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. Author manuscript; available in PMC: 2011 Aug 1.
Published in final edited form as: Neuropsychopharmacology. 2010 Nov 17;36(3):684–691. doi: 10.1038/npp.2010.199

WHITE MATTER CHANGES IN HEALTHY ADOLESCENTS AT FAMILIAL RISK FOR UNIPOLAR DEPRESSION: A DIFFUSION TENSOR IMAGING STUDY

Hao Huang 1, Xin Fan 2, Douglas E Williamson 3, Uma Rao 1
PMCID: PMC3036948  NIHMSID: NIHMS267620  PMID: 21085111

Abstract

Alterations in white matter integrity of several cortical and sub-cortical circuits have been reported in relation to unipolar major depressive disorder. It is not clear whether these white matter changes precede the onset of illness. Thirteen adolescent volunteers with no personal or family history of a psychiatric disorder (controls) and 18 adolescent volunteers with no personal history of a psychiatric illness including depression, but who were at high risk for developing unipolar depression by virtue of parental depression (high-risk youth), underwent diffusion tensor imaging studies. An automated tract-based spatial statistics (TBSS) method, a whole-brain voxel-by-voxel analysis, was used to analyze the scans. Population average diffusion parameter values also were calculated for each tract. Adolescents at high risk for unipolar depression had lower fractional anisotropy (FA) values in the left cingulum, splenium of the corpus callosum, superior longitudinal fasciculi, uncinate and inferior fronto-occipital fasciculi than controls. Altered white matter integrity in healthy adolescents at familial risk for unipolar depression suggests that it might serve as a vulnerability marker for the illness.

Keywords: adolescent, corpus callosum, depression, high-risk, magnetic resonance imaging, tract-based spatial statistical analysis

INTRODUCTION

Unipolar Depression is among the leading causes of disability world-wide (World Bank, 2006). Adolescence is the highest risk period for the development of unipolar depressive disorder, and there is evidence for an increasing secular trend (Hankin et al, 1998; Kessler et al, 2005). There also are data demonstrating that early depressive episodes recur or persist into adult life along with ongoing psychosocial difficulties including disruption in interpersonal relationships, early pregnancy, low educational attainment, poor occupational functioning and unemployment, as well as increased risk for suicidal behavior (Rao and Chen, 2009). A better understanding of the etiology and pathophysiology of adolescent depression will have significant personal and public health impact.

Adolescent depression emerges in the context of ongoing maturational changes in the brain (Ernst and Korelitz, 2009; Paus et al, 2008). In the grey matter, these changes take the form of increased myelination of different cortical connections with a net reduction in volume, but these changes may or may not be related to synaptic pruning (Benes et al, 1994; Giedd et al, 1999; Giorgio et al, 2010; Gogtay et al, 2004; Huttenlocher, 1979). There is a simultaneous increase in white matter density associated with increases in the diameter and myelination of the axons forming the fiber tracts alongside increased neuronal size and proliferation of glia (Giedd et al, 1999; Giorgio et al, 2010; Asato et al, 2010; Paus, 2010; Schmithorst and Yuan, 2010). Disturbances in these developmental patterns can adversely affect behavioral, emotional and cognitive control (Ernst and Korelitz, 2009; Paus et al, 2008).

In vivo structural and functional imaging studies as well as post-mortem investigations of adults suggest that cortical-subcortical neural circuits play an important role in the pathogenesis of depression, especially frontal-striatal-thalamic and limbic-thalamic-frontal networks (Mayberg, 2003; Price and Drevets, 2010; Rogers et al, 1998). Limited structural and functional imaging data in pediatric populations also support these models (Pine, 2002; Rosenberg et al, 2006). White matter abnormalities constitute one element of these dysfunctional networks (Fields, 2008; Maller et al, 2010; Sexton et al, 2009). White matter connects proximal and distal brain regions and creates large-scale neural networks facilitating complex behaviors (Fields, 2008; Le Bihan, 2003).

Diffusion tensor imaging (DTI) is a non-invasive technique for studying the orientation and integration of white matter tracts in vivo by measuring the diffusion of water in neural tissue (Moseley et al, 1990; Basser et al, 1994). Water diffuses more easily along the axis of a fiber bundle than across it, as structures such as the axon membrane and myelin sheath hinder diffusion across the bundle. This directional dependence in water diffusion can provide quantitative measures of white matter microstructural integrity (Beaulieu, 2009). A commonly used metric in DTI is fractional anisotropy (FA) (Pierpaoli and Basser, 1996). FA reflects aspects of membrane integrity and myelin thickness, and decreased FA is associated with disruption of white matter (Beaulieu 2009). Several DTI studies reported reduced FA in adult patients with depression, particularly in the frontal and temporal regions (Kieseppa et al, 2010; Maller et al, 2010; Sexton et al, 2009; Shimony et al, 2009). Microstructural white matter abnormalities also were detected during the first-episode of depression in young adult patients (Ma et al. 2007) as well as in depressed adolescents (Cullen et al, 2010). However, it is not clear if the white matter changes precede the clinical manifestation of illness. The evidence of premorbid white matter abnormalities would suggest that they are vulnerability markers for depression and potentially can be helpful in identifying individuals at greatest risk for the disorder.

The present investigation was undertaken to examine white matter tract integrity in healthy adolescents who were at high risk for developing unipolar depression by virtue of family history. Genetic factors explain about 50%–90% of the variance in FA in large portions of the white matter (Chiang et al, 2009a, 2009b; Kochunov et al, 2010; Liu et al, 2010), and might serve as a vulnerability marker in at-risk individuals. Hence, we hypothesized that adolescents at familial risk for unipolar depression, without clinical manifestation of the disorder, will have reduced FA values in white matter tracts. Tract-based spatial statistics (TBSS 1.0, FMRIB Center, Oxford, United Kingdom), a relatively new software package specifically designed for the analysis of diffusion-weighted data (Smith et al, 2006), was used for whole brain analysis. TBSS was developed to alleviate the alignment problems related to standard voxel-wise analysis. It measures and compares individual subject’s FA values within core or skeletons of white matter voxels. Unlike conventional voxel-wise analysis, no spatial smoothing is necessary where the amount of smoothing can significantly affect the final results. In addition to TBSS analysis, structural integrity of full white matter tracts was assessed with the aid of a digital white matter atlas (Mori et al, 2008) to test if a specific white matter tract detected by TBSS was disrupted entirely. This reduces false positive results of local voxel-based group comparisons.

METHODS AND MATERIALS

Participants

With approval from the local Institutional Review Board, 13 adolescent controls and 18 youth at high risk for depression and were recruited. All participants were between 12–20 years (Controls: mean age = 15.5 years, SD = 3.0; and High-Risk = 15.7 ± 2.3 years), and Tanner Stage III, IV or V of pubertal development (Controls: 15.4%, 23.1%, and 61.5%, respectively; and High-Risk: 16.7%, 16.7%, and 66.6%, respectively) (Marshall and Tanner, 1969, 1970). Adolescents at high-risk for depression had no personal history of a psychiatric disorder, including depression, but at least one biological parent had a history of unipolar major depressive disorder. Controls were free from any type of psychopathology in their lifetime. Controls were not included in the study if any first-degree relative had history of a major psychiatric disorder. All participants were medically healthy and free from alcohol or illicit drug use, as determined by physical examination, full chemistry panel, thyroid function tests, electrocardiogram and urine drug screens. Females with suspected pregnancy were excluded from the study, based on urine pregnancy test.

Sociodemographic Information

Information on race/ethnicity was gathered from self-report, and socioeconomic status (SES) was assessed with the Hollingshead Scale (Hollingshead, 1975). Intelligence Quotient (IQ) was estimated from vocabulary and block design scores using Wechsler Intelligence Scale for Children (WISC IV) (Wechsler 2003) for ages <16 years and Wechsler Adult Intelligence Scale (WISC III) (Wechsler 1997) was employed for ages ≥16 years.

Diagnostic Evaluation

Symptoms of psychiatric disorders were assessed using the Schedule for Affective Disorders and Schizophrenia for School-Age Children - the Present and Lifetime Version (K-SADS-PL). The K-SADS-PL is a semi-structured interview designed to ascertain present and lifetime history of psychiatric illness according to Diagnostic and Statistical Manual of Mental Disorders – Version IV (DSM-IV) criteria (Kaufman et al, 1997). Probes and objective criteria are provided for individual symptoms at both diagnostic and sub-threshold levels. Inter-rater and test-retest reliability have been established, as well as convergent and discriminate validity (Kaufman et al, 1997). The K-SADS-PL was administered separately to the parent and the adolescent, and both were re-interviewed to resolve any discrepancies. Summary scores were tabulated based on the information obtained from both informants. The Hamilton Depression Rating Scale (Hamilton, 1960), a depression severity measure, and Children’s Global Assessment Scale (Shaffer et al, 1983), a global psychosocial functioning measure, also were completed. The adolescent participants completed the Beck Depression Inventory (BDI) for self-assessment of depression severity (Beck et al, 1961).

The Family History-Research Diagnostic Criteria (FH-RDC), a semi-structured interview, was used for the evaluation of psychiatric disorders in family members (Andreasen et al, 1977). A parent was interviewed regarding life-time psychiatric disorders in all first-degree relatives of the adolescent subject (including the self, spouse, and all offspring). The FH-RDC is sensitive in obtaining information from knowledgeable relatives (Thompson et al, 1982).

DTI Acquisition

A 3T Philips Achieva Magnetic Resonance System was used. DTI data were acquired using a single-shot echo-planar imaging (EPI) sequence with SENSE parallel imaging scheme (SENSitivity Encoding, reduction factor = 2.3). The imaging matrix was 112×112 with a field of view of 224×224mm (nominal resolution of 2mm), which was zero filled to 256×256. Axial slices of 2.2 mm thickness were acquired parallel to the anterior-posterior commissure (AC-PC) line. A total of 65 slices covered the entire hemisphere and brainstem without a gap. The echo time (TE) and repetition time (TR) were 97ms and 7.78s, respectively, without cardiac gating. The diffusion weighting was encoded along 30 independent orientations and the b value was 1000s/mm2 (Jones et al, 1999). Imaging time for each sequence was 5 minutes and 15 seconds. To increase the signal-to-noise ratio (SNR), two repetitions were performed, with a total imaging time of 12 minutes. Automated image registration (AIR) was performed on raw diffusion weighted images to correct distortion caused by eddy current (Woods et al, 1998). Six elements of the 3×3 diffusion tensor were determined by multivariate least-square fitting of diffusion weighted images. The tensor was diagonalized to obtain three eigenvalues (λ1−3) and eigenvectors (ν1−3). Anisotropy was measured by calculating FA (Pierpaoli and Basser, 1996). The tensor fitting and FA calculation were done using DtiStudio (Jiang et al, 2006).

Data Analysis

Voxel-wise comparison

TBSS from FMRIB software library (FSL, http://www.fmrib.ox.ac.uk/fsl) was used for voxel-wise comparison (Smith et al, 2006). This voxel-wise method compared the FA values of each group at core or skeletons of the white matter to effectively alleviate the partial volume effects. Modifications were made to the standard TBSS processing pipeline to better incorporate information of white matter labeling from a digital white matter atlas developed in Johns Hopkins University (JHU ICBM-DTI-81; Mori et al, 2008). Specifically, the single subject template used for nonlinear registration process in TBSS is identical to the template used for establishing the digital white matter atlas JHU ICBM-DTI-81. In this way, all the subjects’ FA data were transformed into JHU ICBM-DTI-81 space, and the atlas labeling is overlaid to the mean skeleton in the JHU ICBM-DTI-81 space such that each skeleton voxel could be categorized into one of the 50 major tracts. The details of this method combining TBSS and digital white matter atlas can be found in a previous publication (Fan et al., 2010).

A voxel-wise comparison was conducted in the JHU ICBM-DTI-81 space by using Randomise of TBSS. The significant clusters with p < .001 (uncorrected) in the skeleton voxels of white matter were identified. In order to avoid false positive results due to noise, only clusters with continuous voxels larger than 10 and averaged FA values greater than 0.25 were retained with home-made IDL (ITT, Boulder CO) programs. FA values at the voxels of these clusters were measured to calculate the averaged FA value of each subject with the skeletonised FA data (all_FA_skeletonised) provided by TBSS outcome. The white matter tract to which each cluster belongs was identified with the labeling of ICBM-DTI-81 atlas. These averaged FA values represent the FA measurement of each subject at the cluster location. The group average and standard deviation was calculated and student t tests were performed based on FA measurements at the clusters. In addition, following the small volume FDR correction analysis from Versace et al. (2008) and Cullen et al. (2010), anatomically defined regional masks containing around 1,000 skeleton voxels were selected in the relevant white matter tract and FSL statistical tool was used for multiple comparison correction of the small volume.

Tract-level comparison

Tract-level analysis was based on the FA values at the skeleton voxels after TBSS registration, projection and skeletonization steps. The entire white matter tract was considered to be possibly disrupted if it contained filtered significant clusters revealed by analyses mentioned in the voxel-wise comparison. In most cases, these clusters are small portions of the entire white matter tract. In order to test whether a tract was disrupted, the FA values of all skeleton voxels of a specific tract were averaged. Student t tests were performed with these averaged FA values for tracts containing significantly disrupted clusters. For this exploratory study, no multiple comparison correction was used for the tract-level statistical comparison. Since the template used for nonlinear registration in TBSS is the same as that used to generate the JHU ICBM-DTI-81 atlas, white matter labeling from JHU ICBM-DTI-81 atlas could cover the entire skeletons of major white matter tracts. The overlaid region of the white matter skeleton and JHU ICBM-DTI-81 atlas with a specific white matter labeling was used to calculate the averaged FA representing the integrity of the entire tract.

Figure 1 depicts the TBSS integration process. As shown in Figure 1a, several steps of the TBSS functions were used to project the FA value of white matter to the skeleton or core of the white matter with FA data of all subjects at the atlas space. Figure 1b depicts the ICBM-DTI-81 atlas. By transferring the labeling of the individual white matter tract (e.g., genu of the corpus callosum), we can label the skeleton FA data of all subjects in the ICBM-DTI-81 space, as shown in Figure 1c. Then, the atlas labeling is overlaid to the mean skeleton in the ICBM-DTI-81 space such that each skeleton voxel could be categorized into one of the 50 major tracts.

Figure 1.

Figure 1

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Illustration of tract-based quantification of fractional anisotropy of white matter tracts. White matter tract labels for eleven major tracts are taken from Johns-Hopkins Diffusion Tensor Imaging White Matter Atlas. GCC = genu of the corpus callosum; ACR = anterior corona radiata; EC = external capsule; PLIC = posterior limb of internal capsule; RLIC= retrolenticular limb of internal capsule; PTR = posterior thalamic radiation; SCC = splenium of the corpus callosum; CgC = cingulate cortex; SLF = superior longitudinal fasciculus; FX = fornix; ALIC = anterior limb of internal capsule.

RESULTS

Sociodemographic and Clinical Characteristics of the Sample

Demographic and clinical features of the sample are outlined in Table 1. The groups did not differ significantly with respect to age, gender, ethnicity/race, pubertal status, SES, IQ or psychosocial functioning. The high-risk group reported significantly more depressive symptoms (BDI score) but the mean score was within the normal range.

Table 1.

Sociodemographic and clinical characteristics by diagnosis

Control (n = 13) High-Risk (n = 18) Statistic p
Age (years) 15.5 (3.0) 15.7 (2.3) 0.22 NS
 Gender .01 NS
 male 6 (46.2) 8 (44.4)
 female 7 (53.8) 10 (55.6)
Ethnicity 0.30 NS
 Caucasian 4 (30.8) 5 (27.8)
 Non-Caucasian 9 (69.2) 13 (72.2)
Pubertal status 0.20 NS
 Tanner Stage III 2 (15.4) 3 (16.7)
 Tanner Stage IV 3 (23.1) 3 (16.7)
 Tanner Stage V 8 (61.5) 12 (66.6)
Socioeconomic status1 46.1 (9.7) 40.7 (6.3) 1.83 NS
Beck Depression Inventory 1.9 (3.8) 7.1 (5.9) 2.57 .02
Hamilton Depression Scale 1.7 (1.5) 2.5 (2.7) 0.98 NS
Global Assessment Scale1 86.4 (5.0) 81.2 (9.7) 1.64 NS
Intelligence Quotient1 106.0 (15.3) 101.7 (15.6) 0.59 NS
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1

Higher score is associated with higher socioeconomic status or higher level of functioning

Data in parentheses reflect standard deviations or percentages

Relationship between White Matter Changes and Sociodemographic/Clinical Features

None of the white matter tracts were associated with sociodemographic or clinical variables.

Voxel-wise Comparison of White Matter Changes

The high-risk group had significantly lower FA values in several white matter tracts including the left cingulum bundle, left and right superior longitudinal fasciculus (SLF), left and right combined bundles of uncinate (UNC) and inferior fronto-occipital fasciculus (IFO), and the splenium (posterior-third of the corpus callosum) (see Table 2). It appears that the significant clusters (p<.001, uncorrected; p<.05, after FDR correction of small volume) are not widespread, but quite localized in the white matter (see Figure 2). Including the BDI score as a covariate did not alter the results. Compared to controls, the high-risk group did not show significantly higher FA values in any white matter tracts.

Table 2.

Voxel-based clusters in which adolescents at familial risk for depression had significantly lower fractional anisotropy (p <.001, uncorrected; p<.05, after FDR correction of small volume) compared with controls

Control (n = 13) High-Risk (n = 18) Statistic P
Cingulum (left) 0.41 (0.05) 0.34 (0.03) 5.12 .0001
Superior longitudinal fasciculus
 Left 0.52 (0.03) 0.45 (0.03) 5.57 .0001
 Right 0.49 (0.05) 0.43 (0.04) 3.49 .0008
UNC-IFO
 left 0.62 (0.05) 0.55 (0.04) 3.79 .0003
 right 0.59 (0.05) 0.51 (0.06) 3.65 .0005
Splenium of corpus callosum 0.72 (0.07) 0.57 (0.09) 4.52 .0001
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UNC-IFO = combined uncinate and inferior fronto-occipital fasciculus

Data in parentheses reflect standard deviations

Figure 2.

Figure 2

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The significant clusters obtained from voxel-wise comparisons between control and high-risk groups. Green color indicates white matter skeletons and red color shows clusters with significant FA reduction in the high-risk group (p < .001). Underlying gray scale images are the averaged FA maps, depicting different tracts. Left, middle and right columns of each panel show the images of axial, coronal and sagittal views. White arrows indicate the clusters of the specified white matter tracts in the left cerebral hemisphere if multiple clusters are shown in the image. FA = fractional anisotropy; CG-L = left cingulate, SLF-L = left superior longitudinal fasciculus; SCC = splenium of the corpus callosum; UNC-IFO-L = left uncinate-inferior fronto-occipital fasciculus.

Tract-level Comparison of White Matter Changes

In these analyses, the high-risk group manifested disruptions only in the left and right SLF (uncorrected p = .02 for left SLF, and p = .03 for right SLF) and splenium (uncorrected p = .03), suggesting a large portion of the skeleton voxels in these two tracts have decreased FA values.

DISCUSSION

To the best of our knowledge, this is the first report of white matter tract changes in healthy adolescents at familial risk for unipolar depression. These findings suggest that adolescents at high risk for developing unipolar depression manifest disruptions in several white matter tracts, including the cingulum, SLF, UNC-IFO and corpus callosum, prior to the clinical manifestation of the disorder.

A combined voxel-wise and tract-level analysis was applied in this study. Voxel-wise analysis revealed local white matter structural changes. Although these local changes are significant, they cannot be used to represent the entire white matter tracts which contain many more skeleton voxels than the small clusters. Therefore, a further comparison at the tract level was conducted. Nevertheless, the tract-level analysis has its limitation which would inevitably exclude the tracts that lack significant reduction in overall FA values, thereby possibly missing changes in some potential regions. For example, despite the lack of a significant difference in the entire cingulum bundle, the disruption of a small cluster in it should not be neglected for the cingulum’s role in emotional network (Mayberg, 2003; Price and Drevets, 2010).

These results should be interpreted with caution for the following reasons. The participants were recruited from a group of volunteers based on stringent inclusion/exclusion criteria, and the findings might not be generalizable to community samples of adolescents. The sample sizes were modest and the findings should be replicated in larger samples. In this exploratory study, we did not control for multiple comparisons because we wanted to identify potential white matter changes associated with familial risk for unipolar depression. Hence, some reported changes might result from chance findings. Pubertal status was assessed only from physical characteristics (Marshall and Tanner, 1969, 1970), and gonadal steroid levels were not obtained. Gonadal steroids can potentially influence depressive symptoms and brain development (Angold et al, 1999; Neufang et al, 2009). Although the BDI score (or other clinical variables) did not correlate with FA values in any of the white matter tracts, the higher BDI score in the high-risk group could have contributed to the reduced white matter integrity (Williamson et al., 2010). The groups did not differ significantly on sociodemographic factors and these variables did not affect the FA values. Nevertheless, the modest sample sizes might have mitigated their effects on white matter integrity. Despite these limitations, a state-of-the-art voxel-based method with relatively stringent criteria was utilized to examine white matter tract changes. In addition, the structural integrity of entire white matter tracts was compared between the high risk and control groups with the per-tract average of FA values. Tract-level analysis indicated the structural changes of entire tracts and helped reduce the false positive outcomes which could result from noise effects or local FA changes (e.g., caused by crossing fibers) in voxel-wise analysis.

Prior research has demonstrated evidence of genetic control on the variability in cerebral white matter through FA analysis (Chiang et al, 2009a, 2009b; Kochunov et al, 2010; Liu et al, 2010). Moreover, genetic factors have been shown to mediate the association between white matter integrity and cognitive performance (Chiang et al, 2009b). Linkage analysis identified c15q25 to be associated with FA (Kochunov et al, 2010), which also has been implicated in early-onset recurrent major depressive disorder (Holmans et al, 2004; Shyn and Hamilton, 2010). Reduced FA in major white matter tracts in adolescents at familial risk for depression, coupled with its high heritability, suggests that it might be an endophenotype of genetic susceptibility for depression. Although decreased FA was not associated with depressive symptoms in this study, in a large cohort of non-referred adolescents at high- and low-familial-risk for depression (n = 320), higher frequency of depressive symptoms was associated with lower FA in several white matter tracts including the SLF and splenium (Williamson et al, 2010). Longitudinal follow-up of these cohorts will determine whether reduced FA in high-risk youth will predict the onset of depression. Simultaneous incorporation of environmental factors will provide information on gene-environment interactions in determining the risk for unipolar depression (Johansen-Berg, 2010).

Consistent with prior reports (Cullen et al, 2010; Maller et al, 2010; Sexton et al, 2009), reduced FA was observed in the cingulate gyrus. The cingulum bundle links the cingulate gyrus with the hippocampus and parahippocampal gurus (Schmahmann et al, 2007). The cingulate cortex is crucial for a wide range of emotional and motivational processes as well as spatial working memory, and is involved in the pathophysiology of depression (Mayberg, 2003; Price and Drevets, 2010; Schmahmann et al, 2007; Shimony et al, 2009). In the current study, the changes in the cingulum bundle were localized (observed only in voxel-wise analysis).

SLF is a major bidirectional association tract connecting large parts of the frontal cortex with the parietal, temporal and occipital lobes. It is comprised of several subtypes and each subtype is involved in specific behavioral and cognitive functions (Schmahmann et al, 2007). Lower FA in SLF has been reported in some studies of unipolar depression (Cullen et al, 2010; Zou et al, 2008). The UNC links the rostral portion of the temporal lobe (e.g., amygdala and hippocampus) with inferior portions of the frontal lobe (orbital and medial prefrontal cortices). The UNC has a prolonged period of development (Lebel et al, 2008). The function of UNC is not known but it is thought to be involved in cognitive and socio-emotional regulation, and alterations in this tract have been associated with a variety of psychiatric disorders including depression (Cullen et al, 2010; Schmahmann et al, 2007; Sexton et al, 2009). The IFO connects the inferior and lateral margins of the occipital lobe to the inferolateral and dorsolateral regions of the frontal lobe, and it is involved in emotional visual function (Catani et al, 2002). Alterations in emotional visual perception and reduced FA in IFO have been observed in depression (Cullen et al, 2010; Kieseppa et al, 2010; Phillips et al, 2003). The observed reduction in FA values in these tracts in adolescents at high familial risk for depression prior to the clinical manifestation of illness suggests that reduced FA might be a vulnerability marker for unipolar depression.

The corpus callosum is the largest commissural projection in the central nervous system, and it is comprised of extensive networks sub-serving not only motor and sensory systems but also memory, attention, emotion, language and intelligence (Gazzaniga, 2000; Paul et al, 2006; Tamietto et al, 2007). The corpus callosum develops throughout childhood and adolescence (Giedd et al, 1999; Keshavan et al, 2002), and disruptions in this maturational process might be associated with behavioral and emotional disorders. The splenium specifically communicates somatosensory information between the two halves of the temporal lobe and visual center at the occipital lobe. Altered FA in the corpus callosum has been reported in adult unipolar and bipolar depression (Kieseppa et al, 2010; Maller et al, 2010; Sexton et al, 2009). A recent study of adolescents with bipolar disorder, many of whom were in euthymic state, also reported reduced FA throughout the corpus callosum (Barnea-Goraly et al, 2009). These findings, together with the results from the current study and from a larger sample of adolescents at familial risk for depression (Williamson et al, 2010), suggest that reduced FA in the corpus callosum might be a trait marker for mood disorders, and possibly the splenium might be more vulnerable.

In summary, white matter tract changes were observed before the manifestation of clinical symptoms of depression in at-risk adolescents. Longitudinal studies with larger samples will determine whether the observed microstructural white matter changes in the high-risk youth are associated with increased vulnerability for developing depressive disorder, which could have potential implications for identifying youngsters at highest risk for the disorder.

Acknowledgments

This work was supported in part by grants DA14037, DA15131, DA17804, DA17805, MH62464 and MH68391 from the National Institutes of Health, and by the Sarah M. and Charles E. Seay Endowed Chair in Child Psychiatry at UT Southwestern Medical Center.

Financial Disclosures: The authors do not have any financial conflicts of interest.

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