Child's Nervous System

, Volume 34, Issue 5, pp 901–910 | Cite as

Resting state signal latency predicts laterality in pediatric medically refractory temporal lobe epilepsy

  • Manish N. Shah
  • Anish Mitra
  • Manu S. Goyal
  • Abraham Z. Snyder
  • Jing Zhang
  • Joshua S. Shimony
  • David D. Limbrick
  • Marcus E. Raichle
  • Matthew D. Smyth
Original Paper



Temporal lobe epilepsy (TLE) affects resting state brain networks in adults. This study aims to correlate resting state functional MRI (rsMRI) signal latency in pediatric TLE patients with their laterality.


From 2006 to 2016, 26 surgical TLE patients (12 left, 14 right) with a mean age of 10.7 years (range 0.9–18) were prospectively studied. Preoperative rsMRI was obtained in patients with concordant lateralizing structural MRI, EEG, and PET studies. Standard preprocessing techniques and seed-based rsMRI analyses were performed. Additionally, the latency in rsMRI signal between each 6 mm voxel sampled was examined, compared to the global mean signal, and projected onto standard atlas space for individuals and the cohort.


All but one of the 26 patients improved seizure frequency postoperatively with a mean follow-up of 2.9 years (range 0–7.7), with 21 patients seizure-free. When grouped for epileptogenic laterality, the latency map qualitatively demonstrated that the right TLE patients had a relatively early signal pattern, whereas the left TLE patients had a relatively late signal pattern compared to the global mean signal in the right temporal lobe. Quantitatively, the two groups had significantly different signal latency clusters in the bilateral temporal lobes (p < 0.001).


There are functional MR signal latency changes in medical refractory pediatric TLE patients. Qualitatively, signal latency in the right temporal lobe precedes the mean signal in right TLE patients and is delayed in left TLE patients. With larger confirmatory studies, preoperative rsMRI latency analysis may offer an inexpensive, noninvasive adjunct modality to lateralize pediatric TLE.


Default mode network Functional magnetic resonance imaging Pediatric epilepsy Resting state Temporal lobe epilepsy 


Funding information

Research reported in this publication was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health under Award Number L30 HD089125 (MNS) as well as U54 HD087011 to the Intellectual and Developmental Disabilities Research Center at Washington University (JSS).

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments.

Informed consent

Informed consent was obtained from all study participants.

Supplementary material

381_2018_3770_Fig5_ESM.gif (80 kb)
Supplementary Figure 1

Autocorrelation plot for preprocessed rsMRI data in the temporal lobes. The calculated mean autocorrelation coefficients of 3 time lags (1 TR, 2 TR, 3 TR) for averaged time series (preprocessed rsMRI data) extracted from symmetric regions of interest in the left and right temporal lobes (LTL and RTL) in both patient groups (LTLE and RTLE) show the trend that as lag increases, the autocorrelation coefficient reduces quickly to insignificant. This indicates that the effect of autocorrelation is negligible in the rsMRI temporal latency analysis. (GIF 80 kb)

381_2018_3770_MOESM2_ESM.tif (16.4 mb)
High resolution image (TIFF 16815 kb)
381_2018_3770_Fig6_ESM.gif (201 kb)
Supplementary Figure 2

Standard deviation maps. A. Mean standard deviation map for each patient with LTLE; B. Mean standard deviation map for each patient with RTLE. The standard deviation maps show that the voxel-wise mean standard deviation does not correlate with the temporal latency maps in these patients. (GIF 200 kb)

381_2018_3770_MOESM3_ESM.tif (7.2 mb)
High resolution image (TIFF 7347 kb)
381_2018_3770_MOESM1_ESM.docx (29 kb)
Supplementary Table 1 (DOCX 29 kb)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Manish N. Shah
    • 1
  • Anish Mitra
    • 3
  • Manu S. Goyal
    • 3
  • Abraham Z. Snyder
    • 3
    • 4
  • Jing Zhang
    • 1
  • Joshua S. Shimony
    • 3
  • David D. Limbrick
    • 2
  • Marcus E. Raichle
    • 3
    • 4
    • 5
    • 6
  • Matthew D. Smyth
    • 2
  1. 1.Departments of Pediatric Surgery and NeurosurgeryMcGovern Medical School at UTHealthHoustonUSA
  2. 2.Department of Neurological Surgery, St. Louis Children’s HospitalWashington University School of MedicineSt. LouisUSA
  3. 3.Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisUSA
  4. 4.Department of NeurologyWashington University School of MedicineSt. LouisUSA
  5. 5.Department of Biomedical EngineeringWashington University School of MedicineSt. LouisUSA
  6. 6.Department of Anatomy and NeurobiologyWashington University School of MedicineSt. LouisUSA

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