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• W1573465609 abstract "•The monkey brain is capable of representing numerical and sequence patterns•fMRI responses to number and to sequence are segregated in the monkey•The human inferior frontal gyrus responds to both types of patterns•Humans and monkeys differ even in a simple sequence learning paradigm The ability to extract deep structures from auditory sequences is a fundamental prerequisite of language acquisition. Using fMRI in untrained macaques and humans, we investigated the brain areas involved in representing two abstract properties of a series of tones: total number of items and tone-repetition pattern. Both species represented the number of tones in intraparietal and dorsal premotor areas and the tone-repetition pattern in ventral prefrontal cortex and basal ganglia. However, we observed a joint sensitivity to both parameters only in humans, within bilateral inferior frontal and superior temporal regions. In the left hemisphere, those sites coincided with areas involved in language processing. Thus, while some abstract properties of auditory sequences are available to non-human primates, a recently evolved circuit may endow humans with a unique ability for representing linguistic and non-linguistic sequences in a unified manner. The ability to extract deep structures from auditory sequences is a fundamental prerequisite of language acquisition. Using fMRI in untrained macaques and humans, we investigated the brain areas involved in representing two abstract properties of a series of tones: total number of items and tone-repetition pattern. Both species represented the number of tones in intraparietal and dorsal premotor areas and the tone-repetition pattern in ventral prefrontal cortex and basal ganglia. However, we observed a joint sensitivity to both parameters only in humans, within bilateral inferior frontal and superior temporal regions. In the left hemisphere, those sites coincided with areas involved in language processing. Thus, while some abstract properties of auditory sequences are available to non-human primates, a recently evolved circuit may endow humans with a unique ability for representing linguistic and non-linguistic sequences in a unified manner. A major issue for cognitive neuroscience is to determine how human representational abilities differ from those of other species. Language acquisition is a prime example of fast learning that seems unique to humans. It is often proposed that the faculty of language reflects a broader human-specific ability to acquire and represent recursive structures [1Hauser M.D. Chomsky N. Fitch W.T. The faculty of language: what is it, who has it, and how did it evolve?.Science. 2002; 298: 1569-1579Crossref PubMed Scopus (2666) Google Scholar] or regular combinations of symbols [2Deacon T.W. The Symbolic Species: The Co-Evolution of Language and the Brain. W. W. Norton & Co, New York1997Google Scholar]. Sensitivity to abstract patterns and regularities is essential to mathematics and music, two other faculties uniquely developed in humans [3Dehaene S. Spelke E. Pinel P. Stanescu R. Tsivkin S. Sources of mathematical thinking: behavioral and brain-imaging evidence.Science. 1999; 284: 970-974Crossref PubMed Scopus (1234) Google Scholar, 4Koelsch S. Siebel W.A. Towards a neural basis of music perception.Trends Cogn. Sci. 2005; 9: 578-584Abstract Full Text Full Text PDF PubMed Scopus (389) Google Scholar]. Searching for comparative evidence on the neural representation of rules and symbols may therefore shed a unique light on the evolutionary origins of human cognition. Previous studies have shown that the discovery of numerical or logical patterns called “algebraic rules” [5Marcus G.F. Vijayan S. Bandi Rao S. Vishton P.M. Rule learning by seven-month-old infants.Science. 1999; 283: 77-80Crossref PubMed Scopus (802) Google Scholar] is a powerful mechanism, already available to human infants, and may play an important role in the acquisition of language. In a seminal study [5Marcus G.F. Vijayan S. Bandi Rao S. Vishton P.M. Rule learning by seven-month-old infants.Science. 1999; 283: 77-80Crossref PubMed Scopus (802) Google Scholar], 7-month-old infants were exposed for only 2 min to sequences respecting a regularity such as AAB (all sequences contain two identical sounds followed by a different one). Infants later attended longer to stimuli that violated the rule than to novel stimuli that respected it. Such evidence suggests that infants could detect and memorize at least some aspects of the regular pattern governing the stimuli (e.g., the initial repetition of two sounds, or the change in the last item). It has been claimed that monkeys and some birds may possess the rudiments of this ability [6Gentner T.Q. Fenn K.M. Margoliash D. Nusbaum H.C. Recursive syntactic pattern learning by songbirds.Nature. 2006; 440: 1204-1207Crossref PubMed Scopus (399) Google Scholar, 7Abe K. Watanabe D. Songbirds possess the spontaneous ability to discriminate syntactic rules.Nat. Neurosci. 2011; 14: 1067-1074Crossref PubMed Scopus (136) Google Scholar, 8Wilson B. Slater H. Kikuchi Y. Milne A.E. Marslen-Wilson W.D. Smith K. Petkov C.I. Auditory artificial grammar learning in macaque and marmoset monkeys.J. Neurosci. 2013; 33: 18825-18835Crossref PubMed Scopus (70) Google Scholar], but current evidence remains inconclusive [9Beckers G.J. Bolhuis J.J. Okanoya K. Berwick R.C. Birdsong neurolinguistics: songbird context-free grammar claim is premature.Neuroreport. 2012; 23: 139-145Crossref PubMed Scopus (79) Google Scholar, 10Fitch W.T. Friederici A.D. Artificial grammar learning meets formal language theory: an overview.Philos. Trans. R. Soc. Lond. B Biol. Sci. 2012; 367: 1933-1955Crossref PubMed Scopus (126) Google Scholar, 11ten Cate C. Okanoya K. Revisiting the syntactic abilities of non-human animals: natural vocalizations and artificial grammar learning.Philos. Trans. R. Soc. Lond. B Biol. Sci. 2012; 367: 1984-1994Crossref PubMed Scopus (111) Google Scholar, 12van Heijningen C.A. de Visser J. Zuidema W. ten Cate C. Simple rules can explain discrimination of putative recursive syntactic structures by a songbird species.Proc. Natl. Acad. Sci. USA. 2009; 106: 20538-20543Crossref PubMed Scopus (136) Google Scholar]. Although non-human primates can learn patterns based on number [13Jordan K.E. Maclean E.L. Brannon E.M. Monkeys match and tally quantities across senses.Cognition. 2008; 108: 617-625Crossref PubMed Scopus (72) Google Scholar] or artificial grammars [8Wilson B. Slater H. Kikuchi Y. Milne A.E. Marslen-Wilson W.D. Smith K. Petkov C.I. Auditory artificial grammar learning in macaque and marmoset monkeys.J. Neurosci. 2013; 33: 18825-18835Crossref PubMed Scopus (70) Google Scholar], we still do not know whether and how the neural networks underlying such abstract features differ in monkey and human brains. At the brain level, electrophysiological recordings in monkeys have shown that single neurons in prefrontal and parietal cortical regions can encode motor patterns such as AABB or ABAB, where A and B are unspecified gestures [14Wallis J.D. Anderson K.C. Miller E.K. Single neurons in prefrontal cortex encode abstract rules.Nature. 2001; 411: 953-956Crossref PubMed Scopus (768) Google Scholar, 15Shima K. Isoda M. Mushiake H. Tanji J. Categorization of behavioural sequences in the prefrontal cortex.Nature. 2007; 445: 315-318Crossref PubMed Scopus (151) Google Scholar, 16Eiselt A.K. Nieder A. Representation of abstract quantitative rules applied to spatial and numerical magnitudes in primate prefrontal cortex.J. Neurosci. 2013; 33: 7526-7534Crossref PubMed Scopus (53) Google Scholar]. However, these results raise several issues. First, those brain representations were only studied after extensive training; demonstrations of numerical or symbolic coding in untrained animals, such as the presence of number-tuned neurons in parietal and prefrontal cortex [17Viswanathan P. Nieder A. Neuronal correlates of a visual “sense of number” in primate parietal and prefrontal cortices.Proc. Natl. Acad. Sci. USA. 2013; 110: 11187-11192Crossref PubMed Scopus (103) Google Scholar], are quite scarce. Second, electrophysiological studies, unlike brain-imaging studies [18Gil-da-Costa R. Stoner G.R. Fung R. Albright T.D. Nonhuman primate model of schizophrenia using a noninvasive EEG method.Proc. Natl. Acad. Sci. USA. 2013; 110: 15425-15430Crossref PubMed Scopus (119) Google Scholar, 19Mantini D. Corbetta M. Romani G.L. Orban G.A. Vanduffel W. Evolutionarily novel functional networks in the human brain?.J. Neurosci. 2013; 33: 3259-3275Crossref PubMed Scopus (165) Google Scholar, 20Uhrig L. Dehaene S. Jarraya B. A hierarchy of responses to auditory regularities in the macaque brain.J. Neurosci. 2014; 34: 1127-1132Crossref PubMed Scopus (53) Google Scholar], do not allow for a direct comparison of the neural circuits for sequence representation in monkeys and humans at the whole-brain level. fMRI allows exploring whole-brain activity in monkeys and humans. In a recent fMRI study [20Uhrig L. Dehaene S. Jarraya B. A hierarchy of responses to auditory regularities in the macaque brain.J. Neurosci. 2014; 34: 1127-1132Crossref PubMed Scopus (53) Google Scholar], we demonstrated that, once monkeys repeatedly heard a specific auditory melody aaaab (where a and b are two fixed tones), hearing a deviant sequence aaaaa led to widespread activation in temporal, parietal, and prefrontal cortices, at sites similar to humans [21Bekinschtein T.A. Dehaene S. Rohaut B. Tadel F. Cohen L. Naccache L. Neural signature of the conscious processing of auditory regularities.Proc. Natl. Acad. Sci. USA. 2009; 106: 1672-1677Crossref PubMed Scopus (418) Google Scholar]. This novelty response, however, is ambiguous. It might simply indicate that monkeys can memorize melodies and detect a novel one. Alternatively, it could arise from a sensitivity to the violation of abstract properties such as number (“four sounds plus another one”) or tone-repetition pattern (e.g., “one sound is different” or “the last sound is different”). The present paradigm was therefore designed to probe the sensitivity of monkeys and humans to such abstract auditory properties and to identify whether the two species use similar brain areas for this task. We used fMRI to visualize whole-brain activity while awake monkeys and humans were passively exposed to auditory sequences. During an initial habituation phase, subjects heard sequences with a fixed pattern (AAAB or AAAA). Critically, A and B could be any of several sounds, and duration and temporal spacing were constantly varied such that only the pattern itself could be learned (Figures 1 and S1). Using fMRI, we then tested for brain responses to novel sequences that either respected the original pattern or violated it. The violations consisted in changing the total number of items (e.g., going from AAAB to AB or to AAAAAB), changing the tone-repetition pattern (going from AAAA to AAAB or vice versa), or changing both (e.g., going from AAAA to AAAAAB). Again, controls ensured that discrimination could not be based on other non-numerical parameters (see Figure S1). This design resulted in four test conditions: N−S−, new exemplars of same rule; N+S−, isolated number deviants; N−S+, isolated sequence deviants; and N+S+, double deviants (Figure 1). Importantly, both species were naive to the auditory sequences, had not been actively trained to discriminate them, and simply performed an unrelated eye-fixation task while the auditory stimuli were presented. The first question is, can monkeys identify the invariable pattern underlying the variable sequences? If monkey brains extract the pattern, then they should generalize to novel exemplars and respond only to pattern-violating items. If, on the other hand, monkey brains only store specific melodies in memory, then even the N−S− test sequences should elicit a novelty reaction. Thus, we first examined brain regions responsive to the N−S− test stimuli with only frequency changes. Contrasting N−S− with habituation showed no significant brain responses (voxel-wise, p < 0.005, corrected by false discovery rate [FDR], p < 0.05), suggesting that monkeys extracted the pattern and generalized it to novel items. We then examined the monkey brain responses to pattern-violating sequences. To identify brain regions responsive to number change, we first compared sequences with an isolated violation in number (N+S−) to those without any violation (N−S−) (Figure 2A). Despite a certain variability in the activations observed in individual monkeys (see Figure S2), 14 significant activation peaks were identified in the group analysis (Table S1; p < 0.005, FDR p < 0.05). In parietal areas, the strongest activation was found bilaterally in the ventral part of the intraparietal sulcus (VIP), a site previously found to contain number-sensitive neurons [22Nieder A. Supramodal numerosity selectivity of neurons in primate prefrontal and posterior parietal cortices.Proc. Natl. Acad. Sci. USA. 2012; 109: 11860-11865Crossref PubMed Scopus (126) Google Scholar, 23Nieder A. Diester I. Tudusciuc O. Temporal and spatial enumeration processes in the primate parietal cortex.Science. 2006; 313: 1431-1435Crossref PubMed Scopus (233) Google Scholar] (Figure 2A). Both two- and six-tone number deviants elicited significantly higher VIP activations than the four-tone test stimulus (t test, p < 0.05; Figure S4A). In addition, the posterior bank of the inferior arcuate sulcus (area 6DR, i.e., dorsal premotor F2/F4) was also activated bilaterally, as were the posterior superior temporal sulcus (pSTS), anterior insula (aINS), supplementary motor area (SMA), and anterior cingulate (ACC). Similar results were found when we examined a second, more stringent contrast for a response to number, namely the main effect of number change, regardless of the presence of a concomitant change in sequence [(N+S+) + (N+S−) > (N−S−) + (N−S+)] (Figure 1C): responses were found in areas VIP, 6DR, pSTS, SMA, and ACC, but not aINS (p < 0.005, pFDR < 0.05 corrected). Finally, we computed a third, even more stringent statistic consisting in a conjunction analysis for N+S+ > N−S+ and N+S− > N−S− (Figure 4A), which therefore searched for a replicable response to number change, whether or not there also was a concomitant change in sequence pattern. Again, this conjunction criterion identified areas VIP, pSTS, SMA, and ACC (conjunction null, p < 0.01, pFDR < 0.05). As indicated by the histograms in Figure 2B, those areas showed a positive activation (relative to the mean of the habituation stimuli in this run) whenever the total number of tones suddenly changed and a null or, in some cases, a negative activation (suggesting further habituation) whenever this number remained equal to its habituation value. The main brain regions (VIP and ACC) showing the number effect were also observed in individual monkeys (Figures S2 and S3). Thus, monkeys possess a set of regions responsive to number change, irrespective of concomitant changes in sound frequency, timing, and sequence pattern. When we ran the same paradigm in humans, we observed activations to numerical deviance (N+S− > N−S−) in bilateral intraparietal sulcus (IPS) (p < 0.001, pFDR < 0.05 corrected), at a site plausibly homologous to the monkey site (Figure 2A). As in monkeys, additional activations were observed bilaterally in the pSTS, SMA, medial prefrontal (mPFC), and aINS (Table S1). However, different from monkeys, humans showed an intense activation in bilateral inferior frontal gyri (IFG) (Figure 2A, Human). There was a significant main effect of number in IPS, IFG, pSTS, SMA, medial prefrontal (mPFC), and aINS areas (see plots of activation in Figure 2B). These areas also remained in the conjunction analysis (p < 0.01, pFDR < 0.05 corrected), indicating a context-independent numerical response (Figure 4A). The IPS, left IFG, and SMA closely overlapped with areas active during mental calculation in the same subjects (Figure S4B). In summary, the numerical feature of auditory sequences engaged highly similar networks in both species. The areas involved primarily belonged to a dorsal auditory pathway [24Bizley J.K. Cohen Y.E. The what, where and how of auditory-object perception.Nat. Rev. Neurosci. 2013; 14: 693-707Crossref PubMed Scopus (249) Google Scholar]. The intraparietal cortex, previously involved in number representation [3Dehaene S. Spelke E. Pinel P. Stanescu R. Tsivkin S. Sources of mathematical thinking: behavioral and brain-imaging evidence.Science. 1999; 284: 970-974Crossref PubMed Scopus (1234) Google Scholar, 22Nieder A. Supramodal numerosity selectivity of neurons in primate prefrontal and posterior parietal cortices.Proc. Natl. Acad. Sci. USA. 2012; 109: 11860-11865Crossref PubMed Scopus (126) Google Scholar, 23Nieder A. Diester I. Tudusciuc O. Temporal and spatial enumeration processes in the primate parietal cortex.Science. 2006; 313: 1431-1435Crossref PubMed Scopus (233) Google Scholar], was a dominant node in both species, yet we also observed additional activation of the IFG only in humans. We next probed the sensitivity to sequence changes in both species. In the monkey group analysis, nine cortical regions were activated by isolated sequence changes, i.e., whenever the sequence pattern suddenly changed from AAAB to AAAA or vice versa (N−S+ > N−S−; p < 0.005, pFDR < 0.05 corrected; Figures 1C and 3A ; Table S2). Unlike for number, a ventral auditory pathway was seen. In the frontal cortex, the strongest activations were found bilaterally in area 6VR (i.e., ventral premotor area F5) and the ventral part of dorsolateral prefrontal cortex (VLPFC, area 46v), extending to the anterior insula. In the temporal lobe, the bilateral pSTS and left anterior STS (aSTS) were significantly activated. A subcortical region in the basal ganglia, previously engaged in sequence chunking [25Graybiel A.M. The basal ganglia and chunking of action repertoires.Neurobiol. Learn. Mem. 1998; 70: 119-136Crossref PubMed Scopus (713) Google Scholar], was also activated (left caudate, x = −2, y = 5, z = 4; see Figure S4D). Our second statistical criterion, the main effect of sequence violation [(N+S+) + (N−S+) > (N−S−) + (N+S−)], confirmed the contribution to sequence processing of areas 6VR/VLPFC, pSTS, aSTS, and caudate, but not aINS (Figure 3B). Finally, the most stringent conjunction analysis, searching for areas activated in both contrasts N+S+ > N+S− and N−S+ > N−S− established that areas 6VR/VLPFC, pSTS, and left aSTS reacted to sequence change even when there was a concomitant change in number (conjunction null, p < 0.01, pFDR<0.05 corrected; Figure 4A). The main brain regions showing the sequence effect (6VR and caudate) were also observed in individual monkeys (Figures S2 and S3).Figure 4Evidence for a Uniquely Human Joint Sensitivity to Number and Sequence PatternsShow full caption(A) Conjunction analyses identified the areas for number change detection, regardless of a concomitant change in sequence pattern (conjunction of N+S+ > N−S+ and N+S− > N−S−, shown in red) and, conversely, the areas for sequence change detection, regardless of a concomitant change in number (conjunction of N+S+ > N+S− and N−S+ > N−S−, shown in green). Brain activations in monkeys and humans are projected on lateral and top views of the brains. Maps are thresholded at t > 2.4 (monkey) and at t > 3.0 (human), which corresponds to the conjunction null, p < 0.01, FDR p < 0.05 corrected. Arrows indicate the uniquely human peaks common to both number and sequence conjunction analyses (Table S3).(B) Conjunction map of the two main effects of number and sequence change in humans (contrasts (N+S+) + (N+S−) > (N−S−) + (N−S+) and (N+S+) + (N−S+) > (N−S−) + (N+S−); t > 2.5, conjunction null, p < 0.01, corrected by FDR) superimposed to human cytoarchitectonically defined areas 44 (blue box) and BA45 (green box). Note that the joint activations in IFG are confined to BA44.(C) Activation in subject-specific language-responsive voxels within seven regions of interest (ROIs) in humans. Within each subject, voxels responsive to sentences processing (p < 0.01, uncorrected) in each localizer were identified. Brain activation within those voxels is plotted for the four test conditions. Activations in IFGoper, IFGorb, and pSTS showed significant main effects of number and sequence change (N, number effect; S, sequence effect; ∗∗p < 0.05 corrected; ∗p < 0.05 uncorrected; see Table S4 for p values). Error bars represent ±1 SEM.View Large Image Figure ViewerDownload Hi-res image Download (PPT) (A) Conjunction analyses identified the areas for number change detection, regardless of a concomitant change in sequence pattern (conjunction of N+S+ > N−S+ and N+S− > N−S−, shown in red) and, conversely, the areas for sequence change detection, regardless of a concomitant change in number (conjunction of N+S+ > N+S− and N−S+ > N−S−, shown in green). Brain activations in monkeys and humans are projected on lateral and top views of the brains. Maps are thresholded at t > 2.4 (monkey) and at t > 3.0 (human), which corresponds to the conjunction null, p < 0.01, FDR p < 0.05 corrected. Arrows indicate the uniquely human peaks common to both number and sequence conjunction analyses (Table S3). (B) Conjunction map of the two main effects of number and sequence change in humans (contrasts (N+S+) + (N+S−) > (N−S−) + (N−S+) and (N+S+) + (N−S+) > (N−S−) + (N+S−); t > 2.5, conjunction null, p < 0.01, corrected by FDR) superimposed to human cytoarchitectonically defined areas 44 (blue box) and BA45 (green box). Note that the joint activations in IFG are confined to BA44. (C) Activation in subject-specific language-responsive voxels within seven regions of interest (ROIs) in humans. Within each subject, voxels responsive to sentences processing (p < 0.01, uncorrected) in each localizer were identified. Brain activation within those voxels is plotted for the four test conditions. Activations in IFGoper, IFGorb, and pSTS showed significant main effects of number and sequence change (N, number effect; S, sequence effect; ∗∗p < 0.05 corrected; ∗p < 0.05 uncorrected; see Table S4 for p values). Error bars represent ±1 SEM. Similar analyses in humans revealed that, as in monkeys, isolated sequence deviants activated the bilateral pSTS and temporal polar (TP) cortices (p < 0.001, pFDR < 0.05 corrected; Figure 3A). Also consistent with the monkey results was a bilateral activation in the basal ganglia (putamen, x = 21, y = 9, z = 7, t = 3.62; and x = −23, y = 7, z = 14, t = 3.15; Figure S4D). Furthermore, humans again showed an additional intense frontal activation in the bilateral IFG. All of these areas were confirmed using two additional criteria for sequence responses, namely the main effect of sequence violation (Figure 3B) and the conjunction analysis (Figure 4A). In summary, a striking difference between species was that the IFG was activated by both sequence and number changes in humans, while number and sequence violations seemed to involve distinct networks in monkeys. To quantify this observation, we explored which regions showed intersecting statistical maps for the main effects of number and sequence change, thus possibly operating as integrative structures or “hubs.” In monkeys, no significant regions showed joint effects, either additively or with an interaction of both factors (both p < 0.005, corrected by FDR). Humans were different: conjunction analysis identified additive joint effects of number and sequence in bilateral pSTS and IFG (Figure 4A; Table S3). To confirm this finding with highest statistical sensitivity, we applied a leave-one-out cross-validation approach: we first used number-change (N+ > N−) or sequence-change (S+ > S−) contrasts as functional localizers to define the voxels of interest, in all but one of fMRI runs, then examined the responses to either the N+ > N− or S+ > S− contrasts in the left-out run. In monkeys, the results confirmed that the regions of IPS and ACC/SMA were selectively activated to the number deviants, and regions of aSTS, pSTS, and 6V were activated to the sequence deviants (Figure S3). Crucially, even with this sensitive analysis, no activations were observed in the generalization across conditions (S+ > S− contrast in voxels isolated by the N+ > N− contrast, or vice versa), confirming that the number-change and sequence-change networks involve non-overlapping voxels. Human results, by contrast, not only replicated the brain areas showing the number effect in IPS, SMA, and IFG and the sequence effect in TP, putamen, pSTS, and IFG but also showed cross-condition generalization, with a joint activation of both effects in bilateral pSTS and IFG (Figure S3). To specifically test inferior frontal regions in both monkey and human, we then performed a three-way ANOVA with factors of region (i.e., voxels identified either by the N+/N− contrast or by the S +/S− contrast) × number change × sequence change, using as dependent variable the mean fMRI activation of the cross-validated voxels within human IFG and monkey area F5, respectively. In humans, there was a significant main effect of number (F(1,16) = 8.47, p = 0.01) and sequence (F(1,16) = 4.65, p = 0.03), but no significant interactions (region × number, F(1,16) = 1.29, p = 0.27; region × sequence, F(1,16) = 0.02, p = 0.96; number × sequence, F(1,16) = 0.56, p = 0.46; region × number × sequence, F(1,16) = 0.99, p = 0.33). Hence, in humans, IFG voxels showed joint effects of number and sequence, irrespective of the contrast used to identify them. In monkeys, by contrast, the results showed a significant main effect of sequence (F(1,29) = 29.8, p = 0.0002) and a significant effect in region × sequence interaction (F(1,29) = 4.95, p = 0.03), but no number effect (F(1,29) = 0.10, p = 0.75) or any significant region × number interaction (F(1,29) = 0.72, p = 0.41). This is consistent with monkey IFG responding only to sequence change and only in voxels identified using the sequence change contrast. In the human left hemisphere, the IFG and pSTS regions coincided with those previously identified as forming a core network for language syntax [26Pallier C. Devauchelle A.D. Dehaene S. Cortical representation of the constituent structure of sentences.Proc. Natl. Acad. Sci. USA. 2011; 108: 2522-2527Crossref PubMed Scopus (366) Google Scholar]. Indeed, probabilistic cytoarchitectonic maps located the human IFG conjunction effect to Brodmann’s area 44 (Figure 4B), previously associated with hierarchical linguistic and non-linguistic sequences [27Bahlmann J. Schubotz R.I. Friederici A.D. Hierarchical artificial grammar processing engages Broca’s area.Neuroimage. 2008; 42: 525-534Crossref PubMed Scopus (192) Google Scholar, 28Makuuchi M. Bahlmann J. Anwander A. Friederici A.D. Segregating the core computational faculty of human language from working memory.Proc. Natl. Acad. Sci. USA. 2009; 106: 8362-8367Crossref PubMed Scopus (248) Google Scholar]. The fact that our paradigm involves a minimal form of “syntax,” yet with non-verbal stimuli (tones), may explain why the human IFG was recruited in both left and right hemispheres, as also found during artificial language processing [27Bahlmann J. Schubotz R.I. Friederici A.D. Hierarchical artificial grammar processing engages Broca’s area.Neuroimage. 2008; 42: 525-534Crossref PubMed Scopus (192) Google Scholar], musical syntax processing [4Koelsch S. Siebel W.A. Towards a neural basis of music perception.Trends Cogn. Sci. 2005; 9: 578-584Abstract Full Text Full Text PDF PubMed Scopus (389) Google Scholar], mathematical calculation [3Dehaene S. Spelke E. Pinel P. Stanescu R. Tsivkin S. Sources of mathematical thinking: behavioral and brain-imaging evidence.Science. 1999; 284: 970-974Crossref PubMed Scopus (1234) Google Scholar], and hierarchically organized behavior [29Koechlin E. Jubault T. Broca’s area and the hierarchical organization of human behavior.Neuron. 2006; 50: 963-974Abstract Full Text Full Text PDF PubMed Scopus (365) Google Scholar]. To clarify whether the human left-hemispheric areas in our study corresponded precisely with language-processing regions, we compared the human sequence-change responses to the areas identified in the same group of subjects using an independent language localizer [30Pinel P. Thirion B. Meriaux S. Jobert A. Serres J. Le Bihan D. Poline J.B. Dehaene S. Fast reproducible identification and large-scale databasing of individual functional cognitive networks.BMC Neurosci. 2007; 8: 91Crossref PubMed Scopus (111) Google Scholar]. The results showed overlapping areas for non-verbal sequence change and for verbal sentence processing in TP and putamen. The left IFG showing a sequence effect was just posterior to the activations in sentence processing, with a slight overlap (Figure S4B). A further comparison between our auditory sequence-responsive areas and other sequence-processing studies of the human hierarchical organization of motor actions [29Koechlin E. Jubault T. Broca’s area and the hierarchical organization of human behavior.Neuron. 2006; 50: 963-974Abstract Full Text Full Text PDF PubMed Scopus (365) Google Scholar] and the structure complexity of human language [27Bahlmann J. Schubotz R.I. Friederici A.D. Hierarchical artificial grammar processing engages Broca’s area.Neuroimage. 2008; 42: 525-534Crossref PubMed Scopus (192) Google Scholar] showed highly consistent activations in the inferior frontal regions (Figure S5). Because overlapping fMRI activations may arise spuriously at the group level, we sought to confirm our findings in individual subjects. We used an independent functional localizer for l" @default.
• W1573465609 created "2016-06-24" @default.
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• W1573465609 date "2015-08-01" @default.
• W1573465609 modified "2023-10-13" @default.
• W1573465609 title "Representation of Numerical and Sequential Patterns in Macaque and Human Brains" @default.
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• W1573465609 doi "https://doi.org/10.1016/j.cub.2015.06.035" @default.
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