Skip to main content
NCBI home page
Medicine logoLink to Medicine
. 2016 Mar 7;95(9):e2982. doi: 10.1097/MD.0000000000002982

The White Matter Microintegrity Alterations of Neocortical and Limbic Association Fibers in Major Depressive Disorder and Panic Disorder

The Comparison

Chien-Han Lai 1, Yu-Te Wu 1
Editor: Urs Nater1
PMCID: PMC4782901  PMID: 26945417

Abstract

The studies regarding to the comparisons between major depressive disorder (MDD) and panic disorder (PD) in the microintegrity of white matter (WM) are uncommon. Therefore, we tried to a way to classify the MDD and PD.

Fifty-three patients with 1st-episode medication-naive PD, 54 healthy controls, and 53 patients with 1st-episode medication-naive MDD were enrolled in this study. The controls and patients were matched for age, gender, education, and handedness. The diffusion tensor imaging scanning was also performed. The WM microintegrity was analyzed and compared between 3 groups of participants (ANOVA analysis) with age and gender as covariates.

The MDD group had lower WM microintegrity than the PD group in the left anterior thalamic radiation, left uncinate fasciculus, left inferior fronto-occipital fasciculus, and bilateral corpus callosum. The MDD group had reductions in the microintegrity when compared to controls in the bilateral superior longitudinal fasciculi, inferior longitudinal fasciculi, inferior fronto-occipital fasciculi, and corpus callosum. The PD group had lower microintegrity in bilateral superior longitudinal fasciculi and left inferior fronto-occipital fasciculus when compared to controls.

The widespread pattern of microintegrity alterations in fronto-limbic WM circuit for MDD was different from restrictive pattern of alterations for PD.

INTRODUCTION

The microintegrity of white matter (WM) is an important biomarker to elucidate the pathophysiology for different disorders in psychiatry field. Among the parameters, the fractional anisotropy (FA) can reveal the fluency of signal transmission within the fasciculus, which provides some clues of WM microintegrity. Among the disorders, the differentiation of WM microintegrity between the major depressive disorder (MDD) and panic disorder (PD) is a new field to investigate.

For the MDD, the pathophysiology of WM tracts might be associated with the “limbic-cortico-striato-pallido-thalamic circuit” model for depression.1 The superior longitudinal fasciculus (SLF) connects the precentral gyrus with the Broca regions to form a circuit for higher cortical functions.2 The microintegrity alteration in the left SLF is a core structure for MDD origin.3 The higher density in the WM lesions of the left SLF and corpus callosum (CC) would disturb cognitive and affective functions.4 The alterations in the SLF also corresponded to the neocortical association tracts for Sheline model of depression. The high-risk vulnerable marker for MDD also includes the FA values in the SLF and CC.5 The disturbances in interhemispheric connections of CC are also the possible reason for MDD.6 Our previous report also found significant WM microintegrity alterations in the SLF and anterior thalamic radiation of MDD, which also connect the fronto-limbic regions in the Sheline model.7 The alterations of inferior fronto-occipital fasciculus (IFOF), which also suggested the dysfunction of parieto-occipital regions with the dorsolateral premotor and prefrontal areas in the Sheline model for MDD.8 From the above literature, there are widespread alterations in the microintegrity of WM tracts for MDD.

In the “fear network model” for PD, the SLF and CC also play a significant role in the pathophysiology.9 The SLF is a part of fronto-parietal network and the related spatial working memory.10 The IFOF also influences the frontal-sensory system in fearful network model.9 The early-life stress also causes anxious behaviors and structural changes in the CC.11 Our previous study of PD also replicated the WM alterations in the SLF, IFOF, and CC.12

The clinical overlap of MDD and PD is an important aspect for clinical practice. The genetic–environmental interaction would influence the pathophysiology.13 Due to the clinical overlap, the differentiation between the “limbic-cortico-striato-pallidal-thalamic” circuit model for MDD and “fear network model” for PD is an interesting issue. The “limbic-cortico-striato-pallidal-thalamic” model seemed more extensive than the “fear network model.” The enhanced fear acquisition and elevated synaptic plasticity in the ventral emotional network are also a part of pathogenesis for depression.14 In addition, the enhanced fear perception and emotional disturbance seems to be exaggerated in MDD.15 Based on the involved regions of 2 hypothetical models and more enhanced fear acquisition in MDD, we hypothesized that the MDD patients would have more severe alterations than PD patients in the following WM tracts, such as CC, IFOF, and SLF. In addition, a comparison pattern of WM microintegrity could be revealed after comparisons of MDD and PD.

Participants

All the MDD subjects were 1st-episode, medication-naive patients with a pure MDD diagnosis (DSM-IV criteria) at least moderate severity (Clinician Global Impression of Severity > 4, Hamilton rating scales for depression [HDRS] score > 20, Hamilton rating scales for anxiety [HARS] score < 5). The PD subjects were 1st-episode, medicine-naive patients with a pure PD diagnosis (DSM-IV criteria) at least moderate severity (Clinician Global Impression of Severity > 4, HDRS score < 7, HARS score > 22, Panic Disorder Symptom Severity Scale [PDSS] score > 15). Neither significant comorbidities nor previous psychotherapies were allowed for the eligible participants. The healthy controls had no significant psychiatric illnesses or significant medical illnesses. All of the patients and healthy subjects signed the informed consent that was approved by the 3 Institutional Review Boards at Taipei Tzu Chi Hospital, Cheng Hsin General Hospital, and National Yang-Ming University according to the institute where they were recruited. The patients were enrolled at Taipei Tzu Chi Hospital and Cheng Hsin General Hospital. The controls were enrolled at Taipei Tzu Chi Hospital, Cheng Hsin General Hospital, and National Yang-Ming University. At the time of the magnetic resonance (MR) scanning, none of the participants in the control group received psychotropic treatment. Handedness was also determined.16 The sample of participants has some overlaps of our previous reports.7,12

MR Data Acquisition

The diffusion tensor imaging (DTI) data were obtained with a 3T scanner (Siemens Medical Solutions, Erlangen, Germany) housed at MR Center, National Yang Ming University. The detailed information of scanning parameters please refer to our previous report.7

DTI Analysis

The DTI analysis was performed by the FDT (FMRIB's Diffusion Toolbox v2.0) function that was implemented in the FSL (FMRIB Software Library),17,18 which was developed by the Oxford Center for Functional MRI of the Brain (FMRIB), London, UK. The merged DTI images were preprocessed by reducing the stretches and shears in diffusion weighted images.19 We also used a brain extraction tool20 to remove the nonbrain tissue to obtain the mask to fit a diffusion tensor model. FA maps were computed by the above procedure.

FA images were visually inspected for the orientation and image quality. All the FA volumes were warped to the template by nonlinear registration.21 The mean FA volume of all individuals was thinned to create a mean FA skeleton that represented the centers of all WM tracts for the presentations of the Tract-Based Spatial Statistics results. Individual FA values were warped onto the above mean FA skeleton (threshold: 0.2).

Statistical Analysis

Demographic and clinical data of MDD, PD patients, and controls such as age, HDRS scores, HARS scores, PDSS scores, genders, and handedness would be compared each other by Kruskal–Wallis nonparametric multiple sample test as P < 0.05. The duration of illness in MDD and PD groups was compared by Mann–Whitney U-test with statistical threshold as P < 0.05 (SPSS version 16.0, Chicago, IL).

The randomized function of FSL (version 2.1)22 was performed the voxel-wise analyses for the FA skeletons to compare 2 groups’ FA, respectively (MDD vs PD, MDD vs controls, and PD vs controls). For the main purpose of group comparisons, an ANOVA 1 × 3 factor analysis with group as the main random factor over all subjects. We also included global brain volume, age, gender, and duration of illness as covariates in the above analyses. We used familywise error (FWE) after multiple comparisons to find regions of FA deficits (threshold: FWE P value < 0.05 and cluster threshold > 50 voxels).

The FSL correlation analysis using general lineal model between the scores of clinical rating scales (HDRS, PDSS) and FA (with age and gender as covariates) was also performed (threshold: corrected P < 0.05, multiple comparisons) to delineate individual pathophysiology for MDD and PD.

RESULTS

Demographic and Clinical Data

We enrolled 53 patients with MDD, 53 patients with PD, and 54 controls. There were no significant differences in the age, gender, education years, and handedness between the 3 groups. A main group effect for MDD was observed in the HDRS scores while the patients with MDD were compared to the PD group or controls. In addition, a main group effect for PD was observed in PDSS and HARS scores when the PD patients were compared to the MDD group or controls (Table 1).

TABLE 1.

Demographic Data of Participating Patients and Controls

graphic file with name medi-95-e2982-g001.jpg

Open in a new tab

The Comparison Alterations in the WM Microintegrity of MDD and PD

The patients with MDD had lower FA values in the bilateral CC (Table 2, Figure 1A) and IFOF (Table 2, Figure 1B) than PD patients. In addition, reductions in the FA values were found in the bilateral IFOF (Table 2, Figure 1C), bilateral SLF (Table 2, Figure 1D), CC (Table 2, Figure 1E), and right inferior longitudinal fasciculus (ILF) (Table 2, Figure 1F) of MDD patients when compared to healthy controls. The patients with PD had lower FA values than the controls in the bilateral SLF and right IFOF (Table 2, Figure 1G). In addition, the scores of HDRS were negatively correlated with the FA values in the left SLF of the patients with MDD. There was also a negative correlation between the PDSS scores and the FA values in the right IFOF of PD patients. No significant correlations between the clinical variables and FA values were observed in the control group or across both groups. All the above results were FWE-corrected P value < 0.05 and cluster voxels more than 50.

TABLE 2.

The WM Tract Microintegrity Patterns and Differences in MDD Group, PD Group, and Controls

graphic file with name medi-95-e2982-g002.jpg

Open in a new tab

FIGURE 1.

FIGURE 1

Open in a new tab

The comparison pattern of WM microintegrity alterations in MDD and PD. The patients with MDD had lower FA values in the bilateral CC (A) and IFOF (B) than PD patients. In addition, reductions in the FA values were found in the bilateral IFOF (C), bilateral SLF (D), CC (E), and right ILF (F) of MDD patients when compared to healthy controls. The patients with PD had lower FA values than the controls in the bilateral SLF and right IFOF (G). CC = corpus callosum, FA = fractional anisotropy, IFOF = inferior fronto-occipital fasciculus, ILF = inferior longitudinal fasciculus, MDD = major depressive disorder, PD = panic disorder, SLF = superior longitudinal fasciculus, WM = white matter.

DISCUSSION

In this study, we found a possible way to delineate the MDD and PD. The MDD patients seemed to have more severe WM microintegrity alterations than PD in the bilateral CC and left IFOF. In addition, a more widespread pattern of alterations was discovered in the bilateral IFOF, bilateral SLF, right ILF, and CC of patients with MDD. A relatively limited alteration of the bilateral SLF and right IFOF occurred in PD. According to our knowledge, this should be the 1st study to address the differentiation between MDD and PD in the field of WM microintegrity. Our study showed a possible category model with severity dimension to describe the fundamental differences between the 2 disorders in the WM microintegrity. The category model means the MDD had more widespread alterations than PD, especially in the CC and ILF. The severity dimension means MDD had more severe alterations than PD in the bilateral CC and left IFOF. Our study had a comprehensive comparison between MDD, PD, and controls, which could help confirm the above category and dimension model for the MDD and PD in the WM etiology.

Apart from the comparison, the significance of current study revealed a possible clue for common pathway of “limbic-cortico-striato-pallido-thalamic circuit” model for depression1 and “fear network model” for PD.9 The common WM alteration pathways seemed to include the SLF and IFOF for MDD and PD in current study. It was compatible with the importance of frontal lobe in the pathophysiology of MDD and PD.23–31 The frontal-related WM connections to other lobes, such as parietal, temporal, and occipital lobes, might have great influences in executive function, memory, and visual information processing.32–38 Our results suggested that MDD and PD might have common disturbances in frontal-related WM tracts. The clinical significance in this field can be further evaluated in future study.

The IFOF radiates backward from the frontal lobe via the lateral border of caudate nucleus to the occipital and posterior temporal lobes.39 The SLF is a bidirectional WM tract connecting the anterior and posterior part of brain, including the frontal, parietal, temporal, and occipital lobes. The ILF connects the anterior part of temporal and occipital lobes. It is also an indirect pathway anteriorly joining the uncinate fasciculus to relay the information to frontal lobe.39 The IFOF and ILF are strongly correlated in function40 and the impairments would be associated with thought disorders, visual emotion, and cognitive impairments.41 The IFOF, SLF, and ILF belong to the neocortical association pairs. There is also a moderate correlation between these tracts due to the phylogenetic similarities.40 The more severe alterations in the IFOF and right ILF in MDD might represent the more involvements in the related emotional and cognitive functions.41 The impairments in the SLF occurred in both MDD and PD suggested that the additional visuospatial attention42 would also be altered in MDD and PD. In addition to the neocortical association fibers, the CC plays a major role in the WM pathophysiology of MDD, especially when compared to the PD. The CC connects both hemispheres to control cognition and emotion,4 which would be impaired in depression due to the disturbance of interhemispheric connection.6 The more severe alterations in the WM microintegrity of CC and IFOF in MDD than PD were probably associated with the influences of emotion, cognition, and other limbic-related functions in the CC and IFOF.8,43 In addition, the IFOF connects the frontal lobe with occipital and temporal lobes through the caudate nucleus. The CC also connects the bihemispheric limbic regions. The more involvement of IFOF and CC microintegrity in MDD also corresponded to the “limbic-cortico-striato-pallidal-thalamic” model of depression. It also supported our hypothesis of more widespread alterations of WM tracts in depression due to the clinical severity and function impairments. In the whole view of our results, the neocortical association fibers, such as IFOF, ILF, and SLF, are significantly impaired in MDD and PD. However, the involvement of limbic-related interhemispheric fibers, such as CC, would contribute the differentiation point between MDD and PD.

The findings in current study also replicated the several WM tracts in our previous reports in MDD and PD, respectively.12,44 For the MDD, the CC results replicated the results of several previous reports in MDD8,43,45 and some of the reports also involved the ILF,45 IFOF,8 and SLF,45 which also correspond to our study results. Most results showed widespread alterations in WM microintegrity of MDD patients. However, some studies argued against the association of WM microintegrity disruption and depression,46 or with increased anatomic connectivity of cortico-limbic circuit.47 Our results found no alterations in the WM microintegrity of the cingulum, which has been found with significant alterations in several MDD studies.45 However, our study had less impact from aging, medication, and chronicity, which makes the strengths totally different from previous studies with patients in late-onset depression.48 In summary, the findings in MDD group corresponded to our original hypothesis of “limbic-cortico-striato-pallidal-thalamic” circuit with more widespread involvement of WM tracts.

For the PD, the findings of IFOF and SLF also corresponded to several previous reports.12 However, several studies found increased WM microintegrity in PD.49 Therefore, several review articles criticized the applications of DTI analysis in the study of pathophysiology for PD and the significance of findings.50 However, the IFOF might be important for integration of frontal lobe-related inhibitory control and occipital lobe-related sensory inputs in the “fear network model” for PD.9,12 The fear circuitry theory of PD also included the SLF.51 The overgeneralization of conditioned fear also provoked panic attacks.52 The SLF-related fear modulation also would be associated with anxiety-related microintegrity alterations.53 In summary, the less widespread involvement of WM tracts also corresponded to “fear network model” for PD, which corresponded to our original hypothesis.

LIMITATIONS

Our study had several limitations.

  1. The cross-sectional design and lack of further tractography might limit the interpretations.

  2. DTI is still a nonspecific measure that does not provide information about the underlying causes for the reported microstructural pathology.

  3. The detailed histopathologic validation of FA in healthy humans is still missing, even with some animal evidences.54

  4. The lack of power and limited directional resolution of the suboptimal DTI protocol (30 directions) in this study would be associated with the limited number of structures involved.

  5. The current DTI method does not make it possible to disentangle the white matter tract of the crossing fibers to a high certainty in the brain, and our study results were not confirmed by tractography.

  6. The findings may not be diagnostically specific to MDD as the same white matter tract abnormalities are observed in other disorders, as for example, schizophrenia.

CONCLUSION

The degree of alterations in the neocortical association and interhemispheric fibers might help differentiate MDD and PD. The more widespread pattern of microintegrity alterations in the fronto-limbic WM circuit represents the comparison pattern for MDD, which was different from the restrictive pattern of alterations for PD in the frontal and sensory-related WM circuit.

Acknowledgements

The authors thank the grant support from Taipei Tzu-Chi General Hospital hospital project TCRD-TPE-99-02, TCRD-TPE-100-02 and the project was also supported by grant from the Cheng Hsin General Hospital and National Yang Ming University cooperative project 103F003C02. The authors also thank MR support from National Yang-Ming University, Taiwan, which is in part supported by the MOE plan for the top university.

Footnotes

Abbreviations: CC = corpus callosum, DTI = diffusion tensor imaging, HARS = Hamilton rating scales for anxiety, HDRS = Hamilton rating scales for depression, IFOF = inferior fronto-occipital fasciculus, ILF = inferior longitudinal fasciculus, MDD = major depressive disorder, MR = magnetic resonance, PD = panic disorder, SLF = superior longitudinal fasciculus.

I state that I am the first and corresponding author of this manuscript. All individuals included as authors have contributed substantially to the scientific process leading up to the writing of the paper. In addition, the authors have made a substantial contribution to drafting or critical revision of the manuscript for important intellectual content.

Buddhist Tzu-Chi General Hospital, Taipei Branch hospital project TCRD-TPE-99-02, TCRD-TPE-100-02 and grant from the Cheng Hsin General Hospital and National Yang Ming University cooperative project 103F003C02.

The authors have no conflicts of interest to disclose.

REFERENCES

  • 1.Sheline YI. 3D MRI studies of neuroanatomic changes in unipolar major depression: the role of stress and medical comorbidity. Biol Psychiatry 2000; 48:791–800. [DOI] [PubMed] [Google Scholar]
  • 2.Bernal B, Altman N. The connectivity of the superior longitudinal fasciculus: a tractography DTI study. Magn Reson Imaging 2010; 28:217–225. [DOI] [PubMed] [Google Scholar]
  • 3.Murphy ML, Frodl T. Meta-analysis of diffusion tensor imaging studies shows altered fractional anisotropy occurring in distinct brain areas in association with depression. Biol Mood Anxiety Disord 2011; 1:3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Dalby RB, Chakravarty MM, Ahdidan J, et al. Localization of white-matter lesions and effect of vascular risk factors in late-onset major depression. Psychol Med 2010; 40:1389–1399. [DOI] [PubMed] [Google Scholar]
  • 5.Frodl T, Carballedo A, Fagan AJ, et al. Effects of early-life adversity on white matter diffusivity changes in patients at risk for major depression. J Psychiatry Neurosci 2012; 37:37–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Liao Y, Huang X, Wu Q, et al. Is depression a disconnection syndrome? Meta-analysis of diffusion tensor imaging studies in patients with MDD. J Psychiatry Neurosci 2013; 38:49–56. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Lai CH, Wu YT. Alterations in white matter micro-integrity of the superior longitudinal fasciculus and anterior thalamic radiation of young adult patients with depression. Psychol Med 2014; 44:2825–2832. [DOI] [PubMed] [Google Scholar]
  • 8.Cheng Y, Xu J, Yu H, et al. Delineation of early and later adult onset depression by diffusion tensor imaging. PLoS One 2014; 9:e112307. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Gorman JM, Kent JM, Sullivan GM, et al. Neuroanatomical hypothesis of panic disorder, revised. Am J Psychiatry 2000; 157:493–505. [DOI] [PubMed] [Google Scholar]
  • 10.Vestergaard M, Madsen KS, Baare WF, et al. White matter microstructure in superior longitudinal fasciculus associated with spatial working memory performance in children. J Cogn Neurosci 2010. [DOI] [PubMed] [Google Scholar]
  • 11.Teicher MH, Samson JA, Sheu YS, et al. Hurtful words: association of exposure to peer verbal abuse with elevated psychiatric symptom scores and corpus callosum abnormalities. Am J Psychiatry 2010; 167:1464–1471. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Lai CH, Wu YT. Fronto-occipital fasciculus, corpus callosum and superior longitudinal fasciculus tract alterations of first-episode, medication-naive and late-onset panic disorder patients. J Affect Disord 2013; 146:378–382. [DOI] [PubMed] [Google Scholar]
  • 13.Mosing MA, Gordon SD, Medland SE, et al. Genetic and environmental influences on the co-morbidity between depression, panic disorder, agoraphobia, and social phobia: a twin study. Depress Anxiety 2009; 26:1004–1011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Nissen C, Holz J, Blechert J, et al. Learning as a model for neural plasticity in major depression. Biol Psychiatry 2010; 68:544–552. [DOI] [PubMed] [Google Scholar]
  • 15.Kuhn M, Hoger N, Feige B, et al. Fear extinction as a model for synaptic plasticity in major depressive disorder. PLoS One 2014; 9:e115280. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Oldfield RC. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 1971; 9:97–113. [DOI] [PubMed] [Google Scholar]
  • 17.Smith SM, Jenkinson M, Woolrich MW, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 2004; 23 Suppl 1:S208–S219. [DOI] [PubMed] [Google Scholar]
  • 18.Woolrich MW, Jbabdi S, Patenaude B, et al. Bayesian analysis of neuroimaging data in FSL. Neuroimage 2009; 45 (1 Suppl):S173–S186. [DOI] [PubMed] [Google Scholar]
  • 19.Jenkinson M, Smith S. A global optimisation method for robust affine registration of brain images. Med Image Anal 2001; 5:143–156. [DOI] [PubMed] [Google Scholar]
  • 20.Smith SM. Fast robust automated brain extraction. Hum Brain Mapp 2002; 17:143–155. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Smith SM, Johansen-Berg H, Jenkinson M, et al. Acquisition and voxelwise analysis of multi-subject diffusion data with tract-based spatial statistics. Nat Protoc 2007; 2:499–503. [DOI] [PubMed] [Google Scholar]
  • 22.Nichols TE, Holmes AP. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp 2002; 15:1–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Hasler G, van der Veen JW, Geraci M, et al. Prefrontal cortical gamma-aminobutyric acid levels in panic disorder determined by proton magnetic resonance spectroscopy. Biol Psychiatry 2009; 65:273–275. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Kim B, Kim JH, Kim MK, et al. Frontal white matter alterations in short-term medicated panic disorder patients without comorbid conditions: a diffusion tensor imaging study. PLoS One 2014; 9:e95279. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Lai CH, Wu YT. Frontal regional homogeneity increased and temporal regional homogeneity decreased after remission of first-episode drug-naive major depressive disorder with panic disorder patients under duloxetine therapy for 6 weeks. J Affect Disord 2012; 136:453–458. [DOI] [PubMed] [Google Scholar]
  • 26.Long Z, Medlock C, Dzemidzic M, et al. Decreased GABA levels in anterior cingulate cortex/medial prefrontal cortex in panic disorder. Prog Neuropsychopharmacol Biol Psychiatry 2013; 44:131–135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Na KS, Ham BJ, Lee MS, et al. Decreased gray matter volume of the medial orbitofrontal cortex in panic disorder with agoraphobia: a preliminary study. Prog Neuropsychopharmacol Biol Psychiatry 2013; 45:195–200. [DOI] [PubMed] [Google Scholar]
  • 28.Salomons TV, Dunlop K, Kennedy SH, et al. Resting-state cortico-thalamic-striatal connectivity predicts response to dorsomedial prefrontal rTMS in major depressive disorder. Neuropsychopharmacology 2014; 39:488–498. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Strigo IA, Matthews SC, Simmons AN. Decreased frontal regulation during pain anticipation in unmedicated subjects with major depressive disorder. Transl Psychiatry 2013; 3:e239. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Wang Y, Jia Y, Xu G, et al. Frontal white matter biochemical abnormalities in first-episode, treatment-naive patients with major depressive disorder: a proton magnetic resonance spectroscopy study. J Affect Disord 2012; 136:620–626. [DOI] [PubMed] [Google Scholar]
  • 31.Ye T, Peng J, Nie B, et al. Altered functional connectivity of the dorsolateral prefrontal cortex in first-episode patients with major depressive disorder. Eur J Radiol 2012; 81:4035–4040. [DOI] [PubMed] [Google Scholar]
  • 32.Bagga D, Sharma A, Kumari A, et al. Decreased white matter integrity in fronto-occipital fasciculus bundles: relation to visual information processing in alcohol-dependent subjects. Alcohol 2014; 48:43–53. [DOI] [PubMed] [Google Scholar]
  • 33.Desco M, Navas-Sanchez FJ, Sanchez-Gonzalez J, et al. Mathematically gifted adolescents use more extensive and more bilateral areas of the fronto-parietal network than controls during executive functioning and fluid reasoning tasks. Neuroimage 2011; 57:281–292. [DOI] [PubMed] [Google Scholar]
  • 34.Garavan H, Ross TJ, Murphy K, et al. Dissociable executive functions in the dynamic control of behavior: inhibition, error detection, and correction. Neuroimage 2002; 17:1820–1829. [DOI] [PubMed] [Google Scholar]
  • 35.Gur RC, Turetsky BI, Loughead J, et al. Hemodynamic responses in neural circuitries for detection of visual target and novelty: an event-related fMRI study. Hum Brain Mapp 2007; 28:263–274. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Lin WC, Chou KH, Chen CC, et al. White matter abnormalities correlating with memory and depression in heroin users under methadone maintenance treatment. PLoS One 2012; 7:e33809. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Schmidt D, Krause BJ, Weiss PH, et al. Visuospatial working memory and changes of the point of view in 3D space. Neuroimage 2007; 36:955–968. [DOI] [PubMed] [Google Scholar]
  • 38.Vasic N, Walter H, Sambataro F, et al. Aberrant functional connectivity of dorsolateral prefrontal and cingulate networks in patients with major depression during working memory processing. Psychol Med 2009; 39:977–987. [DOI] [PubMed] [Google Scholar]
  • 39.Ashtari M. Anatomy and functional role of the inferior longitudinal fasciculus: a search that has just begun. Dev Med Child Neurol 2012; 54:6–7. [DOI] [PubMed] [Google Scholar]
  • 40.Wahl M, Li YO, Ng J, et al. Microstructural correlations of white matter tracts in the human brain. Neuroimage 2010; 51:531–541. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Chanraud S, Zahr N, Sullivan EV, et al. MR diffusion tensor imaging: a window into white matter integrity of the working brain. Neuropsychol Rev 2010; 20:209–225. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Thiebaut de Schotten M, Dell’Acqua F, Forkel SJ, et al. A lateralized brain network for visuospatial attention. Nat Neurosci 2011; 14:1245–1246. [DOI] [PubMed] [Google Scholar]
  • 43.Bessette KL, Nave AM, Caprihan A, et al. White matter abnormalities in adolescents with major depressive disorder. Brain Imaging Behav 2014; 8:531–541. [DOI] [PubMed] [Google Scholar]
  • 44.Lai CH, Wu YT. Alterations in white matter micro-integrity of the superior longitudinal fasciculus and anterior thalamic radiation of young adult patients with depression. Psychol Med 2014; 1–8. [DOI] [PubMed] [Google Scholar]
  • 45.de Diego-Adelino J, Pires P, Gomez-Anson B, et al. Microstructural white-matter abnormalities associated with treatment resistance, severity and duration of illness in major depression. Psychol Med 2014; 44:1171–1182. [DOI] [PubMed] [Google Scholar]
  • 46.Choi KS, Holtzheimer PE, Franco AR, et al. Reconciling variable findings of white matter integrity in major depressive disorder. Neuropsychopharmacology 2014; 39:1332–1339. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Fang P, Zeng LL, Shen H, et al. Increased cortical-limbic anatomical network connectivity in major depression revealed by diffusion tensor imaging. PLoS One 2012; 7:e45972. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Taylor WD, MacFall JR, Gerig G, et al. Structural integrity of the uncinate fasciculus in geriatric depression: relationship with age of onset. Neuropsychiatr Dis Treat 2007; 3:669–674. [PMC free article] [PubMed] [Google Scholar]
  • 49.Kim B, Yoo E, Lee JY, et al. The effects of the catechol-O-methyltransferase val158met polymorphism on white matter connectivity in patients with panic disorder. J Affect Disord 2013; 147:64–71. [DOI] [PubMed] [Google Scholar]
  • 50.Del Casale A, Serata D, Rapinesi C, et al. Structural neuroimaging in patients with panic disorder: findings and limitations of recent studies. Psychiatr Danub 2013; 25:108–114. [PubMed] [Google Scholar]
  • 51.de Carvalho MR, Rozenthal M, Nardi AE. The fear circuitry in panic disorder and its modulation by cognitive-behaviour therapy interventions. World J Biol Psychiatry 2010; 11 (2 Pt 2):188–198. [DOI] [PubMed] [Google Scholar]
  • 52.Lissek S, Rabin S, Heller RE, et al. Overgeneralization of conditioned fear as a pathogenic marker of panic disorder. Am J Psychiatry 2010; 167:47–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Baur V, Hanggi J, Rufer M, et al. White matter alterations in social anxiety disorder. J Psychiatr Res 2011; 45:1366–1372. [DOI] [PubMed] [Google Scholar]
  • 54.Song SK, Yoshino J, Le TQ, et al. Demyelination increases radial diffusivity in corpus callosum of mouse brain. Neuroimage 2005; 26:132–140. [DOI] [PubMed] [Google Scholar]

Articles from Medicine are provided here courtesy of Wolters Kluwer Health

RESOURCES