Skip to main content

Clinical application of DEX/CRH test and multi-channel NIRS in patients with depression



To reduce the number of patients with depression, biomarkers for clarifying psychiatric disorders are warranted. Numerous candidates have been proposed; however, near-infrared spectroscopy (NIRS) with multi-channel probes and a dexamethasone/corticotropin-releasing hormone (DEX/CRH) test are still surviving for practical demand. Thirty-one outpatients with depressed moods were analyzed using both biological tests.


The non-suppressors, as indicated by the DEX/CRH test, exhibited a high severity on the Hamilton Depression Scale and severe anxiety on the State Trait Anxiety Scale. In addition, a unique response was identified via NIRS in the same group suggested by the DEX/CRH assessment.


The results obtained from these biological tests did not fit well with the category defined by operative diagnostic criteria, such as the Diagnostic and Statistical Manual of Mental Disorders or The International Classification of Diseases. Thus, it is critical that the utility evaluations of candidate biomarkers not be assessed by comparisons with the categorized criteria for a specific psychiatric disorder.

Trial registration UMIN000013214, Registered 21 February 2014


A depressed mental state is caused by non-specific mental disorders, such as a mood disorder, schizophrenia, substance abuse, a personality disorder, or nearly every psychiatric condition. If it is more prolonged than expected, several types of costs related to individuals with depressed moods will increase. A recent analysis indicated a 21.5 % cost increment, $173.2 billion (2005) to $210.5 billion (2010), within 5 years, and this cost will be further increased [9]. Another analysis with regard to the global DALY (disability-adjusted life year) indicated that the cost associated with depression would represent the primary cost of all disorders, including physical disorders, in 2030, and it ranked 3rd in 2004 ( These analyses have provided warnings regarding the importance of depression prevention. In addition, to perform effective treatment, a precise assessment based on scientific data comprises a key factor.

Evidence regarding the relationship between depression severity and blood flow measurements scaled by Near-Infrared Spectroscopy (NIRS) is accumulating [30, 36, 41]. NIRS comprises a non-invasive imaging device that uses multiple channels to visualize brain activity. It has been developed as a result of the demand to consider a differential diagnosis in patients with depressed moods despite the administration of antidepressant agents for an extended period. A reliable biological marker is required to identify a specific psychiatric disorder because diagnosis in psychiatry currently relies on expressed symptoms or spoken words. NIRS comprises a device that applies knowledge of neuroscience through the assessment of blood flow in the brain; however, another potential candidate for a reliable biomarker depends on an endocrine imbalance. Particularly in the assessment of disturbed regulation of the hypothalamic-pituitary-adrenocortical (HPA) system, dexamethasone/corticotropin-releasing hormone (DEX/CRH) has been extensively investigated with a view towards the differential diagnosis of depression prior to the development of NIRS. Originally, the dexamethasone suppression test (DST) was used to diagnosis Cushing’s syndrome [22]; this endocrine reaction has also been implemented for the diagnosis of endogenous depression. The current standard method is a DEX/CRH test in combination with the administration of CRH, which has been reported to increase the sensitivity from 60 to 80 % for the classic type of depression [12, 13]. There is an unavoidable burden regarding the application of this method for practical usage; however, these two biological methods have been considerably implemented in previous decades for investigating the biomarkers of depression.

In the current study, we investigated patients with a depressed mental state. Following a diagnosis according to the DSM-IV or psychological assessments, two tests using both endocrine (DEX/CRH) and neuroimaging (NIRS) assessments were applied. The aim of the current work was to identify the relationship between the psychiatric diagnosis based on the expressed symptoms and the assessment of two biomarkers.



The subjects comprised 31 outpatients (15 men and 16 women; mean age: 44.2 years, SD: 12.2 years) with a depressive state, who were recruited from patients who attended the Neuropsychiatry Department of Osaka Medical College. The demographic details of the psychiatric diagnoses are shown in Table 1. We have adopted structured clinical interview for DSM-IV TR (SCID) for clinical assessment [7].

Table 1 Psychiatric diagnoses assessed by the DSM-IV TR for study patients (n = 31)

The severity of the index depressive episode was assessed with the 21-item version of the Hamilton depression rating scale (HAMD) [10, 15], on the admission day (DEX/CRH on the 2nd day and NIRS on the 3rd day). Enrolled patients (n = 31) were not strictly limited along with the HAMD score because all of the participants had the difficulty to live their life. Regarding the self-evaluation tool for anxiety, the STAI, the State-Trait Anxiety Inventory [26, 34], was administered to the participants on admission day. The majority of the patients were medicated with antidepressants; however, we did not control for the class of antidepressant medication. In addition, we assessed the psychiatric disorders of patients along with the SCID method; therefore, psychiatric comorbidities were not controlled.

DEX/CRH test

The DEX/CRH test was conducted according to the method described by Zobel et al. [42]. The subjects were pretreated with an oral dose of 1.5 mg of DEX at 2200 h on the 1st admission day. On the next day, a vein was cannulated at 1400 h to collect blood at 1430, 1500, 1530, 1545, and 1600 h via an intravenous catheter. Human CRH (100 μg) was intravenously administered at 1400 h immediately after the initial blood collection. The plasma concentrations of ACTH and cortisol were measured via radioimmunoassay at SRL Corporation (Tokyo, Japan). The detection limits for ACTH and cortisol were 5.0 pg/ml and 1.0 μg/dl, respectively.

The definition of the subtypes of the cortisol suppression pattern followed previously described criteria [19, 14] and included incomplete-suppressors (DEX/CRH-cortisol ≥5 μg/dl, moderate-suppressors (1 μg/dl ≤ DEX/CRH-cortisol <5 μg/dl), and enhanced-suppressors (DEX/CRH-cortisol <1 μg/dl). We also included a non-suppressor group with cortisol >5 μg/dl at the test onset to reduce the number of incomplete suppressors (onset-cortisol <5 μg/dl).

NIRS measurement

The NIRS measurements were performed using a 22-channel ETG-4000 Optical Topography System (Hitachi Medical Corporation, Tokyo, Japan). This machine used two sets of wavelengths of near-infrared light (695 and 830 nm, respectively) to identify differences in the absorption spectrum, which thus enabled the measurement of oxy-Hb and deoxy-Hb [23]. Seventeen emitter probes and 16 detector probes were plugged into a 3 × 11 array. The distance between the pair of emission and detector probes was 3.0 cm; the measuring area between each pair of detector probes was defined as a “channel.” Probes were placed on the frontal region of the participant. The lowest probes were positioned along the Fp1–Fp2 line in accordance with the international 10–20 system used in electroencephalography.

Activation task

Changes in the hemoglobin concentration were measured during the verbal fluency task [37]. The cognitive activation task was structured to include a 30-s pre-task period, a 60-s task period, and a 70-s post-task period. For the pre- and post-task baseline periods, the participants were instructed to consecutively repeat five Japanese vowels (a, i, u, e, o) aloud. During the task periods, they were instructed to generate as many Japanese words as possible that began with a designated syllable. The initial syllables were presented in counterbalanced order among the participants with each syllable changing every 20 s (0–20 s: /to/, /na/, /a/; 20–40 s:/se/, /i/, /ki/; 40–60 s: /o/, /ta/, /ha/) during the 60-s task period.

Measurement environment

Each participant was seated in a comfortable chair and instructed to remain still to prevent movement artifacts; specifically, head movements, strong biting, or unnecessary eyebrow movements were minimized during the NIRS measurements. The data that clearly contained motion artifacts, based on both our observations and NIRS recordings, were excluded from further analyses.

Statistical analysis

All statistical analyses were performed with JMP Pro® software (Ver. 11.0, SAS Institute Japan Ltd., Tokyo, Japan). Intergroup comparisons were conducted for HAMD, STAI scores and the gained scores from NIRS instrument according to the Kruskal–Wallis test. A value of p < 0.05 was considered statistically significant.


According to the previously described criteria for the division of the DEX/CRH response, the numbers of participants in the four groups divided by the cortisol reaction are indicated in Table 2.

Table 2 Number of patients with depression divided by the DEX/CRH test (n = 31)

First, the association between the reaction in the DEX/CRH test and the severity of depression determined by the HAMD assessment was investigated. The HAMD was recorded by another clinician in a blind manner. There was a difference in the mean value of the HAMD score between the non-suppressors and the other two main groups, incomplete suppressors and moderate suppressors (Fig. 1 Kruskal–Wallis analysis Chi square 7.37, p = 0.06). Second, the STAI, which is a self-evaluation tool for anxiety, significantly represented the characteristic psychological feature of the non-suppressor group compared with the other three groups (Fig. 2 Kruskal–Wallis analysis Chi square 7.58, p = 0.06). Moreover, we compared the results of the NIRS assessment with the reaction of the DEX/CRH test. Similar to the HAMD assessment, the NIRS recording was performed by a lab technician in a blind manner. The results indicated that the values of the center of gravity at the frontal lobe in the non-suppressor group, which comprised the representative values in NIRS, were significantly different compared with the other three groups by post hoc analysis (Fig. 3 Kruskal–Wallis analysis Chi square 7.02, p = 0.07). No significant age difference between the groups that were defined by the two biological methods was found (data not shown).

Fig. 1
figure 1

Diamond plot of the HAMD 21 assessment and DEX–CRH test (n = 31). Kruskal–Wallis analysis Chi square 7.37, p = 0.06. Post hoc analysis non-suppressors, 26.3 ± 3.2, vs incomplete suppressors, 13.7 ± 8.2, p = 0.04, vs moderate suppressors, 14.8 ± 5.8, p = 0.03

Fig. 2
figure 2

Diamond plot of the STAI assessment and DEX–CRH test (n = 31). Kruskal–Wallis analysis Chi square 7.58, p = 0.06. Post hoc analysis non-suppressors, 140 ± 4.0, vs incomplete suppressors, 107 ± 18.8, p = 0.03, vs moderate suppressors, 103 ± 18.1, p = 0.01, vs enhanced suppressors, 109 ± 24.6, p = 0.04

Fig. 3
figure 3

Diamond plot of the NIRS assessment (value of the center of gravity at the frontal lobe) and DEX–CRH test (n = 31). Kruskal–Wallis analysis Chi square 7.02, p = 0.07. Post hoc analysis non-suppressors, 82.0 ± 9.6, vs incomplete suppressors, 58.0 ± 11.4, p = 0.02, vs moderate suppressors, 55.5 ± 12.8, p = 0.02, vs enhanced suppressors, 56.9 ± 13.2, p = 0.04


The current sample size is relatively small to provide a definitive conclusion; however, our current work has an advantage in which we employed cases regardless of the diagnosis of psychiatric disorders. Therefore, there is the potential that the novel scientific findings identified from our sample are consistent across diagnoses based on the operative diagnostic criteria, such as the DSM. In addition, this is the first report to assess confirmed cases with depressed mood by two representative assessment methods for depression.

Regarding the DEX/CRH test, it has been reported that 60–80 % of patients with depression exhibited an increased cortisol response prior to the initiation of treatment [18, 42]. It is partly because all participants in this study received medical treatment prior to the assessment, whereas only 11 % (3/31) of the participants in this study exhibited an increase cortisol response (non-suppressors). Moreover, most previous studies utilizing DEX/CRH test have been based on data obtained from inpatients [18, 20], which implies a clinical subgroup with severe symptoms, whereas our current sample consisted of a depression severity at the outpatient level. Moreover, the group that enrolled the largest sample size did not include data for individuals with a HAMD less than 15 [18], whereas the average HAMD score was 15.6 ± 7.5 in our sample. This difference has a significant impact on the interpretation of our data. The percentage of non-suppressors was not high because of the enrolled mild subtype of depression; however, every non-suppressor patient exhibited a high score on the HAMD assessment (average score of HAMD: 27.8) despite inconsistencies in the psychiatric disorders across three patients (two patients: 296.23 major depressive disorder, and one patient: 300.3 obsessive compulsive disorder). Thus, the DEX/CRH approach should again be considered a potentially useful biomarker for dividing the subclasses of depression.

With regard to psychological aspects assessed by STAI, the Kruskal–Wallis analysis indicated there was no significant difference for the divided subgroups regarding the DEX/CRH test; however, a trend was identified particularly for the non-suppressor group (Chi square 7.58, p = 0.06). Anxiety is a non-specific feature of psychiatric disorders. Therefore, the severity of anxiety is not fundamentally considered to be scaled by a biological method. Although the scale is simply based on a self-evaluation scale, our results indicated the potential finding that anxiety across psychiatric disorders, including depression, is represented by a biological scale, such as DEX/CRH. Anxiety-related stimuli cause a systemic response through neural circuits centered on the amygdala, which enhances the increment of cortisol [38]. Therefore, an association between the altered DEX/CRH result and the anxiety scale is reasonable. A previous study demonstrated that individuals with depression and comorbid anxiety (n = 18) related disorders exhibited a significantly lower cortisol level compared with pure depression patients (n = 36) [40]. The non-suppressor group, which showed higher anxiety, more severe depressive symptoms and a distinguished response in the DEX/CRH, should be regarded as one entity in terms of different biological and psychological reactions. Thus, larger sample sizes, especially for the non-suppressor group, are necessary to clarify the association between the STAI scale and DEX/CRH results.

Accumulating evidence supports the relationship between NIRS and the diagnosis of psychiatric disorders [21, 24, 36]. Thus, NIRS is one of the most attractive tools for adopting a biological assessment for clinical practice. One reason is that the device requires only 10–15 min for each individual if VFT is adopted for the assessment. NIRS is sufficiently easy to adopt, even for adolescents, for the evaluation of psychiatric disorders, such as depression, bipolar disorder, and schizophrenia. General interpretation on VFT is a neurocognitive battery for assessing verbal ability or executive control ability. This battery is widely used for assessing these abilities of patients after stroke, patients with Alzheimer’s disease or Parkinson’s disease [33]. In the current study, we have utilized this battery mainly for recording the measurements of oxy-Hb in the brain, therefore the task performance (the number of words) on each individual were not recorded. The comparison of the performance on VFT between each groups will be of interest. Future work should focus on the task performance across various psychiatric disorders. One of the critical issues regarding the NIRS device is that insufficient data are obtained from the perspective of neuroscience. In an analysis of a larger sample than the current work, our group recently demonstrated that the severity of depression scaled by the HAMD was negatively associated with the integral value of the blood flow at the frontal lobe (n = 43) [16]. This finding was characterized by the point that the assessment was not limited to depressed patients who satisfied the criteria defined by the DSM or ICD. Moreover, several biomarkers, such as DEX/CRH, NIRS and other factors, cannot guarantee the specificity of psychiatric disorders defined by operational diagnostic criteria. The current findings indicated that individuals with a Non-Suppression reaction in the DEX/CRH test exhibited a fairly increased value of the center of gravity at the frontal lobe. A general interpretation regarding the increased value of the center of gravity implies the existence of bipolarity [36]. In specific areas such as the left inferior frontal lobe, [oxy-Hb] was decreased in patients with panic disorders [27, 28]. Although the current work did not focus on a specific channel of NIRS, future research should analyze the data for a specific channel by assessing a larger sample series. Thus, a larger sample size is critical to validate the current work to determine the unknown mechanism of endocrine imbalance and brain response. In addition, the biological data from the mentally healthy controls will serve as a useful reference, although the current design has not been adopted to collect them. Moreover, the length of the duration of mental illness has a possible effect on the result from the biological assessment. This should be considered in future studies.

Biological assessments embedded in the diagnosis of specific psychiatric disorders are proceeding. Candidate methods have been evaluated using the concordance rate for diagnosis defined by the operative criteria to date; however, a universally accepted biomarker has not been identified. Despite substantial efforts to identify a biological marker, the methodological approach for objective evaluation is incorrect in terms of the fact that the concordance rate between biological assessments and psychiatric diagnoses based on the artificial operative system is not sufficient to satisfy practical demands. For example, a genetic approach has indicated considerable overlap between schizophrenia and bipolar disorder [6, 29]; therefore, the current understanding regarding these disorders has been modified to indicate that the two disorders are related diseases in terms of the genetic aspects [31]. Moreover, recent reports have indicated considerable genetic overlap across several types of psychiatric disorders [4, 8, 11, 25]. According to a recent perspective based on genetic analyses, Craddock and his group have advocated the theory that psychiatric disorders are considered a spectrum [3]. It is true that a decreased frontal lobe is present in the brains of individuals with schizophrenia and bipolar disorder, as well as depression [5, 35]. Regarding therapeutic agents, antidepressants are effective for the improvement of symptoms in schizophrenia and bipolar disorders [32, 39]. Alternatively, antipsychotic agents are used for the treatment of depression [2]. Based on the traditional diagnostic category, it is impossible to explain the differences between psychiatric disorders by genetic or morphological factors or the responsiveness to therapeutic drugs. Our current finding indicates a trend of correlation between the severity of depression and the response to the DEX/CRH test; however, the diagnostic categories did not sufficiently fit the biological assessment. Moreover, our findings also suggest that the current diagnostic criteria are not valid for the assessment of biological markers in psychiatric disorders. Thus, the diagnostic criteria must be updated based on the contributions of biological assessments, such as DEX/CRH or NIRS [1, 17].



dexamethasone/corticotropin-releasing hormone


near-infrared spectroscopy


adrenocorticotropic hormone


Hamilton rating scale for depression


state-trait anxiety inventory


  1. Adam D. Mental health: on the spectrum. Nature. 2013;496:416–8.

    Article  CAS  PubMed  Google Scholar 

  2. Berman RM, Fava M, Thase ME, Trivedi MH, Swanink R, Mcquade RD, Carson WH, Adson D, Taylor L, Hazel J, Marcus RN. Aripiprazole augmentation in major depressive disorder: a double-blind, placebo-controlled study in patients with inadequate response to antidepressants. CNS Spectr. 2009;14:197–206.

    Article  PubMed  Google Scholar 

  3. Craddock N, Owen MJ. The Kraepelinian dichotomy—going, going… but still not gone. Br J Psychiatr. 2010;196:92–5.

    Article  Google Scholar 

  4. de Rubeis S, He X, Goldberg AP, Poultney CS, Samocha K, Cicek AE, Kou Y, et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature. 2014;515:209–15.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Drevets WC, Price JL, Simpson JR, Todd RD, Reich T, Vannier M, Raichle ME. Subgenual prefrontal cortex abnormalities in mood disorders. Nature. 1997;386:824–7.

    Article  CAS  PubMed  Google Scholar 

  6. Ferreira MA, O’Donovan MC, Meng YA, Jones IR, Ruderfer DM, Jones L, Fan J, et al. Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder. Nat Genet. 2008;40:1056–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. First MB, Spitzer RL, Williams JBW, Gibbon M. Structured clinical interview for DSM-IV-TR (SCID-I)-research version. New York: Biometrics Research, New York State Psychiatric Institute; 2002.

    Google Scholar 

  8. Green EK, Grozeva D, Jones I, Jones L, Kirov G, Caesar S, Gordon-smith K, et al. The bipolar disorder risk allele at CACNA1C also confers risk of recurrent major depression and of schizophrenia. Mol Psychiatr. 2010;15:1016–22.

    Article  CAS  Google Scholar 

  9. Greenberg PE, Fournier AA, Sisitsky T, Pike CT, Kessler RC. The economic burden of adults with major depressive disorder in the United States (2005 and 2010). J Clin Psychiatr. 2015;76(2):155–62.

    Article  Google Scholar 

  10. Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatr. 1960;23:56.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Han K, Holder JLJR, Schaaf CP, Lu H, Chen H, Kang H, Tang J, et al. SHANK3 overexpression causes manic-like behaviour with unique pharmacogenetic properties. Nature. 2013;503:72–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Heuser I, Yassouridis A, Holsboer F. The combined dexamethasone/CRH test: a refined laboratory test for psychiatric disorders. J Psychiatr Res. 1994;28:341–56.

    Article  CAS  PubMed  Google Scholar 

  13. Heuser IJ, Schweiger U, Gotthardt U, Schmider J, Lammers CH, Dettling M, Yassouridis A, Holsboer F. Pituitary-adrenal-system regulation and psychopathology during amitriptyline treatment in elderly depressed patients and normal comparison subjects. Am J Psychiatr. 1996;153:93–9.

    Article  CAS  PubMed  Google Scholar 

  14. Hori H, Ozeki Y, Teraishi T, Matsuo J, Kawamoto Y, Kinoshita Y, Suto S, Terada S, Higuchi T, Kunugi H. Relationships between psychological distress, coping styles, and HPA axis reactivity in healthy adults. J Psychiatr Res. 2010;44:865–73.

    Article  PubMed  Google Scholar 

  15. Kalali A, Williams JBW, Kobak KA, Lipschitz J, Engelhardt N, Evans K, Olin J, Rotheman P, Bech P. The new GRID HAM-D: pilot testing and international field trials. Int J Neuropsychopharmacol. 2002;5:S147.

    Google Scholar 

  16. Kawano M, Kanazawa T, Kikuyama H, Tsutsumi A, Kinoshita S, Kawabata Y, Yamauchi S, et al. Correlation between frontal lobe oxy-hemoglobin and severity of depression assessed using near-infrared spectroscopy. J Affect Disord. 2016;205:154–8.

    Article  CAS  PubMed  Google Scholar 

  17. Koike S, Takizawa R, Nishimura Y, Takano Y, Takayanagi Y, Kinou M, Araki T, Harima H, Fukuda M, Okazaki Y, Kasai K. Different hemodynamic response patterns in the prefrontal cortical sub-regions according to the clinical stages of psychosis. Schizophr Res. 2011;132:54–61.

    Article  PubMed  Google Scholar 

  18. Kunugi H, Ida I, Owashi T, Kimura M, Inoue Y, Nakagawa S, et al. Assessment of the dexamethasone/CRH test as a state-dependent marker for hypothalamic-pituitary-adrenal (HPA) axis abnormalities in major depressive episode: a Multicenter Study. Neuropsychopharmacology. 2006;31:212–20.

    CAS  PubMed  Google Scholar 

  19. Kunugi H, Urushibara T, Nanko S. Combined DEX/CRH test among Japanese patients with major depression. J Psychiatr Res. 2004;38:123–8.

    Article  PubMed  Google Scholar 

  20. Kunzel HE, Binder EB, Nickel T, Ising M, Fuchs B, Majer M, Pfennig A, Ernst G, Kern N, Schmid DA, Uhr M, Holsboer F, Modell S. Pharmacological and nonpharmacological factors influencing hypothalamic-pituitary-adrenocortical axis reactivity in acutely depressed psychiatric in-patients, measured by the Dex–CRH test. Neuropsychopharmacology. 2003;28:2169–78.

    CAS  PubMed  Google Scholar 

  21. Liu X, Sun G, Zhang X, Xu B, Shen C, Shi L, Ma X, Ren X, Feng K, Liu P. Relationship between the prefrontal function and the severity of the emotional symptoms during a verbal fluency task in patients with major depressive disorder: a multi-channel NIRS study. Prog Neuropsychopharmacol Biol Psychiatr. 2014;54:114–21.

    Article  Google Scholar 

  22. Lodish M, Stratakis CA. A genetic and molecular update on adrenocortical causes of Cushing syndrome. Nat Rev Endocrinol. 2016;12:255–62.

    Article  CAS  PubMed  Google Scholar 

  23. Maki A, Yamashita Y, Ito Y, Watanabe E, Mayanagi Y, Koizumi H. Spatial and temporal analysis of human motor activity using noninvasive NIR topography. Med Phys. 1995;22:1997–2005.

    Article  CAS  PubMed  Google Scholar 

  24. Matsubara T, Matsuo K, Nakashima M, Nakano M, Harada K, Watanuki T, Egashira K, Watanabe Y. Prefrontal activation in response to emotional words in patients with bipolar disorder and major depressive disorder. Neuroimage. 2014;85(Pt 1):489–97.

    Article  PubMed  Google Scholar 

  25. Mccarthy SE, Gillis J, Kramer M, Lihm J, Yoon S, Berstein Y, Mistry M, et al. De novo mutations in schizophrenia implicate chromatin remodeling and support a genetic overlap with autism and intellectual disability. Mol Psychiatr. 2014;19:652–8.

    Article  CAS  Google Scholar 

  26. Nakazato K, Mizuguchi T. Development and validation of Japanese version of state-trait anxiety inventory. Shinshin-Igaku. 1982;22:107–12.

    Google Scholar 

  27. NishimuraY Tanii H, Hara N, Inoue K, Kaiya H, Nishida A, Okada M, Okazaki Y. Relationship between the prefrontal function during a cognitive task and the severity of the symptoms in patients with panic disorder: a multi-channel NIRS study. Psychiatr Res. 2009;172(2):168–72.

    Article  Google Scholar 

  28. Nishimura Y, Tanii H, Fukuda M, Kajiki N, Inoue K, Kaiya H, Nishida A, Okada M, Okazaki Y. Frontal dysfunction during a cognitive task in drug-naive patients with panic disorder as investigated by multi-channel near-infrared spectroscopy imaging. Neurosci Res. 2007;59(1):107–12.

    Article  PubMed  Google Scholar 

  29. O’Donovan MC, Craddock N, Norton N, Williams H, Peirce T, Moskvina V, Nikolov I, et al. Identification of loci associated with schizophrenia by genome-wide association and follow-up. Nat Genet. 2008;40:1053–5.

    Article  PubMed  Google Scholar 

  30. Pu S, Nakagome K, Yamada T, Yokoyama K, Matsumura H, Yamada S, Sugie T, Miura A, Mitani H, Iwata M, Nagata I, Kaneko K. Suicidal ideation is associated with reduced prefrontal activation during a verbal fluency task in patients with major depressive disorder. J Affect Disord. 2015;181:9–17.

    Article  PubMed  Google Scholar 

  31. Purcell SM, Wray NR, Stone JL, Visscher PM, O’Donovan MC, Sullivan PF, Sklar P. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009;460:748–52.

    CAS  PubMed  Google Scholar 

  32. Saito M, Yasui-furukori N, Nakagami T, Furukori H, Kaneko S. Dose-dependent interaction of paroxetine with risperidone in schizophrenic patients. J Clin Psychopharmacol. 2005;25:527–32.

    Article  CAS  PubMed  Google Scholar 

  33. Shao Z, Janse E, Visser K, Meyer AS. What do verbal fluency tasks measure? Predictors of verbal fluency performance in older adults. Front Psychol. 2014;5:772.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Spielberger CD. Manual for the state-trait anxiety inventory STAI (form Y) (“ self-evaluation questionnaire”); 1983.

  35. Stip E, Mancini-marie A, Letourneau G, Fahim C, Mensour B, Crivello F, Dollfus S. Increased grey matter densities in schizophrenia patients with negative symptoms after treatment with quetiapine: a voxel-based morphometry study. Int Clin Psychopharmacol. 2009;24:34–41.

    Article  PubMed  Google Scholar 

  36. Takizawa R, Fukuda M, Kawasaki S, Kasai K, Mimura M, Pu S, Noda T, et al. Neuroimaging-aided differential diagnosis of the depressive state. Neuroimage. 2014;85(Pt 1):498–507.

    Article  CAS  PubMed  Google Scholar 

  37. Takizawa R, Kasai K, Kawakubo Y, Marumo K, Kawasaki S, Yamasue H, Fukuda M. Reduced frontopolar activation during verbal fluency task in schizophrenia: a multi-channel near-infrared spectroscopy study. Schizophr Res. 2008;99:250–62.

    Article  PubMed  Google Scholar 

  38. Teicher MH. Biology of anxiety. Med Clin North Am. 1988;72:791–814.

    Article  CAS  PubMed  Google Scholar 

  39. Tohen M, Vieta E, Calabrese J, Ketter TA, Sachs G, Bowden C, Mitchell PB, et al. Efficacy of olanzapine and olanzapine-fluoxetine combination in the treatment of bipolar I depression. Arch Gen Psychiatr. 2003;60:1079–88.

    Article  CAS  PubMed  Google Scholar 

  40. Veen G, Derijk RH, Giltay EJ, van Vliet IM, Van Pelt J, Zitman FG. The influence of psychiatric comorbidity on the dexamethasone/CRH test in major depression. Eur Neuropsychopharmacol. 2009;19:409–15.

    Article  CAS  PubMed  Google Scholar 

  41. Zhang H, Dong W, Dang W, Quan W, Tian J, Chen R, Zhan S, Yu X. Near-infrared spectroscopy for examination of prefrontal activation during cognitive tasks in patients with major depressive disorder: a meta-analysis of observational studies. Psychiatr Clin Neurosci. 2015;69:22–33.

    Article  Google Scholar 

  42. Zobel AW, Nickel T, Sonntag A, Uhr M, Holsboer F, Ising M. Cortisol response in the combined dexamethasone/CRH test as predictor of relapse in patients with remitted depression. a prospective study. J Psychiatr Res. 2001;35:83–94.

    Article  CAS  PubMed  Google Scholar 

Download references

Authors’ contributions

SK carried out the DEX/CRH test and drafted the manuscript. TK designed the study especially for NIRS and revised the manuscript. HK performed the statistical analysis. YH participated in the design of the study and conducted the study. All authors read and approved the final manuscript.


We are grateful to Drs. Makoto Kawano, Masaki Nishiguchi, Yasuo Kawabata, Shigeru Yamauchi, Hiroyuki Uenishi, Seiya Kawashige, Shinichi Imazu, Katsunori Toyoda, Yoshitaka Nishizawa, Mayuko Takahashi, Tatsushi Okayama, Wakako Odo, Kentaro Ide, Soichiro Maruyama, Seiichiro Tarutani, Emi Minami, Ryosuke Katsura, Yuko Higa, Tomoyoshi Nakano, Yoichiro Kubo, Shota Ouchi, and Tetsuya Togashi for helpful effort to collect the samples. We would like to thank Drs. Nanako Saito, Mai Yoshikawa, and other clinical psychotherapists for collecting the psychological data. We would like to thank Drs. Atsushi Tsutsumi and Jun Koh for the helpful discussion.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

Anonymized raw data can be provided if it is needed.

Ethics approval and consent to participate

This study was approved by the ethical committee at Osaka Medical College. All of the participants have provided the written informed consent after the explanation of the study design. This clinical investigation was also approved and registered in the National registration system in Japan (UMIN000013214).


The funding of this research was provided by the department of Neuropsychiatry, Osaka Medical College. No official grant was not used for this work.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Tetsufumi Kanazawa.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kinoshita, S., Kanazawa, T., Kikuyama, H. et al. Clinical application of DEX/CRH test and multi-channel NIRS in patients with depression. Behav Brain Funct 12, 25 (2016).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: