Cerebellar Atrophy and Language Processing in Chronic Left-Hemisphere Stroke.

Chronic stroke results in significant downstream changes at connected cortical sites. However, less is known about the impact of cortical stroke on cerebellar structure. Here, we examined the relationship between chronic stroke, cerebellar volume, cerebellar symmetry, language impairment, and treatment trajectories in a large cohort (N = 249) of chronic left hemisphere (LH) stroke patients with aphasia, using a healthy aging cohort (N = 244) as control data. Cerebellar gray matter volume was significantly reduced in chronic LH stroke relative to healthy control brains. Within the chronic LH stroke group, we observed a robust relationship between cerebellar volume, lesion size, and days post-stroke. Notably, the extent of cerebellar atrophy in chronic LH patients, particularly in the contralesional (right) cerebellar gray matter, explained significant variability in post-stroke aphasia severity, as measured by the Western Aphasia Battery-Revised, above and beyond traditional considerations such as cortical lesion size, days post-stroke, and demographic measures (age, race, sex). In a subset of participants that took part in language treatment studies, greater cerebellar gray matter volume was associated with greater treatment gains. These data support the importance of considering both cerebellar volume and symmetry in models of post-stroke aphasia severity and recovery.

Long-range white matter fibres and post-stroke verbal and non-verbal cognition.

Among stroke survivors, linguistic and non-linguistic impairments exhibit substantial inter-individual variability. Stroke lesion volume and location do not sufficiently explain outcomes, and the neural mechanisms underlying the severity of aphasia or non-verbal cognitive deficits remain inadequately understood. Converging evidence supports the idea that white matter is particularly susceptible to ischaemic injury, and long-range fibres are commonly associated with verbal and non-verbal function. Here, we investigated the relationship among post-stroke aphasia severity, cognition, and white matter integrity. Eighty-seven individuals in the chronic stage of stroke underwent diffusion MRI and behavioural testing, including language and cognitive measures. We used whole-brain structural connectomes from each participant to calculate the ratio of long-range fibres to short-range fibres. We found that a higher proportion of long-range fibres was associated with lower aphasia severity, more accurate picture naming, and increased performance on non-verbal semantic memory/processing and non-verbal reasoning while controlling for lesion volume, key damage areas, age, and years post stroke. Our findings corroborate the hypothesis that, after accounting for age and lesion anatomy, inter-individual differences in post-stroke aphasia severity, verbal, and non-verbal cognitive outcomes are related to the preservation of long-range white matter fibres beyond the lesion.

A Rose by Any Other Name: Mapping Taxonomic and Thematic Naming Errors Poststroke.

Understanding the neurobiology of semantic knowledge is a major goal of cognitive neuroscience. Taxonomic and thematic semantic knowledge are represented differently within the brain's conceptual networks, but the specific neural mechanisms remain unclear. Some neurobiological models propose that the anterior temporal lobe is an important hub for taxonomic knowledge, whereas the TPJ is especially involved in the representation of thematic knowledge. However, recent studies have provided divergent evidence. In this context, we investigated the neural correlates of taxonomic and thematic confrontation naming errors in 79 people with aphasia. We used three complementary lesion-symptom mapping (LSM) methods to investigate how structure and function in both spared and impaired brain regions relate to taxonomic and thematic naming errors. Voxel-based LSM mapped brain damage, activation-based LSM mapped BOLD signal in surviving tissue, and network-based LSM mapped white matter subnetwork integrity to error type. Voxel- and network-based lesion symptom mapping (V- and NLSM) provided converging evidence that damage/disruption of the left mid-to-anterior temporal lobe was associated with a greater proportion of thematic naming errors. activation-based lesion symptom mapping (ALSM) revealed that higher BOLD signal in the left anterior temporal lobe during an in-house naming task was associated with a greater proportion of taxonomic errors on the Philadelphia Naming Test administered outside of the scanner. A lower BOLD signal in the bilateral angular gyrus, precuneus, and right inferior frontal cortex was associated with a greater proportion of taxonomic errors. These findings provide novel evidence that damage to the anterior temporal lobe is especially related to thematic naming errors.

Connectivity of the Piriform Cortex and its Implications in Temporal Lobe Epilepsy.

Background: The piriform cortex has been implicated in the initiation, spread and termination of epileptic seizures. This understanding has extended to surgical management of epilepsy, where it has been shown that resection or ablation of the piriform cortex can result in better outcomes. How and why the piriform cortex may play such a crucial role in seizure networks is not well understood. To answer these questions, we investigated the functional and structural connectivity of the piriform cortex in both healthy controls and temporal lobe epilepsy (TLE) patients. Methods: We studied a retrospective cohort of 55 drug-resistant unilateral TLE patients and 26 healthy controls who received structural and functional neuroimaging. Using seed-to-voxel connectivity we compared the normative whole-brain connectivity of the piriform to that of the hippocampus, a region commonly involved in epilepsy, to understand the differential contribution of the piriform to the epileptogenic network. We subsequently measured the inter-piriform coupling (IPC) to quantify similarities in the inter-hemispheric cortical functional connectivity profile between the two piriform cortices. We related differences in IPC in TLE back to aberrations in normative piriform connectivity, whole brain functional properties, and structural connectivity. Results: We find that relative to the hippocampus, the piriform is functionally connected to the anterior insula and the rest of the salience ventral attention network (SAN). We also find that low IPC is a sensitive metric of poor surgical outcome (sensitivity: 85.71%, 95% CI: [19.12%, 99.64%]); and differences in IPC within TLE were related to disconnectivity and hyperconnectivity to the anterior insula and the SAN. More globally, we find that low IPC is associated with whole-brain functional and structural segregation, marked by decreased functional small-worldness and fractional anisotropy. Conclusions: Our study presents novel insights into the functional and structural neural network alterations associated with this structure, laying the foundation for future work to carefully consider its connectivity during the presurgical management of epilepsy.

Brain-Hand Function Relationships Based on Level of Grasp Function in Chronic Left-Hemisphere Stroke.

BACKGROUND AND OBJECTIVE: The biomarkers of hand function may differ based on level of motor impairment after stroke. The objective of this study was to determine the relationship between resting state functional connectivity (RsFC) and unimanual contralesional hand function after stroke and whether brain-behavior relationships differ based on level of grasp function. METHODS: Sixty-two individuals with chronic, left-hemisphere stroke were separated into three functional levels based on Box and Blocks Test performance with the contralesional hand: Low (moved 0 blocks), Moderate (moved >0% but <90% of blocks relative to the ipsilesional hand), and High (moved ≥90% of blocks relative to the ipsilesional hand). RESULTS: RsFC in the ipsilesional and interhemispheric motor networks was reduced in the Low group compared to the Moderate and High groups. While interhemispheric RsFC correlated with hand function (grip strength and Stroke Impact Scale Hand) across the sample, contralesional RsFC correlated with hand function in the Low group and no measures of connectivity correlated with hand function in the Moderate and High groups. Linear regression modeling found that contralesional RsFC significantly predicted hand function in the Low group, while no measure correlated with hand function in the High group. Corticospinal tract integrity was the only predictor of hand function for the Moderate group and in an analysis across the entire sample. CONCLUSIONS: Differences in brain-hand function relationships based on level of motor impairment may have implications for predictive models of treatment response and the development of intervention protocols aimed at improving hand function after stroke.

The stroke outcome optimization project: Acute ischemic strokes from a comprehensive stroke center.

Stroke is a leading cause of disability, and Magnetic Resonance Imaging (MRI) is routinely acquired for acute stroke management. Publicly sharing these datasets can aid in the development of machine learning algorithms, particularly for lesion identification, brain health quantification, and prognosis. These algorithms thrive on large amounts of information, but require diverse datasets to avoid overfitting to specific populations or acquisitions. While there are many large public MRI datasets, few of these include acute stroke. We describe clinical MRI using diffusion-weighted, fluid-attenuated and T1-weighted modalities for 1715 individuals admitted in the upstate of South Carolina, of whom 1461 have acute ischemic stroke. Demographic and impairment data are provided for 1106 of the stroke survivors from this cohort. Our validation demonstrates that machine learning can leverage the imaging data to predict stroke severity as measured by the NIH Stroke Scale/Score (NIHSS). We share not only the raw data, but also the scripts for replicating our findings. These tools can aid in education, and provide a benchmark for validating improved methods.

Concurrent tDCS-fMRI after stroke reveals link between attention network organization and motor improvement.

Restoring motor function after stroke necessitates involvement of numerous cognitive systems. However, the impact of damage to motor and cognitive network organization on recovery is not well understood. To discover correlates of successful recovery, we explored imaging characteristics in chronic stroke subjects by combining noninvasive brain stimulation and fMRI. Twenty stroke survivors (6 months or more after stroke) were randomly assigned to a single session of transcranial direct current stimulation (tDCS) or sham during image acquisition. Twenty healthy subjects were included as controls. tDCS was limited to 10 min at 2 mA to serve as a mode of network modulation rather than therapeutic delivery. Fugl-Meyer Assessments (FMA) revealed significant motor improvement in the chronic stroke group receiving active stimulation (p = 0.0005). Motor changes in this group were correlated in a data-driven fashion with imaging features, including functional connectivity (FC), surface-based morphometry, electric field modeling and network topology, focusing on relevant regions of interest. We observed stimulation-related changes in FC in supplementary motor (p = 0.0029), inferior frontal gyrus (p = 0.0058), and temporo-occipital (p = 0.0095) areas, though these were not directly related to motor improvement. The feature most strongly associated with FMA improvement in the chronic stroke cohort was graph topology of the dorsal attention network (DAN), one of the regions surveyed and one with direct connections to each of the areas with FC changes. Chronic stroke subjects with a greater degree of motor improvement had lower signal transmission cost through the DAN (p = 0.029). While the study was limited by a small stroke cohort with moderate severity and variable lesion location, these results nevertheless suggest a top-down role for higher order areas such as attention in helping to orchestrate the stroke recovery process.

The Relationship Between Poststroke Dysphagia and Poststroke Depression and Its Risk Factors.

PURPOSE: A diagnosis of dysphagia and/or depression after stroke can impact the physical, psychological, and social welfare of stroke survivors. Although poststroke depression (PSD) and poststroke dysphagia are known to occur concurrently, there is a paucity of research that has specifically investigated their association. Therefore, we aimed to study the relationship between PSD and poststroke dysphagia during acute inpatient hospitalization and within 90 days after discharge. Furthermore, we aimed to evaluate the odds and hazard of being diagnosed with depression after stroke and estimate the time to depression diagnosis from the initial stroke diagnosis in patients with and without a diagnosis of dysphagia. METHOD: Using the acute inpatient hospital data set from our previous work, we pulled additional postdischarge administrative claims data from the 2017 Medicare 5% Limited Data Set and conducted a retrospective, cross-sectional study of patients diagnosed with poststroke dysphagia and PSD. RESULTS: Patients diagnosed with poststroke dysphagia had 2.7 higher odds of being diagnosed with PSD and had an approximately 1.75-fold higher hazard for PSD diagnosis in the 90 days after discharge compared to patients not diagnosed with dysphagia. Risk factors for PSD included having dysphagia, being female, and having dual eligibility. CONCLUSIONS: Our results demonstrated a significant association between PSD and poststroke dysphagia. Additional research should further explore the impact of PSD on poststroke dysphagia.

Acute seizures after spontaneous intracerebral hemorrhage in young individuals: 11-year trends and association with mortality.

BACKGROUND: The rate of spontaneous Intracerebral Hemorrhage (sICH) is rising among young Americans. Trends in acute seizure (AS) incidence in this age group is largely unknown. Further, the association of AS with mortality has not been reported in this age group. The aim of this study is to determine trends in AS among young individuals with sICH. METHODS: The Merative MarketScan® Commercial Claims and Encounters database, for the years 2005 through 2015, served as the data source for this retrospective in-hospital population study. This period was chosen as spontaneous ICH incidence increased among young individuals between 2005 and 2015. Our study population included patients aged 18-64 years with ICH identified using the International Classification of Diseases, Ninth and Tenth Revision (ICD-9/10) codes 430, 431, 432.0, 432.1, 432.9, I61, I61.0, I61.1, I61.2, I61.3, I61.4, I61.5, I61.6, I61.8, and I61.9, excluding those with a prior diagnosis of seizures (ICD-9/10 codes 345.x,780.3x, G40, G41, and R56.8). We computed yearly AS incidence, mortality (in patients with and without seizures), and analyzed trends. We applied a logistic regression model to determine the independent association of AS with mortality accounting for demographic and clinical variables. RESULTS: AS incidence increased linearly between 2005 (incidence rate: 8.1 %) and 2015 (incidence rate: 11.0 %), which represents a 26 % relative increase (P for trends <0.0001). In-hospital mortality rate was 14.3 % among those who developed AS and 11.5 % among those who did not have AS. Overall, between 2005 and 2015, in-hospital mortality decreased from 13.0 % to 9.7 % among patients without AS but remained unchanged among those with AS. Patients who developed AS were 10 % more likely to die than those who did not (OR: 1.10, 95 % confidence interval: 1.02-1.18). CONCLUSIONS: Between 2005 and 2015, the incidence of AS increased by nearly 26 % among young Americans with sICH. In-patient mortality remained unchanged among those who developed seizures but declined among those who did not. The occurrence of AS was independently associated with a 10 % higher risk of in-hospital death.

Age-Related Hearing Loss, Cognitive Decline, and Social Interaction: Testing a Framework.

PURPOSE: Aging increases risk for hearing loss, cognitive decline, and social isolation; however, the nature of their interconnection remains unclear. This study examined the interplay between age-related hearing loss, cognitive decline, and social isolation in adults by testing the ability to understand speech in background noise, a challenge frequently reported by many older adults. METHOD: We analyzed data collected from 128 adults (20-79 years of age, Mage = 51 years) recruited as part of the Aging Brain Cohort at the University of South Carolina repository. The participants underwent testing for hearing, cognition, and social interaction, which included pure-tone audiometry, a words-in-noise (WIN) test, a hearing questionnaire (Speech, Spatial and Qualities of Hearing Scale [SSQ12]), a social questionnaire (Patient-Reported Outcomes Measurement Information System-57 Social), and the Montreal Cognitive Assessment. We used a single pure-tone average (PTA) threshold value and a single WIN threshold value for each participant because there were no differences on average between the left and right ears. RESULTS: Poorer hearing was significantly associated with cognitive decline, through both PTA and WIN thresholds, with a stronger association observed for WIN threshold. Adults with poorer hearing also exhibited greater social isolation, as evidenced by their WIN threshold and SSQ12 score, although not through PTA. This connection was more pronounced with the WIN threshold than with the SSQ12 score. Cognition was not related to social isolation, suggesting that social isolation is affected more by the ability to understand words in noise than by cognition in a nondemented population. CONCLUSIONS: Understanding speech in challenging auditory environments rather than mere threshold detection is strongly linked to social isolation and cognitive decline. Thus, inclusion of a word-recognition-in-noise test and a social isolation survey in clinical settings is warranted. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.26237060.

Microstructural properties in subacute aphasia: concurrent and prospective relationships underpinning recovery.

BACKGROUND: Few investigations examined the relationship between microstructural white matter integrity and subacute post-stroke linguistic performance or the relationship between microstructural integrity and the recovery of language function. We examined two key questions: (1) How does subacute language performance, measured in single words and discourse, relate to the microstructural integrity of key white matter regions of interest in the language network? and (2) Does the integrity of these regions before treatment predict the improvement or resolution of linguistic symptoms immediately and chronically following treatment? METHODS: 58 participants within the first three months of stroke were enrolled in a randomized, single-center, double-blind, sham-controlled, study of anodal transcranial direct current stimulation combined with a computer-delivered speech and language naming therapy for subacute aphasia and were asked to complete magnetic resonance imaging at enrollment. Microstructural integrity was evaluated using diffusion tensor imaging processed with atlas-based segmentation. Regression and correlation analyses were conducted. RESULTS: A subset of 22 participants received diffusion tensor imaging. Picture naming accuracy significantly correlated with lower mean diffusivity (higher microstructural integrity) in the left posterior inferior temporal gyrus. Recovery of naming performance was predicted by days since stroke and baseline microstructural integrity of the left posterior middle temporal gyrus, arcuate fasciculus, and superior longitudinal fasciculus. Recovery of discourse efficiency was significantly predicted by the same model. CONCLUSIONS: This study demonstrates an association between picture naming and discourse and microstructural integrity of the key regions in the language network for patients with subacute post-stroke aphasia. Baseline microstructural integrity significantly predicts language recovery.

Under pressure: the interplay of hypertension and white matter hyperintensities with cognition in chronic stroke aphasia.

While converging research suggests that increased white matter hyperintensity load is associated with poorer cognition, and the presence of hypertension is associated with increased white matter hyperintensity load, the relationship among hypertension, cognition and white matter hyperintensities is not well understood. We sought to determine the effect of white matter hyperintensity burden on the relationship between hypertension and cognition in individuals with post-stroke aphasia, with the hypothesis that white matter hyperintensity load moderates the relationship between history of hypertension and cognitive function. Health history, Fazekas scores for white matter hyperintensities and Wechsler Adult Intelligence Scale Matrix Reasoning subtest scores for 79 people with aphasia collected as part of the Predicting Outcomes of Language Rehabilitation study at the Center for the Study of Aphasia Recovery at the University of South Carolina and the Medical University of South Carolina were analysed retrospectively. We found that participants with a history of hypertension had increased deep white matter hyperintensity severity (P < 0.001), but not periventricular white matter hyperintensity severity (P = 0.116). Moderation analysis revealed that deep white matter hyperintensity load moderates the relationship between high blood pressure and Wechsler Adult Intelligence Scale scores when controlling for age, education, aphasia severity and lesion volume. The interaction is significant, showing that a history of high blood pressure and severe deep white matter hyperintensities together are associated with poorer Matrix Reasoning scores. The overall model explains 41.85% of the overall variation in Matrix Reasoning score in this group of participants. These findings underscore the importance of considering cardiovascular risk factors in aphasia treatment, specifically hypertension and its relationship to brain health in post-stroke cognitive function.

Regional brain aging: premature aging of the domain general system predicts aphasia severity.

Premature brain aging is associated with poorer cognitive reserve and lower resilience to injury. When there are focal brain lesions, brain regions may age at different rates within the same individual. Therefore, we hypothesize that reduced gray matter volume within specific brain systems commonly associated with language recovery may be important for long-term aphasia severity. Here we show that individuals with stroke aphasia have a premature brain aging in intact regions of the lesioned hemisphere. In left domain-general regions, premature brain aging, gray matter volume, lesion volume and age were all significant predictors of aphasia severity. Increased brain age following a stroke is driven by the lesioned hemisphere. The relationship between brain age in left domain-general regions and aphasia severity suggests that degradation is possible to specific brain regions and isolated aging matters for behavior.

The impact of age-related hearing loss on cognitive decline: The mediating role of brain age gap.

In recent years, the relationship between age-related hearing loss, cognitive decline, and the risk of dementia has garnered significant attention. The significant variability in brain health and aging among individuals of the same chronological age suggests that a measure assessing how one's brain ages may better explain hearing-cognition links. The main aim of this study was to investigate the mediating role of Brain Age Gap (BAG) in the association between hearing impairment and cognitive function. This research included 185 participants aged 20-79 years. BAG was estimated based on the difference between participant's brain age (estimated based on their structural T1-weighted MRI scans) and chronological age. Cognitive performance was assessed using the Montreal Cognitive Assessment (MoCA) test while hearing ability was measured using pure-tone thresholds (PTT) and words-in-noise (WIN) perception. Mediation analyses were used to examine the mediating role of BAG in the relationship between age-related hearing loss as well as difficulties in WIN perception and cognition. Participants with poorer hearing sensitivity and WIN perception showed lower MoCA scores, but this was an indirect effect. Participants with poorer performance on PTT and WIN tests had larger BAG (accelerated brain aging), and this was associated with poorer performance on the MoCA test. Mediation analyses showed that BAG partially mediated the relationship between age-related hearing loss and cognitive decline. This study enhances our understanding of the interplay among hearing loss, cognition, and BAG, emphasizing the potential value of incorporating brain age assessments in clinical evaluations to gain insights beyond chronological age, thus advancing strategies for preserving cognitive health in aging populations.

Distinct brain morphometry patterns revealed by deep learning improve prediction of post-stroke aphasia severity.

BACKGROUND: Emerging evidence suggests that post-stroke aphasia severity depends on the integrity of the brain beyond the lesion. While measures of lesion anatomy and brain integrity combine synergistically to explain aphasic symptoms, substantial interindividual variability remains unaccounted. One explanatory factor may be the spatial distribution of morphometry beyond the lesion (e.g., atrophy), including not just specific brain areas, but distinct three-dimensional patterns. METHODS: Here, we test whether deep learning with Convolutional Neural Networks (CNNs) on whole brain morphometry (i.e., segmented tissue volumes) and lesion anatomy better predicts chronic stroke individuals with severe aphasia (N = 231) than classical machine learning (Support Vector Machines; SVMs), evaluating whether encoding spatial dependencies identifies uniquely predictive patterns. RESULTS: CNNs achieve higher balanced accuracy and F1 scores, even when SVMs are nonlinear or integrate linear or nonlinear dimensionality reduction. Parity only occurs when SVMs access features learned by CNNs. Saliency maps demonstrate that CNNs leverage distributed morphometry patterns, whereas SVMs focus on the area around the lesion. Ensemble clustering of CNN saliencies reveals distinct morphometry patterns unrelated to lesion size, consistent across individuals, and which implicate unique networks associated with different cognitive processes as measured by the wider neuroimaging literature. Individualized predictions depend on both ipsilateral and contralateral features outside the lesion. CONCLUSIONS: Three-dimensional network distributions of morphometry are directly associated with aphasia severity, underscoring the potential for CNNs to improve outcome prognostication from neuroimaging data, and highlighting the prospective benefits of interrogating spatial dependence at different scales in multivariate feature space.

Some stroke survivors experience difficulties understanding and producing language. We performed brain imaging to capture information about brain structure in stroke survivors and used it to predict which survivors have more severe language problems. We found that a type of artificial intelligence (AI) specifically designed to find patterns in spatial data was more accurate at this task than more traditional methods. AI found more complex patterns of brain structure that distinguish stroke survivors with severe language problems by analyzing the brain's spatial properties. Our findings demonstrate that AI tools can provide new information about brain structure and function following stroke. With further developments, these models may be able to help clinicians understand the extent to which language problems can be improved after a stroke.

Utility of intracranial EEG networks depends on re-referencing and connectivity choice.

Studies of intracranial EEG networks have been used to reveal seizure generators in patients with drug-resistant epilepsy. Intracranial EEG is implanted to capture the epileptic network, the collection of brain tissue that forms a substrate for seizures to start and spread. Interictal intracranial EEG measures brain activity at baseline, and networks computed during this state can reveal aberrant brain tissue without requiring seizure recordings. Intracranial EEG network analyses require choosing a reference and applying statistical measures of functional connectivity. Approaches to these technical choices vary widely across studies, and the impact of these technical choices on downstream analyses is poorly understood. Our objective was to examine the effects of different re-referencing and connectivity approaches on connectivity results and on the ability to lateralize the seizure onset zone in patients with drug-resistant epilepsy. We applied 48 pre-processing pipelines to a cohort of 125 patients with drug-resistant epilepsy recorded with interictal intracranial EEG across two epilepsy centres to generate intracranial EEG functional connectivity networks. Twenty-four functional connectivity measures across time and frequency domains were applied in combination with common average re-referencing or bipolar re-referencing. We applied an unsupervised clustering algorithm to identify groups of pre-processing pipelines. We subjected each pre-processing approach to three quality tests: (i) the introduction of spurious correlations; (ii) robustness to incomplete spatial sampling; and (iii) the ability to lateralize the clinician-defined seizure onset zone. Three groups of similar pre-processing pipelines emerged: common average re-referencing pipelines, bipolar re-referencing pipelines and relative entropy-based connectivity pipelines. Relative entropy and common average re-referencing networks were more robust to incomplete electrode sampling than bipolar re-referencing and other connectivity methods (Friedman test, Dunn-Sidák test P < 0.0001). Bipolar re-referencing reduced spurious correlations at non-adjacent channels better than common average re-referencing (Δ mean from machine ref = -0.36 versus -0.22) and worse in adjacent channels (Δ mean from machine ref = -0.14 versus -0.40). Relative entropy-based network measures lateralized the seizure onset hemisphere better than other measures in patients with temporal lobe epilepsy (Benjamini-Hochberg-corrected P < 0.05, Cohen's d: 0.60-0.76). Finally, we present an interface where users can rapidly evaluate intracranial EEG pre-processing choices to select the optimal pre-processing methods tailored to specific research questions. The choice of pre-processing methods affects downstream network analyses. Choosing a single method among highly correlated approaches can reduce redundancy in processing. Relative entropy outperforms other connectivity methods in multiple quality tests. We present a method and interface for researchers to optimize their pre-processing methods for deriving intracranial EEG brain networks.

Can machine learning predict late seizures after intracerebral hemorrhages? Evidence from real-world data.

INTRODUCTION: Intracerebral hemorrhage represents 15 % of all strokes and it is associated with a high risk of post-stroke epilepsy. However, there are no reliable methods to accurately predict those at higher risk for developing seizures despite their importance in planning treatments, allocating resources, and advancing post-stroke seizure research. Existing risk models have limitations and have not taken advantage of readily available real-world data and artificial intelligence. This study aims to evaluate the performance of Machine-learning-based models to predict post-stroke seizures at 1 year and 5 years after an intracerebral hemorrhage in unselected patients across multiple healthcare organizations. DESIGN/METHODS: We identified patients with intracerebral hemorrhage (ICH) without a prior diagnosis of seizures from 2015 until inception (11/01/22) in the TriNetX Diamond Network, using the International Classification of Diseases, Tenth Revision (ICD-10) I61 (I61.0, I61.1, I61.2, I61.3, I61.4, I61.5, I61.6, I61.8, and I61.9). The outcome of interest was any ICD-10 diagnosis of seizures (G40/G41) at 1 year and 5 years following the first occurrence of the diagnosis of intracerebral hemorrhage. We applied a conventional logistic regression and a Light Gradient Boosted Machine (LGBM) algorithm, and the performance of the model was assessed using the area under the receiver operating characteristics (AUROC), the area under the precision-recall curve (AUPRC), the F1 statistic, model accuracy, balanced-accuracy, precision, and recall, with and without seizure medication use in the models. RESULTS: A total of 85,679 patients had an ICD-10 code of intracerebral hemorrhage and no prior diagnosis of seizures, constituting our study cohort. Seizures were present in 4.57 % and 6.27 % of patients within 1 and 5 years after ICH, respectively. At 1-year, the AUROC, AUPRC, F1 statistic, accuracy, balanced-accuracy, precision, and recall were respectively 0.7051 (standard error: 0.0132), 0.1143 (0.0068), 0.1479 (0.0055), 0.6708 (0.0076), 0.6491 (0.0114), 0.0839 (0.0032), and 0.6253 (0.0216). Corresponding metrics at 5 years were 0.694 (0.009), 0.1431 (0.0039), 0.1859 (0.0064), 0.6603 (0.0059), 0.6408 (0.0119), 0.1094 (0.0037) and 0.6186 (0.0264). These numerical values indicate that the statistical models fit the data very well. CONCLUSION: Machine learning models applied to electronic health records can improve the prediction of post-hemorrhagic stroke epilepsy, presenting a real opportunity to incorporate risk assessments into clinical decision-making in post-stroke care clinical care and improve patients' selection for post-stroke epilepsy research.

EEG Ictal Power Dynamics, Function-Structure Associations, and Epilepsy Surgical Outcomes.

BACKGROUND AND OBJECTIVES: Postoperative seizure control in drug-resistant temporal lobe epilepsy (TLE) remains variable, and the causes for this variability are not well understood. One contributing factor could be the extensive spread of synchronized ictal activity across networks. Our study used novel quantifiable assessments from intracranial EEG (iEEG) to test this hypothesis and investigated how the spread of seizures is determined by underlying structural network topological properties. METHODS: We evaluated iEEG data from 157 seizures in 27 patients with TLE: 100 seizures from 17 patients with postoperative seizure control (Engel score I) vs 57 seizures from 10 patients with unfavorable surgical outcomes (Engel score II-IV). We introduced a quantifiable method to measure seizure power dynamics within anatomical regions, refining existing seizure imaging frameworks and minimizing reliance on subjective human decision-making. Time-frequency power representations were obtained in 6 frequency bands ranging from theta to gamma. Ictal power spectrums were normalized against a baseline clip taken at least 6 hours away from ictal events. Electrodes' time-frequency power spectrums were then mapped onto individual T1-weighted MRIs and grouped based on a standard brain atlas. We compared spatiotemporal dynamics for seizures between groups with favorable and unfavorable surgical outcomes. This comparison included examining the range of activated brain regions and the spreading rate of ictal activities. We then evaluated whether regional iEEG power values were a function of fractional anisotropy (FA) from diffusion tensor imaging across regions over time. RESULTS: Seizures from patients with unfavorable outcomes exhibited significantly higher maximum activation sizes in various frequency bands. Notably, we provided quantifiable evidence that in seizures associated with unfavorable surgical outcomes, the spread of beta-band power across brain regions is significantly faster, detectable as early as the first second after seizure onset. There was a significant correlation between beta power during seizures and FA in the corresponding areas, particularly in the unfavorable outcome group. Our findings further suggest that integrating structural and functional features could improve the prediction of epilepsy surgical outcomes. DISCUSSION: Our findings suggest that ictal iEEG power dynamics and the structural-functional relationship are mechanistic factors associated with surgical outcomes in TLE.

Verbal working memory and syntactic comprehension segregate into the dorsal and ventral streams.

Syntactic processing and verbal working memory are both essential components to sentence comprehension. Nonetheless, the separability of these systems in the brain remains unclear. To address this issue, we performed causal-inference analyses based on lesion and connectome network mapping using MRI and behavioral testing in 103 individuals with chronic post-stroke aphasia. We employed a rhyme judgment task with heavy working memory load without articulatory confounds, controlling for the overall ability to match auditory words to pictures and to perform a metalinguistic rhyme judgment, isolating the effect of working memory load. We assessed noncanonical sentence comprehension, isolating syntactic processing by incorporating residual rhyme judgment performance as a covariate for working memory load. Voxel-based lesion analyses and structural connectome-based lesion symptom mapping controlling for total lesion volume were performed, with permutation testing to correct for multiple comparisons (4,000 permutations). We observed that effects of working memory load localized to dorsal stream damage: posterior temporal-parietal lesions and frontal-parietal white matter disconnections. These effects were differentiated from syntactic comprehension deficits, which were primarily associated with ventral stream damage: lesions to temporal lobe and temporal-parietal white matter disconnections, particularly when incorporating the residual measure of working memory load as a covariate. Our results support the conclusion that working memory and syntactic processing are associated with distinct brain networks, largely loading onto dorsal and ventral streams, respectively.

Dissociating reading and auditory comprehension in persons with aphasia.

Language comprehension is often affected in individuals with post-stroke aphasia. However, deficits in auditory comprehension are not fully correlated with deficits in reading comprehension and the mechanisms underlying this dissociation remain unclear. This distinction is important for understanding language mechanisms, predicting long-term impairments and future development of treatment interventions. Using comprehensive auditory and reading measures from a large cohort of individuals with aphasia, we evaluated the relationship between aphasia type and reading comprehension impairments, the relationship between auditory versus reading comprehension deficits and the crucial neuroanatomy supporting the dissociation between post-stroke reading and auditory deficits. Scores from the Western Aphasia Battery-Revised from 70 participants with aphasia after a left-hemisphere stroke were utilized to evaluate both reading and auditory comprehension of linguistically equivalent stimuli. Repeated-measures and univariate ANOVA were used to assess the relationship between auditory comprehension and aphasia types and correlations were employed to test the relationship between reading and auditory comprehension deficits. Lesion-symptom mapping was used to determine the dissociation of crucial brain structures supporting reading comprehension deficits controlling for auditory deficits and vice versa. Participants with Broca's or global aphasia had the worst performance on reading comprehension. Auditory comprehension explained 26% of the variance in reading comprehension for sentence completion and 44% for following sequential commands. Controlling for auditory comprehension, worse reading comprehension performance was independently associated with damage to the inferior temporal gyrus, fusiform gyrus, posterior inferior temporal gyrus, inferior occipital gyrus, lingual gyrus and posterior thalamic radiation. Auditory and reading comprehension are only partly correlated in aphasia. Reading is an integral part of daily life and directly associated with quality of life and functional outcomes. This study demonstrated that reading performance is directly related to lesioned areas in the boundaries between visual association regions and ventral stream language areas. This behavioural and neuroanatomical dissociation provides information about the neurobiology of language and mechanisms for potential future treatment interventions.