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AJNR Awards, New Junior Editors, and more. Read the latest AJNR updates

ReplyLetter

Reply:

O. Foesleitner, K.-H. Nenning, L. Bartha-Doering, C. Baumgartner, E. Pataraia, D. Moser, M. Schwarz, V. Schmidbauer, J.A. Hainfellner, T. Czech, C. Dorfer, G. Langs, D. Prayer, S. Bonelli and G. Kasprian
American Journal of Neuroradiology June 2020, 41 (6) E47-E48; DOI: https://doi.org/10.3174/ajnr.A6597
O. Foesleitner
aDepartments of Biomedical Imaging and Image-Guided Therapy
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K.-H. Nenning
aDepartments of Biomedical Imaging and Image-Guided Therapy
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L. Bartha-Doering
bDepartment of Pediatrics and Adolescent Medicine
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C. Baumgartner
cGeneral Hospital Hietzing with Neurological Center RosenhuegelVienna, Austria
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E. Pataraia
dDepartment of Neurology
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D. Moser
dDepartment of Neurology
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M. Schwarz
eDepartments of Biomedical Imaging and Image-Guided Therapy
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V. Schmidbauer
eDepartments of Biomedical Imaging and Image-Guided Therapy
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J.A. Hainfellner
fInstitute of Neurology
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T. Czech
gDepartment of Neurosurgery
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C. Dorfer
gDepartment of Neurosurgery
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G. Langs
hDepartments of Biomedical Imaging and Image-Guided Therapy
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D. Prayer
hDepartments of Biomedical Imaging and Image-Guided Therapy
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S. Bonelli
iDepartment of Neurology
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G. Kasprian
jDepartments of Biomedical Imaging and Image-Guided TherapyMedical University of ViennaVienna, Austria
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We would like to thank Drs Middlebrooks and Sabsevitz for their interesting comment on our recent publication, “Lesion-Specific Language Network Alterations in Temporal Lobe Epilepsy.”1 In this group analysis of patients with temporal lobe epilepsy (TLE) due to different underlying pathologies, we targeted the main question: Can distinct lesion-specific language network changes be identified using functional connectivity (FC) analysis? Indeed, we showed that different etiologies (ie, hippocampal sclerosis, nonlesional temporal lobe epilepsy, and mesiotemporal low-grade glioma) of TLE cause distinct patterns of language network changes.

We agree with the authors that a critical appraisal of the paradigm design is extremely important in presurgical fMRI. However, we would like to emphasize that due the robustness of our FC group analysis results, the main results are very unlikely to change, even after choosing a different paradigm approach. We would like to further comment on the points mentioned.

As outlined in the authors’ comment, Binder et al2 found a significant difference between auditory semantic language tasks with rest versus active, nonlinguistic tasks as a baseline condition.2 Auditory tasks, as exclusively used in the study by Binder et al, cannot be simply extrapolated to our visually presented language paradigms using cross-fixation (with/without alternating hashtags) as a baseline task. The use of cross-fixation as a baseline is common3 and recommended by the American Society of Functional Neuroradiology in their recently published guideline article (see, for instance, the “Antonym Generation” paradigm).4 As expected, cross-fixation typically results in bilateral activations in the primary visual cortex,4 which need to be considered when interpreting the results. Furthermore, we would like to stress that using a homogeneous protocol (ie, the same scanner, same task design, systematic prescanning patient training, quality assessment by on-line processing during fMRI acquisition, the same processing, and the same analysis parameters), we could reduce the risk of a systematic bias. However, we do agree that potential intersubject differences in the ability to “rest” during cross-fixation could have translated into subtle differences of network characteristics.

To our knowledge, the examined etiologies were never proved to differ in the patients’ ability to optimally adhere to the baseline task. Moreover, many of the included patients scored low on verbal fluency and naming tests (36.8% and 69.1%, respectively). Although no group differences were found in their actual language abilities, these facts strongly suggest that the observed lesion-specific changes can be explained by functional connectivity changes in the language domain and not, as otherwise assumed, by differences in baseline task completion.

We do agree that task-based fMRI, regardless of whether active or cross-fixation is used as baseline condition, is not equivalent to pure resting-state fMRI. We absolutely do not and did not recommend to either replace task-based fMRI by resting-state fMRI or vice versa. For the investigation of specific cognitive networks, such as language in our case, task-based fMRI may be superior to resting-state fMRI due to its higher reliability and robustness.5 As outlined in our Materials and Methods section,1 we included all acquired time points (n = 100 per run) into our FC analysis in order not to introduce artificial fluctuations into the frequency spectrum by cutting and concatenating task and resting blocks.6 Moreover, this approach should maximize the available number of time points, which is known to critically influence the reliability of connectivity measures.7 In our opinion, activation analysis and FC are complementary approaches. However, before the full transition of functional connectivity from research to clinics, further studies on the reproducibility and interpretation of these correlation values on a single-subject level are needed. One such promising approach could be connectivity fingerprints, in which individual network features are compared with normative values derived from a large pool of healthy subjects, as recently attempted.8-10

References

  1. 1.
    1. Foesleitner O,
    2. Nenning KH,
    3. Bartha-Doering L, et al
    . Lesion-specific language network alterations in temporal lobe epilepsy. AJNR Am J Neuroradiol 2020;41:147–54 doi:10.3174/ajnr.A6350 pmid:31896570
  2. 2.
    1. Binder JR,
    2. Swanson SJ,
    3. Hammeke TA, et al
    . A comparison of five fMRI protocols for mapping speech comprehension systems. Epilepsia 2008;49:1980–97 doi:10.1111/j.1528-1167.2008.01683.x pmid:18513352
  3. 3.
    1. Benjamin CF,
    2. Dhingra I,
    3. Li AX, et al
    . Presurgical language fMRI: technical practices in epilepsy surgical planning. Hum Brain Mapp 2018;39:4032–42 doi:10.1002/hbm.24229 pmid:29962111
  4. 4.
    1. Black DF,
    2. Vachha B,
    3. Mian A, et al
    . American Society of Functional Neuroradiology-Recommended fMRI paradigm algorithms for presurgical language assessment. AJNR Am J Neuroradiol 2017;38:E65–73 doi:10.3174/ajnr.A5345 pmid:28860215
  5. 5.
    1. Kristo G,
    2. Rutten GJ,
    3. Raemaekers M, et al
    . Task and task-free FMRI reproducibility comparison for motor network identification. Hum Brain Mapp 2014;35:340–52 doi:10.1002/hbm.22180 pmid:22987751
  6. 6.
    1. Ganger S,
    2. Hahn A,
    3. Kublbock M, et al
    . Comparison of continuously acquired resting state and extracted analogues from active tasks. Hum Brain Mapp 2015;36:4053–63 doi:10.1002/hbm.22897 pmid:26178250
  7. 7.
    1. Shah LM,
    2. Cramer JA,
    3. Ferguson MA, et al
    . Reliability and reproducibility of individual differences in functional connectivity acquired during task and resting state. Brain Behav 2016;6:e00456 doi:10.1002/brb3.456 pmid:27069771
  8. 8.
    1. Finn ES,
    2. Shen X,
    3. Scheinost D, et al
    . Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat Neurosci 2015;18:1664–71 doi:10.1038/nn.4135 pmid:26457551
  9. 9.
    1. Amico E,
    2. Goni J
    . The quest for identifiability in human functional connectomes. Sci Rep 2018;8:8254 doi:10.1038/s41598-018-25089-1 pmid:29844466
  10. 10.
    1. Voets NL,
    2. Parker Jones O,
    3. Mars RB, et al
    . Characterising neural plasticity at the single patient level using connectivity fingerprints. Neuroimage Clin 2019;24:101952 doi:10.1016/j.nicl.2019.101952 pmid:31357148
  • © 2020 by American Journal of Neuroradiology
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