Journal of Neuroendocrinology, 2015, 27, 609–615 © 2015 British Society for Neuroendocrinology
ORIGINAL ARTICLE
Alterations of Functional Connectivity Among Resting-State Networks in Hypothyroidism S. Singh*, M. Kumar*, S. Modi*, P. Kaur*, L. R. Shankar† and S. Khushu* *NMR Research Centre, Institute of Nuclear Medicine and Allied Sciences (INMAS), Timarpur, Delhi, India. †Thyroid Research Centre, Timarpur, Delhi, India.
Journal of Neuroendocrinology
Correspondence to: Dr Subash Khushu, NMR Research Centre, INMAS, DRDO, Lucknow Road, Timarpur, Delhi 110054, India (e-mail: [email protected]).
Hypothyroidism affects brain functioning as suggested by various neuroimaging studies. The primary focus of the present study was to examine whether hypothyroidism would impact connectivity among resting-state networks (RSNs) using resting-state functional magnetic resonance imaging (rsfMRI). Twenty-two patients with hypothyroidism and 22 healthy controls were recruited and scanned using rsfMRI. The data were analysed using independent component analysis and a dual regression approach that was applied on five RSNs that were identified using FSL software (http://fsl.fmrib.ox.ac.uk). Hypothyroid patients showed significantly decreased functional connectivity in the regions of the right frontoparietal network (frontal pole), the medial visual network (lateral occipital gyrus, precuneus cortex and cuneus) and the motor network (precentral gyrus, postcentral gyrus, precuneus cortex, paracingulate gyrus, cingulate gyrus and supramarginal gyrus) compared to healthy controls. The reduced functional connectivity in the right frontoparietal network, the medial visual network and the motor network suggests neurocognitive alterations in hypothyroid patients in the corresponding functions. However, the study would be further continued to investigate the effects of thyroxine treatment and correlation with neurocognitive scores. The findings of the present study provide further interesting insights into our understanding of the action of thyroid hormone on the adult human brain. Key words: resting-state fMRI, functional connectivity, hypothyroidism
Hypothyroidism is among the most prevalent endocrine disorders affecting the population worldwide (1). It is characterised by a high thyroid-stimulating hormone (TSH) level and low tri-iodothyronine and thyroxine (T4) levels. A change in thyroid hormone levels in adulthood has been associated with cognitive dysfunction, disturbed attention and depressed moods (2). Several neuropsychological studies have also shown cognitive deficits in attention, language, memory and visuospatial skills, as well as depressive mood and psychomotor slowing in association with hypothyroidism (2–4). Neuroimaging studies have also reported functional, structural and metabolic changes in hypothyroidism (5–9). Functional magnetic resonance imaging (fMRI) studies have shown deficits in brain regions associated with working memory and motor skills in hypothyroid patients (5,6). These studies have shown alterations of taskinduced deactivation within default mode network (DMN) regions during working memory processing (5) and deficits in motor areas during a finger tapping task (6). A previous 31P magnetic resonance spectroscopy study has shown a decreased ratio of phosphocreatine
doi: 10.1111/jne.12282
to inorganic phosphate in the frontal lobe as a result of hypothyroidism, which increased after thyroid hormone treatment (9). These findings suggest that there are reversible changes in the adult cerebral phosphate metabolism in the frontal lobe as a result of hypothyroidism treatment (9). Recent voxel-based morphometry studies in humans (7,8) have also demonstrated grey and white matter volume loss in a hypothyroid population. These studies have shown morphological changes in the motor areas, cerebellum, frontal gyrus, occipital gyrus and temporal gyrus, as well as the hippocampus of hypothyroid patients (7,8). A diffusion tensor imaging study from our group has shown microstructural changes in the white matter fibres of the brain that may contribute to underlying dysfunction in memory in hypothyroid patients (10). However, to the best of our knowledge, no study has assessed brain functional connectivity in an adult hypothyroid population using resting-state fMRI (rsfMRI). rsfMRI represents another fMRI technique for investigating the functional connectivity of the brain that measures spontaneous low-frequency fluctuations in the blood oxygenation level-dependent (BOLD) signal during the resting condition (11). rsfMRI experi-
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ments are less prone to multisite variability, allow a wider range of patients to be scanned and make it possible to study multiple cortical systems from one dataset (12). Application of this technique has allowed the identification of various resting-state networks (RSNs) or spatially distinct areas of the brain that demonstrate synchronous BOLD fluctuations at rest. The major RSNs that have been shown to possess strong temporal coherence include the visual, auditory and language networks, the visual, auditory and language networks, the executive control network, the dorsal and ventral attention network and the DMN, etc. (13–15). The existence of these networks has been consistently verified across healthy controls and diseased conditions. There are several studies that have shown alterations in the functional connectivity network in different pathological conditions, including attention deficit hyperactivity disorder (16), Alzheimer’s disease (17), obsessive compulsive disorder (18), depression (19), bipolar disorder (20) and schizophrenia (21). At present, rsfMRI has not been applied to hypothyroid patients to assess possible alterations in resting-state connectivity patterns in these patients. Accordingly, in the present study, we performed rsfMRI in hypothyroid patients compared to controls (age/sex matched). The present study therefore aimed to investigate the resting-state functional brain connectivity changes associated with hypothyroidism in all resting-state networks as identified by the independent component analysis (ICA) approach.
Materials and methods Subjects Forty-four subjects participated in the present study. Out of 44 participants, 22 participants were hypothyroid and 22 were healthy controls. All hypothyroid patients were newly diagnosed with elevated TSH and low free T4 (FT4) levels, and were recruited from outpatients at Thyroid Research Centre, Institute of Nuclear Medicine and Allied Sciences (INMAS), Defence Research and Development Organization (DRDO), Delhi, India. All control subjects chosen for the study were recruited from the local community of INMAS and none of the group displayed any clinical evidence of stroke, head injury, cardiovascular diseases, history of smoking, alcohol or drug dependence, or psychiatric disorders. Both patients and controls showed no neurological abnormalities on conventional MRI scans. The institutional research ethics committee approved the study and informed consent was obtained from all subjects.
Image acquisition The study was carried out using a 3T whole body MR system (Magnetom Skyra; Siemens, Erfurt, Germany) with a circularly polarised 20 channel matrix head and neck coil and a 45 mT/m actively shielded gradient system. Subjects lay in the supine position with their heads supported and immobilised within the head coil using foam-pads (vendor provided), to minimise head movement and gradient noise. For anatomical reference, a T1-weighted three-dimensional gradient echo sequence (magnetisation-prepared rapid acquisition gradient echo, 160 sagittal slices, slice thickness = 1 mm, field of view = 256 mm, TR = 1900 ms, TE = 2.07 ms) image data set was acquired. Functional brain volumes were acquired using an echo-planar T2*weighted imaging sequence. Each volume consisted of 30 interleaved 5-mm © 2015 British Society for Neuroendocrinology
thick slices without interslice gap (TE = 30 ms, TR = 2000 ms, FOV = 240 mm, flip angle = 90°, voxel size = 3.75 9 3.75 9 5 mm3). Total scanning time was 410 s (205 brain volumes), during which the subjects were asked to keep their eyes closed without thinking of anything in particular and not to fall asleep.
Statistical analysis The resting-state functional data were first pre-processed using the FMRI Expert Analysis Tool (FEAT), which is a part of FSL (FMRIB Software Library; http://fsl.fmrib.ox.ac.uk). For individual subject analyses, functional brain volumes (after deleting the first five brain volumes to ensure steady-state longitudinal magnetisation) were corrected for slice timing and head movement. None of the subjects included in the present study had head movement exceeding 1.5 mm or 1.5° in any direction. The data was smoothed with a Gaussian kernel of full-width at half-maximum of 5 mm and preprocessed with high-pass temporal filtering (with a cut-off of 100 s) and low-pass filtering to retain frequencies ˂ 0.1 Hz to remove signal from non-neuronal causes and improve the signal-to-noise ratio. fMRI volumes were registered to the individual’s structural scan and standard space images [Montreal Neurological Institute (MNI)-152 template] using FMRIB’s Linear Image Registration Tool (FLIRT) (22). Individual structural images for use as high resolution images in the registration step were brain-extracted using the Brain Extraction Tool (BET) (23). When running FEAT, the Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC) ICA (Independent Component Analysis) data exploration option was turned on (with ‘automatic dimensionality estimation’ option) to gain insight into unexpected artefacts or activation in the data. The noise components from the ICAs were noted and removed using MELODIC denoising. The MELODIC toolbox of FSL was then used to decompose the denoised data into a set of 35 time courses and associated spatial maps that jointly describe the temporal and spatial characteristics of underlying hidden signals (components) using probabilistic independent component analysis (24). Out of the 35 independent components (ICs) obtained, again, few were RSNs (with a power spectra graph having a typical mono-peak pattern in the 0.01–0.03 Hz region) and others were noise components. The noise components were again removed using MELODIC denoising and MELODIC was run again to decompose the denoised data into a set of twenty independent components. The between-group analysis of the resting data was carried out using a regression technique (dual regression), which allows for voxel-wise comparisons of resting functional connectivity (25,26). For this, MELODIC was run on the denoised data (entire sample) in Concat-ICA mode (multisession temporal concatenation). A general linear model was then defined to create a multi-subject design matrix defining groups [two groups: Healthy Control (C) and Hypothyroid Patients (H)] and contrast files (two contrasts: C versus H and H versus C). A new regressor for age of the subjects was also added. The set of spatial maps from the group-average analysis was used to generate subject-specific versions of the spatial maps, and associated time-series, using dual regression (25,26). First, for each subject, the group average set of spatial maps is regressed into the subject’s four-dimensional space-time dataset. This results in a set of subject-specific time-series: one per grouplevel spatial map. Next, those time series are regressed into the same fourdimensional dataset, resulting in a set of subject-specific spatial maps, one per group-level spatial map. In accordance with Zuo et al. (27), the spatial maps representing RSNs were selected using a two-step ICs selection process. First, the visual selection of the IC of interest was carried out where we compared our output maps with those found in the literature (15,28–30). As a second step, the power spectra graph output of FSL elaborations showing the variability of low frequency fluctuations in every IC was analysed. The power spectra graph of a network shows a typical mono-peak pattern in the 0.01–0.03 Hz Journal of Neuroendocrinology, 2015, 27, 609–615
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Table 1. Demographic and Clinical Characteristics of the Subjects.
Age (years) Sex (male/female) Education (years) BMI (kg/cm2) FT4 (pmol/l) TSH (lIU/ml)
Results
Normal controls (n = 22)
Hypothyroid patients (n = 22)
28.5 5.82 7/15 12.6 2.56 23.76 3.08 17.23 2.50 2.15 0.35
28.8 5.52 6/16 11.9 3.01 24.24 4.16 3.20 2.68 71.65 58.14
Data are the mean SD. BMI, body mass index; FT4, free thyroxine; TSH, thyroid-stimulating hormone. region, whereas an artefact power spectrum shows a chaotic multi-peak pattern in the 0–0.25 Hz range. Voxel-wise analyses of the group differences between the hypothyroid and control group was carried out using FSL randomised nonparametric permutation-testing with 10 000 permutations for each IC of interest (31). Threshold-free cluster enhancement (32) was used to control for multiple comparisons and the significance threshold was set to P < 0.05 corrected for family-wise error. The results characterised the probabilistic statistical maps, representing the group differences in functional connectivity for all RSNs of interest. The statistical maps were then upsampled to a standard MNI 1-mm brain Montreal atlas to better localise the areas of RSN alterations. The Harvard–Oxford cortical and subcortical atlases (Harvard Centre for Morphometric Analysis), which are provided with the FSL software, were used to identify the anatomical representation of the clusters of the resulting probabilistic independent component analysis maps that showed significant differences between the two groups using the ‘autoaq’ script.
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Clinical assessment Demographic characteristics and serum thyroid hormone levels of patients and controls are presented in Table 1. Serum thyroid indices (FT4 and TSH) were measured with the help of an Electro Chemiluminescence Immuno Assay Kit (Elecsys 2010; Roche Diagnostics, Mannheim, Germany). The thyroid function tests were in the normal range for controls: FT4 = 12.0–22.0 pmol/l and TSH = 0.27–4.2 lIU/ml. All hypothyroid patients were diagnosed with elevated TSH and abnormally low FT4 levels. In hypothyroid patients, the FT4 range was 0.5–7.8 pmol/l and the TSH range was 21–252 lIU/ml.
rsfMRI A total of five components (Fig. 1) were identified as RSNs from the group MELODIC output, including the right frontoparietal network (15), left frontoparietal network (15), medial visual network (15), motor network (15,30) and DMN (15). Hypothyroid patients showed significantly decreased temporal correlation in the right frontoparietal network compared to control subjects in frontal pole (Fig. 2A and Table 2). Significantly decreased functional connectivity was also observed within the medial visual network in hypothyroid patients relative to healthy controls, namely in the lateral occipital gyrus, precuneus cortex and cuneus (Fig. 2B
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Fig. 1. Resting-state networks identified using independent component analysis, which was used for the dual regression analysis: (A) Right frontoparietal attention network. (B) Left frontoparietal attention network. (C) Medial visual network. (D) Motor network. (E) Default-mode network. R, right; L, left. Journal of Neuroendocrinology, 2015, 27, 609–615
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Fig. 2. Output of the randomised analysis for the control versus hypothyroid patient group contrast threshold at P < 0.05 (family-wise error corrected). Reduced connectivity was observed in regions of the (A) right frontoparietal network, (B) medial visual network and (C) Motor network. Results are shown on a Montreal Neurological Institute 1-mm standard atlas. R, right; L, left.
and Table 2). The motor network also showed a decreased connectivity in the precentral gyrus, postcentral gyrus, precuneus cortex, paracingulate gyrus, cingulate gyrus and supramarginal gyrus (Fig. 2C and Table 2). No significant differences between control and hypothyroid subjects were found in the left frontoparietal network and DMN connectivity. In the above RSNs, no clusters were obtained where hypothyroid patients had increased connectivity compared to healthy controls. When a correlation analysis was carried out between RSNs and T4 and TSH values in all the subjects, a positive correlation between the functional connectivity and T4 levels was obtained in the regions of right frontoparietal network and motor network (Fig. 3A,B and Table 3). The regions of motor network also showed negative correlation with TSH values (Fig. 3C and Table 3).
Discussion The present rsfMRI study aimed to investigate whether the intrinsic functional connectivity of RSNs might be altered in hypothyroid patients compared to healthy controls. Because there is nothing © 2015 British Society for Neuroendocrinology
available in the literature concerning resting-state functional connectivity alterations in hypothyroid patients, we studied the RSNs identified in our subjects using ICA. Our results revealed significantly reduced resting-state functional connectivity in regions of the right frontoparietal network, the medial visual network and the motor network in hypothyroid patients. We also found a correlation between the altered functional connectivity in the right frontoparietal network and motor network and T4 and TSH values. The frontoparietal networks consist of the parietal and frontal cortex and are implicated in working memory and cognitive attentional processes (14,33). Our findings indicate that the frontal pole showed decreased functional connectivity in hypothyroid patients compared to healthy controls. The frontal pole also showed positive correlation with T4 values in the right frontoparietal network. The frontal pole is the frontal part of the prefrontal cortex that corresponds to Brodmann’s area 10 of the human brain (34,35). The frontal pole plays a critical role in many aspects of complex cognitive tasks, including relational integration, multitasking and memory tasks (36–38). In an earlier study, Zhu et al. (39) reported that working memory is impaired in hypothyroidism and subclinical Journal of Neuroendocrinology, 2015, 27, 609–615
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Table 2. Summary of the Differences Detected in Control Versus Hypothyroid Patient Group Contrast. Hemisphere Right frontoparietal network Frontal pole Medial visual network Lateral occipital cortex (superior division) Precuneus cortex Cuneus Motor network Precentral gyrus Postcentral gyrus Precuneus cortex Paracingulate gyrus Cingulate gyrus, anterior division Postcentral gyrus Precentral gyrus Precuneus cortex Supramarginal gyrus (anterior division)
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7.23 6.54 1.14 2.20 1.72 4.01 3.76 2.48 1.63
*P < 0.05, family-wise error corrected. Montreal Neurological Institute (MNI) (x,y,z): coordinates of peak locations in the space of the MNI.
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Fig. 3. Statistical map showing regions where the functional connectivity in the (A) right frontoparietal network and (B) motor network is positively correlated with thyroxine values. Statistical map (C) showing the negative correlation of thyroid-stimulating hormone with the functional connectivity in the regions of the motor network. Results are shown on a Montreal Neurological Institute 1-mm standard atlas. R, right; L, left.
hypothyroidism and their fMRI data revealed that the frontal areas were affected by subclinical hypothyroidism, indicating that the executive functions were abnormal in these patients. A recent study Journal of Neuroendocrinology, 2015, 27, 609–615
from our group on hypothyroid patients has also shown a significant reduction in white matter volume in the frontal lobe of hypothyroid patients compared to healthy controls (7). A dysfunction in © 2015 British Society for Neuroendocrinology
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Table 3. Summary of the Networks Showing Correlation Between Functional Connectivity with Thyroxine (T4) and Thyroid-Stimulating Hormone (TSH) Values. Hemisphere Positive correlation between right frontoparietal network and T4 Frontal pole R Superior frontal gyrus Superior frontal gyrus R Paracingulate gyrus Positive correlation b/w motor network and T4 Central opercular cortex L Insular cortex Negative correlation b/w motor network and TSH Supplementary motor cortex L Precentral gyrus Cingulate gyrus, anterior division R
Cluster voxels
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*P < 0.05 Family-wise error corrected. Montreal Neurological Institute (MNI) (x,y,z): coordinates of peak locations in the space of the MNI.
resting-state functional connectivity in the right frontoparietal network in hypothyroid patients may suggest a possible deficit in cognitive functions associated with hypothyroidism. We also found evidence for less functional integrity of the medial visual network in hypothyroid patients, relative to healthy controls. Reduced functional connectivity was observed in areas of the primary visual cortex, including the lateral occipital gyrus, precuneus cortex and cuneus. These regions are typically involved in early visual processing and implicated in previous resting-state network investigations of the visual cortex in healthy young adult populations (30,40). Our findings are very well correlated with a previous single-photon emission computed tomography study that showed a significantly lower regional cerebral blood flow in hypothyroid patients before treatment, mostly in posterior parts of the brain, including the inferior occipital gyrus, cuneus and precuneus regions of the brain (41). In the motor network, hypothyroid patients showed reduced functional connectivity in the precentral gyrus, postcentral gyrus, precuneus cortex, paracingulate gyrus, cingulate gyrus and supramarginal gyrus. During correlation analyses, we found a positive correlation between central opercular cortex and insular cortex and T4 values, as well as a negative correlation between supplementary motor cortex, precentral gyrus and cingulate gyrus and TSH values. These areas are involved in movement planning and execution, sensory processing and motor-learning paradigms and have a putative role in disease-related functional alterations during motor tasks (42). A previous fMRI study has shown motor function deficit in precentral gyrus and postcentral gyrus areas in hypothyroid patients compared to healthy subjects in the execution of the same motor task (6). In a recent voxel-based morphometry study on hypothyroidism, significantly reduced grey and white matter volume was detected in the precentral and postcentral gyrus that corresponds to motor function deficits in hypothyroid patients (7). In another study, reduced regional cerebral blood flow was reported in the right parietooccipital gyri, cuneus, precuneus, posterior cingulate gyrus and pre- and postcentral gyri, which might be related to motor retardation, psychomotor slowness and executive functions in hypothyroidism (41). © 2015 British Society for Neuroendocrinology
Several study limitations should be considered when interpreting the results. First, a lack of neurocognitive scores for the subjects limits the structure–function relationship of the study, including both MRI and cognitive testing within a single sample finding. Second, the data are cross-sectional. Whether these altered neural networks change dynamically after therapy remains to be established in longitudinal studies.
Conclusions The present study is the first rsfMRI study conducted in hypothyroid patients. The altered resting-state connectivity networks, including the right frontoparietal network, the medial visual network and the motor network, may, in part, contribute to the impairments in cognitive functions associated with hypothyroidism. However, the study would be further continued to investigate the effects of thyroxine treatment and correlation with neurocognitive scores. The findings of the present study provide further interesting insights into our understanding of the action of thyroid hormone on the adult human brain.
Acknowledgements This work was performed as a part of Defence Research & Development Organization (DRDO), India sponsored R&D project INM-311. The authors are grateful for the financial support received from the Defence Research & Development Organisation (DRDO), Ministry of Defence, India. The authors desclare that they have no potential conflicts of interest.
Received 5 December 2014, revised 26 February 2015, accepted 2 April 2015
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ORIGINAL ARTICLE
Alterations of Functional Connectivity Among Resting-State Networks in Hypothyroidism S. Singh*, M. Kumar*, S. Modi*, P. Kaur*, L. R. Shankar† and S. Khushu* *NMR Research Centre, Institute of Nuclear Medicine and Allied Sciences (INMAS), Timarpur, Delhi, India. †Thyroid Research Centre, Timarpur, Delhi, India.
Journal of Neuroendocrinology
Correspondence to: Dr Subash Khushu, NMR Research Centre, INMAS, DRDO, Lucknow Road, Timarpur, Delhi 110054, India (e-mail: [email protected]).
Hypothyroidism affects brain functioning as suggested by various neuroimaging studies. The primary focus of the present study was to examine whether hypothyroidism would impact connectivity among resting-state networks (RSNs) using resting-state functional magnetic resonance imaging (rsfMRI). Twenty-two patients with hypothyroidism and 22 healthy controls were recruited and scanned using rsfMRI. The data were analysed using independent component analysis and a dual regression approach that was applied on five RSNs that were identified using FSL software (http://fsl.fmrib.ox.ac.uk). Hypothyroid patients showed significantly decreased functional connectivity in the regions of the right frontoparietal network (frontal pole), the medial visual network (lateral occipital gyrus, precuneus cortex and cuneus) and the motor network (precentral gyrus, postcentral gyrus, precuneus cortex, paracingulate gyrus, cingulate gyrus and supramarginal gyrus) compared to healthy controls. The reduced functional connectivity in the right frontoparietal network, the medial visual network and the motor network suggests neurocognitive alterations in hypothyroid patients in the corresponding functions. However, the study would be further continued to investigate the effects of thyroxine treatment and correlation with neurocognitive scores. The findings of the present study provide further interesting insights into our understanding of the action of thyroid hormone on the adult human brain. Key words: resting-state fMRI, functional connectivity, hypothyroidism
Hypothyroidism is among the most prevalent endocrine disorders affecting the population worldwide (1). It is characterised by a high thyroid-stimulating hormone (TSH) level and low tri-iodothyronine and thyroxine (T4) levels. A change in thyroid hormone levels in adulthood has been associated with cognitive dysfunction, disturbed attention and depressed moods (2). Several neuropsychological studies have also shown cognitive deficits in attention, language, memory and visuospatial skills, as well as depressive mood and psychomotor slowing in association with hypothyroidism (2–4). Neuroimaging studies have also reported functional, structural and metabolic changes in hypothyroidism (5–9). Functional magnetic resonance imaging (fMRI) studies have shown deficits in brain regions associated with working memory and motor skills in hypothyroid patients (5,6). These studies have shown alterations of taskinduced deactivation within default mode network (DMN) regions during working memory processing (5) and deficits in motor areas during a finger tapping task (6). A previous 31P magnetic resonance spectroscopy study has shown a decreased ratio of phosphocreatine
doi: 10.1111/jne.12282
to inorganic phosphate in the frontal lobe as a result of hypothyroidism, which increased after thyroid hormone treatment (9). These findings suggest that there are reversible changes in the adult cerebral phosphate metabolism in the frontal lobe as a result of hypothyroidism treatment (9). Recent voxel-based morphometry studies in humans (7,8) have also demonstrated grey and white matter volume loss in a hypothyroid population. These studies have shown morphological changes in the motor areas, cerebellum, frontal gyrus, occipital gyrus and temporal gyrus, as well as the hippocampus of hypothyroid patients (7,8). A diffusion tensor imaging study from our group has shown microstructural changes in the white matter fibres of the brain that may contribute to underlying dysfunction in memory in hypothyroid patients (10). However, to the best of our knowledge, no study has assessed brain functional connectivity in an adult hypothyroid population using resting-state fMRI (rsfMRI). rsfMRI represents another fMRI technique for investigating the functional connectivity of the brain that measures spontaneous low-frequency fluctuations in the blood oxygenation level-dependent (BOLD) signal during the resting condition (11). rsfMRI experi-
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ments are less prone to multisite variability, allow a wider range of patients to be scanned and make it possible to study multiple cortical systems from one dataset (12). Application of this technique has allowed the identification of various resting-state networks (RSNs) or spatially distinct areas of the brain that demonstrate synchronous BOLD fluctuations at rest. The major RSNs that have been shown to possess strong temporal coherence include the visual, auditory and language networks, the visual, auditory and language networks, the executive control network, the dorsal and ventral attention network and the DMN, etc. (13–15). The existence of these networks has been consistently verified across healthy controls and diseased conditions. There are several studies that have shown alterations in the functional connectivity network in different pathological conditions, including attention deficit hyperactivity disorder (16), Alzheimer’s disease (17), obsessive compulsive disorder (18), depression (19), bipolar disorder (20) and schizophrenia (21). At present, rsfMRI has not been applied to hypothyroid patients to assess possible alterations in resting-state connectivity patterns in these patients. Accordingly, in the present study, we performed rsfMRI in hypothyroid patients compared to controls (age/sex matched). The present study therefore aimed to investigate the resting-state functional brain connectivity changes associated with hypothyroidism in all resting-state networks as identified by the independent component analysis (ICA) approach.
Materials and methods Subjects Forty-four subjects participated in the present study. Out of 44 participants, 22 participants were hypothyroid and 22 were healthy controls. All hypothyroid patients were newly diagnosed with elevated TSH and low free T4 (FT4) levels, and were recruited from outpatients at Thyroid Research Centre, Institute of Nuclear Medicine and Allied Sciences (INMAS), Defence Research and Development Organization (DRDO), Delhi, India. All control subjects chosen for the study were recruited from the local community of INMAS and none of the group displayed any clinical evidence of stroke, head injury, cardiovascular diseases, history of smoking, alcohol or drug dependence, or psychiatric disorders. Both patients and controls showed no neurological abnormalities on conventional MRI scans. The institutional research ethics committee approved the study and informed consent was obtained from all subjects.
Image acquisition The study was carried out using a 3T whole body MR system (Magnetom Skyra; Siemens, Erfurt, Germany) with a circularly polarised 20 channel matrix head and neck coil and a 45 mT/m actively shielded gradient system. Subjects lay in the supine position with their heads supported and immobilised within the head coil using foam-pads (vendor provided), to minimise head movement and gradient noise. For anatomical reference, a T1-weighted three-dimensional gradient echo sequence (magnetisation-prepared rapid acquisition gradient echo, 160 sagittal slices, slice thickness = 1 mm, field of view = 256 mm, TR = 1900 ms, TE = 2.07 ms) image data set was acquired. Functional brain volumes were acquired using an echo-planar T2*weighted imaging sequence. Each volume consisted of 30 interleaved 5-mm © 2015 British Society for Neuroendocrinology
thick slices without interslice gap (TE = 30 ms, TR = 2000 ms, FOV = 240 mm, flip angle = 90°, voxel size = 3.75 9 3.75 9 5 mm3). Total scanning time was 410 s (205 brain volumes), during which the subjects were asked to keep their eyes closed without thinking of anything in particular and not to fall asleep.
Statistical analysis The resting-state functional data were first pre-processed using the FMRI Expert Analysis Tool (FEAT), which is a part of FSL (FMRIB Software Library; http://fsl.fmrib.ox.ac.uk). For individual subject analyses, functional brain volumes (after deleting the first five brain volumes to ensure steady-state longitudinal magnetisation) were corrected for slice timing and head movement. None of the subjects included in the present study had head movement exceeding 1.5 mm or 1.5° in any direction. The data was smoothed with a Gaussian kernel of full-width at half-maximum of 5 mm and preprocessed with high-pass temporal filtering (with a cut-off of 100 s) and low-pass filtering to retain frequencies ˂ 0.1 Hz to remove signal from non-neuronal causes and improve the signal-to-noise ratio. fMRI volumes were registered to the individual’s structural scan and standard space images [Montreal Neurological Institute (MNI)-152 template] using FMRIB’s Linear Image Registration Tool (FLIRT) (22). Individual structural images for use as high resolution images in the registration step were brain-extracted using the Brain Extraction Tool (BET) (23). When running FEAT, the Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC) ICA (Independent Component Analysis) data exploration option was turned on (with ‘automatic dimensionality estimation’ option) to gain insight into unexpected artefacts or activation in the data. The noise components from the ICAs were noted and removed using MELODIC denoising. The MELODIC toolbox of FSL was then used to decompose the denoised data into a set of 35 time courses and associated spatial maps that jointly describe the temporal and spatial characteristics of underlying hidden signals (components) using probabilistic independent component analysis (24). Out of the 35 independent components (ICs) obtained, again, few were RSNs (with a power spectra graph having a typical mono-peak pattern in the 0.01–0.03 Hz region) and others were noise components. The noise components were again removed using MELODIC denoising and MELODIC was run again to decompose the denoised data into a set of twenty independent components. The between-group analysis of the resting data was carried out using a regression technique (dual regression), which allows for voxel-wise comparisons of resting functional connectivity (25,26). For this, MELODIC was run on the denoised data (entire sample) in Concat-ICA mode (multisession temporal concatenation). A general linear model was then defined to create a multi-subject design matrix defining groups [two groups: Healthy Control (C) and Hypothyroid Patients (H)] and contrast files (two contrasts: C versus H and H versus C). A new regressor for age of the subjects was also added. The set of spatial maps from the group-average analysis was used to generate subject-specific versions of the spatial maps, and associated time-series, using dual regression (25,26). First, for each subject, the group average set of spatial maps is regressed into the subject’s four-dimensional space-time dataset. This results in a set of subject-specific time-series: one per grouplevel spatial map. Next, those time series are regressed into the same fourdimensional dataset, resulting in a set of subject-specific spatial maps, one per group-level spatial map. In accordance with Zuo et al. (27), the spatial maps representing RSNs were selected using a two-step ICs selection process. First, the visual selection of the IC of interest was carried out where we compared our output maps with those found in the literature (15,28–30). As a second step, the power spectra graph output of FSL elaborations showing the variability of low frequency fluctuations in every IC was analysed. The power spectra graph of a network shows a typical mono-peak pattern in the 0.01–0.03 Hz Journal of Neuroendocrinology, 2015, 27, 609–615
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Table 1. Demographic and Clinical Characteristics of the Subjects.
Age (years) Sex (male/female) Education (years) BMI (kg/cm2) FT4 (pmol/l) TSH (lIU/ml)
Results
Normal controls (n = 22)
Hypothyroid patients (n = 22)
28.5 5.82 7/15 12.6 2.56 23.76 3.08 17.23 2.50 2.15 0.35
28.8 5.52 6/16 11.9 3.01 24.24 4.16 3.20 2.68 71.65 58.14
Data are the mean SD. BMI, body mass index; FT4, free thyroxine; TSH, thyroid-stimulating hormone. region, whereas an artefact power spectrum shows a chaotic multi-peak pattern in the 0–0.25 Hz range. Voxel-wise analyses of the group differences between the hypothyroid and control group was carried out using FSL randomised nonparametric permutation-testing with 10 000 permutations for each IC of interest (31). Threshold-free cluster enhancement (32) was used to control for multiple comparisons and the significance threshold was set to P < 0.05 corrected for family-wise error. The results characterised the probabilistic statistical maps, representing the group differences in functional connectivity for all RSNs of interest. The statistical maps were then upsampled to a standard MNI 1-mm brain Montreal atlas to better localise the areas of RSN alterations. The Harvard–Oxford cortical and subcortical atlases (Harvard Centre for Morphometric Analysis), which are provided with the FSL software, were used to identify the anatomical representation of the clusters of the resulting probabilistic independent component analysis maps that showed significant differences between the two groups using the ‘autoaq’ script.
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Clinical assessment Demographic characteristics and serum thyroid hormone levels of patients and controls are presented in Table 1. Serum thyroid indices (FT4 and TSH) were measured with the help of an Electro Chemiluminescence Immuno Assay Kit (Elecsys 2010; Roche Diagnostics, Mannheim, Germany). The thyroid function tests were in the normal range for controls: FT4 = 12.0–22.0 pmol/l and TSH = 0.27–4.2 lIU/ml. All hypothyroid patients were diagnosed with elevated TSH and abnormally low FT4 levels. In hypothyroid patients, the FT4 range was 0.5–7.8 pmol/l and the TSH range was 21–252 lIU/ml.
rsfMRI A total of five components (Fig. 1) were identified as RSNs from the group MELODIC output, including the right frontoparietal network (15), left frontoparietal network (15), medial visual network (15), motor network (15,30) and DMN (15). Hypothyroid patients showed significantly decreased temporal correlation in the right frontoparietal network compared to control subjects in frontal pole (Fig. 2A and Table 2). Significantly decreased functional connectivity was also observed within the medial visual network in hypothyroid patients relative to healthy controls, namely in the lateral occipital gyrus, precuneus cortex and cuneus (Fig. 2B
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Fig. 1. Resting-state networks identified using independent component analysis, which was used for the dual regression analysis: (A) Right frontoparietal attention network. (B) Left frontoparietal attention network. (C) Medial visual network. (D) Motor network. (E) Default-mode network. R, right; L, left. Journal of Neuroendocrinology, 2015, 27, 609–615
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Fig. 2. Output of the randomised analysis for the control versus hypothyroid patient group contrast threshold at P < 0.05 (family-wise error corrected). Reduced connectivity was observed in regions of the (A) right frontoparietal network, (B) medial visual network and (C) Motor network. Results are shown on a Montreal Neurological Institute 1-mm standard atlas. R, right; L, left.
and Table 2). The motor network also showed a decreased connectivity in the precentral gyrus, postcentral gyrus, precuneus cortex, paracingulate gyrus, cingulate gyrus and supramarginal gyrus (Fig. 2C and Table 2). No significant differences between control and hypothyroid subjects were found in the left frontoparietal network and DMN connectivity. In the above RSNs, no clusters were obtained where hypothyroid patients had increased connectivity compared to healthy controls. When a correlation analysis was carried out between RSNs and T4 and TSH values in all the subjects, a positive correlation between the functional connectivity and T4 levels was obtained in the regions of right frontoparietal network and motor network (Fig. 3A,B and Table 3). The regions of motor network also showed negative correlation with TSH values (Fig. 3C and Table 3).
Discussion The present rsfMRI study aimed to investigate whether the intrinsic functional connectivity of RSNs might be altered in hypothyroid patients compared to healthy controls. Because there is nothing © 2015 British Society for Neuroendocrinology
available in the literature concerning resting-state functional connectivity alterations in hypothyroid patients, we studied the RSNs identified in our subjects using ICA. Our results revealed significantly reduced resting-state functional connectivity in regions of the right frontoparietal network, the medial visual network and the motor network in hypothyroid patients. We also found a correlation between the altered functional connectivity in the right frontoparietal network and motor network and T4 and TSH values. The frontoparietal networks consist of the parietal and frontal cortex and are implicated in working memory and cognitive attentional processes (14,33). Our findings indicate that the frontal pole showed decreased functional connectivity in hypothyroid patients compared to healthy controls. The frontal pole also showed positive correlation with T4 values in the right frontoparietal network. The frontal pole is the frontal part of the prefrontal cortex that corresponds to Brodmann’s area 10 of the human brain (34,35). The frontal pole plays a critical role in many aspects of complex cognitive tasks, including relational integration, multitasking and memory tasks (36–38). In an earlier study, Zhu et al. (39) reported that working memory is impaired in hypothyroidism and subclinical Journal of Neuroendocrinology, 2015, 27, 609–615
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Table 2. Summary of the Differences Detected in Control Versus Hypothyroid Patient Group Contrast. Hemisphere Right frontoparietal network Frontal pole Medial visual network Lateral occipital cortex (superior division) Precuneus cortex Cuneus Motor network Precentral gyrus Postcentral gyrus Precuneus cortex Paracingulate gyrus Cingulate gyrus, anterior division Postcentral gyrus Precentral gyrus Precuneus cortex Supramarginal gyrus (anterior division)
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7.23 6.54 1.14 2.20 1.72 4.01 3.76 2.48 1.63
*P < 0.05, family-wise error corrected. Montreal Neurological Institute (MNI) (x,y,z): coordinates of peak locations in the space of the MNI.
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Fig. 3. Statistical map showing regions where the functional connectivity in the (A) right frontoparietal network and (B) motor network is positively correlated with thyroxine values. Statistical map (C) showing the negative correlation of thyroid-stimulating hormone with the functional connectivity in the regions of the motor network. Results are shown on a Montreal Neurological Institute 1-mm standard atlas. R, right; L, left.
hypothyroidism and their fMRI data revealed that the frontal areas were affected by subclinical hypothyroidism, indicating that the executive functions were abnormal in these patients. A recent study Journal of Neuroendocrinology, 2015, 27, 609–615
from our group on hypothyroid patients has also shown a significant reduction in white matter volume in the frontal lobe of hypothyroid patients compared to healthy controls (7). A dysfunction in © 2015 British Society for Neuroendocrinology
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Table 3. Summary of the Networks Showing Correlation Between Functional Connectivity with Thyroxine (T4) and Thyroid-Stimulating Hormone (TSH) Values. Hemisphere Positive correlation between right frontoparietal network and T4 Frontal pole R Superior frontal gyrus Superior frontal gyrus R Paracingulate gyrus Positive correlation b/w motor network and T4 Central opercular cortex L Insular cortex Negative correlation b/w motor network and TSH Supplementary motor cortex L Precentral gyrus Cingulate gyrus, anterior division R
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*P < 0.05 Family-wise error corrected. Montreal Neurological Institute (MNI) (x,y,z): coordinates of peak locations in the space of the MNI.
resting-state functional connectivity in the right frontoparietal network in hypothyroid patients may suggest a possible deficit in cognitive functions associated with hypothyroidism. We also found evidence for less functional integrity of the medial visual network in hypothyroid patients, relative to healthy controls. Reduced functional connectivity was observed in areas of the primary visual cortex, including the lateral occipital gyrus, precuneus cortex and cuneus. These regions are typically involved in early visual processing and implicated in previous resting-state network investigations of the visual cortex in healthy young adult populations (30,40). Our findings are very well correlated with a previous single-photon emission computed tomography study that showed a significantly lower regional cerebral blood flow in hypothyroid patients before treatment, mostly in posterior parts of the brain, including the inferior occipital gyrus, cuneus and precuneus regions of the brain (41). In the motor network, hypothyroid patients showed reduced functional connectivity in the precentral gyrus, postcentral gyrus, precuneus cortex, paracingulate gyrus, cingulate gyrus and supramarginal gyrus. During correlation analyses, we found a positive correlation between central opercular cortex and insular cortex and T4 values, as well as a negative correlation between supplementary motor cortex, precentral gyrus and cingulate gyrus and TSH values. These areas are involved in movement planning and execution, sensory processing and motor-learning paradigms and have a putative role in disease-related functional alterations during motor tasks (42). A previous fMRI study has shown motor function deficit in precentral gyrus and postcentral gyrus areas in hypothyroid patients compared to healthy subjects in the execution of the same motor task (6). In a recent voxel-based morphometry study on hypothyroidism, significantly reduced grey and white matter volume was detected in the precentral and postcentral gyrus that corresponds to motor function deficits in hypothyroid patients (7). In another study, reduced regional cerebral blood flow was reported in the right parietooccipital gyri, cuneus, precuneus, posterior cingulate gyrus and pre- and postcentral gyri, which might be related to motor retardation, psychomotor slowness and executive functions in hypothyroidism (41). © 2015 British Society for Neuroendocrinology
Several study limitations should be considered when interpreting the results. First, a lack of neurocognitive scores for the subjects limits the structure–function relationship of the study, including both MRI and cognitive testing within a single sample finding. Second, the data are cross-sectional. Whether these altered neural networks change dynamically after therapy remains to be established in longitudinal studies.
Conclusions The present study is the first rsfMRI study conducted in hypothyroid patients. The altered resting-state connectivity networks, including the right frontoparietal network, the medial visual network and the motor network, may, in part, contribute to the impairments in cognitive functions associated with hypothyroidism. However, the study would be further continued to investigate the effects of thyroxine treatment and correlation with neurocognitive scores. The findings of the present study provide further interesting insights into our understanding of the action of thyroid hormone on the adult human brain.
Acknowledgements This work was performed as a part of Defence Research & Development Organization (DRDO), India sponsored R&D project INM-311. The authors are grateful for the financial support received from the Defence Research & Development Organisation (DRDO), Ministry of Defence, India. The authors desclare that they have no potential conflicts of interest.
Received 5 December 2014, revised 26 February 2015, accepted 2 April 2015
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