An fMRI, DTI and Neurophysiological Examination of Atypical Organization of Motor Cortex in Ipsilesional Hemisphere Following Post-Stroke Recovery.

OBJECTIVES: We report a 61-year-old woman who developed left hemiparesis following a right frontal stroke. She underwent rehabilitation and regained function of the left side of her body. Three years after her first stroke, she developed a large left subdural hematoma and again presented with left hemiparesis. MATERIALS AND METHODS: Prior to the cranioplasty, an fMRI scan involving left and right hand movement, arm movement, and foot peddling were conducted in order to determine whether the patient showed ipsilateral activation for the motor tasks, thus explaining the left hemiparesis following the left subdural hematoma. Diffusion tensor imaging (DTI) tractography was also collected to visualize the motor and sensory tracts. RESULTS: The fMRI results revealed activation in the expected contralateral left primary motor cortex (M1) for the right-sided motor tasks, and bilateral M1 activation for the left-sided motor tasks. Intraoperative neurophysiology confirmed these findings, whereby electromyography revealed left-sided (i.e., ipsilateral) responses for four of the five electrode locations. The DTI results indicated that the corticospinal tracts and spinothalamic tracts were within normal limits and showed no displacement or disorganization. CONCLUSIONS: These results suggest that there may have been reorganization of the M1 following her initial stroke, and that the left hemisphere may have become involved in moving the left side of the body thereby leading to left hemiparesis following the left subdural hematoma. The findings suggest that cortical reorganization may occur in stroke patients recovering from hemiparesis, and specifically, that components of motor processing subserved by M1 may be taken over by ipsilateral regions.

Structural connectivity predicts functional activation during lexical and sublexical reading.

A critical question in neuroscience is the extent to which structural connectivity of the brain predicts localization of brain function. Recent research has suggested that anatomical connectivity can predict functional magnetic resonance imaging (fMRI) responses in several cognitive domains, including face, object, scene, and body processing, and development of word recognition skills (Osher et al., 2016; Saygin et al., 2016). However, this technique has not yet been extended to skilled word reading. Thus, we developed a computational model that relates anatomical connectivity (measured using probabilistic tractography) of individual cortical voxels to fMRI responses of the same voxels during lexical and sublexical reading tasks. Our results showed that the model built from structural connectivity was able to accurately predict functional responses of individual subjects based on their structural connectivity alone. This finding was apparent across the cortex, as well as to specific regions of interest associated with reading, language, and spatial attention. Further, we identified the structural connectivity networks associated with different aspects of skilled reading using connectivity analyses, and showed that interconnectivity between left hemisphere language and right hemisphere attentional areas underlies both lexical and sublexical reading. This work has important implications for understanding how structural connectivity contributes to reading and suggests that there is a relationship between skilled reading and neuroanatomical brain connectivity that future research should continue to explore.

Language lateralization differences between left and right temporal lobe epilepsy as measured by overt word reading fMRI activation and DTI structural connectivity.

In cases of brain disease such as temporal lobe epilepsy (TLE), damage may lead to functional reorganization and a shift in language dominance to homolog regions in the other hemisphere. If the effects of TLE on language dominance are hemisphere-focused, then brain regions and connections involved in word reading should be less left-lateralized in left temporal lobe epilepsy (lTLE) than right temporal lobe epilepsy (rTLE) or healthy controls, and the opposite effect should be observed in patients with rTLE. In our study, functional magnetic resonance imaging (fMRI) showed that patients with rTLE had more strongly lateralized left hemisphere (LH) activation than patients with lTLE and healthy controls in language-related brain regions (pars opercularis and fusiform gyrus (FuG)). Corresponding with this difference, diffusion tensor imaging (DTI) found differences in connectivity indicative of patients with lTLE having greater tract integrity than patients with rTLE in the right hemisphere (RH) uncinate fasciculus (UF), inferior longitudinal fasciculus (ILF), and inferior fronto-occipital fasciculus (IFOF) using the network-based statistic analysis method. The UF, ILF, and IFOF tract integrity have previously been associated with lexical (whole-word) processing abilities. Multivariate distance matrix regression provided converging evidence for regions of the IFOF having different connectivity patterns between groups with lTLE and rTLE. This research demonstrates language lateralization differences between patient groups with lTLE and rTLE, and corresponding differences in the connectivity strength of the ILF, IFOF, and UF. This research provides a novel approach to measuring lateralization of language in general, and the fMRI and DTI findings were integral for guiding the neurosurgeons performing the TLE resections. This approach should inform future studies of language lateralization and language reorganization in patients such as those with TLE.

Comparing models of information transfer in the structural brain network and their relationship to functional connectivity: diffusion versus shortest path routing.

The relationship between structural and functional connectivity in the human brain is a core question in network neuroscience, and a topic of paramount importance to our ability to meaningfully describe and predict functional outcomes. Graph theory has been used to produce measures based on the structural connectivity network that are related to functional connectivity. These measures are commonly based on either the shortest path routing model or the diffusion model, which carry distinct assumptions about how information is transferred through the network. Unlike shortest path routing, which assumes the most efficient path is always known, the diffusion model makes no such assumption, and lets information diffuse in parallel based on the number of connections to other regions. Past research has also developed hybrid measures that use concepts from both models, which have better predicted functional connectivity from structural connectivity than the shortest path length alone. We examined the extent to which each of these models can account for the structure-function relationship of interest using graph theory measures that are exclusively based on each model. This analysis was performed on multiple parcellations of the Human Connectome Project using multiple approaches, which all converged on the same finding. We found that the diffusion model accounts for much more variance in functional connectivity than the shortest path routing model, suggesting that the diffusion model is better suited to describing the structure-function relationship in the human brain at the macroscale.

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.

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 Structural Connectivity Predicts Brain Functional Complexity: Diffusion Tensor Imaging Derived Centrality Accounts for Variance in Fractal Properties of Functional Magnetic Resonance Imaging Signal.

The complexity of brain activity has recently been investigated using the Hurst exponent (H), which describes the extent to which functional magnetic resonance imaging (fMRI) blood oxygen-level dependent (BOLD) activity is simple vs. complex. For example, research has demonstrated that fMRI activity is more complex before than after consumption of alcohol and during task than resting state. The measurement of H in fMRI is a novel method that requires the investigation of additional factors contributing to complexity. Graph theory metrics of centrality can assess how centrally important to the brain network each region is, based on diffusion tensor imaging (DTI) counts of probabilistic white matter (WM) tracts. DTI derived centrality was hypothesized to account for the complexity of functional activity, based on the supposition that more sources of information to integrate should result in more complex activity. FMRI BOLD complexity as measured by H was associated with five brain region centrality measures: degree, eigenvector, PageRank, current flow betweenness, and current flow closeness centrality. Multiple regression analyses demonstrated that eigenvector centrality was the most robust predictor of complexity, whereby greater centrality was associated with increased complexity (lower H). Regions known to be highly connected, including the thalamus and hippocampus, notably were among the highest in centrality and complexity. This research has led to a greater understanding of how brain region characteristics such as DTI centrality relate to the novel Hurst exponent approach for assessing brain activity complexity, and implications for future research that employ these measures are discussed.

Atypical language localization in right temporal lobe epilepsy: An fMRI case report.

We report a 41- year-old, left-handed patient with drug-resistant right temporal lobe epilepsy (TLE). Presurgical fMRI was conducted to examine whether the patient had language functioning in the right hemisphere given that left-handedness is associated with a higher prevalence of right hemisphere dominance for language. The fMRI results revealed bilateral activation in Broca's and Wernicke's areas and activation of eloquent cortex near the region of planned resection in the right temporal lobe. Due to right temporal language-related activation, the patient underwent an awake right-sided temporal lobectomy with intraoperative language mapping. Intraoperative direct cortical stimulation (DCS) was conducted in the regions corresponding to the fMRI activation, and the patient showed language abnormalities, such as paraphasic errors, and speech arrest. The decision was made to abort the planned anterior temporal lobe procedure, and the patient instead underwent a selective amygdalohippocampectomy via the Sylvian fissure at a later date. Post-operatively the patient was seizure-free with no neurological deficits. Taken together, the results support previous findings of right hemisphere language activation in left-handed individuals, and should be considered in cases in which presurgical localization is conducted for left-hand dominant patients undergoing neurosurgical procedures.

FMRI of shared-stream priming of lexical identification by object semantics along the ventral visual processing stream.

Distributed sub-systems of the brain's semantic network have been shown to process semantics associated with visual features of objects (e.g., shape, colour) in the ventral visual processing stream, whereas semantics associated with actions are processed in the dorsal stream. Orthographic lexical processing has also been shown to occur in the ventral stream. Past research from our lab (Neudorf et al., 2019) has demonstrated a temporal (i.e., reaction time) priming advantage for object primes over action primes in the lexical decision task consistent with ventral shared-stream processing of visual feature object semantics and orthographic lexical identification, whereby object primes produced larger priming effects than action primes. The current experiment explored this paradigm using functional magnetic resonance imaging (fMRI) and identified the potential loci of shared-stream processing to regions in the ventral stream just anterior to colour sensitive visual area V4 cortex in the left fusiform gyrus and anterior to lexical and shape sensitive regions in the left fusiform gyrus, as well as in cerebellar lobule VI. Action priming showed more activation than object priming in dorsal stream motion related regions of the right parietal occipital junction, right superior occipital gyrus, and bilateral visual area V3. The fMRI activation observed in this experiment supports the theory that spatially shared-stream activation occurs in the ventral stream during object (but not action) priming of lexical identification, which is consistent with our earlier behavioural research showing that these processes are also temporally shared.

How words and space collide: Lexical and sublexical reading are reliant on separable reflexive and voluntary attention regions in hybrid tasks.

Reading ability requires the coordination of many cognitive processes to be effective, including spatial attention. Recent functional magnetic resonance imaging (fMRI) evidence from Ekstrand et al. (2019) suggests that lexical reading is more associated with reflexive attentional orienting regions, whereas sublexical reading is more associated with voluntary attentional orienting regions. The current research sought to further examine the neuroanatomical relationship between reading and attention using a novel experimental design in fMRI. Participants performed four hybrid attentional orienting and reading-aloud tasks, where a reflexive or voluntary spatial cue preceded a lexical or sublexical target. Results indicated that lexical reading resulted in greater activation in the right temporoparietal junction, a reflexive orienting region. Sublexical reading resulted in greater activation in the left inferior frontal gyrus, left fusiform and inferior temporal gyrus, and right superior parietal lobule and intraparietal sulcus (voluntary orienting regions). Further, we found an interaction between reading and attention in the middle occipital gyrus. This study provides the most direct evidence to date that lexical and sublexical reading recruit differential attentional orienting regions during single-word reading in skilled readers. Implications for models of reading and attention, as well as for strategic remediation of their dysfunction, are discussed.