Skip to main content
NCBI home page
Human Brain Mapping logoLink to Human Brain Mapping
. 2015 Sep 14;36(12):5051–5063. doi: 10.1002/hbm.22992

Hippocampal‐DMN disconnectivity in MS is related to WM lesions and depression

Maria A Rocca 1,2, Emanuele Pravatà 1,3, Paola Valsasina 1, Marta Radaelli 2, Bruno Colombo 2, Laura Vacchi 1, Claudio Gobbi 4, Giancarlo Comi 2, Andrea Falini 5, Massimo Filippi 1,2,✉
PMCID: PMC6869286  PMID: 26366641

Abstract

The hippocampus is part of the default‐mode network (DMN) and is functionally hit early in multiple sclerosis (MS). Hippocampal and DMN dysfunctions have been associated with depression, both in patients with MS and in major depressive disorders. We hypothesized that white matter lesions may contribute, through a disconnection mechanism, to hippocampal dysfunction. To test this, we assessed the relationship between hippocampal resting‐state (RS) functional connectivity (FC) abnormalities with brain T2 lesion volumes and the presence and severity of depression. Structural and RS fMRI images were acquired from 69 patients with cognitively intact MS and 42 matched healthy controls (HC). Depression was quantified using the Montgomery–Asberg Depression Rating Scale. Seed‐voxel hippocampal RS FC was assessed. SPM8 was used for between‐group comparisons and correlation analysis between RS FC abnormalities with clinical and structural MRI variables. Compared to HC, patients with MS showed a significant atrophy of the whole brain and left hippocampus (P < 0.001), and a distributed pattern of decreased RS FC between the hippocampi and several cortical–subcortical regions, which were mostly located within the DMN. Reduced hippocampal RS FC with regions of the DMN was strongly correlated with higher T2 lesion volume, longer disease duration, and the severity of depression and disability. In patients with cognitively preserved MS, brain focal WM lesions are related to the functional integration of the hippocampus to other brain regions of the DMN, suggesting a disconnection syndrome. Such a disruption of hippocampal RS FC is likely to contribute to the occurrence of depression and to clinical disability. Hum Brain Mapp 36:5051–5063, 2015. © 2015 Wiley Periodicals, Inc.

Keywords: hippocampus, default‐mode network, resting‐state fMRI, multiple sclerosis, WM lesions, depression

INTRODUCTION

Beyond focally demyelinated lesions in the white matter (WM), damage to the gray matter (GM) is known to occur from the earliest clinical stages of multiple sclerosis (MS) [Geurts and Barkhof, 2008; Kutzelnigg et al., 2005]. Such a damage contributes to some of the clinical manifestations of the disease, including cognitive deficits [Rovaris et al., 2000]. As part of the archeo‐cortex, the hippocampus is another target of demyelination and neuronal loss, which typically take place in MS [Geurts et al., 2007; Papadopoulos et al., 2009]. Hippocampal involvement in MS is clinically relevant, since it has been associated to deficits of verbal and visuospatial memory [Longoni et al., 2015; Roosendaal et al., 2008; Sicotte et al., 2008]. Studies in patients with major depressive disorders have also suggested that the hippocampus is likely involved in the pathogenesis of depression, as shown by a hyperactivity of the hypothalamic–pituitary–adrenal (HPA) axis [Pariante and Lightman 2008], impaired neurogenesis [Miller and Hen, 2015], and reduced hippocampal volumes [Koolschijn et al., 2009]. Depression also affects more than 50% of patients with MS and has been associated with structural hippocampal abnormalities [Gold et al., 2010, 2014; Kiy et al., 2011].

Resting‐state (RS) functional MRI (fMRI) is a task‐free technique which provides an unbiased assessment of the architecture and integrity of brain functional networks by evaluating spontaneous fluctuations of the blood‐oxygen‐level‐dependent (BOLD) signal occurring at rest [Biswal et al., 1995; Cordes et al., 2000]. The application of RS fMRI methods is contributing to provide valuable insights into the pathophysiological mechanisms of several neurological and psychiatric conditions [Greicius, 2008; Greicius et al., 2007; Rocca et al., 2010, 2014]. In this context, the intrinsic activity of the default‐mode network (DMN), a medial cortical network involving several fronto‐parietal brain regions and the hippocampus, which is active at rest, and deactivated during performance of goal‐oriented tasks, is of particular interest [Raichle et al., 2001]. In patients with major depressive disorders, DMN abnormalities have been shown [Bluhm et al., 2009; Chen et al., 2015; Zeng et al., 2012; Zhu et al., 2012] and have been linked to aberrant psychological processes occurring in this condition, such as alterations of self‐referential schemes, cognitive biases, ruminations, and processing mode (over‐general vs. concrete) [Belzung et al., 2015]. An abnormal RS functional connectivity (FC) of the hippocampus with frontal, temporal, and parietal regions has also been demonstrated in these patients [Cao et al., 2012; Shu et al., 2014; Zeng et al., 2012]. Interestingly, abnormalities of the DMN and hippocampal/parahippocampal regions had the highest discriminative power in classifying major depressive individuals vs. healthy controls [Zeng et al., 2012]. At present, only two studies assessed hippocampal RS FC in MS [Hulst et al., in press; Roosendaal et al., 2010] and showed abnormal hippocampal connectivity not only in patients with memory deficits [Hulst et al., in press], but also in those with minimal hippocampal structural damage and intact spatial memory functions [Roosendaal et al., 2010]. To date, there are no studies relating modifications of hippocampal connectivity with depressive symptoms in patients with MS.

In this study, we wished to gain some additional insights into the mechanisms leading to hippocampal dysfunction in MS and its relevance for depression. Our working hypothesis was that WM focal lesions may contribute, through a disconnection mechanism, to hippocampal dysfunction and depression. To test this, we assessed the relationship between abnormalities of hippocampal RS FC with brain T2 lesion volumes (LV) and depression in a relatively large cohort of patients with MS. Since disability has been associated with more severe depression [Chwastiak et al., 2002], we also investigated the correlation between hippocampal disconnectivity and the Expanded Disability Status Scale (EDSS) score.

MATERIALS AND METHODS

Ethics Committee Approval

Approval was received from the local ethical standards committee on human experimentation, and written informed consent was obtained from all subjects prior to study enrolment.

Subjects

Sixty‐nine consecutive relapse‐onset patients with MS and 42 gender‐ and age‐matched healthy controls (HC) were enrolled. All HC had to have no previous history of neurological, psychiatric, or cardiovascular disorders, and a normal neurological examination. To be included, patients had to be relapse‐ and steroid‐free in the previous three months; no significant medical illnesses or substance abuse that could interfere with cognitive functioning; no other major systemic, psychiatric, or neurological diseases. To avoid possible confounding effects on the interpretation of our analysis of correlation with depression, patients with fatigue (defined as a score ≥ 4 at the Fatigue Severity Scale) [Krupp et al., 1989] and cognitive impairment were excluded from the study. Cognitive impairment was evaluated based on the performance at the Brief Repeatable Battery of Neuropsychological Tests [Rao, 1990]. Performance at each test of the battery was considered pathological if patients’ score was <2 SD compared to the Italian normative data [Amato et al., 2006]. To be included, patients had to have abnormalities at less than 2 tests of the battery.

Clinical Assessment

On the same day of MRI acquisition, neurological disability was rated by an experienced observer, blinded to MRI data, using the EDSS. Depressive symptoms were assessed in all patients by administrating the Montgomery–Asberg Depression Rating Scale (MADRS) [Montgomery and Asberg, 1979], a standardized measure of mood disorders, which has been previously used in MS [Amato et al., 2006; Gobbi et al., 2013]. Cumulative scores range from 0 (no depression) to 60 (severe depression).

MRI Acquisition

Using a 3.0 Tesla Philips Intera scanner, the following brain sequences were acquired from all study subjects: (a) dual‐echo (DE) turbo spin echo (TSE) (repetition time (TR)/echo time (TE) = 2,599/16–80 ms; echo train length = 6; flip angle = 90°, 44 contiguous, 3‐mm‐thick, axial slices, matrix size = 256 × 256, field of view [FOV] = 240 × 240 mm2); (b) 3D T1‐weighted fast field echo (FFE) (TR/TE = 25/4.6 ms, flip angle = 30°, 220 contiguous, axial slices, voxel size = 0.89 × 0.89 × 0.8 mm3, matrix size = 256 × 256, FOV = 230 × 230 mm2); and (c) T2*‐weighted single‐shot echo planar imaging (EPI) sequence for RS fMRI (TR = 3,000 ms, TE = 35 ms, flip angle = 90°, FOV = 240 mm2, matrix = 128 × 128, slice thickness = 4 mm, 200 sets of 30 contiguous axial slices after automatic discard of the first two images, parallel to the AC–PC plane, with a total acquisition duration of about 10 min). All subjects were instructed to remain motionless, without thinking anything in particular during scanning. No subject reported to be fallen asleep during scanning, according to a questionnaire delivered immediately after the MRI session.

Structural MRI Analysis

T2 hyperintense and T1 hypointense LV were quantified using a local thresholding segmentation technique (Jim 5, Xinapse Systems Ltd., Northants, UK).

SIENAx and FIRST (version 2.6, both part of FSL 4.1, http://fsl.fmrib.ox.ac.uk/fsl) were used to obtain the normalized brain volume (NBV) and hippocampal volumes from the 3D FFE images. The scaling factor calculated with SIENAx was applied to obtain a normalized hippocampal volume (NHV). Before volume calculation, to correct for misclassification of WM lesions, all pixels classified as GM, but laying neither in cortical GM nor in subcortical GM, were reassigned to WM [Chard et al., 2010].

RS fMRI Data Pre‐processing

To correct for minor head movements, SPM8 (http://www.fil.ion.ucl.ac.uk/spm) was used to realign the raw RS fMRI images to the mean of each session with a six degree rigid‐body transformation. Data were subsequently normalized to the standard SPM8 EPI template using a non‐linear transformation. Then, using the REST software (http://resting-fmri.sourceforge.net), a band‐pass filtering between 0.01 and 0.08 Hz was applied to partially remove low‐frequency drifts and physiological high‐frequency noise, and subsequently to remove non‐neuronal sources of synchrony between RS fMRI time series by regressing out the six motion parameters estimated by SPM8 and the average signals of the ventricular cerebrospinal fluid (CSF) and WM. Finally, images were smoothed using a 3D 6‐mm Gaussian kernel.

RS FC Analysis

Statistical maps of RS FC between the left (L) and right (R) hippocampi, separately, and the remaining voxels of the brain were obtained for each subject using a seed‐region correlation approach [Biswal et al., 1995]. Briefly, masks of the L and R hippocampi obtained for each subject using FIRST were coregistered and normalized to the RS fMRI mean image (obtained after realignment). Then, RS FC was investigated by calculating the correlation coefficients between the time series extracted from the L and R hippocampi and any other voxel in the brain. A Fisher's z transform was used to improve the Gaussianity of the obtained correlation coefficients [Lowe et al., 1998].

Statistical Analysis

Between‐group differences in demographic and clinical variables were investigated using the χ 2 test and the Mann–Whitney U test, as appropriate (SPSS software, version 21.0). Correlations between clinical and conventional MRI variables were assessed using the Spearman's Rank correlation coefficient.

Individual RS FC maps of z‐scores were entered into SPM8 random‐effect analysis to assess RS FC of L and R hippocampus, separately, in HC and patients with MS (one‐sample t test), and between‐group differences (two‐sample t test, age and gender adjusted). In order to verify which clusters of between‐group difference in RS FC were retained within the DMN, the comparison was repeated using the DMN template freely provided by GIFT software (http://icatb.sourceforge.net/groupica.htm) [Franco et al., 2009] as explicit mask. In patients with MS, multiple regression models were run to assess correlations of abnormal RS FC with clinical (disease duration, EDSS, MADRS) and structural MRI (T2 LV, T1 LV, NBV) variables. Correlations were investigated only in brain regions showing abnormal RS FC in patients with MS vs. HC, including age, gender, and NHV as nuisance covariates. All RS FC results were assessed at a threshold of P < 0.05, family‐wise error corrected (FWE) for multiple comparisons and also tested at a P < 0.001, uncorrected (cluster extent = 20 voxels).

RESULTS

Demographic, Clinical, and Structural MRI Findings

The main demographic, clinical, and structural MRI characteristics of patients with MS and HC are reported in Table 1. Results of the neuropsychological evaluation are reported in the Supporting Information Table. Thirty‐nine patients (57%) were free from depressive symptoms (MADRS score = range 0–8), 27 patients (39%) had a mild depression (MADRS score = range 9–17), and 3 patients (4%) had a moderate depression (MADRS score = range 18–34) [Mittmann et al., 1997]. Compared to HC, patients with MS had reduced NBV (P < 0.001), average NHV (P = 0.002), and left NHV (P < 0.001). No significant correlation was found between MADRS and EDSS score (P = 0.4), disease duration (P = 0.6), T2 LV (P = 0.6), T1 LV (P = 0.8), and NHV (P = 0.7). NHV was correlated with T2 LV (r = −0.48, P < 0.001) and EDSS (r = −0.33, P < 0.005).

Table 1.

Main demographic, clinical and structural MRI findings from healthy controls and patients with multiple sclerosis (MS)

HC MS patients P
Number of subjects 42 69 —
Women/men 22/20 36/33 0.6**
Mean age (SD) [years] 36.4 (11.1) 37.5 (9.4) 0.4*
Mean disease duration (range) [years] — 10.8 (1–32) —
Median EDSS (range) — 1.5 (0.0–6.5) —
Median MADRS score (range) — 8.0 (0–30) —
Mean T2 LV (SD) [mL] — 6.7 (5.7) —
Mean T1 LV (SD) [mL] — 4.6 (4.4) —
Mean NBV(SD) [mL] 1,584 (87) 1,522 (79) <0.001*
Mean NHV (SD) [mL] 4.9 (0.5) 4.7 (0.5) 0.002*
Mean L NHV (SD) [mL] 5.0 (0.4) 4.6 (0.6) <0.001*
Mean R NHV (SD) [mL] 4.9 (0.6) 4.7 (0.5) 0.7*
Open in a new tab

*Mann–Withney U test.

**χ2 test.

HC = healthy controls; EDSS = Expanded Disability Status Scale; MADRS= Montgomery‐Asberg Depression Rating Scale; LV = lesion volume; NBV= normalized brain volume; NHV = normalized hippocampal volume, R = right; L = left.

Hippocampal RS FC

Areas showing positive significant correlations (P < 0.001, FWE corrected) with the L and R hippocampus, separately, in HC and patients with MS are shown in Figure 1. Both groups experienced a distributed RS FC of the L and R hippocampi with several limbic, neocortical, and basal ganglia regions. Compared with HC, patients with MS showed reduced RS FC between the L and R hippocampi and several regions located in the frontal, temporal and parietal lobes, the cingulate cortex, insulae, thalami, caudate nuclei, and cerebellum (Table 2; Fig. 1). The large majority of clusters showing significantly decreased RS FC in patients with MS vs HC was located within the DMN (Table 2). No area of increased hippocampal RS FC was found in patients with MS compared to HC.

Figure 1.

Figure 1

Open in a new tab

Hippocampal RS FC in MS patients and HC. A: RS FC analysis showing the positive correlations between the left and right hippocampus and all other brain voxels in patients with MS and healthy controls (HC) (within‐group analysis, one‐sample t‐tests, P < 0.001, family‐wise error corrected). B, C: Areas of decreased hippocampal RS FC in MS patients vs HC (P < 0.001, uncorrected) are superimposed onto a standard template mask of the default mode network (light blue areas) (Franco et al., 2009). Images are in radiological convention. See text for further details.

Table 2.

Brain areas with reduced hippocampal resting state (RS) functional connectivity (FC) in multiple sclerosis (MS) patients compared to healthy controls (HC) (p<0.001, uncorrected)

Anatomical regions Side MNI coordinates BA t values k MNI coordinates (DMN masked) t values k
x y z x y z
Left hippocampus Caudate nucleus§ R 8 10 10 — 4.99* 311 14 18 2 3.53 25
Thalamus 16 −20 14 2.96 311 16 −20 14 3.0 5
Caudate nucleus§ L −8 10 10 — 4.88* 266 −14 20 6 3.0 10
Thalamus −10 −14 6 3.84 266 −10 −16 10 2.96 4
Putamen§ R 26 −8 6 — 4.19* 56 18 18 −2 3.3 5
Middle frontal gyrus§ L −30 44 8 47 3.9* 25 −30 44 8 3.9 25
Insula§ L −34 18 6 48 3.9 49 −34 20 10 3.41 7
Insula§ R 40 16 6 48 3.83 43 40 18 8 3.52 6
Middle frontal gyrus L −24 24 56 8 3.12 10 — — — — —
Supramarginal gyrus§ R 62 −46 32 48 3.74 51 62 −46 32 3.74 49
Angular gyrus§ L −58 −56 26 22 3.71 15 −58 −56 26 3.71 15
Parahippocampal gyrus§ L −30 −42 −6 37 3.58 10 −30 −42 −6 3.58 8
Precuneus§ R 8 −60 24 23 3.35 10 8 −60 24 3.27 5
Medial orbitofrontal gyrus§ R 2 58 −6 10 3.27 5 2 58 −6 3.20 4
Superior temporal gyrus L −54 −14 2 48 3.2 5 — — — — —
Cerebellum§ R 14 −44 −16 30 3.18 5 14 −44 −16 3.18 3
Right Hippocampus Caudate nucleus§ R 10 16 10 — 5.37* 270 12 22 6 3.28 7
Caudate nucleus§ −8 10 8 4.43 311 −12 20 6 3.2 5
Thalamus L −10 −12 10 — 3.85 194 −10 −16 10 3.1 4
Angular gyrus§ R 46 −60 38 39 4.35* 364 46 −60 38 4.35 362*
Middle frontal gyrus§ L −28 44 10 47 3.87 15 −28 44 10 3.87 15
Angular gyrus§ L −56 −56 26 22 3.79 21 −56 −56 26 3.79 21
Putamen§ R 26 −6 8 — 3.72 37 20 20 −2 3.2 4
Inferior frontal Gyrus§ R 38 24 26 48 3.42 12 38 24 26 3.42 7
Putamen L −26 −8 6 — 3.33 8 — — — — —
Cerebellum§ R 16 −26 −32 — 3.23 5 14 −44 −16 3.32 4
Precuneus§ R 6 −60 24 23 3.12 5 6 −60 24 3.0 4
Cingulum§ R 4 42 30 32 3.1 5 4 42 30 3.1 4
Open in a new tab

MNI = Montreal Neurological Institute; BA = Brodmann area; k = cluster extent.

*P < 0.05, family‐wise corrected for multiple comparisons.

Clusters retained within the mask of the default mode network (DMN) (Franco et al., 2009) are marked with§, and corresponding cluster peaks and t values are provided

Correlations Between Hippocampal RS FC and Structural MRI Measures

Table 3 and Figure 2 summarize the results of the analysis of correlation between hippocampal RS FC and structural MRI variables in patients with MS (P < 0.001). Higher T2 LV and T1 LV were significantly correlated to reduced hippocampal RS FC (both for the L and R hippocampus, r ranging from −0.33 to −0.46) with several areas located in the frontal‐parietal‐temporo‐occipital lobes, bilaterally, corresponding to regions which are part of the DMN. Lower NBV was significantly related to reduced R hippocampal RS FC with the R superior frontal gyrus (r = 0.36) and with the R calcarine sulcus (r = 0.39).

Table 3.

Correlations between structural MRI measures and resting state (RS) functional connectivity (FC) between the hippocampus (left and right) and other brain regions in multiple sclerosis (MS) patients (P < 0.001)

Anatomical regions Side BA MNI coordinates t valuesa r
x Y z
T2 LV L Hippocampus Superior frontal gyrus R 10 18 64 2 2.7 −0.37
Middle occipital gyrus L 39 −40 −68 24 3.4 −0.44
Precuneus L 23 −10 −58 20 2.9 −0.39
R Hippocampus Precuneus R 30 4 −60 18 3.9 −0.43
Medial orbitofrontal gyrus L 11 2 62 −8 3.9 −0.45
Middle occipital gyrus L 39 −42 −68 24 2.9 −0.36
Medial orbitofrontal gyrus R 10 8 58 28 2.8 −0.34
T1 LV L Hippocampus Precuneus L 23 −10 −58 20 2.9 −0.39
Middle temporal gyrus L 39 58 −70 20 2.8 −0.37
Posterior cingulum R 23 2 −62 20 3.3 −0.43
Middle occipital gyrus L 39 −40 −68 24 3.0 −0.40
R Hippocampus Precuneus L 23 −6 −64 20 3.7 −0.46
Medial orbitofrontal gyrus R 11 2 62 −8 3.6 −0.43
Superior frontal gyrus R 10 8 58 28 2.9 −0.35
Middle occipital gyrus L 39 −42 −68 24 2.8 −0.34
Middle temporal gyrus L 39 −46 −66 26 2.7 −0.33
NBV R Hippocampus Calcarine/precuneus R 23 2 −62 18 3.0 0.39
Superior frontal gyrus R 10 22 70 8 2.8 0.36
Open in a new tab
a

t values derived from linear contrasts on beta estimates of SPM8 multiple regression models; critical t value of significance=2.7.

MNI = Montreal Neurological Institute; BA = Brodmann area; LV = lesion volume; R = right; L = left; NBV = normalized brain volume.

Correlations were investigated only in brain regions showing abnormal RS FC in MS patients vs healthy controls.

Figure 2.

Figure 2

Open in a new tab

Correlation between hippocampal RS FC and MRI measures in MS patients. Results (and illustrative scatterplots) of the analysis of correlation in patients with MS between reduced RS FC of the left (L) (green) and right (R) (red) hippocampus and brain T2 lesion volume (LV), T1 LV, and normalized brain volume (NBV) (P < 0.001, uncorrected). The T2 LV and T1 LV results are superimposed onto a standard template mask of the default mode network (light blue areas) (Franco et al., 2009). Green scatterplots refer to correlations with L hippocampal RS FC, while red scatterplots refer to correlations with R hippocampal RS FC. Images are in radiological convention. See text for further details.

Correlations Between Hippocampal RS FC and Depression

Table 4 and Figure 3 summarize the results of the analysis of correlation between reduced hippocampal RS FC and depression in patients with MS (P < 0.001). Both for the L and R hippocampus, reduced RS FC with the R middle temporal gyrus was significantly correlated with the severity of depression (r = −0.35 and −0.29, respectively). For the R hippocampus, significant correlation was also found between higher MADRS and reduced RS FC with the L precuneus and R superior orbitofrontal gyrus (r = −0.29 and −0.32, respectively).

Table 4.

Correlations between clinical measures and resting state (RS) functional connectivity (FC) between the hippocampus (left and right) and other brain regions in multiple sclerosis (MS) patients (P < 0.001)

Anatomical regions Side BA MNI coordinates t valuesa r
x y z
MADRS L Hippocampus Middle temporal gyrus R 22 66 −48 12 3.0 −0.35
R Hippocampus middle temporal gyrus R 21 64 −50 16 2.7 −0.29
Precuneus R 7 10 −64 38 2.8 −0.29
Superior orbitofrontal gyrus L 11 −18 48 −12 2.8 −0.32
Disease duration L Hippocampus Hippocampus L 27 −20 −38 4 3.2 −0.42
Angular gyrus L 39 −54 −52 36 2.9 −0.37
Angular gyrus R 39 54 −66 32 3.4 −0.44
Middle frontal gyrus L 9 −30 30 44 3.2 −0.42
Middle temporal gyrus R 22 54 −50 18 3.1 −0.41
R Hippocampus Angular gyrus R 39 42 −64 36 3.0 −0.39
Angular gyrus L 39 −42 −66 44 3.2 −0.41
Middle frontal gyrus L 9 −30 28 44 3.3 −0.43
Superior frontal gyrus R 10 10 56 28 2.7 −0.34
Middle cingulate cortex L 23 −2 −36 42 2.8 −0.37
EDSS L Hippocampus Middle occipital gyrus R 39 40 −80 24 4.4 −0.50
Middle frontal gyrus L 46 −40 34 40 2.7 −0.31
Middle occipital gyrus L 39 −46 84 28 4.1 −0.48
Precuneus L 7 −6 −66 50 2.7 −0.32
R Hippocampus Middle occipital gyrus L 39 −58 −70 24 3.8 −0.44
Middle frontal gyrus L 45 −42 32 38 2.7 −0.33
Angular gyrus R 39 36 −66 42 3.2 −0.39
Middle occipital gyrus R 39 44 −78 28 2.7 −0.29
Angular gyrus L 7 −36 −60 30 3.1 −0.38
Open in a new tab
a

t values derived from linear contrasts on beta estimates of SPM8 multiple regression models; critical t value of significance=2.7.

MNI = Montreal Neurological Institute; BA = Brodmann area; L = left; R = right; EDSS = Expanded Disability Status Scale; MADRS = Montgomery–Asberg Depression Rating Scale.

Correlations were investigated only in brain regions showing abnormal RS FC in MS patients vs healthy controls.

Figure 3.

Figure 3

Open in a new tab

Correlation between hippocampal RS FC and depression and clinical measures in MS patients. Results (and illustrative scatterplots) of the analysis of correlation in patients with MS between reduced RS FC of the left (L) (green) and right (R) (red) hippocampus and Montgomery–Asberg Depression Rating Scale (MADRS), disease duration, and Expanded Disability Status Scale (EDSS) (P < 0.001, uncorrected). Green scatterplots refer to correlations with L hippocampal RS FC, while red scatterplots refer to correlations with R hippocampal RS FC. Images are in radiological convention. See text for further details. Abbreviations: MTG = middle temporal gyrus; MOG = middle occipital gyrus.

Correlations Between Hippocampal RS FC and Other Clinical Measures

Table 4 and Figure 3 summarize the results of the analysis of correlation between reduced hippocampal RS FC and clinical variables in patients with MS (P < 0.001). A longer disease duration was significantly correlated to reduced hippocampal RS FC (both for the L and the R hippocampus) with the contralateral hippocampus, L angular gyrus, and L middle frontal gyrus (r ranging from −0.37 to −0.43). A longer disease duration was also significantly correlated with reduced RS FC between the L hippocampus and the R angular gyrus and middle temporal gyrus (MTG) (r = −0.44 and −0.39, respectively), as well as between the R hippocampus, the R superior frontal gyrus, and the L middle cingulate cortex (r = −0.34 and −0.37, respectively).

Higher EDSS was significantly correlated to reduced hippocampal RS FC (both for the L and R hippocampus) with the bilateral middle occipital gyrus, bilateral angular gyrus, and the L middle frontal gyrus (r ranging from −0.29 to −0.50).

DISCUSSION

In this study, we tested the hypothesis that clinical symptoms attributable to a hippocampal dysfunction in patients with MS might be, at least in part, due to a functional disconnection of this structure due to the presence of lesions in the brain WM. Previous RS FC studies in healthy subjects have demonstrated that the hippocampus is part of the DMN [Greicius et al., 2004; Vincent et al., 2006], an integrated brain system which is active at rest and plays an established role in the hierarchical organization of all brain networks [Crossley et al., 2014; Gusnard et al., 2001; Raichle et al., 2001]. Abnormalities of DMN RS FC are thought to be involved in the clinical symptomatology of a number of brain disorders, including MS [Buckner et al., 2008]. Disrupted DMN connectivity has been also found in individuals with major depression [Bluhm et al., 2009; Chen et al., 2015; Zeng et al., 2012; Zhu et al., 2012]. As a consequence, among the many hippocampal RS FC links found to be damaged in patients with cognitively‐intact MS, the disruption of those connecting the hippocampus to the main hubs of the DMN (i.e., the anterior medial prefrontal regions and the precunei) [Buckner et al., 2008; Fox et al., 2005] are of particular interest in the context of depression. Such findings indicate the occurrence of an early detachment of the hippocampal functional subset from the rest of the DMN in patients with MS, independently from the development of relevant cognitive impairment, and suggest a link between altered DMN RS FC and the complex cognitive and emotional disturbances related to depression. Although additional longitudinal investigations in patients with MS with different degrees of cognitive deficits are needed to assess the impact of such a hippocampal disconnectivity on behavioral and cognitive functions, it is worth noting that in our cohort hippocampal disconnection was correlated to disease duration, suggesting a loosening of hippocampal functional synchronization with time in these patients.

The notion that the hippocampus is involved in the pathogenesis of depression is supported by previous studies in psychiatric patients with major depression [Koolschijn et al., 2009] and depressed MS patients [Gold et al., 2014; Kiy et al., 2011], which have shown that tissue loss in this structure is related to the severity of depression. Our study showed a significant association between depression and reduced RS FC in the orbitofrontal cortex, MTG, and precuneus. The limbic‐cortical circuit involving the medial orbitofrontal cortex is considered important in mood and affect regulation [Mayberg, 2003], and the involvement of orbitofrontal regions in depression has been well documented, both in patients with MS [Feinstein et al., 2004; Gobbi et al., 2013] and in other neurological conditions [Canu et al., 2015; Kostic et al., 2010; Mayberg et al., 1992]. The MTG is located in the dorsal attention system and is involved in cued attention and working memory. The correlation we found between decreased MTG connectivity and higher MADRS scores is in line with previous studies of major depression [Ma et al., 2012] and might be related with an abnormal processing of complex emotional visual stimuli. The precuneus has a crucial role in the integration of mental processing, through its role in cognitive control processes such as visual imagery, episodic memory, and self‐directed operations [Cavanna and Trimble, 2006]. Abnormal RS FC of the precuneus has been found in depressed patients [Peng et al., 2015; Zhu et al., 2012], which was thought to be linked to a significant dissociation between the DMN and relevant sensory, motor, and attention‐processing systems. In patients with MS, a more prominent role for depression of the right hippocampus, particularly in women, was postulated by structural MRI investigations [Gold et al., 2014]. In our cohort (which included both women and men), a correlation between abnormalities of RS FC and depression was found for both the right and left hippocampus, thus not supporting such a laterality effect. Noteworthy, abnormalities of hippocampal connectivity do not seem to be driven by atrophy of this structure, since tissue loss was present only in the left and not the right hippocampus, suggesting that modifications of function might precede those of structure in the pathogenesis of depression. Clearly, as suggested by post mortem investigations, we cannot exclude that the presence of other pathological abnormalities in the hippocampi, including focal lesions and extensive demyelination [Dutta et al., 2011; Geurts et al., 2007], might have led to such an hippocampal dysfunction. In this perspective, synaptic alterations have been correlated to hippocampal demyelination in the absence of neuronal loss in a post mortem study [Dutta et al., 2011].

Differently from previous atrophy studies, which found no relationship between hippocampal abnormalities and disability [Longoni et al., 2015; Sicotte et al., 2008], in our study hippocampal RS FC disruption was correlated to the severity of disability, which might be due to the role played by the hippocampus in the control of the so‐called ventral striatal loop, a complex circuit including the fornix, ventral striatum, ventral pallidum, and prefrontal cortex, which is involved in motor behavior [Duvernoy, 2005]. We tend to exclude that the correlation we found between RS FC abnormalities and disability might be influenced by depression, since no significant association was found between EDSS and MADRS, and only a minimal overlap was found between regions of abnormal RS FC correlated with EDSS and those correlated with MADRS.

Another intriguing finding of our investigation comes from the analysis of correlation between hippocampal RS FC abnormalities and lesional measures of disease burden in the WM, which showed a distributed association between reduced hippocampal RS FC with many DMN regions and higher T2 LV and T1 LV. These results suggest that the accumulation of focal WM lesions diffusely in the brain is likely to be one of the factors affecting the altered interaction between the hippocampal formation and other regions of the DMN. The notion that WM lesions have an influence on the hippocampus in MS is not novel, since it has been shown by previous atrophy studies, which found more pronounced hippocampal atrophy in patients with higher T2 LV [Longoni et al., 2015; Sicotte et al., 2008]. We found that such an effect of lesions on the hippocampus might also result in a disturbed functional interaction at a network level. Different mechanisms might contribute to explain the effect of lesions on hippocampal RS FC, including transynaptic degeneration of afferent/efferent WM tracts connected to the hippocampus passing through WM lesions and/or the selective involvement of WM tracts which contribute to the architecture of the DMN (e.g., the cingulum, superior‐fronto‐occipital fasciculus, and corpus callosum) [van den Heuvel et al., 2009]. The effect of lesions on hippocampal disconnectivity was much more pronounced and distributed than that of global atrophy, which influenced hippocampal connectivity only with a few and sparse cortical areas. This suggests that WM macroscopic damage accumulation and tissue loss play different roles in the pathophysiology of hippocampal dysfunctional changes.

Our study is not without limitations. First, it is cross‐sectional. As a consequence it was not possible to establish the exact temporal relationship between WM lesion formation and hippocampal disconnectivity, and clinical outcomes, including depression. Future longitudinal investigations are therefore warranted. Second, correlations between RS FC abnormalities and structural damage were assessed by using T2/T1 LV, which are global, relatively unspecific measures of brain damage. Future studies including more sophisticated measures of structural damage (such as diffusion tensor MRI and tractography) might be able to disclose closer associations between depression, RS FC abnormalities, and regional distribution of WM abnormalities. Third, hippocampal function was investigated by analyzing depression. As a consequence, these findings need to be replicated for memory impairment, another deficit related to hippocampal damage/dysfunction. However, the selection of patients with MS with isolated memory impairment might result relatively difficult, since these patients typically experience a multidomain cognitive impairment. Finally, the evaluation of hippocampal structural and functional circuitry in patients at the clinical onset of the disease (i.e., clinically isolated syndromes) is of paramount importance to define whether such a hippocampal disconnectivity might represent an early biomarker of the disease, useful to monitor the dynamics of its manifestations and progression.

Authors Contribution

M.A. Rocca: study concept and design; acquisition of data; analysis and interpretation of data; drafting the article, and final approval of the version to be published. E. Pravatà: analysis and interpretation of data; drafting the article, and final approval of the version to be published. P. Valsasina: study design; statistical analysis; analysis and interpretation of data; revising the article for important intellectual content; and final approval of the version to be published. M. Radaelli: patient recruitment; revising the article for important intellectual content; and final approval of the version to be published. B. Colombo: patient recruitment; revising the article for important intellectual content; and final approval of the version to be published. L. Vacchi: neuropsychological evaluation (including assessment of fatigue and depression), analysis and interpretation of data; revising the article for important intellectual content; and final approval of the version to be published. C. Gobbi: interpretation of the data; revising the article for important intellectual content; and final approval of the version to be published. G. Comi: interpretation of the data; revising the article for important intellectual content; and final approval of the version to be published. A. Falini: acquisition of the data; revising the article for important intellectual content; and final approval of the version to be published. M. Filippi: study concept and design; revising the article for important intellectual content; and final approval of the version to be published; study supervision and coordination.

Supporting information

Supporting Information

Click here for additional data file. (14.5KB, docx)

Conflict of interests: E. Pravatà, P. Valsasina, M. Radaelli, B. Colombo, L. Vacchi, C. Gobbi, and A. Falini report no disclosures. M.A. Rocca received speakers honoraria from Biogen Idec, Novartis, Genzyme and ExCemed and receives research support from the Italian Ministry of Health and Fondazione Italiana Sclerosi Multipla. G. Comi has received compensation for consulting services and/or speaking activities from Novartis, Teva Pharmaceutical Ind., Sanofi, Genzyme, Merck Serono, Biogen Bayer, Actelion and ExCemed. M. Filippi is Editor‐in‐Chief of the Journal of Neurology; serves on scientific advisory boards for Teva Pharmaceutical Industries; has received compensation for consulting services and/or speaking activities from Biogen Idec, Excemed, Novartis, and Teva Pharmaceutical Industries; and receives research support from Biogen Idec, Teva Pharmaceutical Industries, Novartis, Italian Ministry of Health, Fondazione Italiana Sclerosi Multipla, Cure PSP, Alzheimer's Drug Discovery Foundation (ADDF), the Jacques and Gloria Gossweiler Foundation (Switzerland), and ARiSLA (Fondazione Italiana di Ricerca per la SLA).

REFERENCES

  1. Amato MP, Portaccio E, Goretti B, Zipoli V, Ricchiuti L, De Caro MF, Patti F, Vecchio R, Sorbi S, Trojano M (2006): The Rao's Brief Repeatable Battery and Stroop Test: Normative values with age, education and gender corrections in an Italian population. Mult Scler 12:787–793. [DOI] [PubMed] [Google Scholar]
  2. Belzung C, Willner P, Philippot P (2015): Depression: From psychopathology to pathophysiology. Curr Opin Neurobiol 30:24–30. [DOI] [PubMed] [Google Scholar]
  3. Biswal B, Yetkin FZ, Haughton VM, Hyde JS (1995): Functional connectivity in the motor cortex of resting human brain using echo‐planar MRI. Magn Reson Med 34:537–541. [DOI] [PubMed] [Google Scholar]
  4. Bluhm R, Williamson P, Lanius R, Theberge J, Densmore M, Bartha R, Neufeld R, Osuch E (2009): Resting state default‐mode network connectivity in early depression using a seed region‐of‐interest analysis: Decreased connectivity with caudate nucleus. Psychiatry Clin Neurosci 63:754–761. [DOI] [PubMed] [Google Scholar]
  5. Buckner RL, Andrews‐Hanna JR, Schacter DL (2008): The brain's default network: Anatomy, function, and relevance to disease. Ann N Y Acad Sci 1124:1–38. [DOI] [PubMed] [Google Scholar]
  6. Canu E, Kostic M, Agosta F, Munjiza A, Ferraro PM, Pesic D, Copetti M, Peljto A, Tosevski DL, Filippi M (2015): Brain structural abnormalities in patients with major depression with or without generalized anxiety disorder comorbidity. J Neurol 262:1255–1265. [DOI] [PubMed] [Google Scholar]
  7. Cao X, Liu Z, Xu C, Li J, Gao Q, Sun N, Xu Y, Ren Y, Yang C, Zhang K (2012): Disrupted resting‐state functional connectivity of the hippocampus in medication‐naive patients with major depressive disorder. J Affect Disord 141:194–203. [DOI] [PubMed] [Google Scholar]
  8. Cavanna AE, Trimble MR (2006): The precuneus: A review of its functional anatomy and behavioural correlates. Brain 129:564–583. [DOI] [PubMed] [Google Scholar]
  9. Chard DT, Jackson JS, Miller DH, Wheeler‐Kingshott CA (2010): Reducing the impact of white matter lesions on automated measures of brain gray and white matter volumes. J Magn Reson Imaging 32:223–228. [DOI] [PubMed] [Google Scholar]
  10. Chen Y, Wang C, Zhu X, Tan Y, Zhong Y (2015): Aberrant connectivity within the default mode network in first‐episode, treatment‐naive major depressive disorder. J Affect Disord 183:49–56. [DOI] [PubMed] [Google Scholar]
  11. Chwastiak L, Ehde DM, Gibbons LE, Sullivan M, Bowen JD, Kraft GH (2002): Depressive symptoms and severity of illness in multiple sclerosis: Epidemiologic study of a large community sample. Am J Psychiatry 159:1862–1868. [DOI] [PubMed] [Google Scholar]
  12. Cordes D, Haughton VM, Arfanakis K, Wendt GJ, Turski PA, Moritz CH, Quigley MA, Meyerand ME (2000): Mapping functionally related regions of brain with functional connectivity MR imaging. AJNR Am J Neuroradiol 21:1636–1644. [PMC free article] [PubMed] [Google Scholar]
  13. Crossley NA, Mechelli A, Scott J, Carletti F, Fox PT, McGuire P, Bullmore ET (2014): The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain 137:2382–2395. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Dutta R, Chang A, Doud MK, Kidd GJ, Ribaudo MV, Young EA, Fox RJ, Staugaitis SM, Trapp BD (2011): Demyelination causes synaptic alterations in hippocampi from multiple sclerosis patients. Ann Neurol 69:445–454. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Duvernoy HM (2005): The Human Hippocampus: Functional Anatomy, Vascularization and Serial Sections with MRI. Berlin: Springer. [Google Scholar]
  16. Feinstein A, Roy P, Lobaugh N, Feinstein K, O'Connor P, Black S (2004): Structural brain abnormalities in multiple sclerosis patients with major depression. Neurology 62:586–590. [DOI] [PubMed] [Google Scholar]
  17. Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME (2005): The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci USA 102:9673–9678. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Franco AR, Pritchard A, Calhoun VD, Mayer AR (2009): Interrater and intermethod reliability of default mode network selection. Hum Brain Mapp 30:2293–2303. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Geurts JJ, Barkhof F (2008): Grey matter pathology in multiple sclerosis. Lancet Neurol 7:841–851. [DOI] [PubMed] [Google Scholar]
  20. Geurts JJ, Bo L, Roosendaal SD, Hazes T, Daniels R, Barkhof F, Witter MP, Huitinga I, van der Valk P (2007): Extensive hippocampal demyelination in multiple sclerosis. J Neuropathol Exp Neurol 66:819–827. [DOI] [PubMed] [Google Scholar]
  21. Gobbi C, Rocca M, Riccitelli G, Pagani E, Messina R, Preziosa P, Colombo B, Rodegher M, Falini A, Comi G, Filippi M (2013): Influence of the topography of brain damage on depression and fatigue in patients with multiple sclerosis. Mult Scler 20:192–201. [DOI] [PubMed] [Google Scholar]
  22. Gold SM, Kern KC, O'Connor MF, Montag MJ, Kim A, Yoo YS, Giesser BS, Sicotte NL (2010): Smaller cornu ammonis 2‐3/dentate gyrus volumes and elevated cortisol in multiple sclerosis patients with depressive symptoms. Biol Psychiatry 68:553–559. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Gold SM, O'Connor MF, Gill R, Kern KC, Shi Y, Henry RG, Pelletier D, Mohr DC, Sicotte NL (2014): Detection of altered hippocampal morphology in multiple sclerosis‐associated depression using automated surface mesh modeling. Hum Brain Mapp 35:30–37. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Greicius M (2008): Resting‐state functional connectivity in neuropsychiatric disorders. Curr Opin Neurol 21:424–430. [DOI] [PubMed] [Google Scholar]
  25. Greicius MD, Srivastava G, Reiss AL, Menon V (2004): Default‐mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI. Proc Natl Acad Sci USA 101:4637–4642. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Greicius MD, Flores BH, Menon V, Glover GH, Solvason HB, Kenna H, Reiss AL, Schatzberg AF (2007): Resting‐state functional connectivity in major depression: Abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol Psychiatry 62:429–437. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Gusnard DA, Akbudak E, Shulman GL, Raichle ME (2001): Medial prefrontal cortex and self‐referential mental activity: Relation to a default mode of brain function. Proc Natl Acad Sci USA 98:4259–4264. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hulst HE, Schoonheim MM, Van Geest Q, Uitdehaag BM, Barkhof F, Geurts JJ: Memory impairment in multiple sclerosis: Relevance of hippocampal activation and hippocampal connectivity. Mult Scler DOI: 10.1177/1352458514567727. [DOI] [PubMed] [Google Scholar]
  29. Kiy G, Lehmann P, Hahn HK, Eling P, Kastrup A, Hildebrandt H (2011): Decreased hippocampal volume, indirectly measured, is associated with depressive symptoms and consolidation deficits in multiple sclerosis. Mult Scler 17:1088–1097. [DOI] [PubMed] [Google Scholar]
  30. Koolschijn PC, van Haren NE, Lensvelt‐Mulders GJ, Hulshoff Pol HE, Kahn RS (2009): Brain volume abnormalities in major depressive disorder: A meta‐analysis of magnetic resonance imaging studies. Hum Brain Mapp 30:3719–3735. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Kostic VS, Agosta F, Petrovic I, Galantucci S, Spica V, Jecmenica‐Lukic M, Filippi M (2010): Regional patterns of brain tissue loss associated with depression in Parkinson disease. Neurology 75:857–863. [DOI] [PubMed] [Google Scholar]
  32. Krupp LB, LaRocca NG, Muir‐Nash J, Steinberg AD (1989): The fatigue severity scale. Application to patients with multiple sclerosis and systemic lupus erythematosus. Arch Neurol 46:1121–1123. [DOI] [PubMed] [Google Scholar]
  33. Kutzelnigg A, Lucchinetti CF, Stadelmann C, Bruck W, Rauschka H, Bergmann M, Schmidbauer M, Parisi JE, Lassmann H (2005): Cortical demyelination and diffuse white matter injury in multiple sclerosis. Brain 128:2705–2712. [DOI] [PubMed] [Google Scholar]
  34. Longoni G, Rocca MA, Pagani E, Riccitelli GC, Colombo B, Rodegher M, Falini A, Comi G, Filippi M (2015): Deficits in memory and visuospatial learning correlate with regional hippocampal atrophy in MS. Brain Struct Funct 220:435–444. [DOI] [PubMed] [Google Scholar]
  35. Lowe MJ, Mock BJ, Sorenson JA (1998): Functional connectivity in single and multislice echoplanar imaging using resting‐state fluctuations. Neuroimage 7:119–132. [DOI] [PubMed] [Google Scholar]
  36. Ma C, Ding J, Li J, Guo W, Long Z, Liu F, Gao Q, Zeng L, Zhao J, Chen H (2012): Resting‐state functional connectivity bias of middle temporal gyrus and caudate with altered gray matter volume in major depression. PLoS One 7:e45263. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Mayberg HS (2003): Modulating dysfunctional limbic‐cortical circuits in depression: Towards development of brain‐based algorithms for diagnosis and optimised treatment. Br Med Bull 65:193–207. [DOI] [PubMed] [Google Scholar]
  38. Mayberg HS, Starkstein SE, Peyser CE, Brandt J, Dannals RF, Folstein SE (1992): Paralimbic frontal lobe hypometabolism in depression associated with Huntington's disease. Neurology 42:1791–1797. [DOI] [PubMed] [Google Scholar]
  39. Miller BR, Hen R (2015): The current state of the neurogenic theory of depression and anxiety. Curr Opin Neurobiol 30:51–58. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Mittmann N, Mitter S, Borden EK, Herrmann N, Naranjo CA, Shear NH (1997): Montgomery‐Asberg severity gradations. Am J Psychiatry 154:1320–1321. [DOI] [PubMed] [Google Scholar]
  41. Montgomery SA, Asberg M (1979): A new depression scale designed to be sensitive to change. Br J Psychiatry 134:382–389. [DOI] [PubMed] [Google Scholar]
  42. Papadopoulos D, Dukes S, Patel R, Nicholas R, Vora A, Reynolds R (2009): Substantial archaeocortical atrophy and neuronal loss in multiple sclerosis. Brain Pathol 19:238–253. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Pariante CM, Lightman SL (2008): The HPA axis in major depression: Classical theories and new developments. Trends Neurosci 31:464–468. [DOI] [PubMed] [Google Scholar]
  44. Peng D, Liddle EB, Iwabuchi SJ, Zhang C, Wu Z, Liu J, Jiang K, Xu L, Liddle PF, Palaniyappan L, Fang Y (2015): Dissociated large‐scale functional connectivity networks of the precuneus in medication‐naive first‐episode depression. Psychiatry Res 232:250–256. [DOI] [PubMed] [Google Scholar]
  45. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL (2001): A default mode of brain function. Proc Natl Acad Sci USA 98:676–682. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Rao SM and the Cognitive Function Study Group of the National Multiple Sclerosis Society (1990): A manual for the Brief Repeatable Battery of Neuropsychological Tests in multiple sclerosis. Medical College of Wisconsin, Milwaukee, WI. [Google Scholar]
  47. Rocca MA, Valsasina P, Absinta M, Riccitelli G, Rodegher ME, Misci P, Rossi P, Falini A, Comi G, Filippi M (2010): Default‐mode network dysfunction and cognitive impairment in progressive MS. Neurology 74:1252–1259. [DOI] [PubMed] [Google Scholar]
  48. Rocca MA, Valsasina P, Absinta M, Moiola L, Ghezzi A, Veggiotti P, Amato MP, Horsfield MA, Falini A, Comi G, Filippi M (2014): Intranetwork and internetwork functional connectivity abnormalities in pediatric multiple sclerosis. Hum Brain Mapp 35:4180–4192. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Roosendaal SD, Moraal B, Vrenken H, Castelijns JA, Pouwels PJ, Barkhof F, Geurts JJ (2008): In vivo MR imaging of hippocampal lesions in multiple sclerosis. J Magn Reson Imaging 27:726–731. [DOI] [PubMed] [Google Scholar]
  50. Roosendaal SD, Hulst HE, Vrenken H, Feenstra HE, Castelijns JA, Pouwels PJ, Barkhof F, Geurts JJ (2010): Structural and functional hippocampal changes in multiple sclerosis patients with intact memory function. Radiology 255:595–604. [DOI] [PubMed] [Google Scholar]
  51. Rovaris M, Filippi M, Minicucci L, Iannucci G, Santuccio G, Possa F, Comi G (2000): Cortical/subcortical disease burden and cognitive impairment in patients with multiple sclerosis. AJNR Am J Neuroradiol 21:402–408. [PMC free article] [PubMed] [Google Scholar]
  52. Shu H, Yuan Y, Xie C, Bai F, You J, Li L, Li SJ, Zhang Z (2014): Imbalanced hippocampal functional networks associated with remitted geriatric depression and apolipoprotein E epsilon4 allele in nondemented elderly: A preliminary study. J Affect Disord 164:5–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Sicotte NL, Kern KC, Giesser BS, Arshanapalli A, Schultz A, Montag M, Wang H, Bookheimer SY (2008): Regional hippocampal atrophy in multiple sclerosis. Brain 131:1134–1141. [DOI] [PubMed] [Google Scholar]
  54. van den Heuvel MP, Mandl RC, Kahn RS, Hulshoff Pol HE (2009): Functionally linked resting‐state networks reflect the underlying structural connectivity architecture of the human brain. Hum Brain Mapp 30:3127–3141. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Vincent JL, Snyder AZ, Fox MD, Shannon BJ, Andrews JR, Raichle ME, Buckner RL (2006): Coherent spontaneous activity identifies a hippocampal‐parietal memory network. J Neurophysiol 96:3517–3531. [DOI] [PubMed] [Google Scholar]
  56. Zeng LL, Shen H, Liu L, Wang L, Li B, Fang P, Zhou Z, Li Y, Hu D (2012): Identifying major depression using whole‐brain functional connectivity: A multivariate pattern analysis. Brain 135:1498–1507. [DOI] [PubMed] [Google Scholar]
  57. Zhu X, Wang X, Xiao J, Liao J, Zhong M, Wang W, Yao S (2012): Evidence of a dissociation pattern in resting‐state default mode network connectivity in first‐episode, treatment‐naive major depression patients. Biol Psychiatry 71:611–617. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supporting Information

Click here for additional data file. (14.5KB, docx)

Articles from Human Brain Mapping are provided here courtesy of Wiley

RESOURCES