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.

A WORLDWIDE ENIGMA STUDY ON EPILEPSY-RELATED GRAY AND WHITE MATTER COMPROMISE ACROSS THE ADULT LIFESPAN.

Objectives: Temporal lobe epilepsy (TLE) is commonly associated with mesiotemporal pathology and widespread alterations of grey and white matter structures. Evidence supports a progressive condition although the temporal evolution of TLE is poorly defined. This ENIGMA-Epilepsy study utilized multimodal magnetic resonance imaging (MRI) data to investigate structural alterations in TLE patients across the adult lifespan. We charted both grey and white matter changes and explored the covariance of age-related alterations in both compartments. Methods: We studied 769 TLE patients and 885 healthy controls across an age range of 17-73 years, from multiple international sites. To assess potentially non-linear lifespan changes in TLE, we harmonized data and combined median split assessments with cross-sectional sliding window analyses of grey and white matter age-related changes. Covariance analyses examined the coupling of grey and white matter lifespan curves. Results: In TLE, age was associated with a robust grey matter thickness/volume decline across a broad cortico-subcortical territory, extending beyond the mesiotemporal disease epicentre. White matter changes were also widespread across multiple tracts with peak effects in temporo-limbic fibers. While changes spanned the adult time window, changes accelerated in cortical thickness, subcortical volume, and fractional anisotropy (all decreased), and mean diffusivity (increased) after age 55 years. Covariance analyses revealed strong limbic associations between white matter tracts and subcortical structures with cortical regions. Conclusions: This study highlights the profound impact of TLE on lifespan changes in grey and white matter structures, with an acceleration of aging-related processes in later decades of life. Our findings motivate future longitudinal studies across the lifespan and emphasize the importance of prompt diagnosis as well as intervention in patients.

Optimizing Surgical Planning for Epilepsy Patients With Multimodal Neuroimaging and Neurophysiology Integration: A Case Study.

SUMMARY: Current preoperative evaluation of epilepsy can be challenging because of the lack of a comprehensive view of the network's dysfunctions. To demonstrate the utility of our multimodal neurophysiology and neuroimaging integration approach in the presurgical evaluation, we present a proof-of-concept for using this approach in a patient with nonlesional frontal lobe epilepsy who underwent two resective surgeries to achieve seizure control. We conducted a post-hoc investigation using four neuroimaging and neurophysiology modalities: diffusion tensor imaging, resting-state functional MRI, and stereoelectroencephalography at rest and during seizures. We computed region-of-interest-based connectivity for each modality and applied betweenness centrality to identify key network hubs across modalities. Our results revealed that despite seizure semiology and stereoelectroencephalography indicating dysfunction in the right orbitofrontal region, the maximum overlap on the hubs across modalities extended to right temporal areas. Notably, the right middle temporal lobe region served as an overlap hub across diffusion tensor imaging, resting-state functional MRI, and rest stereoelectroencephalography networks and was only included in the resected area in the second surgery, which led to long-term seizure control of this patient. Our findings demonstrated that transmodal hubs could help identify key areas related to epileptogenic network. Therefore, this case presents a promising perspective of using a multimodal approach to improve the presurgical evaluation of patients with epilepsy.

Progressive lesion necrosis is related to increasing aphasia severity in chronic stroke.

BACKGROUND: Volumetric investigations of cortical damage resulting from stroke indicate that lesion size and shape continue to change even in the chronic stage of recovery. However, the potential clinical relevance of continued lesion growth has yet to be examined. In the present study, we investigated the prevalence of lesion expansion and the relationship between expansion and changes in aphasia severity in a large sample of individuals in the chronic stage of aphasia recovery. METHODS: Retrospective structural MRI scans from 104 S survivors with at least 2 observations (k = 301 observations; mean time between scans = 31 months) were included. Lesion demarcation was performed using an automated lesion segmentation software and lesion volumes at each timepoint were subsequently calculated. A linear mixed effects model was conducted to investigate the effect of days between scan on lesion expansion. Finally, we investigated the association between lesion expansion and changes on the Western Aphasia Battery (WAB) in a group of participants assessed and scanned at 2 timepoints (N = 54) using a GLM. RESULTS: Most participants (81 %) showed evidence of lesion expansion. The mixed effects model revealed lesion volumes significantly increase, on average, by 0.02 cc each day (7.3 cc per year) following a scan (p < 0.0001). Change on language performance was significantly associated with change in lesion volume (p = 0.025) and age at stroke (p = 0.031). The results suggest that with every 10 cc increase in lesion size, language performance decreases by 0.9 points, and for every 10-year increase in age at stroke, language performance decreases by 1.9 points. CONCLUSIONS: The present study confirms and extends prior reports that lesion expansion occurs well into the chronic stage of stroke. For the first time, we present evidence that expansion is predictive of longitudinal changes in language performance in individuals with aphasia. Future research should focus on the potential mechanisms that may lead to necrosis in areas surrounding the chronic stroke lesion.

Impact of white matter networks on risk for memory decline following resection versus ablation in temporal lobe epilepsy.

BACKGROUND: With expanding neurosurgical options in epilepsy, it is important to characterise each options' risk for postoperative cognitive decline. Here, we characterise how patients' preoperative white matter (WM) networks relates to postoperative memory changes following different epilepsy surgeries. METHODS: Eighty-nine patients with temporal lobe epilepsy with T1-weighted and diffusion-weighted imaging as well as preoperative and postoperative verbal memory scores (prose recall) underwent either anterior temporal lobectomy (ATL: n=38) or stereotactic laser amygdalohippocampotomy (SLAH; n=51). We computed laterality indices (ie, asymmetry) for volume of the hippocampus and fractional anisotropy (FA) of two deep WM tracts (uncinate fasciculus (UF) and inferior longitudinal fasciculus (ILF)). RESULTS: Preoperatively, left-lateralised FA of the ILF was associated with higher prose recall (p<0.01). This pattern was not observed for the UF or hippocampus (ps>0.05). Postoperatively, right-lateralised FA of the UF was associated with less decline following left ATL (p<0.05) but not left SLAH (p>0.05), while right-lateralised hippocampal asymmetry was associated with less decline following both left ATL and SLAH (ps<0.05). After accounting for preoperative memory score, age of onset and hippocampal asymmetry, the association between UF and memory decline in left ATL remained significant (p<0.01). CONCLUSIONS: Asymmetry of the hippocampus is an important predictor of risk for memory decline following both surgeries. However, asymmetry of UF integrity, which is only severed during ATL, is an important predictor of memory decline after ATL only. As surgical procedures and pre-surgical mapping evolve, understanding the role of frontal-temporal WM in memory networks could help to guide more targeted surgical approaches to mitigate cognitive decline.

Improved naming in patients with Broca's aphasia with tDCS.

BACKGROUND: Language impairment (aphasia) is a common neurological deficit after strokes. For individuals with chronic aphasia (beyond 6 months after the stroke), language improvements with speech therapy (ST) are often limited. Transcranial direct current stimulation (tDCS) is a promising approach to complement language recovery but interindividual variability in treatment response is common after tDCS, suggesting a possible relationship between tDCS and type of linguistic impairment (aphasia type). METHODS: This current study is a subgroup analysis of a randomised controlled phase II futility design clinical trial on tDCS in chronic post-stroke aphasia. All participants received ST coupled with tDCS (n=31) vs sham tDCS (n=39). Confrontation naming was tested at baseline, and 1, 4, and 24 weeks post-treatment. RESULTS: Broca's aphasia was associated with maximal adjunctive benefit of tDCS, with an average improvement of 10 additional named items with tDCS+ST compared with ST alone at 4 weeks post-treatment. In comparison, tDCS was not associated with significant benefits for other aphasia types F(1)=4.23, p=0.04. Among participants with Broca's aphasia, preservation of the perilesional posterior inferior temporal cortex was associated with higher treatment benefit (R=0.35, p=0.03). CONCLUSIONS: These results indicate that adjuvant tDCS can enhance ST to treat naming in Broca's aphasia, and this may guide intervention approaches in future studies.

Bridging the Gap: Association between Objective and Subjective Outcomes of Communication Performance in People with Parkinson's Disease Evaluated for Deep Brain Stimulation.

Background: Decrements in verbal fluency following deep brain stimulation (DBS) in people with Parkinson's disease (PwP) are common. As such, verbal fluency tasks are used in assessing DBS candidacy and target selection. However, the correspondence between testing performance and the patient's perception of communication abilities is not well-established. Methods: The Communication Participation Item Bank (CPIB) was administered to 85 PwP during pre-DBS neuropsychological evaluations. Central tendencies for CPIB responses and correlations between CPIB total scores, clinical and demographic factors, and language-based tasks were examined. Results: Most PwP indicated some degree of communication interference on the CPIB. Worse scores on semantic fluency and greater motor impairment were associated with more communication interference. Conclusions: Our findings suggest an incomplete correspondence between commonly used language-based tests and patient-reported outcomes of communication abilities. The need for a functional communication instrument that reflects the different aspects of communication abilities in functional contexts is emphasized.

Semantic Categorization of Naming Responses Based on Prearticulatory Electrical Brain Activity.

PURPOSE: Object naming requires visual decoding, conceptualization, semantic categorization, and phonological encoding, all within 400 to 600 ms of stimulus presentation and before a word is spoken. In this study, we sought to predict semantic categories of naming responses based on prearticulatory brain activity recorded with scalp EEG in healthy individuals. METHODS: We assessed 19 healthy individuals who completed a naming task while undergoing EEG. The naming task consisted of 120 drawings of animate/inanimate objects or abstract drawings. We applied a one-dimensional, two-layer, neural network to predict the semantic categories of naming responses based on prearticulatory brain activity. RESULTS: Classifications of animate, inanimate, and abstract responses had an average accuracy of 80%, sensitivity of 72%, and specificity of 87% across participants. Across participants, time points with the highest average weights were between 470 and 490 milliseconds after stimulus presentation, and electrodes with the highest weights were located over the left and right frontal brain areas. CONCLUSIONS: Scalp EEG can be successfully used in predicting naming responses through prearticulatory brain activity. Interparticipant variability in feature weights suggests that individualized models are necessary for highest accuracy. Our findings may inform future applications of EEG in reconstructing speech for individuals with and without speech impairments.

Network coupling and surgical treatment response in temporal lobe epilepsy: A proof-of-concept study.

OBJECTIVE: This proof-of-concept study aimed to examine the overlap between structural and functional activity (coupling) related to surgical response. METHODS: We studied intracranial rest and ictal stereoelectroencephalography (sEEG) recordings from 77 seizures in thirteen participants with temporal lobe epilepsy (TLE) who subsequently underwent resective/laser ablation surgery. We used the stereotactic coordinates of electrodes to construct functional (sEEG electrodes) and structural connectomes (diffusion tensor imaging). A Jaccard index was used to assess the similarity (coupling) between structural and functional connectivity at rest and at various intraictal timepoints. RESULTS: We observed that patients who did not become seizure free after surgery had higher connectome coupling recruitment than responders at rest and during early and mid seizure (and visa versa). SIGNIFICANCE: Structural networks provide a backbone for functional activity in TLE. The association between lack of seizure control after surgery and the strength of synchrony between these networks suggests that surgical intervention aimed to disrupt these networks may be ineffective in those that display strong synchrony. Our results, combined with findings of other groups, suggest a potential mechanism that explains why certain patients benefit from epilepsy surgery and why others do not. This insight has the potential to guide surgical planning (e.g., removal of high coupling nodes) following future research.

White matter hyperintensity load mediates the relationship between age and cognition.

To elucidate the relationship between age and cognitive decline, it is important to consider structural brain changes such as white matter hyperintensities (WMHs), which are common in older age and may affect behavior. Therefore, we aimed to investigate if WMH load is a mediator of the relationship between age and cognitive decline. Healthy participants (N = 166, 20-80 years) completed the Montreal Cognitive Assessment (MoCA). WMHs were manually delineated on FLAIR scans. Mediation analysis was conducted to determine if WMH load mediates the relationship between age and cognition. Older age was associated with worse cognition (p < 0.001), but this was an indirect effect: older participants had more WMHs, and, in turn, increased WMH load was associated with worse MoCA scores. WMH load mediates the relationship between age and cognitive decline. Importantly, this relationship was not moderated by age (i.e., increased WMH severity is associated with poorer MoCA scores irrespective of age). Across all ages, high cholesterol was associated with increased WMH severity.

Brain health imaging markers, post-stroke aphasia and Cognition: A scoping review.

For the past decade, brain health has been an emerging line of scientific inquiry assessing the impact of age-related neurostructural changes on cognitive decline and recovery from brain injury. Typically, compromised brain health is attributed to the presence of small vessel disease (SVD) and brain tissue atrophy, which are represented by various neuroimaging features. However, to date, the relationship between brain health markers and chronic aphasia severity remains unclear. Thus, the goal of this scoping review was to assess the current body of evidence regarding the relationship between SVD-related brain health biomarkers and post-stroke aphasia and cognition. In all, 187 articles were identified from 3 databases, of which 16 articles met the criteria for inclusion. Among these studies, 11 focused on cognition rather than aphasia, while 2 investigated both. Of the 10 studies that used white matter hyperintensities (WMHs) as an indicator of SVD severity, 8 studies (80%) demonstrated a relationship between WMH load and worse cognition in stroke patients. Interestingly, among the studies that specifically investigated aphasia, all 5 studies (100%) demonstrated a relationship between SVD and worse language performance. They also indicated that factors other than brain health (e.g., lesion, age, time post onset) played an important role in determining aphasia severity at a single timepoint. These findings suggest that brain health is likely a crucial factor in the context of aphasia recovery, possibly indicating the necessity of cognitive reserve thresholds for the multimodal cognitive demands associated with language recovery. While SVD and structural brain health are not commonly considered as predictors of aphasia severity, more comprehensive models incorporating brain health have the potential to improve prognosis of post-stroke cognitive and language deficits. Given the variability in the existing literature, a uniform grading system for overall SVD would be beneficial for future research on the mechanisms related to brain networks and neuroplasticity, and their translational impact.

Subacute aphasia recovery is associated with resting-state connectivity within and beyond the language network.

OBJECTIVE: To examine changes to connectivity after aphasia treatment in the first 3 months after stroke. METHODS: Twenty people experiencing aphasia within the first 3 months of stroke completed MRI before and immediately following 15 hours of language treatment. They were classified based on their response to treatment on a naming test of nouns as either high responders (10% improvement or more), or low responders (<10% improvement). Groups were similar in age, gender distribution, education, days since stroke, stroke volume, and baseline severity. Resting-state functional connectivity analysis was limited to the connectivity of the left fusiform gyrus with the bilateral inferior frontal gyrus, supramarginal gyrus, angular gyrus, and superior, middle, and inferior temporal gyrus, based on previous studies showing the importance of left fusiform gyrus in naming performance. RESULTS: Baseline ipsilateral connectivity between the left fusiform gyrus and the language network was similar between high and low responders to therapy when controlling for stroke volume. Following therapy, change in connectivity was significantly greater among high responders between the left fusiform gyrus and the ipsilateral and contralateral pars triangularis, ipsilateral pars opercularis and superior temporal gyrus, and contralateral angular gyrus when compared with low responders. INTERPRETATION: An account of these findings incorporates primarily proximal connectivity restoration, but also potentially reflects select contralateral compensatory reorganization. The latter is often associated with chronic recovery, reflecting the transitional nature of the subacute period.

Lower socioeconomic status is associated with premature brain aging.

BACKGROUND: Premature age-related brain changes may be influenced by physical health factors. Lower socioeconomic status (SES) is often associated with poorer physical health. In this study, we aimed to investigate the relationship between SES and premature brain aging. METHODS: Brain age was estimated from T1-weighted images using BrainAgeR in 217 participants from the ABC@UofSC Repository. The difference between brain and chronological age (BrainGAP) was calculated. Multiple regression models were used to predict BrainGAP with age, SES, body mass index, diabetes, hypertension, sex, race, and education as predictors. SES was calculated from size-adjusted household income and the cost of living. RESULTS: Fifty-five participants (25.35%) had greater brain age than chronological age (premature brain aging). Multiple regression models revealed that age, sex, and SES were significant predictors of BrainGAP with lower SES associated with greater BrainGAP (premature brain aging). CONCLUSIONS: This study demonstrates that lower SES is an independent contributor to premature brain aging. This may provide additional insight into the mechanisms associated with brain health, cognition, and resilience to neurological injury.

Dynamic network properties of the superior temporal gyrus mediate the impact of brain age gap on chronic aphasia severity.

Brain structure deteriorates with aging and predisposes an individual to more severe language impairments (aphasia) after a stroke. However, the underlying mechanisms of this relation are not well understood. Here we use an approach to model brain network properties outside the stroke lesion, network controllability, to investigate relations among individualized structural brain connections, brain age, and aphasia severity in 93 participants with chronic post-stroke aphasia. Controlling for the stroke lesion size, we observe that lower average controllability of the posterior superior temporal gyrus (STG) mediates the relation between advanced brain aging and aphasia severity. Lower controllability of the left posterior STG signifies that activity in the left posterior STG is less likely to yield a response in other brain regions due to the topological properties of the structural brain networks. These results indicate that advanced brain aging among individuals with post-stroke aphasia is associated with disruption of dynamic properties of a critical language-related area, the STG, which contributes to worse aphasic symptoms. Because brain aging is variable among individuals with aphasia, our results provide further insight into the mechanisms underlying the variance in clinical trajectories in post-stroke aphasia.

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

Emerging evidence suggests that post-stroke aphasia severity depends on the integrity of the brain beyond the stroke lesion. While measures of lesion anatomy and brain integrity combine synergistically to explain aphasic symptoms, significant interindividual variability remains unaccounted for. A possible explanatory factor may be the spatial distribution of brain atrophy beyond the lesion. This includes not just the specific brain areas showing atrophy, but also distinct three-dimensional patterns of atrophy. Here, we tested whether deep learning with Convolutional Neural Networks (CNN) on whole brain morphometry (i.e., segmented tissue volumes) and lesion anatomy can better predict which individuals with chronic stroke (N=231) have severe aphasia, and whether encoding spatial dependencies in the data might be capable of improving predictions by identifying unique individualized spatial patterns. We observed that CNN achieves significantly higher accuracy and F1 scores than Support Vector Machine (SVM), even when the SVM is nonlinear or integrates linear and nonlinear dimensionality reduction techniques. Performance parity was only achieved when the SVM was directly trained on the latent features learned by the CNN. Saliency maps demonstrated that the CNN leveraged widely distributed patterns of brain atrophy predictive of aphasia severity, whereas the SVM focused almost exclusively on the area around the lesion. Ensemble clustering of CNN saliency maps revealed distinct morphometry patterns that were unrelated to lesion size, highly consistent across individuals, and implicated unique brain networks associated with different cognitive processes as measured by the wider neuroimaging literature. Individualized predictions of severity depended on both ipsilateral and contralateral features outside of the location of stroke. Our findings illustrate that three-dimensional network distributions of atrophy in individuals with aphasia are directly associated with aphasia severity, underscoring the potential for deep learning to improve prognostication of behavioral outcomes from neuroimaging data, and highlighting the prospective benefits of interrogating spatial dependence at different scales in multivariate feature space.

Withdrawal of antiseizure medications after MRI-Guided laser interstitial thermal therapy in extra-temporal lobe epilepsy.

PURPOSE: This study investigated the success rate of antiseizure medications (ASMs) withdrawal following MRI Guided Laser Interstitial Thermal Therapy (MRg-LITT) for extra-temporal lobe epilepsy (ETLE), and identified predictors of seizure recurrence. METHODS: We retrospectively assessed 27 patients who underwent MRg-LITT for ETLE. Patients' demographics, disease characteristics, and post-surgical outcomes were evaluated for their potential to predict seizure recurrence associated with ASMs withdrawal. RESULTS: The median period of observation post MRg-LITT was 3 years (range 18 - 96 months) and the median period to initial ASMs reduction was 0.5 years (range 1-36 months). ASMs reduction was attempted in 17 patients (63%), 5 (29%) of whom had seizure recurrence after initial reduction. Nearly all patient who relapsed regained seizure control after reinstitution of their ASMs regimen. Pre-operative seizure frequency (p = 0.002) and occurrence of acute post-operative seizures (p = 0.01) were associated with increased risk for seizure recurrence post ASMs reduction. At the end of the observation period, 11% of patients were seizure free without drugs, 52% were seizure free with drugs and 37% still experienced seizures despite ASMs. Compared with pre-operative status, the number of ASMs was reduced in 41% of patients, unchanged in 55% of them and increased in only 4% of them. CONCLUSIONS: Successful MRg-LITT for ETLE allows for ASMs reduction in a significant portion of patients and complete ASMs withdrawal in a subset of them. Patients with higher pre-operative seizure frequency or occurrence of acute post operative seizures exhibit higher chances relapse post ASMs reduction.

Associations of small vessel disease and acute symptomatic seizures in ischemic stroke patients.

BACKGROUND AND PURPOSE: Cerebral microbleeds (CMBs), markers of small vessel disease are frequent in ischemic stroke, yet the association with acute symptomatic seizures (ASS) has not been well characterized. METHODS: A retrospective cohort of hospitalized patients with anterior circulation ischemic stroke. The association of CMBs with acute symptomatic seizures was assessed using a logistic regression model and causal mediation analysis. RESULTS: Of 381 patients, 17 developed seizures. Compared with patients without CMBs, those with CMBs had a three-fold higher unadjusted odds of seizures (unadjusted OR: 3.84, 95% 1.16-12.71, p = 0.027). After adjusting for confounders such as stroke severity, cortical infarct location, and hemorrhagic transformation, the association between CMBs and ASS was attenuated (adjusted OR: 3.11, 95%CI: 0.74-11.03, p = 0.09). The association was not mediated by stroke severity. CONCLUSION: In this cohort of hospitalized patients with anterior circulation ischemic stroke, CMBs were more likely to be found in patients with ASS than those without ASS, an association that was attenuated when accounting for stroke severity, cortical infarct location, and hemorrhagic transformation. Evaluation of the long-term risk of seizures associated with CMBs and other markers of small vessel disease is warranted.

How can graph theory inform the dual-stream model of speech processing? a resting-state fMRI study of post-stroke aphasia.

The dual-stream model of speech processing has been proposed to represent the cortical networks involved in speech comprehension and production. Although it is arguably the prominent neuroanatomical model of speech processing, it is not yet known if the dual-stream model represents actual intrinsic functional brain networks. Furthermore, it is unclear how disruptions after a stroke to the functional connectivity of the dual-stream model's regions are related to specific types of speech production and comprehension impairments seen in aphasia. To address these questions, in the present study, we examined two independent resting-state fMRI datasets: (1) 28 neurotypical matched controls and (2) 28 chronic left-hemisphere stroke survivors with aphasia collected at another site. Structural MRI, as well as language and cognitive behavioral assessments, were collected. Using standard functional connectivity measures, we successfully identified an intrinsic resting-state network amongst the dual-stream model's regions in the control group. We then used both standard functional connectivity analyses and graph theory approaches to determine how the functional connectivity of the dual-stream network differs in individuals with post-stroke aphasia, and how this connectivity may predict performance on clinical aphasia assessments. Our findings provide strong evidence that the dual-stream model is an intrinsic network as measured via resting-state MRI, and that weaker functional connectivity of the hub nodes of the dual-stream network defined by graph theory methods, but not overall average network connectivity, is weaker in the stroke group than in the control participants. Also, the functional connectivity of the hub nodes predicted specific types of impairments on clinical assessments. In particular, the relative strength of connectivity of the right hemisphere's homologues of the left dorsal stream hubs to the left dorsal hubs versus right ventral stream hubs is a particularly strong predictor of post-stroke aphasia severity and symptomology.

Convolutional Neural Network Algorithm to Determine Lateralization of Seizure Onset in Patients With Epilepsy: A Proof-of-Principle Study.

BACKGROUND AND OBJECTIVES: A new frontier in diagnostic radiology is the inclusion of machine-assisted support tools that facilitate the identification of subtle lesions often not visible to the human eye. Structural neuroimaging plays an essential role in the identification of lesions in patients with epilepsy, which often coincide with the seizure focus. In this study, we explored the potential for a convolutional neural network (CNN) to determine lateralization of seizure onset in patients with epilepsy using T1-weighted structural MRI scans as input. METHODS: Using a dataset of 359 patients with temporal lobe epilepsy (TLE) from 7 surgical centers, we tested whether a CNN based on T1-weighted images could classify seizure laterality concordant with clinical team consensus. This CNN was compared with a randomized model (comparison with chance) and a hippocampal volume logistic regression (comparison with current clinically available measures). Furthermore, we leveraged a CNN feature visualization technique to identify regions used to classify patients. RESULTS: Across 100 runs, the CNN model was concordant with clinician lateralization on average 78% (SD = 5.1%) of runs with the best-performing model achieving 89% concordance. The CNN outperformed the randomized model (average concordance of 51.7%) on 100% of runs with an average improvement of 26.2% and outperformed the hippocampal volume model (average concordance of 71.7%) on 85% of runs with an average improvement of 6.25%. Feature visualization maps revealed that in addition to the medial temporal lobe, regions in the lateral temporal lobe, cingulate, and precentral gyrus aided in classification. DISCUSSION: These extratemporal lobe features underscore the importance of whole-brain models to highlight areas worthy of clinician scrutiny during temporal lobe epilepsy lateralization. This proof-of-concept study illustrates that a CNN applied to structural MRI data can visually aid clinician-led localization of epileptogenic zone and identify extrahippocampal regions that may require additional radiologic attention. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that in patients with drug-resistant unilateral temporal lobe epilepsy, a convolutional neural network algorithm derived from T1-weighted MRI can correctly classify seizure laterality.