The role of nonverbal communication in patients with post-stroke language impairment (aphasia) is not yet fully understood. This study investigated how aphasic patients perceive and produce co-speech gestures during face-to-face interaction, and whether distinct brain lesions would predict the frequency of spontaneous co-speech gesturing. For this purpose, we recorded samples of conversations in patients with aphasia and healthy participants. Gesture perception was assessed by means of a head-mounted eye-tracking system, and the produced co-speech gestures were coded according to a linguistic (...) classification system. The main results are that meaning-laden gestures (e.g., iconic gestures representing object shapes) are more likely to attract visual attention than meaningless hand movements, and that patients with aphasia are more likely to fixate co-speech gestures overall than healthy participants. This implies that patients with aphasia may benefit from the multimodal information provided by co-speech gestures. On the level of co-speech gesture production, we found that patients with damage to the anterior part of the arcuate fasciculus showed a higher frequency of meaning-laden gestures. This area lies in close vicinity to the premotor cortex and is considered to be important for speech production. This may suggest that the use of meaning-laden gestures depends on the integrity of patients’ speech production abilities. (shrink)

Ten years ago, researchers using event-related brain potentials to study language comprehension were puzzled by what looked like a Semantic Illusion: Semantically anomalous, but structurally well-formed sentences did not affect the N400 component—traditionally taken to reflect semantic integration—but instead produced a P600 effect, which is generally linked to syntactic processing. This finding led to a considerable amount of debate, and a number of complex processing models have been proposed as an explanation. What these models have in common is that they (...) postulate two or more separate processing streams, in order to reconcile the Semantic Illusion and other semantically induced P600 effects with the traditional interpretations of the N400 and the P600. Recently, however, these multi-stream models have been called into question, and a simpler single-stream model has been proposed. According to this alternative model, the N400 component reflects the retrieval of word meaning from semantic memory, and the P600 component indexes the integration of this meaning into the unfolding utterance interpretation. In the present paper, we provide support for this “Retrieval–Integration ” account by instantiating it as a neurocomputational model. This neurocomputational model is the first to successfully simulate the N400 and P600 amplitude in language comprehension, and simulations with this model provide a proof of concept of the single-stream RI account of semantically induced patterns of N400 and P600 modulations. (shrink)

Individuals with agrammatic Broca's aphasia experience difficulty when processing reversible non-canonical sentences. Different accounts have been proposed to explain this phenomenon. The Trace Deletion account attributes this deficit to an impairment in syntactic representations, whereas others propose that the underlying structural representations are unimpaired, but sentence comprehension is affected by processing deficits, such as slow lexical activation, reduction in memory resources, slowed processing and/or intermittent deficiency, among others. We test the claims of two processing accounts, slowed processing and intermittent deficiency, (...) and two versions of the Trace Deletion Hypothesis, in a computational framework for sentence processing implemented in ACT-R. The assumption of slowed processing is operationalized as slow procedural memory, so that each processing action is performed slower than normal, and intermittent deficiency as extra noise in the procedural memory, so that the parsing steps are more noisy than normal. We operationalize the TDH as an absence of trace information in the parse tree. To test the predictions of the models implementing these theories, we use the data from a German sentence—picture matching study reported in Hanne, Sekerina, Vasishth, Burchert, and De Bleser. The data consist of offline and online measures. From among the models considered, the model assuming that both slowed processing and intermittent deficiency are present emerges as the best model of sentence processing difficulty in aphasia. The modeling of individual differences suggests that, if we assume that patients have both slowed processing and intermittent deficiency, they have them in differing degrees. (shrink)

Research investigating the brain basis of language comprehension has associated the left anterior temporal lobe with sentence-level combinatorics. Using magnetoencephalography, we test the parsing strategy implemented in this brain region. The number of incremental parse steps from a predictive left-corner parsing strategy that is supported by psycholinguistic research is compared with those from a less-predictive strategy. We test for a correlation between parse steps and source-localized MEG activity recorded while participants read a story. Left-corner parse steps correlated with activity in (...) the left ATL around 350–500 ms after word onset. No other correlations specific to sentence comprehension were observed. These data indicate that the left ATL engages in combinatoric processing that is well characterized by a predictive left-corner parsing strategy. (shrink)

The relationship between the neurobiology of speech and music has been investigated for more than a century. There remains no widespread agreement regarding how (or to what extent) music perception utilizes the neural circuitry that is engaged in speech processing, particularly at the cortical level. Prominent models such as Patel’s Shared Syntactic Integration Resource Hypothesis (SSIRH) and Koelsch’s neurocognitive model of music perception suggest a high degree of overlap, particularly in the frontal lobe, but also perhaps more distinct representations in (...) the temporal lobe with hemispheric asymmetries. The present meta-analysis study used activation likelihood estimate analyses to identify the brain regions consistently activated for music as compared to speech across the functional neuroimaging (fMRI and PET) literature. Eighty music and 91 speech neuroimaging studies of healthy adult control subjects were analyzed. Peak activations reported in the music and speech studies were divided into four paradigm categories: passive listening, discrimination tasks, error/anomaly detection tasks and memory-related tasks. We then compared activation likelihood estimates within each category for music versus speech, and each music condition with passive listening. We found that listening to music and to speech preferentially activate distinct temporo-parietal bilateral cortical networks. We also found music and speech to have shared resources in the left pars opercularis but speech-specific resources in the left pars triangularis. The extent to which music recruited speech-activated frontal resources was modulated by task. While there are certainly limitations to meta-analysis techniques particularly regarding sensitivity, this work suggests that the extent of shared resources between speech and music may be task-dependent and highlights the need to consider how task effects may be affecting conclusions regarding the neurobiology of speech and music. (shrink)

When the language-dominant hemisphere is damaged by a focal lesion, the brain may reorganize the language network through functional and structural changes known as adaptive plasticity. Adaptive plasticity is documented for triggers including ischemic, tumoral, and epileptic focal lesions, with effects in clinical practice. Many questions remain regarding language plasticity. Different lesions may induce different patterns of reorganization depending on pathologic features, location in the brain, and timing of onset. Neuroimaging provides insights into language plasticity due to its non-invasiveness, ability (...) to image the whole brain, and large-scale implementation. This review provides an overview of language plasticity on MRI with insights for patient care. First, we describe the structural and functional language network as depicted by neuroimaging. Second, we explore language reorganization triggered by stroke, brain tumors, and epileptic lesions and analyze applications in clinical diagnosis and treatment planning. By comparing different focal lesions, we investigate determinants of language plasticity including lesion location and timing of onset, longitudinal evolution of reorganization, and the relationship between structural and functional changes. (shrink)

ObjectiveTo compare techniques for computing clustering and switching scores in terms of agreement, correlation, and empirical value as predictors of incident cognitive impairment.MethodsWe transcribed animal and letter F fluency recordings on 640 cases of ICI and matched controls from a national epidemiological study, amending each transcription with word timings. We then calculated clustering and switching scores, as well as scores indexing speed of responses, using techniques described in the literature. We evaluated agreement among the techniques with Cohen’s κ and calculated (...) correlations among the scores. After fitting a base model with raw scores, repetitions, and intrusions, we fit a series of Bayesian logistic regression models adding either clustering and switching scores or speed scores, comparing the models in terms of several metrics. We partitioned the ICI cases into acute and progressive cases and repeated the regression analysis for each group.ResultsFor animal fluency, we found that models with speed scores derived using the slope difference algorithm achieved the best values of the Watanabe–Akaike Information Criterion, but with good net reclassification improvement only for the progressive group. For letter fluency, different models excelled for prediction of acute and progressive cases. For acute cases, NRI was best for speed scores derived from a network model, while for progressive cases, the best model used clustering and switching scores derived from the same network model. Combining variables from the best animal and letter F models led to marginal improvements in model fit and NRI only for the all-cases and acute-cases analyses.ConclusionSpeed scores improve a base model for predicting progressive cognitive impairment from animal fluency. Letter fluency scores may provide complementary information. (shrink)