Language is a uniquely human cognitive function hence its decline or loss is usually devastating to the victim. The first description of an aphasic patients dates back to the nineteenth century (Broca, 1861; Wernicke 1874). Various models of language processing have been developed since then (1
). In the original description by Broca, the non-fluent aphasic patient experienced a lesion in the left inferior frontal lobe (4
). In 1874, Wernicke described patients with posterior superior temporal lobe lesions who presented with fluent speech but made frequent paraphasic errors; they also had naming, auditory comprehension, and repetition impairments (5
). Lichtheim later hypothesized that there is a brain region “where concepts are elaborated
”, the conceptual or semantic processing center in the left hemisphere (3
). Based on these and other findings, Geschwind added tertiary association areas including left angular and supramarginal gyri to the language processing model developing what is called today a Wernicke-Lichtheim-Geschwind theory; this model is still in use (2
). These observations from acquired aphasias laid the foundation for modern studies with recently developed imaging techniques such as positron emission tomography (PET) and functional magnetic resonance imaging (fMRI).
A variety of fMRI language paradigms have been designed to investigate multifaceted aspects of language processing and can be classified in respect to the type of stimuli and targeted cognitive function (6
). In right handed subjects, language lateralization to the left hemisphere has been conclusively established in 90–96% of them based on clinical data and the results of the fMRI and other neuroimaging studies (7
). A similar incidence of left-hemispheric language lateralization has been observed in subjects performing a semantic association task (8
). While language lateralization, whether tested with semantic or other language tasks is not in dispute, the complexity of the processes associated with semantic decision makes them suitable for exploring the entire language circuitry and associated cognitive functions.
The fMRI task used in the present study is a variant of the semantic decision/tone decision task first introduced by Binder et al. (9
). Performance of this task requires perceptual analysis of speech sounds (“phonetic processing”) and retrieval of previously stored semantic information. This process activates the syntactic processing network along with verbal working memory. For the active and control conditions to be performed correctly, mental resources required for low-level auditory processing and maintaining attention need to be recruited (9
). In one previous study, a contrast between semantic decision and tone decision detected robust activations in the left lateralized language networks including frontal, posterior superior temporal and angular gyri. The observed lateralization was much stronger for this contrast when compared to the semantic decision – rest contrast (10
). The semantic decision/tone decision (SDTD) task has been previously used to investigate not only the language trajectories associated with age in healthy left- and right-handed adults (8
) but also as means of estimating language lateralization in epilepsy patients undergoing presurgical evaluation (12
) or to evaluate post-stroke aphasia recovery (15
). Furthermore, this highly-reliable fMRI task has shown superior correlation values with intracarotid amobarbital test (IAT) when compared to a covert verb generation fMRI task (14
). Hence, as shown in previous studies, the SDTD fMRI task appears to be well suited for language network evaluation in health and disease. Prior to applying the proposed data analysis methods to a cohort of epilepsy patients (16
), an investigation of healthy subjects performing SDTD task is needed in order to establish the performance of the healthy network associated with semantic processing and to hypothesize regarding the effects of chronic, medication-resistant epilepsy on such a network.
Therefore, in this study, we used the SDTD fMRI task and an alternative neuroimaging data analysis method – group independent component analysis (ICA) – to investigate the neural substrates of semantic decision. ICA is a data-driven method capable of investigating the spatial and temporal behavior of fMRI data without an a priori
defined timecourse or hemodynamic response (17
). In this method, the timecourse for each brain voxel from each subject is first normalized to a percent signal change from the mean. Thereafter, data are subjected to a two stage data reduction step at subject and group level using principal component analysis (PCA). Data reduction steps are followed by the ICA decomposition which is based on a stochastic algorithm. Therefore, depending on the initial conditions (i.e., the starting point), ICA decomposition can result in different solutions (i.e., different independent components). For this reason, the algorithm is run repeatedly with clustering analysis to determine the most reliable IC components (19
). The IC timecourses of these reliable components are further analyzed using Fourier transforms to determine the most task-related components subserving the semantic decision task for the entire group. ICA has been successfully used to investigate the effects of alcohol intoxication on simulated driving (20
), modular musical perception (22
), story comprehension and covert verb generation (23
), story listening (24
) or verb generation (25
). ICA is not predicated upon an expected neural response (26
). In contrast to ICA, general linear model (GLM) is a model based fMRI data analysis method that can also allow correlations between various error terms (e.g., data noise) (26
). In particular, GLM assumes that all brief neural events evoke a hemodynamic response function (HRF) of the same shape and that the time series is modeled as an impulse train of neural events convolved with this fixed-shaped HRF (28
). However, considering the within and across subject HRF variations, assuming a fixed shape for the entire population may not be ideal (29
). The assumption of fixed-shaped HRF is even more concerning when atypical language behavior or cerebro-vascular reactivity is investigated in subjects where the response is expected to be different because of the underlying brain pathology. Finally, the number of cognitive components involved in semantic decision may exceed the number of activated regions identified by these model-based analyses resulting in an oversimplified picture of semantic decision.
Therefore, the main goal of the current investigation was to examine the behavior of the network that underlies the semantic decision in healthy subjects. The previous, hypothesis driven GLM analysis of these data focused on the contrast between tone decision and semantic decision (14
). The current ICA approach is different in that it provides explicit information about the main effects of the semantic decision component of the task rather than the contrast between the two conditions. Further, this approach also provides information about the specific components that contribute to semantic decision (31
). Therefore, the information provided by ICA is complementary to a standard GLM and can be utilized to corroborate or to strengthen the interpretation of fMRI findings.
In general, speech comprehension, classically associated with left temporal lobe (5
), depends on common systems for processing speech sounds (phonology) and word meanings (lexical semantics). According to the simple view of reading
, word recognition and linguistic comprehension are assumed to be the two basic components of reading comprehension. Further, it is also presumed that the comprehension component of this model (or view) is utilized to make sense out of the linguistic information (32
). Finally, a recent study has also highlighted the importance of semantic processing in word identification and reading disability (34
). Thus, investigating the neural basis of semantic processing has far reaching implications and may provide insight into different mechanisms subserving the human language system.
To place our analysis in a theoretical framework, we will present our findings in relation to an extended version of Wernicke-Lichtheim-Geschwind model for language processing (2
). Recent neuroimaging studies also support a functional segregation of Broca’s and Wernicke’s areas (35
). Thus, as an end-effect of this work, we will propose a cognitive model for semantic decision based on a two-route model for language processing implicating a direct as well as an indirect route between Broca’s and Wernicke’s areas. In developing this model, we take into account the left lateralization of the language functions observed with many semantic processing tasks; the typical left language lateralization can be attributed to the early structural asymmetries found in the prenatal brain (36
). Thus, these prenatal neuroanatomical asymmetries are hypothesized to be the driving factor subserving functional lateralization and localization over the course of language development (38
). Finally, our results also highlight the advantages of combining both modular and connectionist approaches to investigating cognitive functions.
We expected cognitive components such as auditory, attention, phonological processing, semantic association and verbal memory to be identified in order to establish normative baseline for a semantic language task that could be then utilized to evaluate language processing in a disease state, e.g., epilepsy. Here we present an approach with ICA that circumvents some of the methodological and interpretational difficulties inherent to more conventional analyses of fMRI data and propose a hierarchical cognitive model associated with semantic decision tasks that could be further investigated using functional/effective connectivity analysis techniques such as structural equation modeling (SEM) (39
) or dynamical causal modeling (DCM) (41