Structural connectivity predicts functional activation during lexical and sublexical reading

Differences between Alzheimer's disease and mild cognitive impairment using brain networks from magnetic resonance texture analysis

Several studies have aimed at identifying biomarkers in the initial phases of Alzheimer's disease (AD). Conversely, texture features, such as those from gray-level co-occurrence matrices (GLCMs), have highlighted important information from several types of medical images. More recently, texture-based brain networks have been shown to provide useful information in characterizing healthy individuals. However, no studies have yet explored the use of this type of network in the context of AD. This work aimed to employ texture brain networks to investigate the distinction between groups of patients with amnestic mild cognitive impairment (aMCI) and mild dementia due to AD, and a group of healthy subjects. Magnetic resonance (MR) images from the three groups acquired at two instances were used. Images were segmented and GLCM texture parameters were calculated for each region. Structural brain networks were generated using regions as nodes and the similarity among texture parameters as links, and graph theory was used to compute five network measures. An ANCOVA was performed for each network measure to assess statistical differences between groups. The thalamus showed significant differences between aMCI and AD patients for four network measures for the right hemisphere and one network measure for the left hemisphere. There were also significant differences between controls and AD patients for the left hippocampus, right superior parietal lobule, and right thalamus-one network measure each. These findings represent changes in the texture of these regions which can be associated with the cortical volume and thickness atrophies reported in the literature for AD. The texture networks showed potential to differentiate between aMCI and AD patients, as well as between controls and AD patients, offering a new tool to help understand these conditions and eventually aid early intervention and personalized treatment, thereby improving patient outcomes and advancing AD research.

Activity in the pontine reticular nuclei scales with handgrip force in humans

The neural pathways that contribute to force production in humans are currently poorly understood, as the relative roles of the corticospinal tract and brainstem pathways, such as the reticulospinal tract (RST), vary substantially across species. Using functional magnetic resonance imaging (fMRI), we aimed to measure activation in the pontine reticular nuclei (PRN) during different submaximal handgrip contractions to determine the potential role of the PRN in force modulation. Thirteen neurologically intact participants (age: 28 ± 6 yr) performed unilateral handgrip contractions at 25%, 50%, 75% of maximum voluntary contraction during brain scans. We quantified the magnitude of PRN activation from the contralateral and ipsilateral sides during each of the three contraction intensities. A repeated-measures ANOVA demonstrated a significant main effect of force (P = 0.012, [Formula: see text] = 0.307) for PRN activation, independent of side (i.e., activation increased with force for both contralateral and ipsilateral nuclei). Further analyses of these data involved calculating the linear slope between the magnitude of activation and handgrip force for each region of interest (ROI) at the individual-level. One-sample t tests on the slopes revealed significant group-level scaling for the PRN bilaterally, but only the ipsilateral PRN remained significant after correcting for multiple comparisons. We show evidence of task-dependent activation in the PRN that was positively related to handgrip force. These data build on a growing body of literature that highlights the RST as a functionally relevant motor pathway for force modulation in humans.NEW & NOTEWORTHY In this study, we used a task-based functional magnetic resonance imaging (fMRI) paradigm to show that activity in the pontine reticular nuclei scales linearly with increasing force during a handgrip task. These findings directly support recently proposed hypotheses that the reticulospinal tract may play an important role in modulating force production in humans.

Comparison of local activation, functional connectivity, and structural connectivity in the N-back task

The N-back task is widely used to investigate working memory. Previous functional magnetic resonance imaging (fMRI) studies have shown that local brain activation depends on the difficulty of the N-back task. Recently, changes in functional connectivity and local activation during a task, such as a single-hand movement task, have been reported to give the distinct information. However, previous studies have not investigated functional connectivity changes in the entire brain during N-back tasks. In this study, we compared alterations in functional connectivity and local activation related to the difficulty of the N-back task. Because structural connectivity has been reported to be associated with local activation, we also investigated the relationship between structural connectivity and accuracy in a N-back task using diffusion tensor imaging (DTI). Changes in functional connectivity depend on the difficulty of the N-back task in a manner different from local activation, and the 2-back task is the best method for investigating working memory. This indicates that local activation and functional connectivity reflect different neuronal events during the N-back task. The top 10 structural connectivities associated with accuracy in the 2-back task were locally activated during the 2-back task. Therefore, structural connectivity as well as fMRI will be useful for predicting the accuracy of the 2-back task.

Mechanisms underlying category learning in the human ventral occipito-temporal cortex

The human ventral occipito-temporal cortex (VOTC) has evolved into specialized regions that process specific categories, such as words, tools, and animals. The formation of these areas is driven by bottom-up visual and top-down nonvisual experiences. However, the specific mechanisms through which top-down nonvisual experiences modulate category-specific regions in the VOTC are still unknown. To address this question, we conducted a study in which participants were trained for approximately 13 h to associate three sets of novel meaningless figures with different top-down nonvisual features: the wordlike category with word features, the non-wordlike category with nonword features, and the visual familiarity condition with no nonvisual features. Pre- and post-training functional MRI (fMRI) experiments were used to measure brain activity during stimulus presentation. Our results revealed that training induced a categorical preference for the two training categories within the VOTC. Moreover, the locations of two training category-specific regions exhibited a notable overlap. Remarkably, within the overlapping category-specific region, training resulted in a dissociation in activation intensity and pattern between the two training categories. These findings provide important insights into how different nonvisual categorical information is encoded in the human VOTC.

Texture-based brain networks for characterization of healthy subjects from MRI

Brain networks have been widely used to study the relationships between brain regions based on their dynamics using, e.g. fMRI or EEG, and to characterize their real physical connections using DTI. However, few studies have investigated brain networks derived from structural properties; and those have been based on cortical thickness or gray matter volume. The main objective of this work was to investigate the feasibility of obtaining useful information from brain networks derived from structural MRI, using texture features. We also wanted to verify if texture brain networks had any relation with established functional networks. T1-MR images were segmented using AAL and texture parameters from the gray-level co-occurrence matrix were computed for each region, for 760 subjects. Individual texture networks were used to evaluate the structural connections between regions of well-established functional networks; assess possible gender differences; investigate the dependence of texture network measures with age; and single out brain regions with different texture-network characteristics. Although around 70% of texture connections between regions belonging to the default mode, attention, and visual network were greater than the mean connection value, this effect was small (only between 7 and 15% of these connections were larger than one standard deviation), implying that texture-based morphology does not seem to subside function. This differs from cortical thickness-based morphology, which has been shown to relate to functional networks. Seventy-five out of 86 evaluated regions showed significant (ANCOVA, p < 0.05) differences between genders. Forty-four out of 86 regions showed significant (ANCOVA, p < 0.05) dependence with age; however, the R2 indicates that this is not a linear relation. Thalamus and putamen showed a very unique texture-wise structure compared to other analyzed regions. Texture networks were able to provide useful information regarding gender and age-related differences, as well as for singling out specific brain regions. We did not find a morphological texture-based subsidy for the evaluated functional brain networks. In the future, this approach will be extended to neurological patients to investigate the possibility of extracting biomarkers to help monitor disease evolution or treatment effectiveness.

What's in a game: Video game visual-spatial demand location exhibits a double dissociation with reading speed

This research sought to clarify the nature of the relationship between video game experience, attention, and reading. Previous studies have suggested playing action video games can improve reading ability in children with dyslexia. Other research has linked video game experience with visual-spatial attention, and visual-spatial attention with reading. We hypothesized that the visual-spatial demands of video games may drive relationships with reading through attentional processing. In this experiment we used a hybrid attention/reading task to explore the relationship between video game visual-spatial demands, reading and attention. We also developed novel visual-spatial demand measures using participants' top five played video games for an individual-specific measure of visual demands. Peripheral visual demands in video games were associated with faster reading times, while central visual demands were associated with slower reading times for both phonetic decoding and lexical reading. In addition, video game experience in terms of hours spent playing video games each week interacted with the cueing effect size in the lexical reading condition, with experienced video game players exhibiting a larger cueing effect than participants with less video game experience. These results suggest that exposure to peripheral visual spatial demands in video games may be related to both lexical and sublexical reading processes in hybrid attentional reading tasks such as ours with skilled adult readers, which has implications not only for models of how ventral and dorsal stream reading and visual-spatial attention are integrated, but also for the development of dyslexia diagnostics and remediation.

The effects of bilateral posterior parietal cortex tRNS on reading performance

According to established cognitive neuroscience knowledge based on studies on disabled and typically developing readers, reading is based on a dual-stream model in which a phonological-dorsal stream (left temporo-parietal and inferior frontal areas) processes unfamiliar words and pseudowords, whereas an orthographic-ventral stream (left occipito-temporal and inferior frontal areas) processes known words. However, correlational neuroimaging, causal longitudinal, training, and pharmacological studies have suggested the critical role of visuo-spatial attention in reading development. In a double blind, crossover within-subjects experiment, we manipulated the neuromodulatory effect of a short-term bilateral stimulation of posterior parietal cortex (PPC) by using active and sham tRNS during reading tasks in a large sample of young adults. In contrast to the dual-stream model predicting either no effect or a selective effect on the stimulated phonological-dorsal stream (as well as to a general multisensory effect on both reading streams), we found that only word-reading performance improved after active bilateral PPC tRNS. These findings demonstrate a direct neural connectivity between the PPC, controlling visuo-spatial attention, and the ventral stream for visual word recognition. These results support a neurobiological model of reading where performance of the orthographic-ventral stream is boosted by an efficient deployment of visuo-spatial attention from bilateral PPC stimulation.

The Brain Connectome for Chinese Reading

Chinese, as a logographic language, fundamentally differs from alphabetic languages like English. Previous neuroimaging studies have mainly focused on alphabetic languages, while the exploration of Chinese reading is still an emerging and fast-growing research field. Recently, a growing number of neuroimaging studies have explored the neural circuit of Chinese reading. Here, we summarize previous research on Chinese reading from a connectomic perspective. Converging evidence indicates that the left middle frontal gyrus is a specialized hub region that connects the ventral with dorsal pathways for Chinese reading. Notably, the orthography-to-phonology and orthography-to-semantics mapping, mainly processed in the ventral pathway, are more specific during Chinese reading. Besides, in addition to the left-lateralized language-related regions, reading pathways in the right hemisphere also play an important role in Chinese reading. Throughout, we comprehensively review prior findings and emphasize several challenging issues to be explored in future work.

Visuo-spatial attention deficit in children with reading difficulties

Although developmental reading disorders (developmental dyslexia) have been mainly associated with auditory-phonological deficits, recent longitudinal and training studies have shown a possible causal role of visuo-attentional skills in reading acquisition. Indeed, visuo-attentional mechanisms could be involved in the orthographic processing of the letter string and the graphemic parsing that precede the grapheme-to-phoneme mapping. Here, we used a simple paper-and-pencil task composed of three labyrinths to measure visuo-spatial attention in a large sample of primary school children (n = 398). In comparison to visual search tasks requiring visual working memory, our labyrinth task mainly measures distributed and focused visuo-spatial attention, also controlling for sensorimotor learning. Compared to typical readers (n = 340), children with reading difficulties (n = 58) showed clear visuo-spatial attention impairments that appear not linked to motor coordination and procedural learning skills implicated in this paper and pencil task. Since visual attention is dysfunctional in about 40% of the children with reading difficulties, an efficient reading remediation program should integrate both auditory-phonological and visuo-attentional interventions.

Structural Brain Imaging Predicts Individual-Level Task Activation Maps Using Deep Learning

Accurate individual functional mapping of task activations is a potential tool for biomarker discovery and is critically important for clinical care. While structural imaging does not directly map task activation, we hypothesized that structural imaging contains information that can accurately predict variations in task activation between individuals. To this end, we trained a convolutional neural network to use structural imaging (T1-weighted, T2-weighted, and diffusion tensor imaging) to predict 47 different functional MRI task activation volumes across seven task domains. The U-Net model was trained on 591 subjects and then subsequently tested on 122 unrelated subjects. The predicted activation maps correlated more strongly with their actual maps than with the maps of the other test subjects. An ablation study revealed that a model using the shape of the cortex alone or the shape of the subcortical matter alone was sufficient to predict individual-level differences in task activation maps, but a model using the shape of the whole brain resulted in markedly decreased performance. The ablation study also showed that the additional information provided by the T2-weighted and diffusion tensor imaging strengthened the predictions as compared to using the T1-weighted imaging alone. These results indicate that structural imaging contains information that is predictive of inter-subject variability in task activation mapping and that cortical folding patterns, as well as microstructural features, could be a key component to linking brain structure to brain function.

Is human face recognition lateralized to the right hemisphere due to neural competition with left-lateralized visual word recognition? A critical review

The right hemispheric lateralization of face recognition, which is well documented and appears to be specific to the human species, remains a scientific mystery. According to a long-standing view, the evolution of language, which is typically substantiated in the left hemisphere, competes with the cortical space in that hemisphere available for visuospatial processes, including face recognition. Over the last decade, a specific hypothesis derived from this view according to which neural competition in the left ventral occipito-temporal cortex with selective representations of letter strings causes right hemispheric lateralization of face recognition, has generated considerable interest and research in the scientific community. Here, a systematic review of studies performed in various populations (infants, children, literate and illiterate adults, left-handed adults) and methodologies (behavior, lesion studies, (intra)electroencephalography, neuroimaging) offers little if any support for this reading lateralized neural competition hypothesis. Specifically, right-lateralized face-selective neural activity already emerges at a few months of age, well before reading acquisition. Moreover, consistent evidence of face recognition performance and its right hemispheric lateralization being modulated by literacy level during development or at adulthood is lacking. Given the absence of solid alternative hypotheses and the key role of neural competition in the sensory-motor cortices for selectivity of representations, learning, and plasticity, a revised language-related neural competition hypothesis for the right hemispheric lateralization of face recognition should be further explored in future research, albeit with substantial conceptual clarification and advances in methodological rigor.

Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity

Although functional connectivity and associated graph theory measures (e.g., centrality; how centrally important to the network a region is) are widely used in brain research, the full extent to which these functional measures are related to the underlying structural connectivity is not yet fully understood. Graph neural network deep learning methods have not yet been applied for this purpose, and offer an ideal model architecture for working with connectivity data given their ability to capture and maintain inherent network structure. Here, we applied this model to predict functional connectivity from structural connectivity in a sample of 998 participants from the Human Connectome Project. Our results showed that the graph neural network accounted for 89% of the variance in mean functional connectivity, 56% of the variance in individual-level functional connectivity, 99% of the variance in mean functional centrality, and 81% of the variance in individual-level functional centrality. These results represent an important finding that functional centrality can be robustly predicted from structural connectivity. Regions of particular importance to the model's performance as determined through lesioning are discussed, whereby regions with higher centrality have a higher impact on model performance. Future research on models of patient, demographic, or behavioural data can also benefit from this graph neural network method as it is ideally-suited for depicting connectivity and centrality in brain networks. These results have set a new benchmark for prediction of functional connectivity from structural connectivity, and models like this may ultimately lead to a way to predict functional connectivity in individuals who are unable to do fMRI tasks (e.g., non-responsive patients).

Brain-behavior dynamics between the left fusiform and reading

The visual word form area (VWFA) plays a significant role in the development of reading skills. However, the developmental course and anatomical properties of the VWFA have only limitedly been investigated. The aim of the current longitudinal MRI study was to investigate dynamic, bidirectional relations between reading, and the structure of the left fusiform gyrus at the early-to-advanced reading stage. More specifically, by means of bivariate correlations and a cross-lagged panel model (CLPM), the interrelations between the size of the left fusiform gyrus and reading skills (an average score of a word and pseudo-word reading task) were studied in a longitudinal cohort of 43 Flemish children (29M, 14F) with variable reading skills in grade 2 (the early stage of reading) and grade 5 (the advanced stage of reading) of primary school. Results revealed that better reading skills at grade 2 lead to a larger size of the left fusiform gyrus at grade 5, whereas there are no directional effects between the size of the left fusiform gyrus at grade 2 and reading skills at grade 5. Hence, according to our results, there is behavior-driven brain plasticity and no brain-driven reading change between the early and advanced stage of reading. Together with pre-reading brain studies showing predictive relations to later reading scores, our results suggest that the direction of brain-behavioral influences changes throughout the course of reading development.