minor; F-St, frontostriatal fibers; UF, uncinate fasciculus.
showing a decrease in the 17-item Hamilton Depression Rating Scale scores by more than 50% after 6 months and an additional subject becoming a responder at the 12-month time point (9/11: 82% response rate). The four-bundle white matter blueprint was reliably defined and precisely implanted in each of the 11 subjects. A group probabilistic tractography blueprint for individualized, patient-specific, deterministic tractography targeting confirmed the retrospective findings. This targeting method confirms and validates the conceptualization of a network model with the cingulate as a hub where engagement of remote areas of the depression network is needed for the adequate antidepressant effect.
CONCLUSIONS The cingulate cortex, in its many functional and anatomic subdivisions, plays an essential role as a hub in the center of neural networks that are involved in manifestations of mood and emotion. The anatomic and functional
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Chapter 21
Cingulate-mediated depressive symptoms in neurologic disease and therapeutics PATRICIO RIVA-POSSE1, PAUL E. HOLTZHEIMER2, AND HELEN S. MAYBERG3* Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
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Departments of Psychiatry and Surgery, Geisel School of Medicine at Dartmouth, Dartmouth Hitchcock Medical Center, Lebanon, NH, United States 3
Departments of Neurology, Neurosurgery, Psychiatry, and Neuroscience, Center of Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, United States
Abstract The depressive syndrome includes a number of symptoms that are clinically diverse. Research in the past decades has consistently demonstrated that the cingulate cortex plays an essential role in these manifestations. With anatomic studies initially showing volumetric changes, followed by the insights that functional imaging and physiology contributed to neuroscience and psychiatry, the distinct areas of the cingulate subdivisions were seen to have unique contributions. The subcallosal cingulate, with its functional responsivity to mood states and to antidepressant therapies, has been identified as a central node within the mood regulation network. In this chapter, detailed descriptions of the anatomic and functional changes that are seen in depression will be discussed. Finally, a focus on the development of deep brain stimulation in the subcallosal cingulate area will be used to emphasize the conceptualization of a network model with the cingulate as a hub, where engagement of remote areas of the depression network is needed to treat depression.
OVERVIEW Patients with depressive disorders share a number of symptoms, but there are many possible combinations of symptoms that result in the same syndrome. The presence of depressed mood and/or anhedonia is required, but the disruption of cognition, motor function, and homeostatic processes such as sleep, appetite, libido, and drive and motivation can be present in various manners, making the disorder a highly heterogeneous one. Therefore, it manifests as a multidimensional disorder (Zimmerman et al., 2015). Some patients may suffer from severe insomnia, flattened affective response, and appetite loss, while others have profound hypersomnia,
increased mood reactivity, and significantly increased appetite; still other patients within each of these groups may have varying levels of disturbance of cognition, psychomotor function, and libido and varying levels of anxiety. Additionally, antidepressant treatments fail to achieve full response in many patients. The first line of antidepressant medications, modulating the monoaminergic system (serotonin, dopamine, norepinephrine), have an efficacy of around 60%, leaving a significant number of this population having an inadequate response. Evidence-based psychotherapies (such as cognitive behavioral therapy and interpersonal psychotherapy) are clearly effective in many, but not
*Correspondence to: Helen S. Mayberg, MD, Center of Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, 1000 10th Avenue G10-47, New York, NY, 10019, United States. Tel: +1-212-523-8276, Fax: +1-212-523-8331, E-mail: helen. [email protected]
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all, depressed patients. Electroconvulsive therapy (ECT) is the most effective acute treatment for depression, but it fails in up to 20%–30% of patients (Haq et al., 2015). The past decades’ efforts in neurobiology of depression have tried to understand better this clinical variability of the diagnosis and dramatic differences in symptom presentations and clinical response. Unfortunately, efforts to date have been largely unsuccessful in translating neurobiologic findings into personalized treatment options with higher efficacy. Specific dysfunctions in the functional and structural connectivity of circuits that manage emotional, cognitive, and other mood-related functions define unique biotypes of depression and anxiety. A neural circuit approach to depression may generate different depression biotypes, unlike traditional diagnostic categories, which are likely to overlap, interact, or co-occur in individual patients (Williams, 2016). This chapter will review the basis for the role of the cingulate cortex in the neurobiology of depression, with the subgenual anterior cingulate cortex (sACC) as a critical node in neural networks involved in negative mood regulation and antidepressant treatment response. Focusing specifically on the cingulate cortex, it will describe the anatomic and functional studies supporting the position of ACC in depression. Emphasizing the nodal approach, the sACC in its relations to other regions in the brain is proposed as a central player in the circuits involved in mood and emotion regulation.
IS CINGULATE CORTEX THE ORIGIN OF DEPRESSIVE SYMPTOMS? As discussed earlier, the depressive syndrome is polymorphous and not explained uniformly by the involvement or lack of function of a single region, area, or network. In other disorders, such as the parkinsonian syndrome, a large component of it can be explained by single lesions to the extrapyramidal system. Interestingly, even the parkinsonian model of disease has evolved in recent years beyond the simplistic explanation of an extrapyramidal liaison model, with the understanding of other pathways, neurotransmitters, and disease mechanisms that exceed the previously dopaminergic deficit model (Barone, 2010). Research that in the last few decades has moved beyond the lesion model has highlighted the importance of the cingulate cortex in depression. Neuroimaging techniques introduced the possibility of identifying the role of the anterior cingulate cortex in the pathophysiology of depression. This region has been identified to
have a leading position and it has been verified through imaging data (both structural and functional) as well as though physiology. Imaging data suggest that certain subdivisions of the ACC play critical roles in the neurobiology of depression. Further, the importance of the sACC as a critical node in mood regulation networks involved in negative mood and treatment response has been confirmed many times. Over the last two to three decades, significant technical advances in neuroimaging have allowed investigators to define multiple aspects of brain structure, function, and neurochemical processes to a degree that was previously impossible. Data from these investigations have been used to further develop and redefine existing neurobiologic models of depression. The last few years have allowed for research of the neurobiology of depression in vivo. This has led to advances in the study of brain function in depressed states. The ability to directly investigate brain function in depressed patients has been greatly advanced by developments in neuroimaging and neurophysiology. Imaging studies have shown that the functioning of the ACC (and especially the sACC) is key in the neurobiology of the disease. The role of the ACC does not fit classic “lesion-deficit” expectations, and it is a primary dynamic modulator within a larger, multicomponent mood regulation system. The importance of this shift in thinking about the biology of depression cannot be overemphasized. This new framework changes the understanding of antidepressant treatments from interventions designed to correct a deficit to interventions that modulate function within a dynamic, dysfunctional system. This change in the paradigm provides a better understanding of the structure and function of this system providing the basis for optimizing the use of currently available treatments and, importantly, for the development of novel interventions that more directly target dysfunctional components.
Anatomic characteristics of the cingulate cortex in depression Volumetric imaging studies are well established in the study of depression and have been used to help identify the brain regions that are most likely involved in the pathophysiology of the disease. A large number of imaging studies using structural magnetic resonance imaging (sMRI) have investigated structural brain changes associated with major depressive disorder (MDD). Data from a systematic review and meta-analysis of 40 studies indicate that reduction of gray matter volume in subjects with MDD is consistently observed in cortical
CINGULATE-MEDIATED DEPRESSIVE SYMPTOMS (prefrontal and ACC) as well as hippocampus and subcortical (caudate and putamen) regions compared to healthy individuals without MDD (Schur et al., 2016). A large meta-analysis by the ENIGMA group of 20 cohorts using harmonized protocols (2148 MDD patients and 7957 healthy controls) indicated that cortical thinning occurs in subjects with MDD, possibly in a way impacted by development, across a variety of structures including the orbitofrontal cortex, insula, and the ACC and posterior cingulate cortex (PCC; Schmaal et al., 2017). Volume differences between depressed and nondepressed subjects have been reported for the prefrontal cortex, hippocampus, amygdalae, and various basal ganglia structures (Sheline, 2003). Even considering for genetic and gender variances, the sACC volume in depressed patients is smaller than that in control subjects. The sACC is significantly reduced in volume, compared with controls, in patients with familial depression, patients with familial mood disorder have smaller sACC compared with controls, and mood disorder patients without a family history do not have this volume reduction (Drevets et al., 1997; Hirayasu et al., 1999). Finally, in a group of elderly depressed patients, significantly reduced ACC volume was found compared with controls—the volume of interest included both ACC and anterior midcingulate cortex (aMCC) regions; a number of other inferior frontal regions also showed volume reduction in these older depressed patients (Ballmaier et al., 2004). Additionally, gray matter volumes both before and after antidepressant treatment may be related to the prognosis of the illness. For example, thicker aMCC at baseline is associated with greater symptom improvement over follow-up (Phillips et al., 2015). The relationship between structural brain changes and illness burden, which may indicate changes in cortical volume, is a state-related rather than trait-related marker and thus may be reversible upon treatment or remission. In a study comparing volume changes in patients with depression, patients whose depression remitted during a 3-year follow-up period had less volume decline than nonremitted patients in the left hippocampus, left ACC, left dorsomedial prefrontal cortex, and bilaterally in the dorsolateral prefrontal cortex (Frodl et al., 2008). Volumetric reductions of the sACC, but also of the hippocampus, basal ganglia, and orbitofrontal cortex are consistently found in patients with depression, with more persistent and chronic forms of the illness being associated with greater impact on regional brain volumes (Lorenzetti et al., 2009). Volumetric studies in depression suffer from a number of limitations. While they provide important information on the structure of the brain, they only indirectly inform
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on potential functional differences. Further, techniques for defining and measuring regions of interest differ among studies. Also, most studies included phenomenologically heterogeneous patients. For example, many studies combined patients with unipolar and bipolar depression as well as patients with and without familial history of mood disorder. Given their phenomenological overlap, it is indeed likely that unipolar and bipolar depression share some pathophysiologic features; however, it is also likely that these two illnesses have important biologic differences. Several studies have suggested that smaller ACC volumes are associated with bipolar disorder in general, although the specific regions of the ACC investigated have differed among studies (Lochhead et al., 2004; McDonald et al., 2004; Sassi et al., 2004; Wilke et al., 2004). Further, it is likely that familial mood disorders may be biologically distinct from nonfamilial disorders. The presence or absence of psychosis may also be a confounder. Gender and other genetic factors may influence volumetric findings, especially in the ACC; however, these factors were not carefully controlled for in the majority of investigations. Stage of illness and treatment history may also impact brain structures in mood disorders (Sheline, 2003; Sassi et al., 2004). Finally, age and comorbid diseases (e.g., hypertension, smoking, anxiety disorders, and substance abuse) may affect the volume of brain structures. Despite these limitations, several studies have found significant structural abnormalities in the ACC (especially the sACC) in patients with depression and other mood disorders. Data suggest that gender, genetics, and treatment history may impact these structural changes. Volume reductions in the sACC may be primarily related to loss of glial cells; however, it is unclear whether such loss is due to a reduction in astrocytes or oligodendrocytes, although data suggest oligodendrocyte abnormalities in depression (Hamidi et al., 2004; Aston et al., 2005). If loss of oligodendrocytes in the ACC is confirmed, this would suggest abnormalities in the ability to properly myelinate axons resulting in abnormal neural connectivity and interaction of sACC with other parts of the neural network involved in mood regulation. Pezawas et al. (2005) showed that, in healthy subjects, the volume of the sACC was smaller in subjects carrying the allele for the short version (s-allele) of the gene for the promoter region of the serotonin transporter (5-HTTLPR) compared with subjects without this allele. This group also found that subjects carrying the s-allele had functional “decoupling” of activity in the ACC from activity in the amygdala. Carrying the 5-HTTLPR s-allele has been associated with trait
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anxiety and has been shown to increase the likelihood of developing depression in response to stressful experiences (Lesch et al., 1996; Caspi et al., 2003; Kendler et al., 2005).
FUNCTIONAL ABNORMALITIES IN CINGULATE CORTEX AND THEIR RELATION TO DEPRESSIVE SYMPTOMATOLOGY Within the cingulate cortex, the subgenual region is a particular area of interest as several studies show a dysfunctional state in depression. The sACC is a critical brain region in emotion processing and the pathogenesis of mood disorders. Its metabolism is increased during depressive states (Kennedy et al., 2001; Drevets et al., 2002). The metabolism in this region is also positively correlated with depression and anxiety severity (Osuch et al., 2000). A sadness induction experiment in healthy controls has shown increases in blood flow in the sACC (Liotti et al., 2000). Patients who respond appropriately to antidepressant treatment have a decline in metabolism from an abnormal elevation toward normality (Mayberg et al., 2000). While the local manifestations of depressive symptoms are more and more evident, in the last decade there has been an increased interest in the description of dysfunctional networks as an explanatory model for cognitive and affective pathologies in depression. These connectivity alterations in MDD are related primarily to three major brain networks: the default mode network (DMN), the salience network (SN), and the central executive network (CEN). Each of these networks has critical nodes involving cingulate regions (Raichle et al., 2001). Resting-state sACC and thalamic functional connectivity with the DMN are significantly greater in depressed subjects. Within depressed subjects, the length of the current depressive episode is positively correlated with functional connectivity in the sACC (Greicius et al., 2007). Meta-analytic findings show reliably increased functional connectivity between the DMN and sACC, predicting levels of depressive rumination (Dutta et al., 2014; Hamilton et al., 2015). The affective-salience circuit is critically involved in the direction of motivated behavior in response to the presence of perceived threats or rewards. Meta-analytic data support the finding of heightened connectivity and hyperactivity of the amygdala as well as hyperactivity of the MCC and anterior insula following exposure to negative stimuli in patients with MDD (Hamilton et al., 2012). Resting-state and task-based functional imaging data from the Prediction of Remission to Individual and Combined Treatments (PReDICT) study indicate that functional connectivity of the sACC was differentially associated with outcomes of remission and
treatment failure in patients with depression (Dunlop et al., 2017). In this study, the resting-state functional connectivity of the SCC to three regions (left anterior ventrolateral prefrontal cortex/insula, the dorsal midbrain, and the left ventromedial prefrontal cortex) was differentially associated with outcomes of remission and treatment failure to cognitive behavioral therapy (CBT) and antidepressant medication. Data using extended electroencephalography from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) trial indicate that increased right ACC theta band activity (Pizzagalli et al., 2018) and right ACC–right anterior insula connectivity (Whitton et al., 2019) are incrementally predictive of antidepressant treatment response. In summary, it can be argued that there is extensive evidence of network dysfunction or disrupted network organization as a neurobiologic cause for or consequence of MDD.
MULTINODAL NETWORK MODEL OF MOOD REGULATION The conceptualization of mood disorders as network models has grown in the last decades and the understanding of distinct but closely related processes in the depressive syndrome has increased. Cognitive and sensorimotor processing are associated with prefrontal, parietal, midcingulate, and posterior cingulate cortices. The PCC in particular represents a functional hub of the default mode network (DMN; Raichle et al., 2001). Major depression is associated with altered static functional connectivity in various brain networks, particularly the DMN (Wise et al., 2017). Cognitive processing of emotional stimuli (as well as emotional processing of cognitive stimuli) is most associated with medial frontal and orbitofrontal cortices and pregenual ACC (pACC). Other aspects of cognitive-emotional processing are associated with amygdala, basal ganglia, thalamic, and midbrain structures. Ventral structures that include the sACC, insula, hypothalamus, and brainstem nuclei are central to autonomic, circadian, homeostatic, and drive processes. Following on this, specific behavioral or emotional states involve the integrated activity of all of these regions. Therefore, dysfunction of these networks may occur rarely because of an abnormality within a single region, but mostly due to abnormal functional connectivity of these nodes. As discussed earlier, lesion models in depression are not truly representative of the disease, but the group dysfunction may provide a better explanation. Subdivisions of the cingulate cortex are also functionally segregated: the MCC is involved mostly in cognitive processing and the sACC in emotional and autonomic experiences, with the MCC involved in mediating the
CINGULATE-MEDIATED DEPRESSIVE SYMPTOMS interaction of the first two. The pACC and MCC regions are involved in cognitive processes, pACC is involved in overt cognitive-emotional processing, and the sACC is involved in autonomic, circadian/drive processes. Functional imaging shows that sACC activity is linked to negative mood and sadness (in both healthy controls as well as patients with depression) (Phan et al., 2002). The many different depressive types may then reflect unique manifestations of specific cingulate subdivisions and their interactions with other regions of the mood network. A recent study using functional magnetic resonance imaging (fMRI) in a large multisite sample (n ¼ 1188) described that patients with depression could be subdivided into four distinct biotypes defined by distinct patterns of dysfunctional connectivity in limbic and frontostriatal networks. This clustering enabled the development of diagnostic classifiers (biomarkers) with high (82%–93%) sensitivity and specificity for depression subtypes. While these biotypes did not allow for differences recognizable only by clinical features, they were able to be associated with selective response to antidepressant treatments (i.e., repetitive transcranial magnetic stimulation; rTMS) (Drysdale et al., 2017). The use of resting-state fMRI has also been shown to indicate that connectivity changes in the network is predictive of response to rTMS, with nonresponders displaying hyperconnectivity in ACC to ventromedial prefrontal cortex, PCC to precuneus, MCC, and insula, which are hub regions of the DMN and salience networks (Ge et al., 2017). It is then possible to hypothesize that treatments with different primary mechanisms of action likely act at different points within the circuit. For example, certain “first-line” treatments, such as serotonergic antidepressant medications and CBT, may have different primary sites of action within the network (frontal cortex for CBT and midbrain-subcortical regions for medications) but rely on intact connections between various regions of the circuit and the ability of these connected regions to respond appropriately (i.e., changes in midbrainsubcortical regions with medications must be able to result in downstream functional changes in frontal cortex; vice versa for CBT). Similarly, poor adaptive capacity within the network may underlie lack of response to common treatments and explain why progressively more aggressive treatments (such as ECT and surgery) are needed to ameliorate symptoms. This model was proposed and confirmed using structural equation modeling (SEM) of data from prior combined imaging-treatment studies (Seminowicz et al., 2004). Based on SEM analyses, independent groups of depressed patients can be characterized by a neural network model that includes sACC area 25 and pregenual area 24; orbital (BA 11), medial (BA 10), and dorsolateral prefrontal cortex (BA 9); and the anterior thalamus
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and hippocampus. Variability in the model across different patient groups was related to differences in the strength of interactions between area 25 and other model constituents, with differences across groups best explained by treatment response outcomes rather than any symptom, severity, or classification variable. Patients who responded to medications were distinguished by a dominant area 25–BA 9–hippocampal connectivity pattern, while CBT responders where defined by an area 25–area 24–BA 11–BA 10 connectivity pattern. Importantly, medication nonresponders showed a distinct third connectivity pattern involving area 25, area 24, and ventral-subcortical regions (BA 11, anterior thalamus), without significant involvement of either prefrontal or medial frontal regions. These different studies confirm the idea that no unique brain region is responsible for the illness state—rather, it is the interplay both structurally and functionally of all brain regions that then lead to diverse manifestations of the same syndrome (whether with clinical characteristics, suggestive of treatment decisions or predictive of outcomes). Subgroups of depressed patients (defined by differences in treatment response and nonresponse, rather than specific symptoms or other illness features), manifest subtle but distinct differences in the functional integrity of some but not all paths within a more general mood regulation network. The functional connectivity of subdivisions of the ACC plays a critical role in these networks.
THERAPEUTIC IMPLICATIONS OF sACC INVOLVEMENT IN DEPRESSION: DEEP BRAIN STIMULATION Based on the modeling approaches described earlier in the chapter, it was determined that the sACC (Area 25) was overactive and had abnormal connectivity with other brain regions involved in mood regulation in patients with treatment-resistant depression. It was then hypothesized that high-frequency deep brain stimulation (DBS) of this region in such patients might help correct these abnormalities and restore mood regulation. In a small pilot study of this approach in six severely treatment– resistant depressed patients (five had failed ECT), four patients responded at 6 months, with three in remission (Mayberg et al., 2005). The initial cohort of 6 patients was increased to 20 patients, who were implanted and monitored for antidepressant effects of chronic stimulation. One month after surgery, 35% of patients met the criteria for response, with 10% of patients in remission. Six months after surgery, 60% of patients were responders and 35% met the criteria for remission, benefits that were largely maintained at 12 months (Lozano et al., 2008). Notably, the antidepressant effects of DBS were observed to be maintained. The long-term follow-up of
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this cohort of patients showed sustained reduction in the mood rating scales. After 1 year of DBS, 62.5% of patients were responders (50% decline in the Hamilton Depression Rating Scale from baseline), and the response rate after 2 years was 46%, with 75% response rate after 3 years of stimulation. These patients were considered to have the highest level of treatment resistance, having been in the current depressive episode for almost 7 years, having profound occupational impairment (only 10% were employed), and having tried and relapsed after electroconvulsive therapy. DBS did not only cause a symptoms reduction that was remarkable and sustained, but functional impairment in the areas of physical health and social functioning progressively also improved up to the last follow-up visit (Kennedy et al., 2011). No significant stimulation-related adverse events were reported during this follow-up, although two patients died by suicide during depressive relapses. The initial enthusiasm led the field to attempt to replicate these findings in different centers. Gradually, a number of single- and multicenter studies were published, demonstrating similar degrees of efficacy in open-label designs. Seventeen patients (seven of them with bipolar 2 disorder) were implanted at Emory University. Holtzheimer et al. (2012) included not only participants with unipolar depression but also patients with bipolar 2 disorder (seven patients). The severity of these participants was similar and the response rates were 41% at 6 months and 92% after 2 years of stimulation (12 of the 17 patients having reached the latter time point by the time of publication). In the last few years, other centers have described similar outcomes, with many case series (Lozano et al., 2012; Puigdemont et al., 2012; Merkl et al., 2013). Puigdemont et al. reported response and remission rates of 87.5% and 37.5%, respectively, in eight patients with severe TRD at 6 months. These dramatic improvements were sustained after 12 months with 62.5% response and 50% remission rates. Another group in Germany implanted six subjects and explored acute effects of stimulation but also described long-term antidepressant effects, with two of the six patients in remission of depression after 6 months of stimulation. Importantly, they did not describe side effects secondary to high-voltage stimulation (Merkl et al., 2013). An early meta-analysis described 6-month pooled response rates of 53.9% and 39.9% at 12 months (Berlim et al., 2014). There was an additional multicenter study conducted in three different sites in Canada that implanted 21 patients (Lozano et al., 2012). Forty-eight percent of patients were responders at 6 months. This study corroborated the results of previous reports showing that the outcome of SCG DBS was replicable. An industry-sponsored, multicenter study was conducted with the initial intention of
recruiting 200 patients in the United States. The study was halted after a futility analysis determined that the likelihood of it achieving its primary outcome was low (Holtzheimer et al., 2017). At the time of the interruption, 90 patients had been implanted in the sACC. The study involved randomizations to either active (n ¼ 60) or sham (n ¼ 30) stimulation for the initial 24 weeks of the study. The primary outcome was frequency of response (defined as a 40% or greater reduction in depression severity from baseline) averaged over months 4–6 of the double-blind phase. Both groups showed improvement, but there was no statistically significant difference in response during the doubleblind, sham-controlled phase (12 [20%] patients in the stimulation group vs five [17%] patients in the control group). Notably, the response rates improved in the long-term follow-up phase with results that doubled the initial response rates at 18, 24, and 30 months; half of the patients who were in the study showed a positive effect of chronic DBS. The reasons for failure in studies of this kind are multiple and can many times be attributed to patient selection and study design, among other factors (Schlaepfer, 2015). In the sACC, the variations in anatomy and its white matter connections are not evident in standard neuroimaging sequences (Hamani et al., 2009). The difference between responders and nonresponders to DBS is not evidenced by the anatomic coordinates that were used to guide surgical implantation. Instead, effective DBS in the sACC appears to be related to the network involvement and the white matter fibers that not only are related to the local cingulate effects but the connections to other regions involved the “depression network.” This was better understood when the imaging analysis was done using diffusion tensor imaging tractography. Riva-Posse et al. (2014) did a retrospective analysis of the initial 17 subjects who had received open-label stimulation at their center. The authors described that all responders shared a common tractography map. Using models that calculate volume of stimulation with individualized stimulation parameters, each contact had a unique set of white matter fibers. When all the responders were compiled, a clear pattern showed fibers that connected the sACC to the rest of the ACC (via the cingulum bundle), bilateral medial frontal cortices (through the forceps minor), subcortical nuclei, and the thalamus (via uncinate fasciculus and frontostriatal fibers). Once the pattern was identified in the retrospective analysis, a prospective testing of this hypothesis was conducted. Eleven subjects were implanted in the sACC using target selection for the DBS that was based on the connectivity map that was present in responders (Riva-Posse et al., 2018) (Fig. 21.1). This approach resulted in a significant increase in the response rate, with 8/11 (73%) of patients
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findings in recent years point toward cingulate cortex as a primary dynamic modulator within a larger, multicomponent mood regulation system. Its relations to different frontal and parietal regions, insula, and subcortical nuclei, and the individual network contributions to the complex syndromal presentations seen in clinical practice emphasize the continued need for cingulate research.
REFERENCES
Fig. 21.1. Structural connectivity-based target selection. (A) Individualized deterministic tractography target selection in one subject: optimal target location within sACC region with modeled stimulation impacting necessary fiber bundles for effective DBS. Blue sphere represents the estimated volume of activated tissue with standard stimulation settings. The deterministic tractography shows fibers traveling through that sphere, with cable models in the cingulum bundle, the forceps minor, the uncinate fasciculus, and frontostriatal fibers. (B and C) Postimplantation computerized tomography (CT) merged on presurgical MRI showing sagittal (B) and coronal (C) views of the location of the DBS lead in the subcallosal cingulate. CB, cingulum bundle; FM, forceps minor; F-St, frontostriatal fibers; UF, uncinate fasciculus.
showing a decrease in the 17-item Hamilton Depression Rating Scale scores by more than 50% after 6 months and an additional subject becoming a responder at the 12-month time point (9/11: 82% response rate). The four-bundle white matter blueprint was reliably defined and precisely implanted in each of the 11 subjects. A group probabilistic tractography blueprint for individualized, patient-specific, deterministic tractography targeting confirmed the retrospective findings. This targeting method confirms and validates the conceptualization of a network model with the cingulate as a hub where engagement of remote areas of the depression network is needed for the adequate antidepressant effect.
CONCLUSIONS The cingulate cortex, in its many functional and anatomic subdivisions, plays an essential role as a hub in the center of neural networks that are involved in manifestations of mood and emotion. The anatomic and functional
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