Identifying neural targets for enhancing phonological processing with transcranial alternate current stimulation
Bibliographic record
Abstract
Language is organized in distributed networks in the human brain. Numerous previous neurostimulation studies have demonstrated the functional relevance of several regions in the left hemisphere for different aspects of language comprehension and production (e.g. Ref. [[1]Hartwigsen G. The neurophysiology of language: insights from non-invasive brain stimulation in the healthy human brain.Brain Lang. 2015; 148: 81-94https://doi.org/10.1016/j.bandl.2014.10.007Crossref PubMed Scopus (40) Google Scholar], for review). In particular, the posterior part of the left inferior prefrontal cortex (IPC) has been identified as key region for processing the sound of words (i.e., phonological processing) [[2]Gough P.M. Nobre A.C. Devlin J.T. Dissociating linguistic processes in the left inferior frontal cortex with transcranial magnetic stimulation.J Neurosci. 2005; 25: 8010-8016https://doi.org/10.1523/JNEUROSCI.2307-05.2005Crossref PubMed Scopus (245) Google Scholar]. Aside from probing the functional relevance of specialized language regions, there is increasing interest in enhancing language performance with neurostimulation to provide effective training and treatment protocols for language disorders [[3]Hamilton R.H. Chrysikou E.G. Coslett B. Mechanisms of aphasia recovery after stroke and the role of noninvasive brain stimulation.Brain Lang. 2011; 118: 40-50https://doi.org/10.1016/J.BANDL.2011.02.005Crossref PubMed Scopus (0) Google Scholar]. Various neurostimulation protocols show promise but have inconsistent and variable effects across studies [[4]Biou E. Cassoudesalle H. Cogné M. Sibon I. de Gabory I. Dehail P. et al.Transcranial direct current stimulation in post-stroke aphasia rehabilitation: a systematic review.Ann Phys Rehab Med. 2019; 62: 104-121https://doi.org/10.1016/j.rehab.2019.01.003Crossref PubMed Scopus (33) Google Scholar]. One reason for this inconsistency is the current lack of understanding of neurostimulation effects at the network level since most language studies focused on the behavioral level only. Some studies provide first evidence that participants with increased functional connectivity (FC) between the left IPC and the rest of the brain in the alpha frequency band during task-free states have better language abilities [[5]Dubovik S. Pignat J.M. Ptak R. Aboulafia T. Allet L. Gillabert N. et al.The behavioral significance of coherent resting-state oscillations after stroke.Neuroimage. 2012; 61: 249-257https://doi.org/10.1016/J.NEUROIMAGE.2012.03.024Crossref PubMed Scopus (0) Google Scholar,[6]Guggisberg A.G. Rizk S. Ptak R. di Pietro M. Saj A. Lazeyras F. et al.Two intrinsic coupling types for resting-state integration in the human brain.Brain Topogr. 2015; 28: 318-329https://doi.org/10.1007/S10548-014-0394-2Crossref PubMed Google Scholar]. Consequently, increasing spontaneous alpha-band network interactions of the IPC may enhance language processing. Transcranial alternating current stimulation (tACS) may be particularly suited for this purpose, as it allows targeting and modulating different brain rhythms of interest. Indeed, previous research has shown that alpha-tACS over the bilateral prefrontal cortex facilitates phonological decisions [[7]Moliadze V. Sierau L. Lyzhko E. Stenner T. Werchowski M. Siniatchkin M. et al.After-effects of 10 Hz tACS over the prefrontal cortex on phonological word decisions.Brain Stimul. 2019; 12: 1464-1474https://doi.org/10.1016/J.BRS.2019.06.021Abstract Full Text Full Text PDF PubMed Google Scholar]. However, the neural correlates of such modulatory effects remain largely unclear. In particular, it remains unknown if the prefrontal cortex is the optimal target for enhancing phonological processing and if tACS modulates alpha-band oscillations and FC at the network level. Here, we address these questions based on a re-analysis of a previous dataset [[7]Moliadze V. Sierau L. Lyzhko E. Stenner T. Werchowski M. Siniatchkin M. et al.After-effects of 10 Hz tACS over the prefrontal cortex on phonological word decisions.Brain Stimul. 2019; 12: 1464-1474https://doi.org/10.1016/J.BRS.2019.06.021Abstract Full Text Full Text PDF PubMed Google Scholar]. Details on the study sample and experimental procedures have been reported previously [[7]Moliadze V. Sierau L. Lyzhko E. Stenner T. Werchowski M. Siniatchkin M. et al.After-effects of 10 Hz tACS over the prefrontal cortex on phonological word decisions.Brain Stimul. 2019; 12: 1464-1474https://doi.org/10.1016/J.BRS.2019.06.021Abstract Full Text Full Text PDF PubMed Google Scholar] and are provided in the Supplementary Information. In brief, subjects performed a phonological decision task on two- and three-syllable nouns after receiving tACS at 10 Hz, 16.6 Hz (control frequency) or sham stimulation over the bilateral prefrontal cortex. For the current analysis, we localized resting-state oscillations before and after stimulation with minimum variance beamformers and computed oscillation power as well as the absolute imaginary coherence as index of FC. The main analysis was done in the alpha (7.5–12.5 Hz) frequency band as this corresponds to the stimulated frequency, but to examine specificity, we additionally investigated delta (0.5–3.5 Hz), theta (3.5–7.5 Hz), and beta (12.5–20.5 Hz) frequency bands. At each voxel, we computed global FC as the mean FC with all other voxels, hence obtaining the graph theoretical measure of weighted node degree (WND). We first asked which neural patterns were associated with better behavioral performance in the language task. Behavioral performance was quantified with the inverse of the reaction time (iRT). A correlation between resting-state FC and iRT revealed that the more alpha-band WND at left inferior parietal (r = 0.45, p = 0.027) and left temporo-parietal areas (r = 0.50, p = 0.013) increased after compared to before 10 Hz stimulation, the faster the phonological response speed (Fig. 1A). Similarly, greater levels of alpha-band WND at the same areas, measured after 10 Hz stimulation, were also correlated with faster iRT (r > 0.46, p < 0.023, Fig. 1B). This suggests that temporo-parietal regions may play a role in behavioral improvements and, in particular, that alpha-band WND at these areas is a potentially useful marker for behavioral tACS effects. Conversely, greater increases of WND at the homologous right temporo-parietal areas correlated with worse behavioral performance (r = −0.43, p = 0.038). Changes in resting-state FC at the other frequency bands did not correlate with iRT (p > 0.35), demonstrating the specificity of the observed effects. In summary, we identify global interactions of the left temporo-parietal area with the whole brain as neural correlate of stimulation-induced performance increases. In line with our previous observations in healthy humans as well as stroke patients [[5]Dubovik S. Pignat J.M. Ptak R. Aboulafia T. Allet L. Gillabert N. et al.The behavioral significance of coherent resting-state oscillations after stroke.Neuroimage. 2012; 61: 249-257https://doi.org/10.1016/J.NEUROIMAGE.2012.03.024Crossref PubMed Scopus (0) Google Scholar,[6]Guggisberg A.G. Rizk S. Ptak R. di Pietro M. Saj A. Lazeyras F. et al.Two intrinsic coupling types for resting-state integration in the human brain.Brain Topogr. 2015; 28: 318-329https://doi.org/10.1007/S10548-014-0394-2Crossref PubMed Google Scholar], abundant spontaneous network communication in the alpha-frequency range is associated with better performance. Our previous work suggests this to be a general mechanism beyond the language domain that also applies to motor, cognitive, and visual perception performance [[8]Allaman L. Mottaz A. Guggisberg A.G. Disrupted resting-state EEG alpha-band interactions as a novel marker for the severity of visual field deficits after brain lesion.Clin Neurophysiol. 2021; 132: 2101-2109https://doi.org/10.1016/j.clinph.2021.05.029Crossref PubMed Scopus (0) Google Scholar]. Coherent alpha activity could thus represent integration over different cortical areas [[9]Nikolaev A.R. Ivanitsky G.A. Ivanitsky A.M. Posner M.I. Abdullaev Y.G. Correlation of brain rhythms between frontal and left temporal (Wernicke's) cortical areas during verbal thinking.Neurosci Lett. 2001; 298: 107-110https://doi.org/10.1016/S0304-3940(00)01740-7Crossref PubMed Scopus (29) Google Scholar], which likely represents a domain-global mechanism. Next, we tested if 10 Hz tACS could induce changes in alpha-band WND. We indeed noticed an increase at the left posterior temporal lobe after 10 Hz tACS (p < 0.05, uncorrected, Fig. 1C). Significant voxels corresponded to the left temporal parts of Brodmann area 40 of the Montreal Neurological Institute template. This increase did not survive correction for multiple comparisons in a whole-brain analysis, but was significant after small-volume correction in the left temporal Brodmann area 40 (t23 = 2.4, p = 0.028). Stimulation with 16.6 Hz (p = 0.11) and sham stimulation (p = 0.45) did not induce a significant change of alpha-band WND at this area. No significant changes in oscillation power were observed after any stimulation condition in prefrontal or parieto-temporal brain areas at any frequency band (p > 0.17). There was no correlation of oscillation power at any band with the inverse reaction time (p > 0.09). Collectively, our results show the relevance of remote stimulation effects. Contrary to our hypothesis, the region where FC was associated with behavioral improvements was not located in the prefrontal cortex. Indeed, we did not observe any modulatory tACS effects at the stimulation site but only at temporo-parietal areas. Notably, the modulatory effect was relatively small. The spatially distant effect may have been induced through fronto-parietal projections, which are known to contribute to working memory [[10]Jones K.T. Johnson E.L. Berryhill M.E. Frontoparietal theta-gamma interactions track working memory enhancement with training and tDCS.Neuroimage. 2020; 211: 116615https://doi.org/10.1016/J.NEUROIMAGE.2020.116615Crossref PubMed Scopus (0) Google Scholar]. Our results thus suggest that global FC of temporo-parietal areas may be a better and potentially behaviorally useful marker for language performance and modulatory stimulation effects than local activity of prefrontal areas. Furthermore, the ability of tACS to enhance alpha-band network communication at the stimulation site is limited, at least if it is applied at prefrontal areas. Future studies should evaluate if we can obtain greater effects on neural communication and phonological processing with the electrodes placed above temporo-parietal areas. This work is supported by the Swiss National Science Foundation ( 51AU40_180888 ) to AGG and by the Max Planck Society and the German Research Foundation ( DFG, HA 6314/3-1 , HA 6314/4-1 ) to GH.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".