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Table of Contents    
Year : 2018  |  Volume : 66  |  Issue : 5  |  Page : 1367-1369

Assessing functional connectivity of brain network in children with nocturnal enuresis using resting state functional magnetic resonance imaging

Department of Radiology, Vijaya Diagnostics, Krishna Institute of Medical Sciences, Hyderabad, Telangana, India

Date of Web Publication17-Sep-2018

Correspondence Address:
Dr. Rammohan Vadapalli
Department of Radiology, Vijaya Diagnostics, Krishna Institute of Medical Sciences, Hyderabad, Telangana
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0028-3886.241392

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How to cite this article:
Vadapalli R. Assessing functional connectivity of brain network in children with nocturnal enuresis using resting state functional magnetic resonance imaging. Neurol India 2018;66:1367-9

How to cite this URL:
Vadapalli R. Assessing functional connectivity of brain network in children with nocturnal enuresis using resting state functional magnetic resonance imaging. Neurol India [serial online] 2018 [cited 2019 Jul 17];66:1367-9. Available from:

I read with interest the article titled " Degree centrality and voxel-mirrored homotopic connectivity in children with nocturnal enuresis - A functional MRI study" by Jiang et al.[1] The focus of the article is to investigate the intensity of functional connectivity among the nodes of brain network and between two hemispheres in children with nocturnal enuresis and to compare them with the findings in normal children using resting state functional magnetic resonance imaging (fMRI). For addressing the functional connectivity metrics, it is important to understand the structural and functional correlates of nocturnal enuresis in the brain along with the methods of ideal and recommended data acquisition for resting state fMRI study.

  Nocturnal Enuresis and the Human Brain: the Key Areas of Interest Top

Multiple contributory factors are associated with nocturnal enuresis (NE), including heredity, polyuria, detrusor muscle dysfunction, as well as sleep and central nervous system abnormalities.[2] A delay in the maturation of the central nervous system may be an underlying etiology for nocturnal enuresis as it can induce functional and structural alterations in the concerned brain areas.[3]


The central bladder control mechanism involves the thalamus, which relays signals from the prefrontal cortex,[4],[5] in addition to regulating sleep and wakefulness. Children with NE do not have involuntary urination during daytime, hence a sleep disorder can be one of the underlying pathogenetic factors for NE.[6],[7],[8] Thalamic dysfunction due to a developmental delay could, therefore, be an important factor in children with NE.

Medial frontal gyrus

The prefrontal cortex is implicated in the decision-making plan of complex behaviour and in the carrying out and moderation of social conduct.[9] This area is connected to the periaqueductal gray (PAG) matter, the anterior cingulate cortex (ACC), the insula, the hypothalamus and the thalamus. All these regions interconnect with each other to control micturition.[10]

Fowler's preliminary working model states that higher brain centres exert control over the lower urinary tract. The medial prefrontal cortex is involved in the storage and voiding phases.[9] Generally, the prefrontal cortex is associated with the decision making in voiding.[9],[10]


In healthy people, the insula shows activation when the bladder is full.[9] Insular and visceral sensations are related. In the insula, the signals related to visceral sensations are transformed into a conscious internal signal,[9] thus aiding in the interoceptive awareness of the state of the bladder.

Anterior cingulate cortex

Beckel and Holstege have published that the ACC plays a key role in voluntary voiding and is also involved in the attention and introspection (that convey that the bladder is full and that it is time to void) and in the executive control (that decides whether or not the place of voiding is appropriate).[7],[8]

  Other Brain Regions Top

The globus pallidus is known to be activated in normal subjects during micturition but not at rest.[8] The precise function of the globus pallidus in micturition control is still unclear. It has also been established that the peri-aqueductal gray matter plays an important role in bladder control.[11],[12],[13],[14] Involuntary voiding is inhibited mainly by the brain stem and is controlled by some superior regions such as the thalamus, the supplementary motor area (SMA), the anterior cingulate cortex, and the prefrontal cortex.[10],[11],[12]

  Nocturnal Enuresis and Resting State Functional Magnetic Resonance Imaging Top

Lei et al.,[11],[12] have studied the changes in spontaneous brain activity in children with primary monosymptomatic nocturnal enuresis (PMNE) using statistical parametric mapping (SPM) and a resting-state fMRI data analysis toolkit (REST). They concluded that children with NE exhibited significant differences in the amplitude of low frequency fluctuations (slow fluctuations in activity are intrinsic features of the resting brain that determine correlated activity between areas of the brain and the resting state networks; when the frequency of these fluctuations ranges between 0.01 and 0.1 Hz, the fluctuations are termed as low frequency fluctuations) or regional homogeneity (ReHo; a voxel-based measure of brain activity where the similarity or synchronization between the time series of a given voxel and its nearest neighbours is evaluated) in the left inferior frontal gyrus, medial frontal gyrus (Brodmann area, BA 10), and left midbrain (periaqueductal gray matter). Abnormalities in the BA 10 and the inferior frontal gyrus may affect the children's decision-making with regard to voiding; abnormalities in the midbrain and peri aqueductal gray matter in children with primary monosymptomatic nocturnal enuresis cause an intrinsic dysfunction of the signal transmission in their bladder control network.[13],[14],[15],[16],[17]

Concerns regarding the reproducibility of findings have been raised in the field of resting-state functional magnetic resonance imaging. However, at the present time, little is known about the operationally defined reproducibility of resting-state functional magnetic resonance imaging. It is also not known up to what extent this reproducibility is affected by multiple comparison correction strategies and sample sizes.[17],[18] The ideal platform for data acquisition while performing a resting state fMRI study is on a 3Tesla (T) magnetic resonance scanner due to the higher inherent singal-to-noise ratio and the fidelity of the blood oxygen level dependent signals.

Functional MRI at 3T has emerged as a routine workhorse for neurosciences, including the fields of neurology, neurosurgery, psychology and psychiatry, enabling a non-invasive measurement of brain structure, function and functional connectivity. The acquisition of signals utilizing blood oxygen level dependent (BOLD)-based fMRI is constantly challenged by the physiology-related signals like the head or brain motion, the cerebrospinal fluid and brain pulsations, the blood flow, and the susceptibility differences in the neighbourhood regions of neuronal activity. Many pre-processing and data cleaning strategies have been used to address these confounding factors; however, their efficiency is yet to be fully validated. In particular, signal fluctuations related blood flow to the brain may mask the BOLD signal changes due to “true” neuronal activation. Novel MR imaging techniques and methods to separate these physiological signal interferences have evolved like the fast multiband echo-planar imaging techniques in combination with fast temporal internal component analysis, aiming to separate the various physiological signals that mimick neuronal activity.[19]

Task-related fMRI uses an informative framework, which is provided by the paradigm and timing of the study for the identification of any specific local brain activation. This framework does not exist for a resting state study. Hence, the correlation of temporal signal fluctuations in one cerebral region with another region could potentially mimic true inter- connected neuronal activation (as seen in the case of blood flow).[18],[19]

Therefore, it is of utmost importance to select the magnetic resonance imaging methods for a resting state experiment in such a way that enables the differentiation of neuron-specific effects from the basic cerebral physiological functions in any of the connective networks.[19]

The interference induced by physiological signal changes during the magnetic resonance imaging study may be considered as a source of new information rather than a nuisance. It is well known that, currently, the complexity of the structured brain is being undersampled both at a macroscopic and a microscopic level.[18] One should be very cautious in the interpretation of neurological and scientific findings, especially when comparing different groups (e.g., subjects of different age groups, gender, with eyes open or closed, on different medications, etc.). Hence our goal should be to sample the brain activity utilizing layer-specific high resolution sampling using a high-field MRI platform, with cutting edge techniques like the multiband echo-planar imaging with a low repetition times (of less than 0.5 sec) with an optimal permissible specific absorption rate.[18],[19]

  References Top

Jiang K, Ding L, Li H, Shen H, Zheng A, Zhao F, et al. Degree centrality and voxel-mirrored homotopic connectivity in children with nocturnal enuresis: A functional MRI study. Neurol India 2018:66:1359-64.  Back to cited text no. 1
Neveus T. Diagnosis and management of nocturnal enuresis. Curr Opin Pediatr 2009;21:199-202.  Back to cited text no. 2
Hallioglu O, Ozge A, Comelekoglu U, Topaloglu AK, Kanik A, Duzovali O, et al. Evaluation of cerebral maturation by visual and quantitative analysis of resting electroencephalography in children with primary nocturnal enuresis. J Child Neurol 16:714-18.  Back to cited text no. 3
Karlidag R, Ozisik HI, Soylu A, Kizkin S, Sipahi B, Unal S et al. Topographic abnormalities in event-related potentials in children with monosymptomatic nocturnal enuresis. Neurourol Urodyn 23:237-40.  Back to cited text no. 4
Guillery RW, Sherman SM. Thalamic relay functions and their role in corticocortical communication: Generalizations from the visual system. Neuron 2002;33:163-75.  Back to cited text no. 5
Critchley HD, Wiens S, Rotshtein P, Ohman A, Dolan RJ. Neural systems supporting interoceptive awareness. Nat Neurosci 2004;7:189-95.  Back to cited text no. 6
Beckel JM, Holstege G. Neurophysiology of the lower urinary tract. Handb Exp Pharmacol 2011;202:149-69.  Back to cited text no. 7
Matsuura S, Kakizaki H, Mitsui T, Shiga T, Tamaki N, Koyanagi T, et al. Human brain region response to distention or cold stimulation of the bladder: A positron emission tomography study. J Urol 2002;168:2035-39.  Back to cited text no. 8
Fowler CJ, Griffiths DJ. A decade of functional brain imaging applied to bladder control. Neurourol Urodyn 2010;29:49-55.  Back to cited text no. 9
Griffiths D, Tadic SD. Bladder control, urgency, and urge incontinence: evidence from functional brain imaging. Neurourol Urodyn 2008;27:466-74.  Back to cited text no. 10
Lei D, Ma J, Shen X, Du X, Shen G, Du X, et al. (2012) Changes in the brain microstructure of children with primary monosymptomatic nocturnal enuresis: A diffusion tensor imaging study. PLoS ONE 2012;7: e31023. doi: 10.1371/journal.pone.0031023.  Back to cited text no. 11
Lei D, Ma J, Du X, Shen G, Tian M, Li G. Altered brain activation during response inhibition in children with primary nocturnal enuresis: An fMRI study. Hum Brain Mapp 2012;33:2913-9.  Back to cited text no. 12
Lei D, Ma J, Du X, Shen G, Tian M, Li G. (Spontaneous brain activity changes in children with primary monosymptomatic nocturnal enuresis: A resting-state fMRI study. Neurourol Urodyn 2012;31:99-104.  Back to cited text no. 13
Lei D, Ma J, Zhang J, Wang M, Zhang K, Chen F, Suo X, et al. Connectome-scale assessments of functional connectivity in children with primary monosymptomatic nocturnal enuresis. Biomed Res Int (2015) 2015:463708. Available from: [Last accessed on 2018 Oct 08].  Back to cited text no. 14
Liu D, Dong Z, Zuo X, Wang J, Zang Y. Eyes-open/eyes-closed dataset sharing for reproducibility evaluation of resting state fMRI data analysis methods. Neuroinformatics 2013;11:469-76.  Back to cited text no. 15
Yu B, Guo Q, Fan G, Ma H, Wang L, Liu M. Evaluation of working memory impairment in children with primary nocturnal enuresis: Evidence from event-related functional magnetic resonance imaging. J Paediatr Child Health 2011;47:429-35.  Back to cited text no. 16
Zou QH, Zhu CZ, Yang Y, Zuo XN, Long XY, Cao QJ, et al. An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF. J Neurosci Methods 2008; 172:137-41.  Back to cited text no. 17
Chen X, Lu B, Yan CG. A comprehensive assessment of reproducibility of R-fMRI metrics on the impact of different strategies for multiple comparison correction and sample sizes. Human B rain Mapping 2017; doi: 10.1002/hbm.23843.  Back to cited text no. 18
Boubela RN, Kalcher K, Nasel C, Moser E. Scanning fast and slow: current limitations of 3 Tesla functional MRI and future potential. Front Phys 2014. Available from: [Last accessed on 2018 Sep 07].  Back to cited text no. 19


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