The anatomy of language has been investigated with PET or fMRI for more than 20years. Here I attempt to provide an overview of the brain areas associated with heard speech, speech production and ...reading. The conclusions of many hundreds of studies were considered, grouped according to the type of processing, and reported in the order that they were published. Many findings have been replicated time and time again leading to some consistent and undisputable conclusions. These are summarised in an anatomical model that indicates the location of the language areas and the most consistent functions that have been assigned to them. The implications for cognitive models of language processing are also considered. In particular, a distinction can be made between processes that are localized to specific structures (e.g. sensory and motor processing) and processes where specialisation arises in the distributed pattern of activation over many different areas that each participate in multiple functions. For example, phonological processing of heard speech is supported by the functional integration of auditory processing and articulation; and orthographic processing is supported by the functional integration of visual processing, articulation and semantics. Future studies will undoubtedly be able to improve the spatial precision with which functional regions can be dissociated but the greatest challenge will be to understand how different brain regions interact with one another in their attempts to comprehend and produce language.
The anatomy of language has been investigated with PET or fMRI for more than 20 years. Here I attempt to provide an overview of the brain areas associated with heard speech, speech production and ...reading. The conclusions of many hundreds of studies were considered, grouped according to the type of processing, and reported in the order that they were published. Many findings have been replicated time and time again leading to some consistent and undisputable conclusions. These are summarised in an anatomical model that indicates the location of the language areas and the most consistent functions that have been assigned to them. The implications for cognitive models of language processing are also considered. In particular, a distinction can be made between processes that are localized to specific structures (e.g. sensory and motor processing) and processes where specialisation arises in the distributed pattern of activation over many different areas that each participate in multiple functions. For example, phonological processing of heard speech is supported by the functional integration of auditory processing and articulation; and orthographic processing is supported by the functional integration of visual processing, articulation and semantics. Future studies will undoubtedly be able to improve the spatial precision with which functional regions can be dissociated but the greatest challenge will be to understand how different brain regions interact with one another in their attempts to comprehend and produce language.
In this review of 100 fMRI studies of speech comprehension and production, published in 2009, activation is reported for: prelexical speech perception in bilateral superior temporal gyri; meaningful ...speech in middle and inferior temporal cortex; semantic retrieval in the left angular gyrus and pars orbitalis; and sentence comprehension in bilateral superior temporal sulci. For incomprehensible sentences, activation increases in four inferior frontal regions, posterior planum temporale, and ventral supramarginal gyrus. These effects are associated with the use of prior knowledge of semantic associations, word sequences, and articulation that predict the content of the sentence. Speech production activates the same set of regions as speech comprehension but in addition, activation is reported for: word retrieval in left middle frontal cortex; articulatory planning in the left anterior insula; the initiation and execution of speech in left putamen, pre‐SMA, SMA, and motor cortex; and for suppressing unintended responses in the anterior cingulate and bilateral head of caudate nuclei. Anatomical and functional connectivity studies are now required to identify the processing pathways that integrate these areas to support language.
The ventral occipitotemporal cortex (vOT) is involved in the perception of visually presented objects and written words. The Interactive Account of vOT function is based on the premise that ...perception involves the synthesis of bottom-up sensory input with top-down predictions that are generated automatically from prior experience. We propose that vOT integrates visuospatial features abstracted from sensory inputs with higher level associations such as speech sounds, actions and meanings. In this context, specialization for orthography emerges from regional interactions without assuming that vOT is selectively tuned to orthographic features. We discuss how the Interactive Account explains left vOT responses during normal reading and developmental dyslexia; and how it accounts for the behavioural consequences of left vOT damage.
We consider between-subject variance in brain function as data rather than noise. We describe variability as a natural output of a noisy plastic system (the brain) where each subject embodies a ...particular parameterisation of that system. In this context, variability becomes an opportunity to: (i) better characterise typical versus atypical brain functions; (ii) reveal the different cognitive strategies and processing networks that can sustain similar tasks; and (iii) predict recovery capacity after brain damage by taking into account both damaged and spared processing pathways. This has many ramifications for understanding individual learning preferences and explaining the wide differences in human abilities and disabilities. Understanding variability boosts the translational potential of neuroimaging findings, in particular in clinical and educational neuroscience.
A wealth of scientifically and clinically relevant information is hidden, and potentially invalidated, when data are averaged across subjects.
There is growing interest in using neuroimaging to explain differences in human abilities and disabilities. Progress in this endeavour requires us to treat intersubject variability as data rather than noise.
Our plastic and noisy brains intrinsically change the parameterisation of each individual’s brain, providing a rich opportunity to understand differences in brain function.
Normal variability can be used to decode different neural pathways that can sustain the same task (degeneracy).
This is of paramount importance for understanding why patients have variable outcomes after damage to seemingly similar brain regions.
Electrophysiological studies in humans and animals suggest that noninvasive neurostimulation methods such as transcranial direct current stimulation (tDCS) can elicit long-lasting 1, ...polarity-dependent 2 changes in neocortical excitability. Application of tDCS can have significant and selective behavioral consequences that are associated with the cortical location of the stimulation electrodes and the task engaged during stimulation 3–8. However, the mechanism by which tDCS affects human behavior is unclear. Recently, functional magnetic resonance imaging (fMRI) has been used to determine the spatial topography of tDCS effects 9–13, but no behavioral data were collected during stimulation. The present study is unique in this regard, in that both neural and behavioral responses were recorded using a novel combination of left frontal anodal tDCS during an overt picture-naming fMRI study. We found that tDCS had significant behavioral and regionally specific neural facilitation effects. Furthermore, faster naming responses correlated with decreased blood oxygen level-dependent (BOLD) signal in Broca's area. Our data support the importance of Broca's area within the normal naming network and as such indicate that Broca's area may be a suitable candidate site for tDCS in neurorehabilitation of anomic patients, whose brain damage spares this region.
► This is a novel application of concurrent A-tDCS and fMRI during speech production ► Left frontal A-tDCS speeds up spoken naming responses ► Left frontal A-tDCS elicits a regionally specific neural effect in Broca's area ► Decreased BOLD signal in Broca's area correlates with faster naming responses
This paper provides a historical and future perspective on how neuropsychology and neuroimaging can be used to develop cognitive models of human brain functions. Section 1 focuses on the emergence of ...cognitive modelling from neuropsychology, why lesion location was considered to be unimportant and the challenges faced when mapping symptoms to impaired cognitive processes. Section 2 describes how established cognitive models based on behavioural data alone cannot explain the complex patterns of distributed brain activity that are observed in functional neuroimaging studies. This has led to proposals for new cognitive processes, new cognitive strategies and new functional ontologies for cognition. Section 3 considers how the integration of data from lesion, behavioural and functional neuroimaging studies of large cohorts of brain damaged patients can be used to determine whether inter-patient variability in behaviour is due to differences in the premorbid function of each brain region, lesion site or cognitive strategy. This combination of neuroimaging and neuropsychology is providing a deeper understanding of how cognitive functions can be lost and re-learnt after brain damage – an understanding that will transform our ability to generate and validate cognitive models that are both physiologically plausible and clinically useful.
This paper provides a worked example of using Dynamic Causal Modelling (DCM) and Parametric Empirical Bayes (PEB) to characterise inter-subject variability in neural circuitry (effective ...connectivity). It steps through an analysis in detail and provides a tutorial style explanation of the underlying theory and assumptions (i.e, priors). The analysis procedure involves specifying a hierarchical model with two or more levels. At the first level, state space models (DCMs) are used to infer the effective connectivity that best explains a subject's neuroimaging timeseries (e.g. fMRI, MEG, EEG). Subject-specific connectivity parameters are then taken to the group level, where they are modelled using a General Linear Model (GLM) that partitions between-subject variability into designed effects and additive random effects. The ensuing (Bayesian) hierarchical model conveys both the estimated connection strengths and their uncertainty (i.e., posterior covariance) from the subject to the group level; enabling hypotheses to be tested about the commonalities and differences across subjects. This approach can also finesse parameter estimation at the subject level, by using the group-level parameters as empirical priors. The preliminary first level (subject specific) DCM for fMRI analysis is covered in a companion paper. Here, we detail group-level analysis procedures that are suitable for use with data from any neuroimaging modality. This paper is accompanied by an example dataset, together with step-by-step instructions demonstrating how to reproduce the analyses.
•This guide walks through a group effective connectivity study using DCM and PEB.•It explains recently developed tools for hierarchical Bayesian modelling.•The appendices clarify the technical detail of the PEB framework and its priors.•An accompanying dataset is provided with step-by-step analysis instructions.
Dynamic Causal Modelling (DCM) is the predominant method for inferring effective connectivity from neuroimaging data. In the 15 years since its introduction, the neural models and statistical ...routines in DCM have developed in parallel, driven by the needs of researchers in cognitive and clinical neuroscience. In this guide, we step through an exemplar fMRI analysis in detail, reviewing the current implementation of DCM and demonstrating recent developments in group-level connectivity analysis. In the appendices, we detail the theory underlying DCM and the assumptions (i.e., priors) in the models. In the first part of the guide (current paper), we focus on issues specific to DCM for fMRI. This is accompanied by all the necessary data and instructions to reproduce the analyses using the SPM software. In the second part (in a companion paper), we move from subject-level to group-level modelling using the Parametric Empirical Bayes framework, and illustrate how to test for commonalities and differences in effective connectivity across subjects, based on imaging data from any modality.
•This guide walks through a group effective connectivity study using DCM and PEB.•Part 1, presented here, covers first level analysis using DCM for fMRI.•It clarifies the specific neural and haemodynamic models in DCM and their priors.•An accompanying dataset is provided with step-by-step analysis instructions.
The left angular gyrus (AG) is reliably activated across a wide range of semantic tasks, and is also a consistently reported component of the so-called default network that it is deactivated during ...all goal-directed tasks. We show here that there is only partial overlap between the semantic system and the default network in left AG and the overlap defines a reliable functional landmark that can be used to segregate functional subdivisions within AG. In 94 healthy human subjects, we collected functional magnetic resonance imaging (fMRI) data during fixation and eight goal directed tasks that involved semantic matching, perceptual matching or speech production in response to familiar or unfamiliar stimuli presented in either verbal (letters) or nonverbal (pictures) formats. Our results segregated three different left AG regions that were all activated by semantic relative to perceptual matching: (1) a midregion (mAG) that overlapped with the default network because it was deactivated during all tasks relative to fixation; (2) a dorsomesial region (dAG) that was more activated by all tasks relative to fixation; and (3) a ventrolateral region (vAG) that was only activated above fixation during semantic matching. By examining the effects of task and stimuli in each AG subdivision, we propose that mAG is involved in semantic associations regardless of the presence or absence of a stimulus; dAG is involved in searching for semantics in all visual stimuli, and vAG is involved in the conceptual identification of visual inputs. Our findings provide a framework for reporting and interpreting AG activations with greater definition.
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