Ann. N.Y. Acad. Sci. ISSN 0077-8923

A N N A L S O F T H E N E W Y O R K A C A D E M Y O F SC I E N C E S Issue: Translational Neuroscience in Psychiatry

In pursuit of neuroimaging biomarkers to guide treatment selection in major depressive disorder: a review of the literature Marc S. Lener and Dan V. Iosifescu Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York Address for correspondence: Marc S. Lener, M.D., Department of Psychiatry, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029. [email protected]

Over the last few decades, neuroimaging techniques have advanced the identification of structural, functional, and neurochemical brain abnormalities that are associated with the increased risk, clinical course, and treatment outcomes of major depressive disorder (MDD). This paper reviews specific neuroimaging abnormalities that, on the basis of early studies, may discriminate between MDD patients who do or do not respond to current therapeutic modalities, such as antidepressants, cognitive behavioral therapy, or novel therapies. Differences in gray matter volume, white matter coherence, brain activity via structural and functional magnetic resonance imaging techniques, and concentrations of specific brain metabolites (as measured with magnetic resonance spectroscopy), are potential biomarkers discussed in this review. Given the heterogeneity of MDD, larger, multisite studies with increased statistical power will be needed to identify more precise imaging biomarkers of treatment response in MDD. Keywords: depression; major depressive disorder; treatment-resistant depression; neuroimaging; voxel based; structural; morphometry; white matter; functional; resting state; SPECT

Introduction Among patients who develop major depressive disorder (MDD), 15–30% will develop a chronic, unremitting depression despite trials of multiple antidepressant medications.1 This poses a significant clinical challenge given that most common antidepressant treatments (such as selective serotonin reuptake inhibitors (SSRI)) and psychotherapies (such as cognitive behavioral therapy (CBT)) are slow to take effect, requiring 6–8 weeks or longer for clinical benefit even among patients who ultimately improve.2 As a consequence, patients frequently suffer from ongoing emotional distress, financial and social difficulties resulting from impaired functioning, negative impact on comorbid medical illness, and suicidality.3 Second, approximately 12–30% of individuals with MDD who fail to respond despite two or more antidepressant treatments during the depressive episode (defined as treatment-resistant depression (TRD)) have a disproportionately high rate of morbidity.4–8 Unfortunately, the absence of a reliable clinical measure to

guide the diagnosis or the next-step treatment selection in MDD (or TRD) significantly limits the ability of clinicians to address these severe problems in a more timely manner. Not surprisingly, the discovery of novel biological targets and the development of more effective and rapid treatment paradigms for MDD has become a priority for the field and for the National Institute of Mental Health (NIMH).9,10 Psychiatric research has struggled to identify biological markers (also known as biomarkers) to be used as reliable and valid measures of psychiatric illness and predictors of treatment outcome. In particular, biomarker identification for MDD has become an important focus of the NIMH and pharmaceutical industry9 and advances in neuroimaging techniques and other neurobiological investigations in MDD. In a recent study using fluorodeoxyglucose (FDG) positron emission tomography (PET), a specific metabolic signature in the insula was found to discriminate between responders to CBT and responders to antidepressants.11 If replicated, this result may become a major finding and a potential doi: 10.1111/nyas.12759

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unique neuroimaging biomarker. However, in this study there were too few patients who completed and survived analysis to account for the vast clinical and neurobiological heterogeneity. A more likely scenario is that neuroimaging biomarkers will enhance detection of vulnerable, protective, and treatment-responsive groups in conjunction with clinical measures as well as genetic and epigenetic biomarkers. It is also possible that biomarkers and specific neuroimaging-defined mechanisms will not describe MDD as a whole, but rather subtypes of MDD or even syndromes that have been defined within the NIMH-proposed Research Domain Criteria (RDoC).12 While a variety of biomarkers have been studied in an effort to (1) enhance the precision of the diagnosis of MDD and to (2) increase the ability to select efficacious next-step treatments, this review focuses primarily on the second category (i.e., biomarkers of treatment efficacy). We will present data from selected neuroimaging studies in an effort to summarize biomarker investigations, provide insights into what has been learned from these studies, and discuss their aid in focusing subsequent studies, particularly in the context of the paradigm represented by the NIMH RDoC. We present data highlighting converging results in the same brain regions and/or brain circuits/networks implicated in MDD, emphasizing (where applicable) the data pertaining to TRD. We chose this approach as evidence suggests that a convergence of multiple imaging and nonimaging techniques may be necessary to identify and confirm specific neurocircuits that underlie this disorder. Methods We used PubMed to search MEDLINE for articles published between January 2000 and July 2014, with an emphasis on papers published after January 2011 by using terms related to depression, MDD, or TRD. We then paired these core terms with secondary search terms—neuroimaging, voxel based, morphometry, structural, white matter, functional, resting state, PET, single-photon emission computed tomography (SPECT), spectroscopy, magnetic resonance spectroscopy (MRS)—and excluded studies that did not include an MDD group. We primarily focused on patients between the ages of 18 and

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65 years, with exception to three studies of adolescents (12–19 years old) with MDD (one longitudinal study and two cross-sectional studies that were compared with findings from adult MDD), one study of adults with MDD (>18 years old), and four crosssectional studies of the elderly (>65 years old) with MDD. The reference lists of selected studies were additionally searched manually for relevant studies. Where applicable, reviews and meta-analyses were used to summarize findings earlier than January 2011, particularly for studies identifying neuroimaging biomarkers of illness (which was a secondary focus of this manuscript). A more in-depth review of the literature was performed for studies identifying neuroimaging biomarkers of treatment response. Two researchers screened each abstract of the retrieved articles. The articles were included if the abstract referred to primary data collection as a part of the design and if end points of the study were pertinent to clinical outcomes. Brain regional structural abnormalities implicated in MDD An accumulating body of literature investigating patients with MDD has demonstrated a diffuse pattern of gray matter volume abnormalities. Metaanalyses of voxel-based morphometry (VBM) studies investigating patients with MDD as compared to healthy controls (HC) most robustly and notably implicate a significant gray matter volume reduction within the prefrontal cortex (PFC) (including the dorsolateral PFC (dlPFC), medial PFC (mPFC), ventrolateral PFC (vlPFC), and orbitofrontal cortex (OFC)) and limbic areas (such as the hippocampus, amygdala, and the anterior cingulate cortex (ACC)).13–18 It is posited that these regional volumetric changes result in abnormal functioning within a larger network of neural circuits.19 Although the exact neuropathology associated with gray matter volumetric reductions found in patients with MDD is not well understood, postmortem neuropathological studies have shown evidence of a decrement in glial cell quantity, neuron cell size, synaptic density, and neuronal cell quantity in late stages of the illness.20–24 Given that the majority of structural imaging investigations in MDD are cross-sectional studies, it is not known whether these abnormalities confer susceptibility

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to the development of MDD, represent a pathological consequence or compensatory change after depressive episodes, or are a direct result of medication. In a VBM study comparing MDD patients with HC groups (with and without a family history of MDD), the HC group with a positive family history demonstrated a significantly smaller hippocampal (P = 0.042; k = 597; T = 3.20) and dlPFC (P = 0.031; k = 128; T = 3.32) gray matter volume compared with the HC group without a family history.25 Although the at-risk HC group surprisingly demonstrated a significantly greater reduction in right hippocampal volume compared with patients with MDD (P = 0.017; k = 1156; T = 3.54), the MDD group exhibited smaller volumes in the ACC, dorsal mPFC (dmPFC), and the basal ganglia (all P < 0.049) compared with both HC groups, suggesting that illness vulnerability might be associated with volumetric reductions in the dmPFC and hippocampus. Of note, the hippocampus is a particularly sensitive brain region in patients with MDD; hippocampal volumetric decreases have been reported in association with the duration of illness and number of depressive episodes.26,27 Such hippocampal volumetric reductions in MDD patients have been shown to associate with poorer response to antidepressant treatment,28–31 are greater in magnitude in unmedicated than in medicated MDD patients,32 and may be reversed in patients who receive electroconvulsive therapy (ECT).33 In a longitudinal study of adolescent patients at risk for psychiatric illness, volumetric changes in the hippocampus (e.g., attenuated growth of the hippocampus; F = 6.79, P = 0.011), amygdala (F = 7.61; P = 0.007), and putamen (F = 5.69; P = 0.020) were associated with depression onset, suggesting that developmental abnormalities of the hippocampus and related limbic areas may confer risk of developing MDD.34 To date, only a handful of studies have investigated structural brain abnormalities in association with treatment response in patients with MDD (Table S1). One approach consists of cross-sectional comparisons between MDD patients with depressive episodes in remission and MDD patients with highly recurrent depressive episodes (e.g., Refs. 28, 31, and 35) or MDD patients currently depressed.36–38 Such studies can help identify markers of resilience (remitted depression) as well as markers of unfavorable clinical course (as in 52

TRD). In one study, patients with remitted (as opposed to recurrent) depression demonstrated an increased volume in the subgenual PFC (F = 4.32; P = 0.02) as compared to HCs, suggesting that this regional volumetric change may serve as a dual marker for susceptibility and antidepressant treatment response.35 Regarding anterior insular cortical volume in patients with MDD, there have been conflicting findings with respect to volumetric alterations (increased or decreased) and discriminating patients with recurrent and remitted depression.36,38,39 However, in one study that used both a cross-sectional and longitudinal study design, a volume reduction in the insula was associated with melancholic depression and slower treatment recovery (r = –0.39; P = 0.001).40 In a longitudinal structural magnetic resonance imaging (MRI) study conducted over a 1-year follow-up period, MDD patients who had smaller hippocampal volumes were more treatment refractory and had more relapses than MDD patients whose depression had remitted.29 This was replicated by the same group in a 3-year follow-up study of a different MDD patient cohort.30 In other prospective longitudinal studies, antidepressant treatment response has been associated with larger hippocampal volume after improvements of depression scores over a 5-week treatment period,41 larger ACC39 and hippocampal volumes42 over an 8-week treatment period, and a larger OFC volume in association with a 6-month remission during a 12-month treatment period,43 whereas a nonresponse over a 6-week antidepressant treatment period has been associated with reduced gray matter volume in the left dlPFC.44 The timeframe is important, since volumetric changes over short intervals (5 weeks) may have different biological substrates than changes occurring over 6–12 months. Moreover, short-term volumetric reductions are more likely to represent changes in dendritic arborization, while long-term volumetric changes are more likely to represent changes in underlying cellular populations. In a recent meta-analysis of neuroimaging predictors of treatment response, a reduction in hippocampal volume (standard mean difference: –0.81, 95% CI: –1.36–0.26) was a significant predictor of a lower likelihood of improvement and poorer treatment response.45 Taken together, increased frontal (OFC, subgenual PFC) and hippocampal volumes may

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portend a greater probability of treatment response, whereas decreased frontal (dlPFC), hippocampal, and insular volumes may predict a lower probability of treatment response with antidepressant therapy. In a high-field MRI study of patients with remitted and nonremitted depression compared with HCs, a machine-learning approach was used to make an MDD diagnosis, with an accuracy of between 67.39% (P = 0.01) and 76.09% (P < 0.001), and to distinguish between the two subgroups of MDD, with an accuracy of 69.57% (P = 0.006).46 This further supports that an abnormal network of brain circuits underlies MDD and that a pattern of volumetric abnormalities particular to MDD can be identified using machine-learning algorithms. Structural connectivity in MDD and TRD The investigation of white matter abnormalities in patients with MDD was initially conducted on the basis of global measures of variations in MR signal intensity (“white matter hyperintensities”) and demonstrated an increased incidence of total brain white matter lesions (WMLs) in elderly MDD subjects47,48 and in some,49 but not all, younger MDD cohorts.50,51 Recognition of the increased prevalence of brain WMLs in MDD has led investigators to describe vascular depression, a subtype of MDD characterized by the presence of cerebrovascular disease (demonstrated on neuroimaging scans by brain WMLs) and poor connectivity between limbic and cortical regions involved in emotional regulation.47,52 In some studies (Table S1) the presence of brain WMLs in MDD subjects was associated with lower rates of response to antidepressant treatment, compared with MDD subjects with no WMLs;51,53 higher rates of irritability and anger attacks;54 as well as higher rates of relapse in long-term follow-up.55 The studies using global measures of WMLs did not inform, however, on the specific circuits affected in MDD; this lack of specificity may also explain the negative results. These limitations were, in part, overcome by the advent of diffusion tensor imaging (DTI), an imaging technique that utilizes properties of water diffusion to measure the integrity of brain tissue. A growing body of DTI studies in MDD has been subject to extensive reviews56 and meta-analyses57 suggesting that microstructural changes in cortical– subcortical white matter in patients with MDD may give rise to a “disconnection syndrome” within

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key frontolimbic neural circuits and contribute to the emergence of depression. Fractional anisotropy (FA), a measure of white matter coherence (and an indirect measure of the integrity of white matter circuits), is reduced as a result of impairments in the myelination of axons or axonal membranes and/or of a decreased density of axons. FA reductions have been found in frontal regions (e.g., superior and middle frontal white matter, and body/genu of the corpus callosum),56–61 white matter tracts interconnecting limbic areas (e.g., anterior cingulum, fornix, and uncinate fasciculus),58,62,63 and thalamocortical white matter relays57,64 primarily in elderly patients, but also in middle-aged and young patients with recurrent MDD. In addition to prior work showing gray matter volumetric changes within regions that are interconnected by these abnormal white matter tracts,13,25,65 this body of DTI studies is consistent with a dysfunctional neural network66 in depressed patients. However, there are multiple other studies63,67–70 showing no significant differences in FA or other measures of white matter coherence between MDD and matched HCs. It is likely that subjects with significant white matter abnormalities may represent a subgroup of MDD subjects (more prevalent in TRD, as shown below) and that studies of large numbers of treatment-naive MDD subjects may result in a dilution effect. Although the presence of heterogeneity within patients diagnosed with MDD was posited nearly a half century ago,71 these results highlight the importance of revisiting clinical heterogeneity to identify subgroups of patients who may have different clinical, genetic, neurobiological, and treatment effectiveness profiles.72–79 There are fewer structural imaging studies that investigate patients with TRD than MDD.80–86 Although similar frontolimbic abnormalities are seen in patients with TRD and in those with treatment-responsive MDD,83,84 lesions of specific white matter tracts appear to be more severe in TRD patients.85 WMLs in circuits connecting frontostriatal80 and limbic areas appear to be more severe in TRD, particularly in the hippocampus,81,82 and may aid in distinguishing patients who are treatment resistant from those who are treatment responsive. In a whole-brain DTI study of patients with TRD, remitted recurrent MDD, first-episode MDD, and HCs, it was found that patients with TRD had significant FA reductions in white matter within the ventromedial PFC (vmPFC) as compared to the patients

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with remitted recurrent MDD (P = 0.03).86 In the analysis of prefrontal cortical white matter, Hamilton Depression Rating Scale (HDRS or HAM-D) scores and the number of previous episodes were significant predictors of decreased FA values, accounting for 12% of the variance. Therefore, a frontolimbic disconnection, as demonstrated by reductions in FA within select frontal, striatal, and limbic regions, may be most robustly associated with patients who have unremitting or treatment-resistant forms of MDD. Several studies have investigated white matter abnormalities as a predictive biomarker of the efficacy of diverse treatments, including antidepressants,87 deep-brain stimulation (DBS),88,89 and transcranial magnetic stimulation (TMS).90 In a DTI study of a large sample (n = 74) of elderly MDD patients over 60 years of age, higher FA in the superior frontal gyri and bilateral ACC correlated with nonremission over a 12-week course of sertraline, suggesting that these regions may be involved in the mechanism of antidepressant response and that their abnormal FA can predict both the risk of depression and SSRI treatment response.87 In contrast to this finding, in another large but younger sample of MDD patients, altered connectivity for the cingulum part of the cingulate and stria terminalis tracts significantly predicted remission with 62% accuracy using a cross-validated logistic regression model.91 This suggests that dysconnectivity in MDD may exist within a specific combination of tracts (as opposed to just one), allowing for more accurate treatment prediction. Two overlapping white matter tracts that were previously shown to be effective targets of DBS (subcallosal cingulum and the anterior limb of the internal capsule) were examined to determine shared white matter connections. In a probabilistic connectivity map, Gutman et al. found shared regions within the frontal pole, medial temporal lobe, nucleus accumbens, dorsal thalamus, and hypothalamus,88 suggesting that the efficacy of DBS may rely on correcting central dysfunctional circuits. In an exploratory DTI study of 30 younger patients with TRD enrolled in a double-blind, randomized repetitive TMS (rTMS) study, baseline FA reductions in the left middle frontal gyrus predicted an improvement of depressive symptoms in the active (rTMS) group but not in the sham group.90 FA values were later increased after rTMS 54

treatment. This suggests that rTMS may give rise to a corrective process in specific white matter tracts that can be clinically tracked through a pre- and postprocedure MRI. Future DTI studies will be necessary to replicate and extend these initial results. Functional dysconnectivity in MDD Functional brain imaging techniques such as perfusion SPECT and functional MRI (fMRI) allow for interrogation of functional brain activity in patients with psychiatric disorders. In this review, we focus on studies using fMRI in MDD patients compared with HCs, and within MDD patients as a discriminator of treatment response. Functional MRI is an MRI-based method that measures the blood oxygen–level dependent response, a proxy for regional neural activity. In studies of depression, fMRI can be used in research paradigms designed to test abnormal brain activation patterns in association with a specific cognitive and/or emotionally salient task or can be used to test abnormalities of functional connectivity unrelated to a specific task, known as “resting-state” brain activity. Previously published reviews and meta-analyses of task-related and resting-state fMRI studies have found abnormalities in frontal (dlPFC, vlPFC, and mPFC), limbic (ACC, amygdala), thalamostriatal (thalamus, globus pallidus, putamen, and caudate), and insular cortical brain activation across domains of emotion processing, cognitive control, affective cognition, reward processing, and resting-state functional connectivity.92–95 Owing to the centrality of emotional dysregulation found in depressed patients, the majority of task-related fMRI studies in depression have targeted emotion-processing brain networks through the induction of both implicit (e.g., automatic) or explicit (e.g., effortful) behavioral responses to affect-laden stimuli. The consensus among studies has shown that, relative to HCs, patients with MDD show hyperactivation within limbic regions and hypoactivation of cortical systems in response to emotionally salient stimuli,96–101 a pattern that is, in part, reversed with antidepressant treatment.102 This altered activation in top-down (or high-level cognitive interpretation of emotion) and bottom-up (or encoding of affect-laden stimuli to form a perception) prefrontal–subcortical circuitry has been suggested to underlie the failure to regulate mood in patients with a range of psychiatric disorders,

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such as MDD,103–105 bipolar disorder,106 anxiety disorders,107,108 posttraumatic stress disorder,109 and borderline personality disorder.110 This relative lack of specificity is unsurprising given that each patient group exhibits a varying form of emotion dysregulation. What may underlie the differences between these groups beyond the environmental and contextual aspects are the differences in the degree of recruitment of cognitive regulatory networks,108,110,111 differential involvement of reward networks,109 or a biologically driven fluctuant pattern as seen with bipolar disorder.106 Two other important task-related fMRI paradigms that have been used to investigate functional brain networks that underlie motivational anhedonia and ruminative brooding in patients with MDD include those that examine reward processing and self-referential processing, respectively. Reward-processing fMRI paradigms detect underlying reward network dysfunction to explain motivational and consummatory anhedonia, hallmark features of the symptom cluster in depression. In response to positively valent images (i.e., images that evoke pleasant emotion, such as images of food or sexually arousing images of people), MDD patients showed decreased activation in the mPFC and increased activation in the inferior frontal cortex, ACC, thalamus, putamen, and insula.112 In a study investigating neural responses to positive- and negative-valence stimuli in a group of depression patients compared with HCs, the vmPFC appeared to demonstrate a double-dissociation pattern of activity successfully delineating the HC from the MDD group.113 In a study using a monetary incentive delay task, patients with MDD showed significantly weaker responses to gains in the left nucleus accumbens and the caudate bilaterally. Furthermore, volumetric reductions in the caudate were associated with anhedonic symptoms and depression, suggesting a major role for basal ganglia and nucleus accumbens activity in mediating consummatory anhedonia.114 In patients with MDD (as well as those with anxiety disorders), rumination is persistent, repetitive, and self-critical in nature, characteristic of a depressive episode,115 and may represent a failure to regulate affect or emotional distance when recalling negative autobiographical memories.116 Studies that have used various strategies to induce rumination

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demonstrate hyperactivity in the subgenual ACC (sgACC) and dmPFC116–118 and hypoactivity in the dorsomedial thalamus and ventral striatum.119 In a study conducted by Yoshimura et al., activity in the rostral ACC was functionally connected to the mPFC and the amygdala during rumination and mediated the correlation between mPFC activity and depressive symptoms, suggesting that rostral ACC activity may lie at the centerpiece of limbic system dysfunction in depression.118 Finally, in a study that required subjects to passively examine negative pictures and actively reappraise them, MDD patients, but not HCs, exhibited a failure to reduce activity in the vmPFC, ACC, and other regions while both looking at negative pictures and reappraising them, demonstrating a failure to normally downregulate activity broadly within the default-mode network.120 Studies using fMRI to predict treatment response in patients with MDD have used task-related, resting-state, and combined approaches in search of a signature of functional brain activation unique to responders. Although not supported by all studies,121–124 the most consistently replicated finding in task-related fMRI studies of MDD patients is that of increased ACC activation in response to negative stimuli as a marker of improvement in depressive symptoms and a predictor of an antidepressant response to venlafaxine,125 fluoxetine,39 and a variety of other antidepressant medications126,127 (Table S1). This finding reinforces previous and influential work demonstrating a link between ACC hypermetabolism and antidepressant treatment response in patients with MDD.128 In a recent metaanalysis of 23 fMRI studies investigating neural activity before and after antidepressant treatment, 19 of the studies demonstrated that increased neural activity in the rostral ACC robustly (Cohen’s d value: 0.918) predicted better antidepressant treatment response across studies and across different interventions, such as SSRIs, atypical antidepressants, ketamine, sleep deprivation, and rTMS.129 Moreover, ACC hyperactivation has been shown to be a potential biomarker of an antidepressant response to ketamine using other techniques measuring neural activity, such as magnetoencephalography (MEG). In a recent MEG study (n = 11 MDD and 11 HCs), presentation of fearful faces resulted in robust increases in pretreatment ACC

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activity that positively correlated with a subsequent rapid antidepressant response to ketamine.130 In a subsequent MEG study by the same group using a working memory task to engage the pregenual ACC, patients who showed the least engagement of the pregenual ACC in response to increased working memory load showed the greatest symptomatic improvement within 4 h of ketamine administration (r = 0.82; P = 0.0002; false discovery rate 75% correct classification of response, >70% correct classification of remission).133 In 18 F-FDG PET studies, responses to antidepressants were associated with hypoactivity in the midbrain,134 whereas nonresponse to CBT has been associated with pretreatment hypermetabolism in a region located between the pregenual and subgenual cingulate cortices in a small group of MDD patients receiving either CBT or venlafaxine.135 Although studies have remained inconsistent, it has been proposed that, while ACC hyperactivity predicts positive response to antidepressant medication, ACC hypoactivity may predict response to CBT.45,136,137 In contrast to these findings, a recent FDG PET study found that hypometabolism in the right anterior insula was associated with remission after CBT and poor response to escitalopram, while insula hypermetabolism was associated with remission after escitalopram and poor response to CBT.11 This has been the most compelling argument to date in support of a predictive neuroimaging biomarker with the ability to discriminate by treatment type. Given the small sample size of this study and the discordant results with the previous literature, further replication studies with larger sample sizes would be necessary to confirm the predictive abilities of this biomarker. 56

Neuroimaging studies of biochemical abnormalities in MDD In contrast to PET, which requires a radiotracer tagged specifically to a known molecular target, MRS is an imaging technique that measures the abundance of specific neurochemicals using their specific magnetic resonance signatures. MRS studies in psychiatric disorders most often involve proton (1 H) and phosphorus (31 P) spectroscopy. The former (1 H-MRS) is an important method to identify biochemical regional brain abnormalities by measuring concentrations of certain metabolites associated with neuronal death (e.g., creatinine (Cr), N-acetyl aspartate (NAA), myo-inositol (MI), and choline (CHO)); 1 H-MRS is also used to identify abnormalities in neurotransmitter systems implicated in the pathophysiology of MDD (e.g., gamma-aminobutyric acid (GABA), glutamate (Glu), glutamine (Gln), or the combined Glu–Gln levels (also known as Glx)).138 Studies using 1 H-MRS in patients with MDD have demonstrated higher CHO and CHO/Cr ratios in the basal ganglia, lower Glx levels in the frontal lobes,139–142 and reductions of Glx/Cr and Gln/Cr ratios in the hippocampus.143,144 In a study of MDD patients, PFC concentrations of NAA/Cr were lower in moderately depressed patients when compared to mildly depressed patients and HCs,145 suggesting that reductions of NAA/Cr in the PFC may be larger in more severe depressive episodes, potentially explaining studies that have shown the contrary.139 Finally, reductions in NAA/Cr have been shown to renormalize in association with antidepressant treatment within the basal ganglia146 and mPFC,147 while an increase in CHO compounds were found in association with ECT in the hippocampus after five ECT treatments;148 hippocampal CHO appears to eventually decrease to pretreatment levels after long-term (20 months) follow-up.143 These studies suggest that current treatments for depression may be associated with increased neuronal trophicity and lipid membrane turnover (neuroplasticity). Clearly, further studies will be necessary to determine whether these metabolite changes can predict treatment response. Studies using 1 H-MRS can also detect levels of neurotransmitters associated with glutamate metabolism (such as GABA, Glu, and Gln). GABA concentrations in depressed patients were

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reportedly lower in the occipital cortex149 and more pronounced in a melancholic subtype of depression.150 Occipital GABA levels appeared to increase after successful antidepressant treatment with SSRIs151 or ECT.152 The methodological limitations in these earlier studies (i.e., surface coil used), which restricted GABA measurements to the occipital cortex, were surpassed in subsequent studies of unmedicated MDD patients showing reductions of GABA in the ACC and associations with anhedonia in an adolescent group,153 and reduced GABA in the dorsomedial and dorsal anterolateral PFC154 in adults. Hasler et al. also found reduced Glx levels in MDD patients in the same brain regions as well as within the vmPFC, suggesting that regional alterations of inhibitory/excitatory neurotransmission may exist in MDD patients. Several studies have demonstrated reductions in Glu in the PFC and ACC,155,156 coinciding with a growing number of clinical studies that show a rapid antidepressant response to ketamine, an NMDA glutamate receptor antagonist.157–159 It is yet to be determined whether specific levels of Glu or an excitatory/inhibitory neurotransmitter system imbalance in the PFC and/or ACC can help predict the subset of patients who may be particularly responsive to ketamine or more established antidepressant treatments.160 An emerging and developing hypothesis161,162 that a relationship exists between abnormal inflammatory states and the development of MDD and other stress-related disorders has prompted studies of MDD patients to examine peripheral inflammatory markers and in vivo neuroimaging markers of oxidative stress, specifically of glutathione (GSH), the most prevalent antioxidant in the brain. Dysregulation of the GSH system has been shown in animal models of depression163 and may be related to reductions in glutamatergic activity at the glutamate N-methyl-d-aspartate (NMDA) receptor and attenuation in the production of neurotrophic factors as demonstrated in various neurodegenerative disorders. Although clinical studies have shown limited efficacy, this pattern of abnormality has been reversed through the administration of the GSH precursor N-acetylcysteine.164,165 Owing to greater signal separation techniques,166,167 1 H-MRS detection of GSH has led to a growing number of studies measuring GSH as a marker of oxidative stress in patients with MDD. Reductions in GSH have been associated with MDD168 and, more

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specifically, anhedonia in patients with MDD.169 However, the use of 1 H-MRS GSH has not been promising as a treatment biomarker. Despite a 6-week escitalopram treatment that resulted in an improvement of depression, concentrations of GSH did not change (P = 0.08) following treatment course.168 Further studies using other inflammatory markers may become more fruitful in determining an adequate treatment biomarker. 31 P-MRS is used to determine cerebral levels of high-energy phosphates, including phosphocreatine (PCr) and nucleoside triphosphates (NTPs) such as adenosine triphosphate (ATP), a key intracellular energy carrier and main component of the NTP peaks. MDD patients exhibit abnormalities in brain energy metabolism, reflected by decreases in NTP levels in the frontal lobe and basal ganglia, particularly the ␤-NTP fraction most closely reflecting ATP levels.170–172 While ATP levels are decreased, levels of PCr, a reservoir for high-energy phosphates that can be used to generate ATP, are increased in MDD patients.173 Taken together, these results suggest that the NTP decreases reflect reductions in cellular bioenergetic metabolism, consistent with the previously discussed alterations in brain phospholipid metabolism reflected by increased Cho levels. Both sets of data suggest that mitochondrial dysfunction may play a critical role in the pathophysiology of MDD. Importantly, bioenergetic metabolism has been correlated with the response to antidepressant treatment. MDD subjects who responded to antidepressant treatment had lower NTP and higher PCr levels at baseline than treatment nonresponders.172,173 Baseline PCr was found to be a potentially useful predictor of antidepressant response with 83% sensitivity and 75% specificity.173 Also, during antidepressant treatment, total NTP and ␤-NTP increased while PCr decreased in treatment responders, but these changes were not seen in nonresponders. Conclusion and future directions Major brain regions implicated in structural and functional studies of patients with MDD are summarized in Table 1 and depicted in Figure 1. Given the convergence of structural and functional brain abnormalities within frontolimbic brain regions, symptomatologic manifestation of MDD is likely owing to an interplay of molecular and cellular processes causing sequential changes to specific

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Table 1. Summary of select potential neuroimaging biomarkers of MDD and MDD treatment prediction Gray matter volume Brain regions Prefrontal regions

Limbic regions

Hippocampus Striatal regions

Insular cortex

MDD

White matter (fractional anisotropy)

Treatment biomarker

Treatment biomarker

MDD

⇓dlPFC ⇓vlPFC ⇓mPFC ⇓OFC ⇓ACC ⇓Amyg

⇑OFC

⇓Sup. frontal ⇓Mid. frontal ⇓Body/genu of CC

⇑Sup. frontal ⇓L mid. frontal

⇑ACC

⇑Ant. cingulum

⇓Hippo

⇑Hippo

⇓Ant. cingulum ⇓Fornix ⇓UF ⇓Fornix ⇓Thalamo-cortical WM relays

⇓Ant. insula

⇓UF

Brain activation MDD

Treatment biomarker

⇓dlPFCa ⇓vlPFCa ⇓mPFCa,b ⇑dmPFCc ⇑ACCa,b,c ⇑Amyga

⇑ACC,a,b AD ⇓ACC,a,b CBT

⇑Thalamusb ⇓Thalamusc ⇑Putamenb ⇓Caudateb ⇓NAccb,c ⇑Insulab

a Associated

with emotion processing. with reward processing. c Associated with self-referential processing. b Associated

neural systems involved in emotion regulation, reward, and cognition that can be observed in response to a task or at rest. Peripheral systems, such as adrenal and inflammatory systems, may also be involved in mediating microinjuries to vulnerable brain regions, causing metabolic changes and subsequent imbalances to excitatory and inhibitory

neurotransmitter systems that lead to functional brain abnormalities. Neuroimaging modalities have shown promise in detecting abnormalities associated with MDD and with response to specific treatments. In studies examining the influence of genes on brain volume in MDD, growing evidence supports a role of

Figure 1. Brain regions implicated in structural and functional studies as potential illness and treatment biomarkers in major depressive disorder. (A) Lateral view of the cortical surface of the brain, showing the dorsolateral prefrontal cortex (dlPFC), ventrolateral PFC (vlPFC), orbitofrontal cortex (OFC), and insular cortex (ins). (B) Mid-sagittal view of the brain, showing the medial PFC (mPFC), anterior cingulate cortex (subgenual (sgACC), rostral (rACC), dorsal (dACC)), striatum (stria), amygdala (Am), and hippocampus (Hp). Regions are implicated as a potential (a) illness or (b) treatment biomarker. Of note, the striatum, amygdala, and insula are deep to the region indicated and are not seen on the specimen. Photographs of anatomical specimens courtesy of T.P. Naidich, M.E. Fowkes, and C.Y. Tang.

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the BDNF Val66Met polymorphism on gray matter volumetric reductions (e.g., Refs. 174 and 175) and COMT Val158Met on white matter volumetric reductions.176,177 Therefore, the development of treatment-predictive neuroimaging biomarkers will likely require an integrated approach among different imaging modalities and linking them to peripheral biomarkers (e.g., inflammatory), genetic and epigenetic biomarkers, and clinical symptoms (e.g., anhedonia) or domains (e.g., reward processing). Future directions of research determining biomarkers of MDD have already begun as several groups have formed collaborations in assembling large, multicenter data sets of patients with MDD, such as the initiatives Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC; http://embarc.utsouthwestern.edu) and International Study to Predict Optimized Treatment in Depression (iSPOT-D) (http://www.brainresource. com/research/ispot/ispotd). These large studies using multimodal neuroimaging may help circumvent the limitation of low-powered studies to examine the association of biological and psychological domains of depressive illness with specific neuroimaging biomarkers. Conflicts of interest Dr. Lener declares no conflicts of interest. In the past three years, Dr. Iosifescu has consulted for Avanir, Axsome, CNS Response, INSYS Therapeutics, Lundbeck, Otsuka, Servier, and Sunovion, and has received research support through the Icahn School of Medicine at Mount Sinai from Alkermes, Astra Zeneca, Brainsway, Euthymics, Neosync, Roche, and Shire. Supporting Information Additional supporting information may be found in the online version of this article. Table S1. Reviewed studies of neuroimaging biomarkers of treatment response. References 1. Mueller, T.I., A.C. Leon, M.B. Keller, et al. 1999. Recurrence after recovery from major depressive disorder during 15 years of observational follow-up. Am. J. Psychiatry 156: 1000–1006.

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