The Neurobiology of Agrammatic Sentence Comprehension: A Lesion Study

Corianne Rogalsky1, Arianna N. LaCroix1, Kuan-Hua Chen2,3, Steven W. Anderson2, Hanna Damasio4, Tracy Love5, and Gregory Hickok6

Abstract ■ Broca’s area has long been implicated in sentence compre- average did not exhibit the expected agrammatic compre- hension. Damage to this region is thought to be the central hension pattern—for example, their performance was >80% source of “agrammatic comprehension” in which performance on noncanonical sentences in the sentence–picture matching is substantially worse (and near chance) on sentences with non- task. Patients with ATL damage (n = 18) also did not exhibit canonical word orders compared with canonical word order an agrammatic comprehension pattern. Across our entire sentences (in English). This claim is supported by functional patient sample, the lesions of patients with agrammatic com- neuroimaging studies demonstrating greater activation in prehension patterns in either task had maximal overlap in pos- Broca’s area for noncanonical versus canonical sentences. terior superior temporal and inferior parietal regions. Using However, functional neuroimaging studies also have frequently voxel-based lesion–symptom mapping, we find that lower per- implicated the anterior temporal lobe (ATL) in sentence pro- formances on canonical and noncanonical sentences in each cessing more broadly, and recent lesion–symptom mapping task are both associated with damage to a large left superior studies have implicated the ATL and mid temporal regions in temporal–inferior parietal network including portions of the agrammatic comprehension. This study investigates these ATL, but not Broca’s area. Notably, however, response bias in seemingly conflicting findings in 66 left-hemisphere patients plausibility judgments was significantly associated with damage with chronic focal cerebral damage. Patients completed two to inferior frontal cortex, including gray and white matter in sentence comprehension measures, sentence–picture match- Broca’s area, suggesting that the contribution of Broca’sarea ing and plausibility judgments. Patients with damage including tosentencecomprehensionmayberelatedtotask-related Broca’s area (but excluding the temporal lobe; n =11)on cognitive demands. ■

INTRODUCTION and Broca’s aphasia can be caused by lesions sparing At the turn of the 20th century, a then-decades-long de- Broca’s area (Fridriksson, Bonilha, & Rorden, 2007). bate persisted regarding the functional role of Broca’s Approximately a century after Broca’s initial claims re- area in speech. Broca had proposed in the 1860s that garding his namesake brain area, a new function was tied the “foot of the third frontal convolution”—what we call to the region after it was discovered that Broca’s aphasics Broca’sareatoday—was the seat of articulate speech. are impaired at comprehending sentences that require However, in 1905, Pierre Marie authored a famous article subtle syntactic analysis, such as semantically reversible declaring that “The third frontal convolution plays no sentences with noncanonial word order (It was the cat special role in the function of language” (Marie, 1906), that the dog chased or The dog was chased by the cat) presenting evidence that Broca’s area could be damaged compared with more typical subject–verb–object order- with no effect on articulate speech, and conversely that ings in English (Bradley, Garrett, & Zurif, 1980; Caramazza deficits in articulate speech could be documented with- & Zurif, 1976). Notwithstanding the fact that Broca’s apha- out damage to Broca’s area. Modern data have at the very sia holds a tenuous relation to Broca’s area and the fact least confirmed the confusion, if not resolved the debate that people with conduction aphasia (and therefore fully. Damage to Broca’s area alone does not cause posterior temporal-parietal lesions) have the very same chronic Broca’s aphasia nor significant chronic disruption sentence comprehension trouble (Caramazza & Zurif, of speech articulation (Mohr et al., 1978; Mohr, 1976), 1976), Broca’s area became the theoretical centerpiece in models of the neurology of syntax generally (Zurif, 1980) and the basis of aspects of sentence comprehen- 1Arizona State University, 2University of Iowa, 3University of sion in particular (Caplan & Futter, 1986; Grodzinsky, California, Berkeley, 4University of Southern California, 5University 1986). But like a century ago, “the battle over Broca’s of California, San Diego, 6University of California, Irvine area” (Grodzinsky & Santi, 2008) continues to rage, this

© 2017 Massachusetts Institute of Technology Journal of Cognitive Neuroscience 30:2, pp. 234–255 doi:10.1162/jocn_a_01200 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn_a_01200 by guest on 24 September 2021 time over its role in sentence comprehension. Early dis- word lists (Rogalsky et al., 2011; Humphries et al., 2006), senters attacked the specificity of syntactic function, show- more to syntactic errors than semantic errors (Herrmann, ing that Broca’s aphasics could judge the grammaticality of Obleser, Kalberlah, Haynes, & Friederici, 2012; Friederici, sentences quite well (Wulfeck, 1988; Linebarger, Schwartz, Ruschemeyer, Hahne, & Fiebach, 2003), and more to & Saffran, 1983). This led to more refined theories regard- idioms than literal sentences (Lauro, Tettamanti, Cappa, ing which syntactic functions are affected (Grodzinsky & & Papagno, 2008). The ATL also is more activated by Santi, 2008; Grodzinsky, 1986, 2000). sentence prosody than list prosody (Humphries et al., In the last two decades, the debate has been fueled by 2005). However, Broca’s area, but not the ATL, has been evidence from functional imaging. Some groups have found to be more responsive to noncanonical sentences shown what appears to be rather specific involvement of compared with canonical sentences (Rogalsky & Hickok, Broca’s area during aspects of sentence comprehension 2011; Rogalsky et al., 2008; Ben-Shachar, Palti, & Grodzinsky, (Rogalsky, Almeida, Sprouse, & Hickok, 2015; Fedorenko, 2004; Caplan et al., 1998; Stromswold et al., 1996; see Santi Behr, & Kanwisher, 2011; Fedorenko & Kanwisher, 2011; and Grodzinsky, 2012, for one notable exception). Grodzinsky & Santi, 2008; Santi & Grodzinsky, 2007a, Large-scale lesion mapping methods developed in the 2007b; Caplan, Alpert, Waters, & Olivieri, 2000; Caplan, 2000s (Bates et al., 2003) have offered some hope of Alpert, & Waters, 1999; Dapretto & Bookheimer, 1999; resolving the murky state of affairs regarding the neural Caplan, Alpert, & Waters, 1998, Just, Carpenter, Keller, basis of sentence comprehension. The few such studies that Eddy, & Thulborn, 1996; Stromswold, Caplan, Alpert, & have been undertaken so far mainly implicate temporal- Rauch, 1996) whereas others point to results arguing parietal regions in sentence comprehension (Pillay, Binder, that the effects are confounded with working memory Humphries, Gross, & Book, 2017; Magnusdottir et al., load differences (Rogalsky & Hickok, 2011; Rogalsky, 2013; Thothathiri, Kimberg, & Schwartz, 2012; Dronkers, Matchin, & Hickok, 2008; Kaan & Swaab, 2002; Just et al., Wilkins, Van Valin, Redfern, & Jaeger, 2004). For example, 1996) or cognitive control demands (Novick, Trueswell, & Dronkers et al. (2004) investigated auditory sentence Thompson-Schill, 2005). Still other studies comparing comprehension using subtests of the CYCLE-R clinical activations in response to sentences versus nonsentence battery, in 64 English-speaking chronic stroke patients controls, such as word lists or unintelligible acoustically with left-hemisphere damage. Their recruitment was not matched speech, have not reported Broca’sareainvolve- based on aphasia diagnosis; their sample contained both ment consistently (Rogalsky, Rong, Saberi, & Hickok, patients with and without aphasia. Their voxel-based 2011; Friederici, Kotz, Scott, & Obleser, 2010; Rogalsky lesion–symptom mapping (VLSM) results implicated both & Hickok, 2009; Awad, Warren, Scott, Turkheimer, & Wise, the ATL and Broca’s area in the comprehension of sen- 2007; Humphries, Binder, Medler, & Liebenthal, 2006; tences, but noncanonical and canonical sentences were Spitsyna, Warren, Scott, Turkheimer, & Wise, 2006; Humphries, not examined separately or compared, so the relative con- Love, Swinney, & Hickok, 2005; Crinion, Lambon-Ralph, tributions of Broca’s area and the ATL to noncanonical Warburton, Howard, & Wise, 2003; Friederici, Meyer, & von and canonical sentence comprehension remains unclear. Cramon, 2000; Scott, Blank, Rosen, & Wise, 2000; Stowe A subsequent VLSM study (Magnusdottir et al., 2013) et al., 1998; Mazoyer et al., 1993). did examine noncanonical versus canonical sentence Unlike Broca’s area, the anterior temporal lobe (ATL; comprehension in 50 Icelandic-speaking patients with Rogalsky et al., 2011, 2015; Friederici et al., 2000, 2010; acute left-hemisphere strokes; most testing was com- Rogalsky & Hickok, 2009; Awad et al., 2007; Humphries pleted within 3 days of admission to the hospital. Using et al., 2005, 2006; Spitsyna et al., 2006; Crinion et al., asentence–picture matching task, Magnusdottir et al. 2003; Scott et al., 2000; Stowe et al., 1998; Mazoyer measured performance on sentences with noncanonical et al., 1993) as well as, in some studies, portions of the word order as well as on sentences with canonical word posterior temporal lobe and/or temporal-parietal junc- order. They report that overall sentence comprehension tion (Friederici, 2011; Snijders et al., 2009; Humphries, implicates a large temporal-parietal-occipital area but Binder, Medler, & Liebenthal, 2007; Humphries et al., not any frontal regions including Broca’sarea.Their 2005, 2006; Vandenberghe, Nobre, & Price, 2002) have analysis within each sentence type found that canonical been shown to be sensitive to sentences compared with sentences implicated posterior temporal and inferior a variety of nonsentence control conditions. Portions of parietal areas, whereas noncanonical sentences impli- the ATL (mostly in the temporal pole, i.e., BA 38), are cated Broca’s area as well as more anterior and inferior widely considered to be a multimodal semantic hub temporal lobe areas. They then computed a VLSM of a (Bonner & Price, 2013; Simmons & Martin, 2009); thus, direct contrast of canonical > noncanonical performance, perhaps the ATL’s contribution to sentence comprehen- that is, an agrammatic comprehension pattern, which iden- sion may be due to lexical-semantic processing. How- tified an ATL region in which damage is associated with ever, there is strong neuroimaging evidence suggesting impaired performance on noncanonical sentences com- that the ATL’s contributions to sentence comprehension pared with canonical sentences. The authors conclude that are not solely lexical in nature: Portions of the ATL re- the ATL “plays a crucial role in syntactic processing,” and spond more to pseudoword sentences than to pseudo- that Broca’s area contributes as well.

Rogalsky et al. 235 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn_a_01200 by guest on 24 September 2021 Thothathiri et al. (2012) also examined canonical and Mohr et al., 1978) and thus would not be represented in noncanonical sentence comprehension in 79 chronic an aphasia only sample. Moreover, both studies used a dif- stroke patients with aphasia. Their VLSM analyses iden- ference score variable (canonical > noncanonial) that may tified posterior temporal–inferior parietal regions as yield misleading results with respect to agrammatic com- critical for both canonical and noncanonical sentences. prehension as it is classically defined: canonical perfor- Thothathiri et al. also conducted a VLSM of the differ- mance near ceiling, noncanonial performance at chance ence between canonical and noncanonical scores (i.e., level (Grodzinsky, 1990). Difference scores could pick up canonical > noncanonical performance) but found no effects that are not as extreme as required to diagnose voxels that reached significance. An ROI follow-up, how- agrammatic comprehension (e.g., 10 percentage point ever, implicated posterior temporoparietal regions. Broca’s differences) and would confound cases with correct per- area was not implicated in canonical, noncanonical, or formance at, for example, 90% canonical and 60% non- canonical > noncanonical performance but was identified canonial (arguably a case of agrammatic comprehension) in a VLSM of performance on two-proposition sentences on the one hand and 60% canonical and 30% noncanonial compared with one-proposition sentences, suggesting on the other (clearly not such a case). that its role in sentence comprehension may be as a work- Another problem with previous large-scale studies of ing memory or other task-related resource (Thothathiri sentence comprehension generally and agrammatic com- et al., 2012). prehension more specifically is the use of sentence– Overall, large-scale lesion mapping studies have con- picture matching tasks (e.g., Magnusdottir et al., 2013; verged on the critical role of the posterior temporal- Thothathiri et al., 2012). There is evidence from both parietal region in sentence comprehension generally large group and case studies that performance can dis- but failed to substantially clarify the neural basis of agram- sociate across different types of sentence comprehension matic comprehension: Magnusdottir et al. implicated the tasks, such as sentence–picture matching paradigms with ATL (for canonical > canonical) and Broca’sarea(for whole sentence presentation, self-paced listening, plau- noncanonical sentences alone), and Thothathiri et al. sibility judgments, and enactment (i.e., “object manipula- implicated only parietal-temporal regions for canonical, tion”; Caplan, Michaud, & Hufford, 2013; Gutman, DeDe, noncanonial, and canonical > noncanonial sentence Michaud, Liu, & Caplan, 2010; Tyler, Wright, Randall, measures. The discrepancy in findings between these Marslen-Wilson, & Stamatakis, 2010; Caplan, DeDe, & two studies could be due to methodological differences. Michaud, 2006; Caplan, Waters, & Hildebrandt, 1997; Onedifferenceisthetestingofacute(Magnusdottir Cupples & Inglis, 1993). For example, Tyler et al.’s (2010) et al., 2013) versus chronic stage (Thothathiri et al., study of 14 aphasic patients compared areas of brain dam- 2012) stroke patients. There are several possible differ- age associated with performance on two types of sentence ences in acute versus chronic stage testing that might tasks, sentence–picture matching and plausibility judg- affect VLSM results. For example, quantifiable areas of ments. The sentence–picture matching task identified lesion are typically smaller in the acute stage than in the several regions in which damage resulted in greater im- chronic stage (Birenbaum, Bancroft, & Felsberg, 2011), pairment on noncanonical compared with canonical leading to more focal brain regions being implicated in a sentences: Broca’s area, left posterior middle temporal particular diagnosis in acute patients (e.g., Ochfeld et al., gyrus, left STG, and left supramarginal gyrus. The plausi- 2010). Different brain regions also may be implicated for bility judgment task implicated a subset of these regions, a given task in acute versus chronic stroke because of namely, Broca’s area and the left middle temporal gyrus compensatory neural reorganization that has had more (Tyler et al., 2010). Caplan, Michaud, Hufford, and Makris time to occur in chronic stroke (i.e., in chronic stroke (2016) have examined task-related differences in the re- different brain regions may support a task than in acute gions of brain damage associated with impaired perfor- stroke; Ochfeld et al., 2010; Kertesz, 1997). A second dif- mance of aphasic patients in “offline” (or after-the-fact ference is inclusion criteria (unselected vs. aphasia only): assessment) versus “online” (or during sentence process- Magnusdottir et al. included left-hemisphere stroke pa- ing) tasks; offline measures include sentence–picture tients regardless of aphasia diagnosis; Thothathiri et al. matching and object manipulation error rates; the online only included left-hemisphere stroke patients with a diag- task measure is self-paced listening times. A broad sum- nosis of aphasia. The inclusion of only patients with mary of their findings is that damage to posterior STG, aphasia could bias a sample toward larger lesion sizes inferior parietal regions such as supramarginal gyrus, and possibly exclude patients with more focal lesions and angular gyrus, and Broca’sareacontributetoagrammatic subclinical deficits, which in turn could lead to two dif- sentence comprehension to different degrees based on ferent lesion patterns being implicated in a particular defi- the task and type of noncanonical sentence used (pas- cit. Including only patients with aphasia also potentially sive,object-relative,reflexive,etc.).1 The implication of could bias results against Broca’s area in particular, given this kind of task-related variation is that task demands that restricted chronic damage to Broca’s area may present appear to be driving at least some of the results of map- as nonaphasic dysarthria or apraxia of speech (Graff- ping brain regions involved in sentence comprehen- Radford et al., 2014; Alexander, Naeser, & Palumbo 1990; sion. Therefore, the use of only one task in previously

236 Journal of Cognitive Neuroscience Volume 30, Number 2 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn_a_01200 by guest on 24 September 2021 published large-scale lesion studies is biased toward We first characterized the performance of patients with processes involved in that task. damage to Broca’sarea(n = 11) versus ATL damage (n = This study aimed to help resolve discrepancies regard- 18) on two sentence comprehension tasks—sentence– ing the contributions of Broca’s area and the ATL to sen- picture matching and plausibility judgment—containing tence comprehension and specifically agrammatic both canonical and noncanonical syntactic constructions. comprehension. In addition to whole-brain VLSM analy- This allowed us to determine whether the agrammatic ses and performance-based investigations of the lesions comprehension pattern of performance is causally related associated with agrammatic comprehension, we specifi- to damage in either of these regions. Second, across the cally target the relative contributions of Broca’sarea entire sample, we identified patients with agrammatic and the ATL in sentence comprehension, as these two comprehension as traditionally defined in the literature regions are the primary foci for theories of the neural (Berndt, Mitchum, & Haendiges, 1996; Grodzinsky, 1989), basis of sentence comprehension. Motivated by previous that is, performance at or below chance on the non- lesion work (e.g., Caplan et al., 2016; Thothathiri et al., canonical sentences and significantly above chance on the 2012), we employ two different sentence comprehen- canonical sentences; lesion distribution maps were then sion tasks (sentence–picture matching and plausibility generated to identify areas of lesion overlap across judgments) to help address task-related and cognitive re- patients. This was done separately for the two tasks to allow source contributions to sentence comprehension. There us to assess the typical lesion patterns associated with are very few lesion studies specifically addressing ATL agrammatic comprehension and whether it varied as a function, particularly in relation to sentence processing. function of task. We also investigated agrammatic com- This may in part be due to the rarity of lesions from prehension by conducting a VLSM for each task to identify stroke in the ATL. Typically, ATL damage from stroke brain regions implicated in noncanonical sentence per- coincides with overall large left-hemisphere damage and formance after variance due to canonical performance strokes restricted to the ATL are rare (Holland & Lambon was regressed out. Finally, we conducted VLSMs for over- Ralph, 2010). A more common patient group with focal all performance and for each sentence type in each task ATL damage is that of people with temporal lobe epilepsy to identify the brain regions most implicated in compre- who have undergone anterior temporal lobectomies. hending the sentences more broadly. Patients after a lobectomy have been reported to have If Broca’s area (or the ATL) is causally and differentially semantic, naming, and verbal fluency deficits (Janecek involved in processing noncanonical compared with et al., 2013; Drane et al., 2009; Schwarz & Pauli, 2009; canonical sentences, then: Saykin et al., 1995; Frisk & Milner, 1990; Ellis, Young, & (1) Patients with damage to Broca’s area (or the ATL) Critchley, 1989). Because these patients have medically should perform at or near chance level on non- intractable epilepsy, it is possible that their brains have canonical items for both tasks and substantially better abnormal functional organization before lobectomy in- on canonical sentence structures (the agrammatic cluding laterality of language networks (e.g., Swanson, comprehension pattern). Sabsevitz, Hammeke, & Binder, 2007; Hertz-Pannier (2) Patients with agrammatic comprehension on one or et al., 2002; Swanson et al., 2002), and any findings result- both tasks should have maximal lesion overlap in ing from this population should be interpreted carefully. Broca’s area (or the ATL). But because of the rarity of natural ATL lesions (Holland (3) Noncanonical sentence comprehension, once vari- & Lambon Ralph, 2010), the lobectomy population is ability due to canonical performance is controlled worth investigating. Lexical deficits have been reported for, should implicate Broca’s area (or the ATL). in this group (Huang, Hayman-Abello, Hayman-Abello, (4) Whole-sample VLSM analyses should implicate Broca’s Derry, & McLachlan, 2014; Glosser & Donofrio, 2001), area (or the ATL) for noncanonical but not (or less so) but to our knowledge, the agrammatic pattern of sentence for canonical sentences. comprehension has not been yet explicitly tested in lobec- tomy patients. Kho et al. (2008) report that 16 left lobec- None of these expectations were borne out in the tomy patients as a group (both pre- and postsurgery) data. Instead, the data overall point to temporal/temporal- did not perform significantly differently than controls on parietal areas as the primary region implicated in agram- both active and passive sentences in a sentence–picture matic comprehension and in sentence comprehension matching task. However, the aphasia literature indicates more generally. that the agrammatic performance pattern is more promi- nent for relative clause structures than passives (Berndt, METHODS Mitchum, & Wayland, 1997), so perhaps more sensitive measures are needed to detect agrammatic comprehen- Participants sion patterns. One hundred forty-one patients were recruited via the This study took three approaches to address the rela- Multisite Aphasia Research Consortium as part of an on- tive contributions of Broca’s area, the ATL, and, in a more going research program. Sixty-six of these patients were exploratory fashion, other areas to sentence processing. included in this study based on the following criteria: (i) a

Rogalsky et al. 237 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn_a_01200 by guest on 24 September 2021 Table 1. Patient Characteristics Table 1. (continued)

Age Years Age Years ID Sex (years) Poststroke Aphasia Subtype ID Sex (years) Poststroke Aphasia Subtype A01 F 42 2 None L10 M 67 6 None A02 F 43 4 None L11 M 67 6 None A03 F 61 6 None L12 F 56 10 None A04 F 59 6 None L13 M 53 4 Anomic A05 M 53 5 None L14 M 59 7 None A06 M 44 2 None L15 M 57 3 None A07 M 29 3 None L16 M 66 1 Conduction A08 M 47 14 None L17 M 84 5 Anomic A09 F 39 5 None L18 M 56 5 None A10 F 61 1 None L19 M 68 2 Conduction A11 M 49 4 None L20 M 58 1 Wernicke’s A12 M 31 3 None L21 F 64 2 Anomic A13 F 34 3 None L22 M 68 5 None A14 F 54 1 None L23 M 60 0.5 Conduction A15 F 47 10 None L24 M 72 3 None A16 M 62 0.5 None L25 F 34 2 None A17 F 39 6 None L26 M 57 3 None A18 M 35 2 None L27 F 55 3 None B01 F 61 13 Broca’s L28 F 81 2 None B02 M 78 6 Broca’s L29 M 51 2 None B03 M 59 4 None L30 M 48 9 Mixed nonfluent B04 F 57 10 None L31 F 60 15 Broca B05 M 63 4 None L32 M 60 2 Broca B06 M 68 1 Anomic L33 M 57 2 Mild Broca’s/anomic B07 M 61 13 Anomic L34 M 47 6 Mild BROCA’s/anomic B08 M 72 1 Broca’s L35 F 31 10 Broca’s B09 M 60 0.8 None L36 M 67 1 Wernicke’s B10 M 66 6 Anomic L37 M 51 6 Broca’s

B11 F 70 3 Conduction “A” patients are in the ATL group, “B” patients are in the Broca’s area group, and “L” patients are additional left-hemisphere patients included L01 F 54 28 None in the whole-brain analyses. M = male; F = female. L02 M 66 17 None chronic focal (6 months or more postonset) lesion due to L03 F 75 17 None a stroke in the left hemisphere (n = 48; 17 women, mean L04 F 75 14 None age = 62 years, range = 31–84 years) or a unilateral left tem- L05 M 64 8 Wernicke’s poral lobectomy (n = 18; 10 women, mean age = 46 years, range = 29–62 years),2 (ii) no significant anatomical abnor- L06 F 67 9 Anomic malities other than the signature lesion of their vascular L07 M 43 4 None event (or evidence of surgery for the seizure participants) nor signs of multiple strokes, and (iii) were administered L08 F 79 31 Wernicke’s both of the comprehension tasks of interest. Table 1 L09 F 68 6 None contains additional characteristics of the patients. All stroke patients had no prior history of psychological or

238 Journal of Cognitive Neuroscience Volume 30, Number 2 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn_a_01200 by guest on 24 September 2021 neurological disease. The temporal lobectomy patients all Plausibility judgments. The plausibility judgment task had a seizure disorder that required lobectomy surgery for consists of 80 sentences (20 each of passive, active, subject- the treatment of their seizures. Patients were not selected relative, and object-relative; see Table 3). The sentences for aphasia. Patients were all native speakers of English, ranged in length from 8 to 10 words. The object-relative and the vast majority was strongly right-handed as deter- and subject-relative sentences were statistically indistin- mined by the Edinburgh Handedness Scale: Two patients guishable regarding number of words (M =10.7,SD = were left-handed, and five were ambidextrous. Overall .47). Sentences were presented via headphones. Fifty language abilities were assessed in each participant using percent of sentences were plausible (could happen in the the Western Aphasia Battery (Kertesz, 1982) and/or por- real world); 50% were implausible (see sentences in bold in tions of the Boston DiagnosticAphasiaExamination Table 3). Patients were instructed to respond “yes” or “no” (Goodglass & Kaplan, 1983) and clinician estimations. after listening to each sentence to indicate if the sentence The aphasia subtypes present in the sample were as fol- was “plausible,” that is, whether it “made sense.” Three lows: four Wernicke’s, seven Broca’s, four Conduction, practice trials containing active sentences were first pre- seven Anomic, two mild Broca’s/Anomic, and one Mixed sented to ensure that each patient understood the task. nonfluent. The remainder of the patients (n = 41, includ- Signal detection methods were used to determine how ing all of the lobectomy patients) did not present with clin- well participants could discriminate plausible from im- 0 ically significant language impairments on these measures. plausible sentences. The signal detection measure d 0 Informed consent was obtained from each patient before was calculated for each sentence type. d is a discrimi- participation in the study, and all procedures were in com- nability index indicating the distance between the means 0 pliance with the Code of Ethics of the World Medical Asso- of the signal and the noise; d is reported in z-units, that ciation and approved by the institutional review boards of is, normalized standard deviation units (Swets, 1964). A 0 University of California-Irvine, San Diego State University, larger d indicates a larger separation between the num- University of Southern California, University of Iowa, and ber of “hits,” that is, the number of times a plausible Arizona State University. sentence was identified as plausible and the number of “false alarms,” that is, the number of times an implausible 0 sentence was identified as implausible. Thus, d can be Materials conceptualized as a performance measure that accounts 0 Patients completed two sentence comprehension tasks for response biases. d was used as the dependent mea- as part of the Multisite Aphasia Research Consortium Test sure for the plausibility judgments in the voxel-based Battery: the SOAP Test (a test of syntactic complexity; lesion–symptom analyses described below; however, Love & Oster, 2002) and a plausibility judgment task. proportion correct was used in the behavioral analyses The two tasks are described below. to allow for comparison between the two sentence SOAP syntactic test. The SOAP is described in detail in comprehension tasks. Love and Oster (2002). Briefly, the SOAP is a sentence– picture matching task consisting of 40 trials, each of Imaging and Lesion Analyses which involves a sentence being read to the participant, and the presentation of three colored drawings. The task High-resolution MPRAGE or SPGR MRIs were acquired is to point to the picture that corresponds to the sen- for all participants, except for four patients for whom a tence. There is a target (correct picture), a thematic role CT scan was acquired because of their incompatibilities reversal foil, and an unrelated foil. There are 10 trials of with the MR environment (pacemaker, metal clip, etc.). each of the following sentence types: active, passive, Lesion mapping was performed using MAP-3 lesion anal- subject-relative, and object-relative. See Table 2 for ysis methods (Damasio & Damasio, 2003) implemented on examples. Proportion correct for each sentence type was Brainvox software (Frank, Damasio, & Grabowski, 1997). calculated. This method is described in detail elsewhere (Damasio, 2000). Briefly, the method entails the transfer of the Table 2. Sentence–Picture Matching Task Sentence Examples lesioned brain areas as seen in the patient’s MRI/CT into (for Full List See Love & Oster, 2002) the common space of a template brain. To do so, the template brain is resliced such that each slice maximally Active: The girl in the green shirt corresponds to each slice in the lesion’s native space, chases the small boy. based on anatomical markers (e.g., sulci and subcortical Passive: The boy in the green shirt is structures). The lesion is then manually demarcated on chased by the girl. the template brain’s corresponding slice respecting the Subject-relative: The boy that chases the girl is identifiable landmarks. Each lesion map was completed wearing a green shirt. by an individual with extensive training in this technique and supervised by an expert neuroanatomist (H. D.). The Object-relative: The boy that the girl chases is template brain and resulting lesion maps were then trans- wearing a green shirt. formed into Talairach space by Brainvox and resampled to

Rogalsky et al. 239 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn_a_01200 by guest on 24 September 2021 Table 3. Plausibility Judgment Task Sentences and Table 3. (continued) Sentence Types 20. The noise in the dark basement Passive Practice: was scared by the woman. P1. The pretty nurse in the Active 21. The rabbit that the small carrot OR hospital moved the bed. ate is in the garden. P2. The peach in the orchard Active 22. The young farmer in the red barn Active picked the young child. milked the cow. P3. The bed in the hospital Active 23. The man that trimmed the tall bush SR moved the pretty nurse. is in the yard. Task: 24. The woman that scared the noise SR is in the dark basement. 1. The new teacher in the large Active classroom read the book. 25. The cow that milked the farmer SR is in the barn. 2. The money in the bank Active vault stole the robber. 26. The floor near the window was Passive mopped by the janitor. 3. The noise that scared the woman SR is in the dark basement. 27. The rabbit in the garden was Passive eaten by the carrot. 4. The child in the orchard Active picked the ripe apple. 28. The bush in the yard was trimmed Passive by the tall man. 5. The teacher that read the SR new book is in the classroom. 29. The robber in the bank vault Passive was stolen by the money. 6. The janitor that mopped the SR floor is near the window. 30. The teacher in the classroom was read by the book. Passive 7. The man in the yard was Passive trimmed by the tall bush. 31.Thecowthatthefarmermilkedisinthebarn. OR 8. The book in the classroom was Passive 32. The busy secretary that the phone OR read by the teacher. answered is in the office. 9. The child that the ripe apple OR 33. The young child that picked the SR picked is in the orchard. peach is in the orchard. 10. The janitor near the window Passive 34. The woman that the noise scared OR was mopped by the floor. is in the dark basement. 11. The busy secretary in the office Active 35. The new book in the large Active answered the phone. classroom read the teacher. 12. The teacher that the new book OR 36. The bush that trimmed the SR read is in the classroom. tall man is in the yard. 13. The coffee that the waitress poured OR 37. The money in the bank vault Passive is in the restaurant. was stolen by the robber. 14. The woman in the dark basement was Passive 38. The cow in the barn was Passive scared by the noise. milked by the farmer. 15. The busy secretary that answered SR 39. The rabbit in the nearby garden Active the phone is in the office. ate the small carrot. 16. The tall bush in the front yard Active 40. The phone that the busy secretary OR trimmed the man. answered is in the office. 17. The janitor that the floor OR 41. The waitress that the coffee OR mopped is near the window. poured is in the restaurant. 18. The coffee in the restaurant Active 42. The child in the orchard was Passive poured the waitress. picked by the ripe apple. 19. The floor near the window Active 43. The phone in the office answered Active mopped the janitor. the busy secretary.

240 Journal of Cognitive Neuroscience Volume 30, Number 2 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn_a_01200 by guest on 24 September 2021 Table 3. (continued) Table 3. (continued) 44. The waitress in the restaurant Active 68. The farmer in the barn was Passive poured the coffee. milked by the cow. 45. The money that stole the robber Active 69. The bush that the tall man OR is in the bank vault. trimmed is in the yard. 46. The peach in the orchard picked SR 70. The rabbit that ate the small SR the young child. carrot is in the garden. 47. The noise that the woman scared is Active 71. The floor that the janitor mopped OR in the dark basement. is near the window. 48. The peach that the young child picked SR 72. The tall man in the front yard Active is in the orchard. trimmed the bush. 49. The loud noise in the dark basement SR 73. The robber in the bank vault stole Active scared the woman. the money. 50. The peach in the orchard was picked by Passive 74. The coffee that poured the SR the young child. waitress is in the restaurant. 51. The book that read the new teacher SR 75. The man that the tall bush OR is in the classroom. trimmed is in the yard. 52. The carrot that ate the small rabbit SR 76. The carrot in the nearby garden Active is in the garden. ate the small rabbit. 53. The young woman in the dark Active 77. The farmer that the cow milked OR basement scared the noise. is in the barn. 54. The waitress that poured the coffee is SR 78. The robber that the money stole OR in the restaurant. is in the bank vault. 55. The janitor near the window mopped Active 79. The carrot that the small rabbit ate OR the floor. is in the garden. 56. The brown cow in the red barn Active 80. The floor that mopped the janitor SR milked the farmer. is near the window.

57. The phone that answered the busy SR Implausible sentences are in bold. OR = object- relative; SR = subject- secretary is in the office. relative. 58. The book that the new teacher read is OR a voxel size of 1 mm3. The MAP-3 method has been shown in the classroom. to have high inter- and intrarater reliability and, in some 59. The robber that stole the money is in SR cases, higher accuracy than some automated methods the bank vault. (Pantazis et al., 2010; Fiez, Damasio, & Grabowski, 2000). 60. The secretary in the office was Passive Figure 1 depicts an overlay of all of the patients’ lesions answered by the phone. maps on the template brain. 61. The coffee was poured by the Passive waitress in the restaurant. ROI Analysis 62. The ripe apple that picked the SR Patients with damage anywhere in Broca’s area, defined child is in the orchard. as the posterior two thirds of the left inferior frontal gy- 63. The phone in the office was answered Passive rus, that is, the pars triangularis and pars opercularis by the secretary. (gray and/or white matter), without any temporal lobe 64. The farmer that milked the cow is SR involvement were identified for further study. Eleven in the barn. patients met these criteria for inclusion in the Broca’s area damage group (Figure 2). The second group iden- 65. The waitress was poured by the Passive tified were patients with damage restricted to the left coffee in the restaurant. ATL, defined as any damage within any left temporal lobe 66. The carrot in the garden was eaten Passive gray and white matter anterior to the anterior edge of by the rabbit. Heschl’s gyrus. Eighteen left temporal resections met 67. The money that the robber stole is in OR these criteria (Figure 2). Sentence comprehension per- the bank vault. formance within and across these two groups was ana- lyzed using a mixed-effect logistic regression for each

Rogalsky et al. 241 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn_a_01200 by guest on 24 September 2021 comprehension patterns within each group for each task. Multiple comparisons were corrected by using Bonferroni- corrected p values, corrected across each group’s contrasts.

Lesion Distributions of Agrammatic Comprehension Agrammatic comprehension has traditionally been de- fined as performing significantly above chance on canon- ical sentences and at or below chance on noncanonical sentences (Berndt et al., 1996; Grodzinsky, 1989). It has been postulated that the strongest test of agrammatic comprehension is above chance performance on all ca- nonical forms and at or below chance on all noncanonical Figure 1. Overlap of all the patients’ lesions included in subsequent forms (Grodzinsky, 1986). To examine the lesions associ- analyses (max overlap = 25). Coordinates are in Talairach space. ated with these criteria, binomial tests ( p < .05, one- tailed) of performance in each task were used to identify task separately using PROC GLIMMIX in SAS software patients in our sample who exhibited canonical sentence Version 9.4. Effects were estimated for group (Broca’s, (i.e., subject-relative plus active) performance significantly ATL) and sentence type (object-relative [OR], subject-rela- above chance and noncanonical sentence (i.e., object- tive [SR], passive, active) on response accuracy (correct vs. relative plus passive) performance not significantly above incorrect) in each task. Mixed-effect models were used as chance. Voxel-wise Liebermeister tests were computed they provide a better fit of binomially distributed categor- using MRICron’s nonparametric mapping (Rorden, Karnath, ical data compared with a repeated-measures ANOVA and & Bonilha, 2007) to identify regions of damage that are account for individual variability by computing a random significantly associated with agrammatism. In addition, intercept and slope per participant, allowing for a maximal maps depicting the lesion distribution of the patients ex- model containing a maximum random effect structure (Barr, hibiting agrammatic comprehension and of the patients Levy, Scheepers, & Tily, 2013; Jaeger, 2008). The fixed not exhibiting agrammatic comprehension in each task effects were Group and Sentence structure. Participants were generated using AFNI’s 3dcalc command. were treated as random effects. Pairwise comparisons were computed to investigate potential agrammatic sentence Voxel-based Lesion–Symptom Mapping In addition to the ROI-based and agrammatic compre- hension lesion comparisons, VLSM (Bates et al., 2003) was used among all of the 66 left-hemisphere patients (Figure 1) to identify voxels anywhere in the left hemi- sphere for which a t test indicates that patients with damage in that voxel perform significantly different than patients who do not have damage in that voxel. For each task, a VLSM was calculated for performance on the noncanonical sentences (object-relative and passive sen- tences), with performance on the canonical (subject- relative and active) sentences as a covariate. In other words, variance due to canonical sentence performance was regressed out. These VLSMs should identify brain re- gions in which damage is associated with noncanonical < canonical performance, but they are not mathematically equivalent to VLSMs of canonical–noncanonical perfor- mance. As discussed in the Introduction, difference score VLSMs might introduce confounds making interpretation of results difficult; thus, we used a covariate approach instead. Separate VLSMs also were completed for each sen- tence type in each task to further explore any differences between lesion patterns associated with noncanonical versus canonical sentence comprehension. A voxel-wise Figure 2. Overlap of the patients’ lesions in the (A) Broca’s area damage group (n = 11, max overlap = 11) and (B) in the anterior temporal threshold of p < .001 was used. Multiple comparisons group (n = 18, max overlap = 15). Coordinates are in Talairach space. were controlled for using a cluster size-based permutation

242 Journal of Cognitive Neuroscience Volume 30, Number 2 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn_a_01200 by guest on 24 September 2021 method: In each voxel, patients’ behavioral scores were the main effect of Group (F =0,p = .99) and the Group × randomly reassigned 1000 times. For each permutation, Sentence type interaction (F =1.5,p = .23) were not the general linear model was refit, a statistical threshold significant. Note that the lack of a main effect of Group of p < .001 identified significant voxels, and the size of indicates that the Broca’s and ATL damage groups do not the largest cluster of significant voxels was recorded. exhibit significant differences in their sentence compre- Voxels in the actual data were identified as significant hension performance. Although the Group × Sentence if they reached a voxel-wise p value of <.001 and were type interaction was not significant, we conducted pair- in a voxel cluster larger than 95% of the largest signifi- wise comparisons to further explore possible agrammatic cant clusters identified in the permutated data sets. (This comprehension patterns within each of the two groups cluster-based permutation method threshold procedure because of our a priori prediction that if Broca’s area or is the “p < .001, corrected” referred to in the subsequent the ATL is causally involved in agrammatic comprehension, results section). Variance due to lesion size (i.e., number of one would expect an agrammatic comprehension pattern voxels marked as lesion on the template brain) was to be present in the corresponding group. Thus, we con- regressed out of all VLSM analyses via an ANCOVA. Only trasted the parameter estimates for object-relative sen- voxels in which a minimum of 10% of patients (n =7) tences versus subject-relative sentences and for passive ’ had damage were included in the VLSM analyses. versus active sentences. In the Broca s area group, there was no significant difference between object-relative and subject-relative performance (β = −1.2, SE =0.6,p = Response Bias .22) and no significant difference between passive and active performance (β =0.7,SE =1.3,p =1).Inthe The existence of task effects in sentence comprehension ATL group, object-relative performance was significantly paradigms raises questions about the role of nonlinguis- lower than subject-relative performance (β = −3.2, SE = tic functions in performance. One potentially relevant 0.78, p = .0005), but there was no significant difference measure, readily calculable from our plausibility task, is between passive and active performance (β = −14.6, SE = response bias. Frontal lobe regions have been implicated 1457.15, p =1.0). in decision-making processes in sentence comprehension The findings for the plausibility judgments are similar tasks (Caplan, Chen, & Waters, 2008) and response bias in to the sentence–picture matching results: There was a other speech tasks such as syllable discrimination (Venezia, significant main effect of Sentence type (F = 12.4, p < Saberi, Chubb, & Hickok, 2012; Hickok, Buchsbaum, .001); the main effect of Group (F = 1.57, p = .22) and Humphries, & Muftuler, 2003; Baker, Blumstein, & the Group × Sentence type interaction (F =0.57,p =.63) Goodglass, 1981). Thus, we examined the neural corre- were not significant. As in the sentence–picture matching lates of one measure of bias in this study. Signal detec- task, we conducted pairwise comparisons based on our tion theory’s C, a representation of response bias, was ’ a priori predictions regarding agrammatic comprehension calculated for each patient sperformanceonthe patterns within each group. The findings for the plausibility object-relative sentences in the plausibility judgment task judgments were in line with those of the sentence–picture (see Venezia et al., 2012, for a summary of signal detection matching task: The Broca’s area group exhibited no theory as it relates to speech stimuli). The object-relative significant difference between object-relative and subject- sentences were the focus of the response bias analysis be- relative performance (β = −0.64, SE =0.42,p =.77),as cause they elicited the largest range of performance and well as between passive and active performance (β = ceiling effects are unlikely. A VLSM was then generated −0.53, SE =0.77,p = 1). In the ATL group, object-relative to identify the areas of damage associated with greater performance was lower than subject-relative performance, response bias. but this difference did not reach significance (β = −0.94, SE = 0.41, p = .14), and the difference between passive β RESULTS and active performance ( =0.71,SE =0.73,p =1.0) was not significant. — ’ Behavioral Results Broca s Area versus It is also noteworthy that the Broca’s area group and ATL Damage the ATL group both performed well above chance on Performance of the Broca’s area damage group (n = 11) the noncanonical sentences—that is, the object-relative and the left anterior temporal damage group (n = 18) on (SOAP mean correct = 85% for the Broca’s group and both sentence tasks is presented in Figure 3 (solid bars). 81% for the ATL group; plausibility judgments mean Within each task, accuracy for the two groups, that is, correct = 89% for the Broca’s group and 93% for the patients with Broca’s area versus ATL damage, was carried ATL group) and passive sentences (SOAP mean correct = out using mixed-model logistic regression with Group 99% for both the Broca’s and ATL groups; plausibility (Broca’s vs. ATL) and Sentence type (active, passive, SR, judgments mean correct = 98% for the Broca’s group and OR) as predictors. 99% for the ATL group). These results do not support In the sentence–picture matching task, there was a sig- the hypothesis that Broca’s area or the ATL is specifically nificant main effect of Sentence type (F = 12.5, p < .001); linked to agrammatic comprehension.

Rogalsky et al. 243 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn_a_01200 by guest on 24 September 2021 Figure 3. Graphs of mean group performance on the (A) sentence–picture matching task and on the (B) plausibility judgments. The light and dark gray solid bars represent the Broca’s and ATL groups, respectively. The striped bars represent performance of all left-hemisphere participants, 0 for example, all participants included in the VLSM analyses. For the plausibility judgments, both proportion correct and d group mean performance are shown. Error bars represent SEM.

Performance on each sentence type within each task be to conduct voxel-wise Liebermeister tests (Rorden for all 66 left-hemisphere patients combined, that is, all et al., 2007) to determine if there are areas of damage patients included in the VLSMs discussed below, are de- that are significantly associated with the agrammatic picted in Figure 3 (striped bars). Performance of a group group identified for each task. However, these tests of 17 patients with right-hemisphere damage due to yielded no significant results even at a liberal uncorrected stroke and no aphasia diagnoses is summarized in the threshold, likely in part due to insufficient power result- supplemental material to provide a baseline of perfor- ing from the small sample sizes of the agrammatic groups mance in each task. (n = 11 and 3, respectively; Rorden et al., 2007). Thus, we took a qualitative approach: Maps of the lesion distri- butions for agrammatic and nonagrammatic comprehen- Results: Agrammatic Comprehension sion in each task were generated to compare the lesions Lesion Distribution associated with agrammatic and nonagrammatic compre- Binomial tests to identify agrammatic comprehension in- hension. These maps indicate the following: The patients dicate that, in the sentence–picture matching task, 11 of with agrammatic comprehension in the sentence–picture the 66 left-hemisphere patients performed significantly matching have a maximum overlap (n = 10 of 11) in the above chance on the canonical sentences and not sig- white matter underlying the left posterior superior and nificantly above chance on the noncanonical sentences middle temporal gyri, with the center of mass being in ( p < .05, one-tailed). In the plausibility judgment task, the white matter underlying the left STG, Talairach co- three left-hemisphere patients performed significantly ordinates = −35 −48 17 (Figure 4A). Conversely, the above chance on the canonical sentences and not sig- patients who did not exhibit agrammatic comprehension nificantly above chance on the noncanonical sentences in the sentence–picture matching task have maximum ( p < .05, one-tailed). At this point perhaps the most overlap in the ATL (n = 19 of 55, Talairach coordinates = robust way to identify regions of damage associated −41 −6 −25); the second greatest area of overlap is in with agrammatic comprehension as defined above would Broca’sarea(n = 13 of 55; Figure 4A). The three patients

244 Journal of Cognitive Neuroscience Volume 30, Number 2 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn_a_01200 by guest on 24 September 2021 Figure 4. Orthogonal views of the lesion distributions for agrammatic and nonagrammatic performance on the (A) sentence–picture matching task and (B) plausibility judgments. Crosshairs are on regions of maximum overlap. Coordinates are in Talairach space.

with agrammatic comprehension in the plausibility judg- lobe: Implicated regions include Heschl’sgyrus,STG, ment task all have lesions overlapping in nearly the entire middle temporal gyrus, supramarginal gyrus, and under- length of the left superior and middle temporal gyri and lying white matter (for sentence–picture matching peak t underlying white matter, extending into the inferior parie- at Talairach coordinates −35 −52 14 in the white matter tal lobe (Figure 4B). As in the sentence–picture matching underlying the posterior STG and number of voxels = task, patients who did not have agrammatic comprehen- 19,650, for plausibility judgments peak t at Talairach sion in the plausibility judgment task have maximum lesion coordinates −54 −26 5 and number of voxels = 13,109, overlap in the ATL (n = 22 of 63, Talairach coordinates = p < .001, corrected; Figure 5). The posterior boundary −43 0 −24) followed by an overlap of n =17of63in ofthemainclustersineachVLSMalsoextendedinto Broca’s area (Figure 4B). the middle occipital gyrus (>20 voxels). These find- The Broca’s area and ATL ROI analyses and the lesion ings align with the agrammatic comprehension lesion distributions of agrammatic comprehension do not spe- distribution results, implicating temporal and inferior cifically implicate Broca’s area or the ATL in agrammatic parietal damage (but not frontal lobe damage) in agram- comprehension. We now turn to whole-brain analyses to matic comprehension. examine the areas of damage associated with sentence comprehension deficits. VLSM Results: All Sentence Types To further characterize the networks involved in sen- VLSM Results: Agrammatic Comprehension tence comprehension, we generated VLSMs for overall For both tasks, the VLSMs for performance on the non- performance and performance within each sentence canonical (OR and passive) sentences with canonical (SR type, in each task, across all 66 left-hemisphere patients. and active) sentence performance as a covariate identi- The VLSMs for overall performance on the sentence– fied large clusters of voxels spanning the left superior picture matching task (proportion correct) and overall 0 temporal lobe and, to a lesser extent, the inferior parietal performance on the plausibility judgments (d )both

Rogalsky et al. 245 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn_a_01200 by guest on 24 September 2021 Figure 5. Representative sagittal slices depicting the VLSM results for performance on the noncanonical (object- relative and passive) sentences with canonical (subject-relative and active) sentence performance as a covariate regressed out on the (A) sentence–picture matching task and (B) plausibility judgments (voxelwise height threshold, p < .001 and permutation-derived cluster threshold, p < .05). Coordinates are in Talairach space.

identified large clusters of voxels spanning the left supe- picture matching task was in the posterior STG, whereas rior temporal lobe and, to a lesser extent, the inferior the peak t for the plausibility judgments was in superior parietal lobe: Implicated regions include the STG, middle temporal cortex just anterior to Heschl’s gyrus. Notably, temporal gyrus, angular gyrus, and underlying white the VLSMs for overall performance in both tasks identified matter (for sentence–picture matching peak t at Talairach significant voxels in the ATL (approximately in BA 38 and coordinates −35 −46 13 and number of voxels = 63,092, anterior BA 22), but no frontal lobe voxels were significant. for plausibility judgments peak t at Talairach coordinates The VLSMs for each sentence type in each task all identify −45 −16 −5 and number of voxels = 40,254 voxels, p < superior temporal and inferior parietal regions similar to .001,corrected;Figure6).Thepeakt value for the sentence– the overall performance VLSMs; no frontal regions were

Figure 6. Representative sagittal slices depicting the VLSM results for overall performance on the (A) sentence–picture matching task and (B) plausibility judgments (voxel-wise height threshold, p < .001 and permutation-derived cluster threshold, p < .05). Coordinates are in Talairach space.

246 Journal of Cognitive Neuroscience Volume 30, Number 2 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn_a_01200 by guest on 24 September 2021 Figure 7. Representative sagittal slices depicting the VLSM results for performance within each sentence type in the sentence–picture matching task (voxel-wise height threshold, p < .001 and permutation-derived cluster threshold, p < .05). Coordinates are in Talairach space.

significant for any sentence type in either task (Figures 7 both directions were included, but the results do not and 8). These results indicate that a network of temporal qualitatively differ by only examining the “yes” biases. and inferior parietal regions primarily support sentence processing. DISCUSSION This study investigated the neural substrates of agram- Response Bias and Sentence Comprehension matic comprehension and sentence comprehension with One representation of bias, C, was calculated for each a particular focus on the role of Broca’sareaandthe patient’s performance on the OR sentences in the plausi- ATL. Patients with chronic, focal brain lesions completed bility task. Of the 66 left-hemisphere patients, 36 patients two measures of sentence comprehension, a sentence– had a bias toward responding “yes” (i.e., yes, the sentence picture matching task (the SOAP syntactic test; Love & is plausible) whereas eight were biased toward a “no” Oster, 2002) and a plausibility judgment task. We imple- response. A VLSM was then generated to identify the areas mented two different types of sentence tasks to be able of damage associated with greater response bias in either to factor out any task-specific findings. Overall, our find- direction. The VLSM of bias identified a cluster pre- ings in lesion patients are highly consistent with previous dominately in the gray and white matter of the left inferior large-scale studies of sentence deficits, which primarily frontal gyrus (pars opercularis) and precentral gyrus and implicate temporal and posterior parietal lobe networks extending into the insula and middle frontal gyrus (peak t (Pillay et al., 2017; Magnusdottir et al., 2013; Thothathiri at Talairach coordinates −50 10 5, number of voxels = et al., 2012; Dronkers et al., 2004). Our study adds to 15,264, p < .001, corrected; Figure 9). The absolute value previous work in that we specifically assessed the contri- of C was used as the variable of interest so that biases in bution of Broca’s area and ATL damage.

Rogalsky et al. 247 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn_a_01200 by guest on 24 September 2021 Figure 8. Representative sagittal slices depicting the VLSM results for performance within each sentence type in the plausibility judgment task (voxel-wise height threshold, p < .001 and permutation-derived cluster threshold, p < .05). Coordinates are in Talairach space.

Sentence Comprehension after Damage to Broca’s Area versus to the ATL First, performance of patients with damage in Broca’s area (with the temporal lobe spared) was compared with performance in patients with damage to the ATL (with all other regions spared). Analyses comparing the Broca’s area and ATL groups revealed highly similar behavioral performance; there were no significant differences in performance between the two groups for any sentence type in either task. The Broca’s area group did not exhibit significantly lower performance for the noncanonical sentences than for the canonical sentences (contrasts of OR vs. SR and passive vs. active performances were not significant in either task). The ATL group did demonstrate significantly lower performance for OR versus SR sentences in the sentence–picture matching task, but in the plausi- bility judgment task, this effect only approached signifi- Figure 9. Orthogonal views of the VLSM results for bias in responses cance ( p = .14); the passive versus active contrast was for OR sentences in the plausibility judgment task (voxel-wise height threshold, p < .001 and permutation-derived cluster threshold, not significant in either task for the ATL group. Both p < .05). Crosshairs are on the peak t value. Coordinates are in groups performed well above chance on the object-relative Talairach space. OR = object-relative. sentences on both tasks (>80% correct on the SOAP task

248 Journal of Cognitive Neuroscience Volume 30, Number 2 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn_a_01200 by guest on 24 September 2021 0 and d > 3 on the plausibility judgment task) in contrast to implicated regions in the STG extended into areas ante- previous claims that damage to Broca’s area is responsible rior to Heschl’s gyrus: For the sentence–picture match- for agrammatic comprehension, that is, near chance per- ing task, 212 voxels were significant on the superior formance on object relatives (Grodzinsky, 1989, 2000). bank of the anterior STG, and for the plausibility judg- The finding that damage to Broca’sareaisnotreliably ment task, 156 voxels were significant on the superior associated with agrammatic comprehension coincides with bank and lateral aspects of the anterior STG. The coordi- previous lesion work (Magnusdottir et al., 2013; Thothathiri nates of the peak t values in the ATL for both of these et al., 2012; Caramazza, Capasso, Capitani, & Miceli, 2005). VLSMs fall within the area of maximum overlap in the The finding that the ATL is not strongly associated with ATL in the agrammatic comprehension sentence–picture agrammatic comprehension is at odds with the agram- matching overlap map. The ATL regions identified by matic comprehension findings that were localized to the these VLSMs do overlap with the ATL regions found in ATL in Magnusdottir et al.’s study of acute stroke patients. some fMRI studies to be more activated in response to sen- However, neural reorganization needs to be considered tences than nonsentence stimuli (e.g., Rogalsky et al., when interpreting our findings in the ATL; our ATL group 2011; Friederici et al., 2010). However, other fMRI findings consists of temporal lobectomy patients who have likely implicate the middle temporal gyrus as well as the STG, and experienced functional neural reorganization due to their the activations extend more toward the temporal pole, medically intractable epilepsy and perhaps additionally extending into approximately BA 38, whereas our VLSM after their temporal resection surgery. findings are more posterior and solely in approximately BA 22 (e.g., Humphries et al., 2006; Stowe et al., 1998; other contrasts in Rogalsky et al., 2011). We found no evidence of Lesion Overlap in Cases of Broca’s area involvement in noncanonial sentence com- Agrammatic Comprehension prehension on either of the tasks. Agrammatic comprehension (significantly above chance To summarize, our VLSMs of noncanonial sentence on canonical sentences, not significantly above chance comprehension primarily implicate posterior temporal on noncanonical sentences) in each task was predomi- and inferior parietal regions with only partial extension nantly associated with superior temporal and inferior into ATL regions previously implicated in sentence pro- parietal damage and not Broca’s area (or ATL) as stan- cessing and no evidence of Broca’s area involvement. dard models would predict. More specifically, the area This finding of temporal and parietal regions implicated of maximum overlap among patients with agrammatic in agrammatic comprehension is highly consistent with comprehension was in the posterior STG. ATL damage previous VLSM studies of agrammatic comprehension was present in two of the three patients with agrammatic (Pillay et al., 2017; Magnusdottir et al., 2013; Thothathiri comprehension on the plausibility judgments and in 7 et al., 2012) as well as previous functional neuroimaging of 11 patients with agrammatic comprehension on the studies of sentence comprehension generally (Friederici, sentence–picturing matching task. Broca’s area damage 2011; Snijders et al., 2009; Humphries et al., 2005, 2006, was present in one of three patients with agrammatic 2007; Vandenberghe et al., 2002). It also should be noted comprehension on the plausibility judgments and in 4 of that the null results in Broca’s area in this study are not a 11 patients with agrammatic comprehension on the product of reduced sensitivity due to multiple compari- sentence–picture matching task. However, the areas of son correction; the uncorrected results also do not impli- greatest overlap in the nonagrammatic comprehension cate Broca’s area. Insufficient power also can be a group in each task were the ATL (35% for each task) fol- concern when interpreting null findings. Although possi- lowed by Broca’s area (27% for plausibility judgments ble, it is unlikely here given that the greatest lesion cov- and 24% for sentence–picture matching). Thus, the agram- erage of our sample is centered in Broca’s area (overlap matic comprehension overlap maps provide evidence for of n = 25 in the pars opercularis, plus an additional five loose relations at best between agrammatic compre- participants not contributing to this maximum overlap hension and ATL or Broca’s area damage. Based on lesion but who do have damage elsewhere in Broca’sarea overlap analyses, then, we conclude that the posterior STG and/or its underlying white matter). is a more critical site in producing agrammatic compre- hension than either Broca’s area or the ATL. VLSM of Sentence Comprehension Generally Whole-brain VLSM analyses examining the relation be- VLSM of Noncanonical Sentence Comprehension tween comprehension on the various sentence types The VLSMs investigating brain regions implicated in non- for the two tasks in the entire left-hemisphere damaged canonical sentence comprehension with canonical sen- sample showed similar patterns of lesion–symptom map- tence comprehension performance as a covariate also pings. Specifically, all sentence types on both tasks im- identified superior temporal and inferior parietal regions plicated superior and middle temporal lobe regions, in both tasks (Figure 5) consistent with the agrammatic including the STG, middle temporal gyrus, and extended comprehension lesion overlap findings (Figure 4). The into inferior parietal cortex. These VLSM analyses of each

Rogalsky et al. 249 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn_a_01200 by guest on 24 September 2021 sentence type all identified more anterior extension in 1976) and more recently has been shown to involve the temporal lobe than that found in the noncanonial damage to the pars opercularis portion of Broca’sarea sentence analysis, which provides some support for the in combination with the posterior superior temporal claim that the ATL is involved in basic combinatorial sen- lobe (Fridriksson, Fillmore, Guo, & Rorden, 2015). Thus, tence processing (Brennan & Pylkkanen, 2012; Brennan classical lesion–symptom evidence is quite consistent et al., 2012; Rogalsky et al., 2011; Rogalsky & Hickok, with a posterior temporal and parietal focus for sentence 2009; Humphries et al., 2006). The ATL regions identified processing including agrammatic comprehension. here overlap with ATL regions previously implicated in Another paradox arises when considering the present sentence processing by fMRI studies (e.g., Rogalsky results alongside of the functional imaging literature, et al., 2011; Friederici et al., 2010; Stowe et al., 1998), which has frequently implicated Broca’s area in sentence although the lesion maps do not extend as anteriorly as processing (e.g., Blank, Balewski, Mahowald, & Fedorenko, some reported fMRI peak activations (e.g., Rogalsky & 2016; Rogalsky et al., 2015; Bedny, Pascual-Leone, Dodell- Hickok, 2009; Vandenberghe et al., 2002). Feder, Fedorenko, & Saxe, 2011). As mentioned in the In contrast to the involvement of at least some por- Introduction, a possible resolution of this paradox comes tions of the ATL in sentence comprehension, Broca’s area from hypotheses that Broca’s area supports performance was not implicated in these analyses, even for the sen- on sentence comprehension tasks through more general tence types (object relatives) that have been classically cognitive functions such as working memory (Pettigrew associated with damage to or activation of the inferior & Hillis, 2014; Rogalsky et al., 2008; Kaan & Swaab, 2002; frontal gyrus. What this finding suggests is that basic re- Just et al., 1996) and/or cognitive control (Novick et al., ceptive processes in auditory language comprehension 2005). The present finding of a link between response bias are supported by the temporal and posterior parietal and Broca’s area lesions may reflect some aspect of cog- lobes rather than frontal lobe regions (Bates et al., 2003). nitive control or working memory impairment (Venezia et al., 2012), as both of these processes have in part been localized to Broca’s area (Novick et al., 2005; Hickok, Interpretation, Limitations, and Comparison to Buchsbaum, Humphries, & Muftuler, 2003; Smith & Previous Work Jonides, 1997). Neuroimaging studies of response selec- Consistent with three recent large-scale studies from tion (in which deficits can result in response bias) consis- three different groups (Pillay et al., 2017; Magnusdottir tently implicate Broca’s area and adjacent pFC (e.g., et al., 2013; Thothathiri et al., 2012), this study found Harding, Yucel, Harrison, Pantelis, & Breakspear, 2015; no evidence for the involvement of Broca’s area in agram- Goghari & MacDonald, 2009). In addition, working matic comprehension in any of our analyses designed memory impairments due to inferior frontal damage may specifically to test the hypothesized linkage. Further- prevent the use of articulatory rehearsal to assist in the more, no evidence was found for the role of Broca’s area maintenance of a sentence that is difficult to comprehend, in sentence processing more generally. The ATL was thereby causing the patient to choose the most likely re- found to be more related to sentence processing gener- sponse based on previous experience (Venezia et al., ally than was Broca’s area, although the ATL’slinkto 2012) which, in the case of sentences heard in everyday agrammatic comprehension was weak in our sample life, the sentence is plausible. These possibilities would (cf. Magnusdottir et al., 2013). The most consistently im- explain Broca’s area activation in many sentence com- plicated region in agrammatic comprehension and sen- prehension studies yet also explain why Broca’s area does tence processing generally was the posterior superior not always activate during sentence comprehension (e.g., temporal and inferior parietal regions, which have Rogalsky et al., 2011; Humphries et al., 2005, 2006; Spitsyna emerged recently as the core of the sentence-processing et al., 2006): Depending on the particular task demands, network. support functions mediated by Broca’sareawillbere- The finding that the superior temporal and inferior cruited or not. This finding also coincides with Thothathiri parietal region is more implicated in sentence compre- et al.’s finding that damage to Broca’s area (specifically hension impairments than Broca’s area presents a para- BA 44), and not temporoparietal regions, is associated with dox given the long-standing association between Broca’s decreased comprehension of two-proposition sentences aphasia and agrammatic comprehension (Grodzinsky, compared with one-proposition sentences. Thothathiri 1986, 2000; Caramazza & Zurif, 1976). The paradox can et al. suggest that increased working memory demands be resolved, however, by noting two facts. The first is that of the two-proposition sentences compared with the agrammatic comprehension is not unique to Broca’s one-proposition sentences explain this effect. Notably, aphasia but has also been documented in conduction several of the recent high-profile functional imaging stud- aphasia(Caramazza&Zurif,1976),whichisassociated ies implicating Broca’s area in sentence processing have with posterior temporal-parietal lesions (Buchsbaum, included a memory task (Blank et al., 2016; Fedorenko, Baldo, et al., 2011; Damasio & Damasio, 1980). The sec- Duncan, & Kanwisher, 2012; Fedorenko, Nieto-Castanon, ond fact is that Broca’s aphasia is not tied to Broca’s area & Kanwisher, 2012a, 2012b; Fedorenko et al., 2011; alone (Fridriksson et al., 2007; Mohr et al., 1978; Mohr, Fedorenko, Hsieh, Nieto-Castanon, Whitfield-Gabrieli,

250 Journal of Cognitive Neuroscience Volume 30, Number 2 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn_a_01200 by guest on 24 September 2021 Table 4. Summary of Neuroimaging Studies Reporting extent than listening (Daneman & Newson, 1992; Slowiaczek Contrasts of Sentences versus Acoustically Matched Control & Clifton, 1980; Baddeley, Thomson, & Buchanan, 1975), Conditions, Sorted by Stimulus Presentation Type the use of written sentences in many past studies appears Response in Broca’s Area to have biased the functional imaging literature toward a Broca’s area-centric perspective. Sentences > Another potentially important consideration in recon- Nonsentences No Difference ciling functional imaging and lesion results regarding Auditory Roder, Stock, Bien, Friederici et al., 2010 sentence comprehension is functional reorganization. Neville, & Rosler, The current study’s participants and those in Dronkers 2002 et al.’s (2004) study were chronic stroke patients (i.e., >6 months and >1 year postonset of stroke, respec- Friederici et al., 2000a Mazoyer et al., 1993 tively), and it can be assumed that their overall language Bedny et al., 2011 Rogalsky & Hickok, 2009 abilities have improved since the initial lesion onset. Okada et al., 2010 Rogalsky et al., 2011 However, studies of acute stroke patients (i.e., within 48 hr of onset) also identify temporal and inferior parietal Rogalsky et al., 2015 Scott et al., 2000 regions (approximately BA 39 and BA 40) as the neural Spitsyna et al., 2006 substrate for comprehending syntactically complex con- Stowe et al., 1998 structions, such as object-cleft sentences and reversible questions (Magnusdottir et al., 2013; Newhart et al., Humphries et al., 2012; Race, Ochfeld, Leigh, & Hillis, 2012). Thus, com- 2005 pensatory plasticity does not appear to explain the failure Crinion et al., 2003 to implicate Broca’s area in sentence comprehension in Friederici et al., 2000a chronic stroke studies. Humphries et al., 2006 Conclusion Awad et al., 2007 This study sought to characterize the contributions of damage to Broca’s area and the ATL to agrammatic com- Written Fedorenko et al., 2010 Vandenberghe et al., prehension. Overall, our findings do not support Broca’s 2002 area or the ATL being causally involved in processing Fedorenko et al., 2011 noncanonical compared with canonical sentences. Specif- ically, we found the following: Fedorenko et al., 2012 (1) Damage to neither Broca’s area nor the ATL resulted Fedorenko et al., 2012a in the classic agrammatic comprehension pattern, Fedorenko et al., 2012b that is, performance at or near chance level on non- Blank et al., 2016 canonical items for both tasks and substantially better on canonical sentence structures. Studies included in the “sentences > nonsentences” column report at (2) Patients with agrammatic comprehension on one or least one peak activation for this contrast in Broca’s area (i.e., posterior 2/3 of the inferior frontal gyrus). both tasks do not have maximal lesion overlap in Broca’s area or the ATL. a This study is listed twice because its contrast of normal sentences versus word lists did not activate Broca’s area, but jabberwocky (3) Noncanonical sentence comprehension, once vari- sentences > word lists did activate Broca’s area. ability due to canonical performance is controlled for, implicated posterior temporal-parietal regions, not Broca’s area or the ATL. & Kanwisher, 2010). The inclusion of a memory task (4) Whole-sample VLSM analyses do not implicate Broca’s likely engages working memory processes, including area in performance on any sentence type in any articulatory rehearsal, which robustly activates Broca’s task but did implicate Broca’s area in the cognitive area (Buchsbaum, Ye, & D’Espositio, 2011; Buchsbaum task demands of the sentence–picture matching task. &D’Esposito, 2008; Hickok et al., 2003; Smith, Jonides, The ATL, in combination with posterior temporal- Marshuetz, & Koeppe, 1998). parietal regions, was implicated in performance on Another relevant factor that is often overlooked in inter- both noncanonical and canonical sentences, suggest- preting functional activation studies of sentence proces- ing that the ATL may play an important role in sen- sing is whether the stimuli are presented visually (written tence comprehension more broadly. sentences) or auditorily. We have noticed that a dispropor- tionate number of sentence processing studies that impli- The present findings also highlight the importance of cate Broca’s area have used written stimuli (Table 4). Given posterior temporal lobe networks in agrammatic compre- that reading involves articulatory processes to a greater hension and sentence comprehension more generally.

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