Impact of brain parcellation on prediction performance in models of cognition and demographics.

Brain connectivity analysis begins with the selection of a parcellation scheme that will define brain regions as nodes of a network whose connections will be studied. Brain connectivity has already been used in predictive modelling of cognition, but it remains unclear if the resolution of the parcellation used can systematically impact the predictive model performance. In this work, structural, functional and combined connectivity were each defined with five different parcellation schemes. The resolution and modality of the parcellation schemes were varied. Each connectivity defined with each parcellation was used to predict individual differences in age, education, sex, executive function, self-regulation, language, encoding and sequence processing. It was found that low-resolution functional parcellation consistently performed above chance at producing generalisable models of both demographics and cognition. However, no single parcellation scheme showed a superior predictive performance across all cognitive domains and demographics. In addition, although parcellation schemes impacted the graph theory measures of each connectivity type (structural, functional and combined), these differences did not account for the out-of-sample predictive performance of the models. Taken together, these findings demonstrate that while high-resolution parcellations may be beneficial for modelling specific individual differences, partial voluming of signals produced by the higher resolution of the parcellation likely disrupts model generalisability.

Using graph theory as a common language to combine neural structure and function in models of healthy cognitive performance.

Graph theory has been used in cognitive neuroscience to understand how organisational properties of structural and functional brain networks relate to cognitive function. Graph theory may bridge the gap in integration of structural and functional connectivity by introducing common measures of network characteristics. However, the explanatory and predictive value of combined structural and functional graph theory have not been investigated in modelling of cognitive performance of healthy adults. In this work, a Principal Component Regression approach with embedded Step-Wise Regression was used to fit multiple regression models of Executive Function, Self-regulation, Language, Encoding and Sequence Processing with a collection of 20 different graph theoretic measures of structural and functional network organisation used as regressors. The predictive ability of graph theory-based models was compared to that of connectivity-based models. The present work shows that using combinations of graph theory metrics to predict cognition in healthy populations does not produce a consistent benefit relative to making predictions based on structural and functional connectivity values directly.

Distinct roles for the anterior temporal lobe and angular gyrus in the spatiotemporal cortical semantic network.

Semantic knowledge is supported by numerous brain regions, but the spatiotemporal configuration of the network that links these areas remains an open question. The hub-and-spokes model posits that a central semantic hub coordinates this network. In this study, we explored distinct aspects that define a semantic hub, as reflected in the spatiotemporal modulation of neural activity and connectivity by semantic variables, from the earliest stages of semantic processing. We used source-reconstructed electro/magnetoencephalography, and investigated the concreteness contrast across three tasks. In a whole-cortex analysis, the left anterior temporal lobe (ATL) was the only area that showed modulation of evoked brain activity from 100 ms post-stimulus. Furthermore, using Dynamic Causal Modeling of the evoked responses, we investigated effective connectivity amongst the candidate semantic hub regions, that is, left ATL, supramarginal/angular gyrus (SMG/AG), middle temporal gyrus, and inferior frontal gyrus. We found that models with a single semantic hub showed the highest Bayesian evidence, and the hub region was found to change from ATL (within 250 ms) to SMG/AG (within 450 ms) over time. Our results support a single semantic hub view, with ATL showing sustained modulation of neural activity by semantics, and both ATL and AG underlying connectivity depending on the stage of semantic processing.

Direct Neural Evidence for the Contrastive Roles of the Complementary Learning Systems in Adult Acquisition of Native Vocabulary.

The Complementary Learning Systems (CLS) theory provides a powerful framework for considering the acquisition, consolidation, and generalization of new knowledge. We tested this proposed neural division of labor in adults through an investigation of the consolidation and long-term retention of newly learned native vocabulary with post-learning functional neuroimaging. Newly learned items were compared with two conditions: 1) previously known items to highlight the similarities and differences with established vocabulary and 2) unknown/untrained items to provide a control for non-specific perceptual and motor speech output. Consistent with the CLS, retrieval of newly learned items was supported by a combination of regions associated with episodic memory (including left hippocampus) and the language-semantic areas that support established vocabulary (left inferior frontal gyrus and left anterior temporal lobe). Furthermore, there was a shifting division of labor across these two networks in line with the items' consolidation status; faster naming was associated with more activation of language-semantic areas and lesser activation of episodic memory regions. Hippocampal activity during naming predicted more than half the variation in naming retention 6 months later.

Combination of structural and functional connectivity explains unique variation in specific domains of cognitive function.

The relationship between structural and functional brain networks has been characterised as complex: the two networks mirror each other and show mutual influence but they also diverge in their organisation. This work explored whether a combination of structural and functional connectivity can improve the fit of regression models of cognitive performance. Principal Component Analysis (PCA) was first applied to cognitive data from the Human Connectome Project to identify latent cognitive components: Executive Function, Self-regulation, Language, Encoding and Sequence Processing. A Principal Component Regression approach with embedded Step-Wise Regression (SWR-PCR) was then used to fit regression models of each cognitive domain based on structural (SC), functional (FC) or combined structural-functional (CC) connectivity. Executive Function was best explained by the CC model. Self-regulation was equally well explained by SC and FC. Language was equally well explained by CC and FC models. Encoding and Sequence Processing were best explained by SC. Evaluation of out-of-sample models' skill via cross-validation showed that SC, FC and CC produced generalisable models of Language performance. SC models performed most effectively at predicting Language performance in unseen sample. Executive Function was most effectively predicted by SC models, followed only by CC models. Self-regulation was only effectively predicted by CC models and Sequence Processing was only effectively predicted by FC models. The present study demonstrates that integrating structural and functional connectivity can help explaining cognitive performance, but that the added explanatory value (in-sample) may be domain-specific and can come at the expense of reduced generalisation performance (out-of-sample).

Using neurostimulation to understand the impact of pre-morbid individual differences on post-lesion outcomes.

Neuropsychological data have proven invaluable in advancing our understanding of higher cognition. The interpretation of such data is, however, complicated by the fact that post-lesion behavioral abnormalities could reflect pre-morbid individual differences in the cognitive domain of interest. Here we exploited the virtual lesion methodology offered by transcranial magnetic stimulation (TMS) to explore the impact of pre-morbid individual differences on post-lesion performance. We applied this approach to the domain of reading, a crucial ability in which there are known to be considerable individual differences in the normal population. As predicted by neuropsychological studies of surface dyslexia in semantic dementia and the connectionist triangle model of reading, previous empirical work has shown that healthy participants vary in their reliance on meaning for reading words with atypical correspondences between spelling and sound. We therefore selected participants who varied along this dimension and applied a virtual lesion to the left anterior temporal lobe. As expected, we observed a significant three-way interaction between "pre-morbid" reading status, stimulation, and word type, such that TMS increased the disadvantage for spelling-sound atypical words more for the individuals with stronger semantic reliance. This successful test-case study provides an approach to understanding the impact of pre-morbid individual variation on post-lesion outcomes that could be fruitfully applied to a variety of cognitive domains.

Triangulation of the neurocomputational architecture underpinning reading aloud.

The goal of cognitive neuroscience is to integrate cognitive models with knowledge about underlying neural machinery. This significant challenge was explored in relation to word reading, where sophisticated computational-cognitive models exist but have made limited contact with neural data. Using distortion-corrected functional MRI and dynamic causal modeling, we investigated the interactions between brain regions dedicated to orthographic, semantic, and phonological processing while participants read words aloud. We found that the lateral anterior temporal lobe exhibited increased activation when participants read words with irregular spellings. This area is implicated in semantic processing but has not previously been considered part of the reading network. We also found meaningful individual differences in the activation of this region: Activity was predicted by an independent measure of the degree to which participants use semantic knowledge to read. These characteristics are predicted by the connectionist Triangle Model of reading and indicate a key role for semantic knowledge in reading aloud. Premotor regions associated with phonological processing displayed the reverse characteristics. Changes in the functional connectivity of the reading network during irregular word reading also were consistent with semantic recruitment. These data support the view that reading aloud is underpinned by the joint operation of two neural pathways. They reveal that (i) the ATL is an important element of the ventral semantic pathway and (ii) the division of labor between the two routes varies according to both the properties of the words being read and individual differences in the degree to which participants rely on each route.

The impact of phonological versus semantic repetition training on generalisation in chronic stroke aphasia reflects differences in dorsal pathway connectivity.

It has been suggested that neuroimaging can be used to inform therapeutic intervention. The current study aimed to determine whether an individual would benefit more from training engaging their intact or their damaged neural pathway. Two males with chronic stroke aphasia participated, with DM showing milder disruption of connectivity along the dorsal language pathway relative to JS, according to distortion corrected diffusion-weighted magnetic resonance imaging. Each patient received two blocks of six repetition training sessions over two weeks, one of which was "phonological" and the other "semantic" in nature. Both phonological and semantic training produced significant gains for both patients for trained items. For the untrained control items, significant gains were specific to training type for each patient. Only phonological training elicited significant generalisation for DM, which was greater than that seen for JS. Conversely, only semantic training elicited significant generalisation for JS, which was greater than that seen for DM. This double dissociation in generalisation effects suggests that a restitutive approach is more effective for patients with milder damage while a compensatory approach may be more effective for those with more severe damage. These results indicate the utility of neuroimaging to optimise relearning strategies and promote generalisation to untrained items.

Taking Sides: An Integrative Review of the Impact of Laterality and Polarity on Efficacy of Therapeutic Transcranial Direct Current Stimulation for Anomia in Chronic Poststroke Aphasia.

Anomia is a frequent and persistent symptom of poststroke aphasia, resulting from damage to areas of the brain involved in language production. Cortical neuroplasticity plays a significant role in language recovery following stroke and can be facilitated by behavioral speech and language therapy. Recent research suggests that complementing therapy with neurostimulation techniques may enhance functional gains, even amongst those with chronic aphasia. The current review focuses on the use of transcranial Direct Current Stimulation (tDCS) as an adjunct to naming therapy for individuals with chronic poststroke aphasia. Our survey of the literature indicates that combining therapy with anodal (excitatory) stimulation to the left hemisphere and/or cathodal (inhibitory) stimulation to the right hemisphere can increase both naming accuracy and speed when compared to the effects of therapy alone. However, the benefits of tDCS as a complement to therapy have not been yet systematically investigated with respect to site and polarity of stimulation. Recommendations for future research to help determine optimal protocols for combined therapy and tDCS are outlined.

Capturing multidimensionality in stroke aphasia: mapping principal behavioural components to neural structures.

Stroke aphasia is a multidimensional disorder in which patient profiles reflect variation along multiple behavioural continua. We present a novel approach to separating the principal aspects of chronic aphasic performance and isolating their neural bases. Principal components analysis was used to extract core factors underlying performance of 31 participants with chronic stroke aphasia on a large, detailed battery of behavioural assessments. The rotated principle components analysis revealed three key factors, which we labelled as phonology, semantic and executive/cognition on the basis of the common elements in the tests that loaded most strongly on each component. The phonology factor explained the most variance, followed by the semantic factor and then the executive-cognition factor. The use of principle components analysis rendered participants' scores on these three factors orthogonal and therefore ideal for use as simultaneous continuous predictors in a voxel-based correlational methodology analysis of high resolution structural scans. Phonological processing ability was uniquely related to left posterior perisylvian regions including Heschl's gyrus, posterior middle and superior temporal gyri and superior temporal sulcus, as well as the white matter underlying the posterior superior temporal gyrus. The semantic factor was uniquely related to left anterior middle temporal gyrus and the underlying temporal stem. The executive-cognition factor was not correlated selectively with the structural integrity of any particular region, as might be expected in light of the widely-distributed and multi-functional nature of the regions that support executive functions. The identified phonological and semantic areas align well with those highlighted by other methodologies such as functional neuroimaging and neurostimulation. The use of principle components analysis allowed us to characterize the neural bases of participants' behavioural performance more robustly and selectively than the use of raw assessment scores or diagnostic classifications because principle components analysis extracts statistically unique, orthogonal behavioural components of interest. As such, in addition to improving our understanding of lesion-symptom mapping in stroke aphasia, the same approach could be used to clarify brain-behaviour relationships in other neurological disorders.

What lies beneath: a comparison of reading aloud in pure alexia and semantic dementia.

Exaggerated effects of word length upon reading-aloud performance define pure alexia, but have also been observed in semantic dementia. Some researchers have proposed a reading-specific account, whereby performance in these two disorders reflects the same cause: impaired orthographic processing. In contrast, according to the primary systems view of acquired reading disorders, pure alexia results from a basic visual processing deficit, whereas degraded semantic knowledge undermines reading performance in semantic dementia. To explore the source of reading deficits in these two disorders, we compared the reading performance of 10 pure alexic and 10 semantic dementia patients, matched in terms of overall severity of reading deficit. The results revealed comparable frequency effects on reading accuracy, but weaker effects of regularity in pure alexia than in semantic dementia. Analysis of error types revealed a higher rate of letter-based errors and a lower rate of regularization responses in pure alexia than in semantic dementia. Error responses were most often words in pure alexia but most often nonwords in semantic dementia. Although all patients made some letter substitution errors, these were characterized by visual similarity in pure alexia and phonological similarity in semantic dementia. Overall, the data indicate that the reading deficits in pure alexia and semantic dementia arise from impairments of visual processing and knowledge of word meaning, respectively. The locus and mechanisms of these impairments are placed within the context of current connectionist models of reading.

Efficient visual object and word recognition relies on high spatial frequency coding in the left posterior fusiform gyrus: evidence from a case-series of patients with ventral occipito-temporal cortex damage.

Recent visual neuroscience investigations suggest that ventral occipito-temporal cortex is retinotopically organized, with high acuity foveal input projecting primarily to the posterior fusiform gyrus (pFG), making this region crucial for coding high spatial frequency information. Because high spatial frequencies are critical for fine-grained visual discrimination, we hypothesized that damage to the left pFG should have an adverse effect not only on efficient reading, as observed in pure alexia, but also on the processing of complex non-orthographic visual stimuli. Consistent with this hypothesis, we obtained evidence that a large case series (n = 20) of patients with lesions centered on left pFG: 1) Exhibited reduced sensitivity to high spatial frequencies; 2) demonstrated prolonged response latencies both in reading (pure alexia) and object naming; and 3) were especially sensitive to visual complexity and similarity when discriminating between novel visual patterns. These results suggest that the patients' dual reading and non-orthographic recognition impairments have a common underlying mechanism and reflect the loss of high spatial frequency visual information normally coded in the left pFG.

Establishing and evaluating the gradient of item naming difficulty in post-stroke aphasia and semantic dementia.

Anomia is a common consequence following brain damage and a central symptom in semantic dementia (SD) and post-stroke aphasia (PSA), for instance. Picture naming tests are often used in clinical assessments and experience suggests that items vary systematically in their difficulty. Despite clinical intuitions and theoretical accounts, however, the existence and determinants of such a naming difficulty gradient remain to be empirically established and evaluated. Seizing the unique opportunity of two large-scale datasets of semantic dementia and post-stroke aphasia patients assessed with the same picture naming test, we applied an Item Response Theory (IRT) approach and we (a) established that an item naming difficulty gradient exists, which (b) partly differs between patient groups, and is (c) related in part to a limited number of psycholinguistic properties - frequency and familiarity for SD, frequency and word length for PSA. Our findings offer exciting future avenues for new, adaptive, time-efficient, and patient-tailored approaches to naming assessment and therapy.

Relating Cognition to both Brain Structure and Function: A Systematic Review of Methods.

Introduction: Cognitive neuroscience explores the mechanisms of cognition by studying its structural and functional brain correlates. Many studies have combined structural and functional neuroimaging techniques to uncover the complex relationship between them. In this study, we report the first systematic review that assesses how information from structural and functional neuroimaging methods can be integrated to investigate the brain substrates of cognition. Procedure: Web of Science and Scopus databases were searched for studies of healthy young adult populations that collected cognitive data and structural and functional neuroimaging data. Results: Five percent of screened studies met all inclusion criteria. Next, 50% of included studies related cognitive performance to brain structure and function without quantitative analysis of the relationship. Finally, 31% of studies formally integrated structural and functional brain data. Overall, many studies consider either structural or functional neural correlates of cognition, and of those that consider both, they have rarely been integrated. We identified four emergent approaches to the characterization of the relationship between brain structure, function, and cognition; comparative, predictive, fusion, and complementary. Discussion: We discuss the insights provided in each approach about the relationship between brain structure and function and how it impacts cognitive performance. In addition, we discuss how authors can select approaches to suit their research questions. Impact statement The relationship between structural and functional brain networks and their relationship to cognition is a matter of current investigations. This work surveys how researchers have studied the relationship between brain structure and function and its impact on cognitive function in healthy adult populations. We review four emergent approaches of quantitative analysis of this multivariate problem; comparative, predictive, fusion, and complementary. We explain the characteristics of each approach, discuss the insights provided in each approach, and how authors can combine approaches to suit their research questions.

Global versus phonemic similarity: Evidence in support of multi-level representation.

There is long-standing debate about the extent to which children cognitively represent words in terms of global properties or phonological segments, yet few studies have investigated how children's sensitivity to phonemic versus global similarity changes over time. The current study uses a mispronunciation-reconstruction task to measure both types of sensitivity within a cross-sectional (N = 90, aged 3;2 to 5;7) and longitudinal sample (N = 23, aged 3;2 to 5;1). The results show that children's sensitivity to phonemes increases over the first two years of school but does not reach adult levels. The findings indicate that global similarity relations remain important throughout development and support the idea of multi-level representation.

Word or word-like? Dissociating orthographic typicality from lexicality in the left occipito-temporal cortex.

Prior lesion and functional imaging studies have highlighted the importance of the left ventral occipito-temporal (LvOT) cortex for visual word recognition. Within this area, there is a posterior-anterior hierarchy of subregions that are specialized for different stages of orthographic processing. The aim of the present fMRI study was to dissociate the effects of subword orthographic typicality (e.g., cider [high] vs. cynic [low]) from the effect of lexicality (e.g., pollen [word] vs. pillen [pseudoword]). We therefore orthogonally manipulated the orthographic typicality of written words and pseudowords (nonwords and pseudohomophones) in a visual lexical decision task. Consistent with previous studies, we identified greater activation for pseudowords than words (i.e., an effect of lexicality) in posterior LvOT cortex. In addition, we revealed higher activation for atypical than typical strings, irrespective of lexicality, in a left inferior occipital region that is posterior to LvOT cortex. When lexical decisions were made more difficult in the context of pseudohomophone foils, left anterior temporal activation also increased for atypical relative to typical strings. The latter finding agrees with the behavior of patients with progressive anterior temporal lobe degeneration, who have particular difficulty recognizing words with atypical orthography. The most novel outcome of this study is that, within a distributed network of regions supporting orthographic processing, we have identified a left inferior occipital region that is particularly sensitive to the typicality of subword orthographic patterns.

The role of plasticity-related functional reorganization in the explanation of central dyslexias.

This investigation explored the hypothesis that patterns of acquired dyslexia may reflect, in part, plasticity-driven relearning that dynamically alters the division of labour (DOL) between the direct, orthography → phonology (O → P) pathway and the semantically mediated, orthography → semantics → phonology (O → S → P) pathway. Three simulations were conducted using a variant of the triangle model of reading. The model demonstrated core characteristics of normal reading behaviour in its undamaged state. When damage was followed by reoptimization (mimicking spontaneous recovery), the model reproduced the deficits observed in the central dyslexias-acute phonological damage combined with recovery matched data taken from a series of 12 phonological dyslexic patients-whilst progressive semantic damage interspersed with recovery reproduced data taken from 100 observations of semantic dementia patients. The severely phonologically damaged model also produced symptoms of deep dyslexia (imageability effects, production of semantic and mixed semantic/visual errors). In all cases, the DOL changed significantly in the recovery period, suggesting that postmorbid functional reorganization is important in understanding behaviour in chronic-stage patients.

Making sense of progressive non-fluent aphasia: an analysis of conversational speech.

The speech of patients with progressive non-fluent aphasia (PNFA) has often been described clinically, but these descriptions lack support from quantitative data. The clinical classification of the progressive aphasic syndromes is also debated. This study selected 15 patients with progressive aphasia on broad criteria, excluding only those with clear semantic dementia. It aimed to provide a detailed quantitative description of their conversational speech, along with cognitive testing and visual rating of structural brain imaging, and to examine which, if any features were consistently present throughout the group; as well as looking for sub-syndromic associations between these features. A consistent increase in grammatical and speech sound errors and a simplification of spoken syntax relative to age-matched controls were observed, though telegraphic speech was rare; slow speech was common but not universal. Almost all patients showed impairments in picture naming, syntactic comprehension and executive function. The degree to which speech was affected was independent of the severity of the other cognitive deficits. A partial dissociation was also observed between slow speech with simplified grammar on the one hand, and grammatical and speech sound errors on the other. Overlap between these sets of impairments was however, the rule rather than the exception, producing continuous variation within a single consistent syndrome. The distribution of atrophy was remarkably variable, with frontal, temporal and medial temporal areas affected, either symmetrically or asymmetrically. The study suggests that PNFA is a coherent, well-defined syndrome and that varieties such as logopaenic progressive aphasia and progressive apraxia of speech may be seen as points in a space of continuous variation within progressive non-fluent aphasia.

Investigating the effect of changing parameters when building prediction models for post-stroke aphasia.

Neuroimaging has radically improved our understanding of how speech and language abilities map to the brain in normal and impaired participants, including the diverse, graded variations observed in post-stroke aphasia. A handful of studies have begun to explore the reverse inference: creating brain-to-behaviour prediction models. In this study, we explored the effect of three critical parameters on model performance: (1) brain partitions as predictive features, (2) combination of multimodal neuroimaging and (3) type of machine learning algorithms. We explored the influence of these factors while predicting four principal dimensions of language and cognition variation in post-stroke aphasia. Across all four behavioural dimensions, we consistently found that prediction models derived from diffusion-weighted data did not improve performance over models using structural measures extracted from T1 scans. Our results provide a set of principles to guide future work aiming to predict outcomes in neurological patients from brain imaging data.