Iowa Brain-Behavior Modeling Toolkit: An Open-Source MATLAB Tool for Inferential and Predictive Modeling of Imaging-Behavior and Lesion-Deficit Relationships.

The traditional analytical framework taken by neuroimaging studies in general, and lesion-behavior studies in particular, has been inferential in nature and has focused on identifying and interpreting statistically significant effects within the sample under study. While this framework is well-suited for hypothesis testing approaches, achieving the modern goal of precision medicine requires a different framework that is predictive in nature and that focuses on maximizing the predictive power of models and evaluating their ability to generalize beyond the data that were used to train them. However, few tools exist to support the development and evaluation of predictive models in the context of neuroimaging or lesion-behavior research, creating an obstacle to the widespread adoption of predictive modeling approaches in the field. Further, existing tools for lesion-behavior analysis are often unable to accommodate categorical outcome variables and often impose restrictions on the predictor data. Researchers therefore often must use different software packages and analytical approaches depending on whether they are addressing a classification vs. regression problem and on whether their predictor data correspond to binary lesion images, continuous lesion-network images, connectivity matrices, or other data modalities. To address these limitations, we have developed a MATLAB software toolkit that supports both inferential and predictive modeling frameworks, accommodates both classification and regression problems, and does not impose restrictions on the modality of the predictor data. The toolkit features both a graphical user interface and scripting interface, includes implementations of multiple mass-univariate, multivariate, and machine learning models, features built-in and customizable routines for hyper-parameter optimization, cross-validation, model stacking, and significance testing, and automatically generates text-based descriptions of key methodological details and modeling results to improve reproducibility and minimize errors in the reporting of methods and results. Here, we provide an overview and discussion of the toolkit's features and demonstrate its functionality by applying it to the question of how expressive and receptive language impairments relate to lesion location, structural disconnection, and functional network disruption in a large sample of patients with left hemispheric brain lesions. We find that impairments in expressive vs. receptive language are most strongly associated with left lateral prefrontal and left posterior temporal/parietal damage, respectively. We also find that impairments in expressive vs. receptive language are associated with partially overlapping patterns of fronto-temporal structural disconnection, and that the associated functional networks are also similar. Importantly, we find that lesion location and lesion-derived network measures are highly predictive of both types of impairment, with predictions from models trained on these measures explaining ~30-40% of the variance on average when applied to data from patients not used to train the models. We have made the toolkit publicly available, and we have included a comprehensive set of tutorial notebooks to support new users in applying the toolkit in their studies.

Generative whole-brain dynamics models from healthy subjects predict functional alterations in stroke at the level of individual patients.

Computational whole-brain models describe the resting activity of each brain region based on a local model, inter-regional functional interactions, and a structural connectome that specifies the strength of inter-regional connections. Strokes damage the healthy structural connectome that forms the backbone of these models and produce large alterations in inter-regional functional interactions. These interactions are typically measured by correlating the time series of the activity between two brain regions in a process, called resting functional connectivity. We show that adding information about the structural disconnections produced by a patient's lesion to a whole-brain model previously trained on structural and functional data from a large cohort of healthy subjects enables the prediction of the resting functional connectivity of the patient and fits the model directly to the patient's data (Pearson correlation = 0.37; mean square error = 0.005). Furthermore, the model dynamics reproduce functional connectivity-based measures that are typically abnormal in stroke patients and measures that specifically isolate these abnormalities. Therefore, although whole-brain models typically involve a large number of free parameters, the results show that, even after fixing those parameters, the model reproduces results from a population very different than that on which the model was trained. In addition to validating the model, these results show that the model mechanistically captures the relationships between the anatomical structure and the functional activity of the human brain.

Inferring the dynamical effects of stroke lesions through whole-brain modeling.

Understanding the effect of focal lesions (stroke) on brain structure-function traditionally relies on behavioral analyses and correlation with neuroimaging data. Here we use structural disconnection maps from individual lesions to derive a causal mechanistic generative whole-brain model able to explain both functional connectivity alterations and behavioral deficits induced by stroke. As compared to other models that use only the local lesion information, the similarity to the empirical fMRI connectivity increases when the widespread structural disconnection information is considered. The presented model classifies behavioral impairment severity with higher accuracy than other types of information (e.g.: functional connectivity). We assessed topological measures that characterize the functional effects of damage. With the obtained results, we were able to understand how network dynamics change emerge, in a nontrivial way, after a stroke injury of the underlying complex brain system. This type of modeling, including structural disconnection information, helps to deepen our understanding of the underlying mechanisms of stroke lesions.

Subcortical-cortical dynamical states of the human brain and their breakdown in stroke.

The mechanisms controlling dynamical patterns in spontaneous brain activity are poorly understood. Here, we provide evidence that cortical dynamics in the ultra-slow frequency range (<0.01-0.1 Hz) requires intact cortical-subcortical communication. Using functional magnetic resonance imaging (fMRI) at rest, we identify Dynamic Functional States (DFSs), transient but recurrent clusters of cortical and subcortical regions synchronizing at ultra-slow frequencies. We observe that shifts in cortical clusters are temporally coincident with shifts in subcortical clusters, with cortical regions flexibly synchronizing with either limbic regions (hippocampus/amygdala), or subcortical nuclei (thalamus/basal ganglia). Focal lesions induced by stroke, especially those damaging white matter connections between basal ganglia/thalamus and cortex, provoke anomalies in the fraction times, dwell times, and transitions between DFSs, causing a bias toward abnormal network integration. Dynamical anomalies observed 2 weeks after stroke recover in time and contribute to explaining neurological impairment and long-term outcome.

Post-stroke reorganization of transient brain activity characterizes deficits and recovery of cognitive functions.

Functional magnetic resonance imaging (fMRI) has been widely employed to study stroke pathophysiology. In particular, analyses of fMRI signals at rest were directed at quantifying the impact of stroke on spatial features of brain networks. However, brain networks have intrinsic time features that were, so far, disregarded in these analyses. In consequence, standard fMRI analysis failed to capture temporal imbalance resulting from stroke lesions, hence restricting their ability to reveal the interdependent pathological changes in structural and temporal network features following stroke. Here, we longitudinally analyzed hemodynamic-informed transient activity in a large cohort of stroke patients (n = 103) to assess spatial and temporal changes of brain networks after stroke. Metrics extracted from the hemodynamic-informed transient activity were replicable within- and between-individuals in healthy participants, hence supporting their robustness and their clinical applicability. While large-scale spatial patterns of brain networks were preserved after stroke, their durations were altered, with stroke subjects exhibiting a varied pattern of longer and shorter network activations compared to healthy individuals. Specifically, patients showed a longer duration in the lateral precentral gyrus and anterior cingulum, and a shorter duration in the occipital lobe and in the cerebellum. These temporal alterations were associated with white matter damage in projection and association pathways. Furthermore, they were tied to deficits in specific behavioral domains as restoration of healthy brain dynamics paralleled recovery of cognitive functions (attention, language and spatial memory), but was not significantly correlated to motor recovery. These findings underscore the critical importance of network temporal properties in dissecting the pathophysiology of brain changes after stroke, thus shedding new light on the clinical potential of time-resolved methods for fMRI analysis.

Mental health in the UK Biobank: A roadmap to self-report measures and neuroimaging correlates.

The UK Biobank (UKB) is a highly promising dataset for brain biomarker research into population mental health due to its unprecedented sample size and extensive phenotypic, imaging, and biological measurements. In this study, we aimed to provide a shared foundation for UKB neuroimaging research into mental health with a focus on anxiety and depression. We compared UKB self-report measures and revealed important timing effects between scan acquisition and separate online acquisition of some mental health measures. To overcome these timing effects, we introduced and validated the Recent Depressive Symptoms (RDS-4) score which we recommend for state-dependent and longitudinal research in the UKB. We furthermore tested univariate and multivariate associations between brain imaging-derived phenotypes (IDPs) and mental health. Our results showed a significant multivariate relationship between IDPs and mental health, which was replicable. Conversely, effect sizes for individual IDPs were small. Test-retest reliability of IDPs was stronger for measures of brain structure than for measures of brain function. Taken together, these results provide benchmarks and guidelines for future UKB research into brain biomarkers of mental health.

Intermittent Theta Burst Stimulation (iTBS) for Treatment of Chronic Post-Stroke Aphasia: Results of a Pilot Randomized, Double-Blind, Sham-Controlled Trial.

BACKGROUND Research indicates intermittent theta burst stimulation (iTBS) is a potential treatment of post-stroke aphasia. MATERIAL AND METHODS In this double-blind, sham-controlled trial (NCT01512264) participants were randomized to receive 3 weeks of sham (G0), 1 week of iTBS/2 weeks of sham (G1), 2 weeks of iTBS/1 week of sham (G2), or 3 weeks of iTBS (G3). FMRI localized residual language function in the left hemisphere; iTBS was applied to the maximum fMRI activation in the residual language cortex in the left frontal lobe. FMRI and aphasia testing were conducted pre-treatment, at ≤1 week after completing treatment, and at 3 months follow-up. RESULTS 27/36 participants completed the trial. We compared G0 to each of the individual treatment group and to all iTBS treatment groups combined (G1₋3). In individual groups, participants gained (of moderate or large effect sizes; some significant at P<0.05) on the Boston Naming Test (BNT), the Semantic Fluency Test (SFT), and the Aphasia Quotient of the Western Aphasia Battery-Revised (WAB-R AQ). In G1₋3, BNT, and SFT improved immediately after treatment, while the WAB-R AQ improved at 3 months. Compared to G0, the other groups showed greater fMRI activation in both hemispheres and non-significant increases in language lateralization to the left hemisphere. Changes in IFG connectivity were noted with iTBS, showing differences between time-points, with some of them correlating with the behavioral measures. CONCLUSIONS The results of this pilot trial support the hypothesis that iTBS applied to the ipsilesional hemisphere can improve aphasia and result in cortical plasticity.

Lesion Quantification Toolkit: A MATLAB software tool for estimating grey matter damage and white matter disconnections in patients with focal brain lesions.

Lesion studies are an important tool for cognitive neuroscientists and neurologists. However, while brain lesion studies have traditionally aimed to localize neurological symptoms to specific anatomical loci, a growing body of evidence indicates that neurological diseases such as stroke are best conceptualized as brain network disorders. While researchers in the fields of neuroscience and neurology are therefore increasingly interested in quantifying the effects of focal brain lesions on the white matter connections that form the brain's structural connectome, few dedicated tools exist to facilitate this endeavor. Here, we present the Lesion Quantification Toolkit, a publicly available MATLAB software package for quantifying the structural impacts of focal brain lesions. The Lesion Quantification Toolkit uses atlas-based approaches to estimate parcel-level grey matter lesion loads and multiple measures of white matter disconnection severity that include tract-level disconnection measures, voxel-wise disconnection maps, and parcel-wise disconnection matrices. The toolkit also estimates lesion-induced increases in the lengths of the shortest structural paths between parcel pairs, which provide information about changes in higher-order structural network topology. We describe in detail each of the different measures produced by the toolkit, discuss their applications and considerations relevant to their use, and perform example analyses using real behavioral data collected from sub-acute stroke patients. We show that analyses performed using the different measures produced by the toolkit produce results that are highly consistent with results that have been reported in the prior literature, and we demonstrate the consistency of results obtained from analyses conducted using the different disconnection measures produced by the toolkit. We anticipate that the Lesion Quantification Toolkit will empower researchers to address research questions that would be difficult or impossible to address using traditional lesion analyses alone, and ultimately, lead to advances in our understanding of how white matter disconnections contribute to the cognitive, behavioral, and physiological consequences of focal brain lesions.

Damage to the shortest structural paths between brain regions is associated with disruptions of resting-state functional connectivity after stroke.

Focal brain lesions disrupt resting-state functional connectivity, but the underlying structural mechanisms are unclear. Here, we examined the direct and indirect effects of structural disconnections on resting-state functional connectivity in a large sample of sub-acute stroke patients with heterogeneous brain lesions. We estimated the impact of each patient's lesion on the structural connectome by embedding the lesion in a diffusion MRI streamline tractography atlas constructed using data from healthy individuals. We defined direct disconnections as the loss of direct structural connections between two regions, and indirect disconnections as increases in the shortest structural path length between two regions that lack direct structural connections. We then tested the hypothesis that functional connectivity disruptions would be more severe for disconnected regions than for regions with spared connections. On average, nearly 20% of all region pairs were estimated to be either directly or indirectly disconnected by the lesions in our sample, and extensive disconnections were associated primarily with damage to deep white matter locations. Importantly, both directly and indirectly disconnected region pairs showed more severe functional connectivity disruptions than region pairs with spared direct and indirect connections, respectively, although functional connectivity disruptions tended to be most severe between region pairs that sustained direct structural disconnections. Together, these results emphasize the widespread impacts of focal brain lesions on the structural connectome and show that these impacts are reflected by disruptions of the functional connectome. Further, they indicate that in addition to direct structural disconnections, lesion-induced increases in the structural shortest path lengths between indirectly structurally connected region pairs provide information about the remote functional disruptions caused by focal brain lesions.

Structural Disconnections Explain Brain Network Dysfunction after Stroke.

Stroke causes focal brain lesions that disrupt functional connectivity (FC), a measure of activity synchronization, throughout distributed brain networks. It is often assumed that FC disruptions reflect damage to specific cortical regions. However, an alternative explanation is that they reflect the structural disconnection (SDC) of white matter pathways. Here, we compare these explanations using data from 114 stroke patients. Across multiple analyses, we find that SDC measures outperform focal damage measures, including damage to putative critical cortical regions, for explaining FC disruptions associated with stroke. We also identify a core mode of structure-function covariation that links the severity of interhemispheric SDCs to widespread FC disruptions across patients and that correlates with deficits in multiple behavioral domains. We conclude that a lesion's impact on the structural connectome is what determines its impact on FC and that interhemispheric SDCs may play a particularly important role in mediating FC disruptions after stroke.

Cortical excitability affects mood state in patients with idiopathic generalized epilepsies (IGEs).

Previously, we demonstrated an association between cortical hyperexcitability and mood disturbance in healthy adults. Studies have documented hyperexcitability in patients with idiopathic generalized epilepsies (IGEs; long-interval intracortical inhibition [LICI]) and high prevalence of mood comorbidities. This study aimed to investigate the influences of cortical excitability and seizure control on mood state in patients with IGEs. Single and paired-pulse transcranial magnetic stimulation (TMS) was applied to 30 patients with IGEs (16 controlled IGEs [cIGEs], 14 with treatment-resistant IGEs [trIGEs]), and 22 healthy controls (HCs) to assess cortical excitability with LICI. The Profile of Mood Sates (POMS) questionnaire was used to assess total mood disturbance (TMD), as well as, six mood domains: Depression, Confusion, Anger, Anxiety, Fatigue, and Vigor. To assess the effects of seizure control (HC vs. cIGEs vs. trIGEs) and LICI response (inhibitory vs. excitatory) on TMD, a two-way multivariate analysis of variance (MANOVA) was performed. Analyses revealed a significant main effect of long-interval intracortical inhibition (LICI) response on TMD (F(1, 46) = 4.69, p = 0.04), but not seizure control (F(2, 46) = 0.288, p = 0.75). Excitatory responders endorsed significantly higher TMD scores, indicating greater mood disturbance, than inhibitory responders (MD = -2.12; T (50) = -2.47, p = 0.04). Also, excitatory responders endorsed more items than inhibitory responders on the Depression (MD = -2.12; T (50) = -2.47, p = 0.04) and Fatigue (MD = -3.42; T (50) = -2.96, p = 0.03) subscales of the POMS. These findings provide further evidence of a relationship between hyperexcitability and mood disturbance, and indicate that cortical excitability may have greater influence on mood state than seizure control in patients with IGEs. Results also support theories for the underlying role of gamma-aminobutyric acid (GABA) network dysfunction in the etiology of depression. To better understand the clinical relevance and causal nature of these relationships, further investigation is warranted.

Cortical excitability and seizure control influence attention performance in patients with idiopathic generalized epilepsies (IGEs).

We recently found that higher cortical excitability is associated with poorer attention performance in healthy adults. While patients with idiopathic generalized epilepsies (IGEs), previously termed genetic generalized epilepsies, are known to demonstrate increased cortical excitability and cognitive deficits, a relationship between these variables in IGEs has not been investigated. Therefore, we aimed to characterize the effects of cortical excitability and seizure control on cognitive performance in IGEs. We studied 30 patients with IGEs (16 patients with controlled IGEs (cIGEs) and 14 patients with treatment-resistant IGEs (trIGEs)) and 24 healthy controls (HCs). Transcranial magnetic stimulation (TMS) was used to measure cortical excitability, including long-interval intracortical inhibition (LICI). Attention was assessed with the Digit Span Forwards, Digit Span Backwards, Trails A, and Flanker tasks. Executive functioning was assessed using Trails B, Stroop Color and Word, and the Wisconsin Card Sorting Task. Two-way multivariate analyses of variance (MANOVAs) were conducted to assess the influences of seizure control (HCs vs. cIGEs vs. trIGEs) and cortical excitability (inhibitory vs. excitatory) on composite measures of attention and executive functions. Attention performance was significantly affected by cortical excitability and seizure control. Participants with primarily excitatory LICI responses, indicating higher cortical excitability, performed worse than inhibitory responders on composite attention (Wilks' lambda = 0.748, F(4, 44) = 3.72, p = 0.011). While participants with cIGEs and trIGEs did not significantly differ in attention performance, participants with trIGEs performed worse on the Digit Forwards (False Discovery Rate (FDR)p < 0.001), Digit Backwards (FDRp = 0.015), and Flanker (FDRp = 0.0075) tasks compared with HCs. These results provide support for the relationship between cortical excitability and attention dysfunction in IGEs. Further investigation is needed to determine whether there is a causal relationship between these variables and whether intracortical gamma-aminobutyric acid (GABA)B networks may be targeted to improve attention deficits in clinical populations with decreased LICI. Findings also suggest that additional research directly comparing cognition in patients with cIGEs and trIGEs is warranted.

Relationship Between Alpha Rhythm and the Default Mode Network: An EEG-fMRI Study.

PURPOSE: Reports of the relationship between the default mode network (DMN) and alpha power are conflicting. Our goal was to assess this relationship by analyzing concurrently obtained EEG/functional MRI data using hypothesis-independent methods. METHODS: We collected functional MRI and EEG data during eyes-closed rest in 20 participants aged 19 to 37 (10 females) and performed independent component analysis on the functional MRI data and a Hamming-windowed fast Fourier transform on the EEG data. We correlated functional MRI fluctuations in the DMN with alpha power. RESULTS: Of the six independent components found to have significant relationships with alpha, four contained DMN-associated regions: One independent component was positively correlated with alpha power, whereas all others were negatively correlated. Furthermore, two independent components with opposite relationships with alpha had overlapping voxels in the medial prefrontal cortex and posterior cingulate cortex, suggesting that subpopulations of neurons within these classic nodes within the DMN may have different relationships to alpha power. CONCLUSIONS: Different parts of the DMN exhibit divergent relationships to alpha power. Our results highlight the relationship between DMN activity and alpha power, indicating that networks, such as the DMN, may have subcomponents that exhibit different behaviors.

Linking left hemispheric tissue preservation to fMRI language task activation in chronic stroke patients.

The preservation of near-typical function in distributed brain networks is associated with less severe deficits in chronic stroke patients. However, it remains unclear how task-evoked responses in networks that support complex cognitive functions such as semantic processing relate to the post-stroke brain anatomy. Here, we used recently developed methods for the analysis of multimodal MRI data to investigate the relationship between regional tissue concentration and functional MRI activation evoked during auditory semantic decisions in a sample of 43 chronic left hemispheric stroke patients and 43 age, handedness, and sex-matched controls. Our analyses revealed that closer-to-normal levels of tissue concentration in left temporo-parietal cortex and the underlying white matter correlated with the level of task-evoked activation in distributed regions associated with the semantic network. This association was not attributable to the effects of left hemispheric lesion or brain volumes, and similar results were obtained when using explicit lesion data. Left temporo-parietal tissue concentration and the associated task-evoked activations predicted patient performance on the in-scanner task, and also predicted patient performance on out-of-scanner naming and verbal fluency tasks. Exploratory analyses using the average HCP-842 tractography dataset revealed the presence of fronto-temporal, fronto-parietal, and temporo-parietal semantic network connections in the locations where tissue concentration was found to correlate with task-evoked activation in the semantic network. In summary, our results link the preservation of left posterior temporo-parietal structures with the preservation of task-evoked semantic network function in chronic left hemispheric stroke patients. Speculatively, this relationship may reflect the status of posterior temporo-parietal areas as cortical and white matter convergence zones that support coordinated processing in the distributed semantic network. Damage to these regions may contribute to atypical task-evoked responses during semantic processing in chronic stroke patients.

Cortical excitability and neuropsychological functioning in healthy adults.

Evidence from clinical populations, such as epilepsy and attention deficit/hyperactivity disorder, suggests a relationship between hyperexcitability and cognitive impairment, but this relationship has not been demonstrated in healthy individuals. Here, we investigate the relationship between cortical excitability and cognitive functioning in healthy adults. Single- and paired-pulse TMS was applied to 20 healthy adults to measure cortical excitability and long-interval intracortical inhibition (LICI). A neuropsychological battery was administered to assess aspects of attention, executive function, and mood. Participants with primarily excitatory responses to the LICI paradigm performed worse on a composite measure of attention and reported more negative mood states than participants with primarily inhibitory responses. Thus, differences in attention and mood among healthy adults are related to differences in cortical excitability as measured by LICI. This is consistent with a role for GABAB inhibitory circuits in regulating attention and mood, and suggests that individual variability in these domains may reflect variability in cortical excitability. This study demonstrates preliminary evidence that increased cortical excitability is associated with poorer cognition and mood in healthy adults. These findings provide new insight into the presence of cognitive dysfunction in several patient populations with hyperexcitability and support the development of neurostimulation interventions for clinical use.

Damage to white matter bottlenecks contributes to language impairments after left hemispheric stroke.

Damage to the white matter underlying the left posterior temporal lobe leads to deficits in multiple language functions. The posterior temporal white matter may correspond to a bottleneck where both dorsal and ventral language pathways are vulnerable to simultaneous damage. Damage to a second putative white matter bottleneck in the left deep prefrontal white matter involving projections associated with ventral language pathways and thalamo-cortical projections has recently been proposed as a source of semantic deficits after stroke. Here, we first used white matter atlases to identify the previously described white matter bottlenecks in the posterior temporal and deep prefrontal white matter. We then assessed the effects of damage to each region on measures of verbal fluency, picture naming, and auditory semantic decision-making in 43 chronic left hemispheric stroke patients. Damage to the posterior temporal bottleneck predicted deficits on all tasks, while damage to the anterior bottleneck only significantly predicted deficits in verbal fluency. Importantly, the effects of damage to the bottleneck regions were not attributable to lesion volume, lesion loads on the tracts traversing the bottlenecks, or damage to nearby cortical language areas. Multivariate lesion-symptom mapping revealed additional lesion predictors of deficits. Post-hoc fiber tracking of the peak white matter lesion predictors using a publicly available tractography atlas revealed evidence consistent with the results of the bottleneck analyses. Together, our results provide support for the proposal that spatially specific white matter damage affecting bottleneck regions, particularly in the posterior temporal lobe, contributes to chronic language deficits after left hemispheric stroke. This may reflect the simultaneous disruption of signaling in dorsal and ventral language processing streams.

Retinotopic patterns of functional connectivity between V1 and large-scale brain networks during resting fixation.

Psychophysical and neurobiological evidence suggests that central and peripheral vision are specialized for different functions. This specialization of function might be expected to lead to differences in the large-scale functional interactions of early cortical areas that represent central and peripheral visual space. Here, we characterize differences in whole-brain functional connectivity among sectors in primary visual cortex (V1) corresponding to central, near-peripheral, and far-peripheral vision during resting fixation. Importantly, our analyses reveal that eccentricity sectors in V1 have different functional connectivity with non-visual areas associated with large-scale brain networks. Regions associated with the fronto-parietal control network are most strongly connected with central sectors of V1, regions associated with the cingulo-opercular control network are most strongly connected with near-peripheral sectors of V1, and regions associated with the default mode and auditory networks are most strongly connected with far-peripheral sectors of V1. Additional analyses suggest that similar patterns are present during eyes-closed rest. These results suggest that different types of visual information may be prioritized by large-scale brain networks with distinct functional profiles, and provide insights into how the small-scale functional specialization within early visual regions such as V1 relates to the large-scale organization of functionally distinct whole-brain networks.

The canonical semantic network supports residual language function in chronic post-stroke aphasia.

Current theories of language recovery after stroke are limited by a reliance on small studies. Here, we aimed to test predictions of current theory and resolve inconsistencies regarding right hemispheric contributions to long-term recovery. We first defined the canonical semantic network in 43 healthy controls. Then, in a group of 43 patients with chronic post-stroke aphasia, we tested whether activity in this network predicted performance on measures of semantic comprehension, naming, and fluency while controlling for lesion volume effects. Canonical network activation accounted for 22%-33% of the variance in language test scores. Whole-brain analyses corroborated these findings, and revealed a core set of regions showing positive relationships to all language measures. We next evaluated the relationship between activation magnitudes in left and right hemispheric portions of the network, and characterized how right hemispheric activation related to the extent of left hemispheric damage. Activation magnitudes in each hemispheric network were strongly correlated, but four right frontal regions showed heightened activity in patients with large lesions. Activity in two of these regions (inferior frontal gyrus pars opercularis and supplementary motor area) was associated with better language abilities in patients with larger lesions, but poorer language abilities in patients with smaller lesions. Our results indicate that bilateral language networks support language processing after stroke, and that right hemispheric activations related to extensive left hemispheric damage occur outside of the canonical semantic network and differentially relate to behavior depending on the extent of left hemispheric damage. Hum Brain Mapp 38:1636-1658, 2017. © 2016 Wiley Periodicals, Inc.

Age-Dependent Cortical Thinning of Peripheral Visual Field Representations in Primary Visual Cortex.

The cerebral cortex changes throughout the lifespan, and the cortical gray matter in many brain regions becomes thinner with advancing age. Effects of aging on cortical thickness (CT) have been observed in many brain regions, including areas involved in basic perceptual functions such as processing visual inputs. An important property of early visual cortices is their topographic organization-the cortical structure of early visual areas forms a topographic map of retinal inputs. Primary visual cortex (V1) is considered to be the most basic cortical area in the visual processing hierarchy, and is topographically organized from posterior (central visual representation) to anterior (peripheral visual representation) along the calcarine sulcus. Some studies have reported strong age-dependent cortical thinning in portions of V1 that likely correspond to peripheral visual representations, while there is less evidence of substantial cortical thinning in central V1. However, the effect of aging on CT in V1 as a function of its topography has not been directly investigated. To address this gap in the literature, we estimated the CT of different eccentricity sectors in V1 using T1-weighted MRI scans acquired from groups of healthy younger and older adults, and then assessed whether between-group differences in V1 CT depended on cortical eccentricity. These analyses revealed age-dependent cortical thinning specific to peripheral visual field representations in anterior portions of V1, but did not provide evidence for age-dependent cortical thinning in other portions of V1. Additional analyses found similar effects when analyses were restricted to the gyral crown, sulcul depth and sulcul wall, indicating that these effects are not likely due to differences in gyral/sulcul contributions to our regions of interest (ROI). Importantly, this finding indicates that age-dependent changes in cortical structure may differ among functionally distinct zones within larger canonical cortical areas. Likely relationships to known age-related declines in visual performance are discussed to provide direction for future research in this area.

Cortical thickness in human V1 associated with central vision loss.

Better understanding of the extent and scope of visual cortex plasticity following central vision loss is essential both for clarifying the mechanisms of brain plasticity and for future development of interventions to retain or restore visual function. This study investigated structural differences in primary visual cortex between normally-sighted controls and participants with central vision loss due to macular degeneration (MD). Ten participants with MD and ten age-, gender-, and education-matched controls with normal vision were included. The thickness of primary visual cortex was assessed using T1-weighted anatomical scans, and central and peripheral cortical regions were carefully compared between well-characterized participants with MD and controls. Results suggest that, compared to controls, participants with MD had significantly thinner cortex in typically centrally-responsive primary visual cortex - the region of cortex that normally receives visual input from the damaged area of the retina. Conversely, peripherally-responsive primary visual cortex demonstrated significantly increased cortical thickness relative to controls. These results suggest that central vision loss may give rise to cortical thinning, while in the same group of people, compensatory recruitment of spared peripheral vision may give rise to cortical thickening. This work furthers our understanding of neural plasticity in the context of adult vision loss.