Stable multivariate lesion symptom mapping.

Multivariate lesion-symptom mapping (MLSM) considers lesion information across the entire brain to predict impairments. The strength of this approach is also its weakness-considering many brain features together synergistically can uncover complex brain-behavior relationships but exposes a high-dimensional feature space that a model is expected to learn. Successfully distinguishing between features in this landscape can be difficult for models, particularly in the presence of irrelevant or redundant features. Here, we propose stable multivariate lesion-symptom mapping (sMLSM), which integrates the identification of reliable features with stability selection into conventional MLSM and describe our open-source MATLAB implementation. Usage is showcased with our publicly available dataset of chronic stroke survivors (N=167) and further validated in our independent public acute stroke dataset (N = 1106). We demonstrate that sMLSM eliminates inconsistent features highlighted by MLSM, reduces variation in feature weights, enables the model to learn more complex patterns of brain damage, and improves model accuracy for predicting aphasia severity in a way that tends to be robust regarding the choice of parameters for identifying reliable features. Critically, sMLSM more consistently outperforms predictions based on lesion size alone. This advantage is evident starting at modest sample sizes (N>75). Spatial distribution of feature importance is different in sMLSM, which highlights the features identified by univariate lesion symptom mapping while also implicating select regions emphasized by MLSM. Beyond improved prediction accuracy, sMLSM can offer deeper insight into reliable biomarkers of impairment, informing our understanding of neurobiology.

The Influence of Structural Brain Changes on Cognition in the Context of Healthy Aging: Exploring Mediation Effects Through gBAT-The Graphical Brain Association Tool.

The contribution of age-related structural brain changes to the well-established link between aging and cognitive decline is not fully defined. While both age-related regional brain atrophy and cognitive decline have been extensively studied, the specific mediating role of age-related regional brain atrophy on cognitive functions is unclear. This study introduces an open-source software tool with a graphical user interface that streamlines advanced whole-brain mediation analyses, enabling researchers to systematically explore how the brain acts as a mediator in relationships between various behavioral and health outcomes. The tool is showcased by investigating regional brain volume as a mediator to determine the contribution of age-related brain volume loss toward cognition in healthy aging. We analyzed regional brain volumes and cognitive testing data (Montreal Cognitive Assessment [MoCA]) from a cohort of 131 neurologically healthy adult participants (mean age 50 ± 20.8 years, range 20-79, 73% females) drawn from the Aging Brain Cohort Study at the University of South Carolina. Using our open-source tool developed for evaluating brain-behavior associations across the brain and optimized for exploring mediation effects, we conducted a series of mediation analyses using participant age as the predictor variable, total MoCA and MoCA subtest scores as the outcome variables, and regional brain volume as potential mediators. Age-related atrophy within specific anatomical networks was found to mediate the relationship between age and cognition across multiple cognitive domains. Specifically, atrophy in bilateral frontal, parietal, and occipital areas, along with widespread subcortical regions mediated the effect of age on total MoCA scores. Various MoCA subscores were influenced by age through atrophy in distinct brain regions. These involved prefrontal regions, sensorimotor cortex, and parieto-occipital areas for executive function subscores, prefrontal and temporo-occipital regions, along with the caudate nucleus for attention and concentration subscores, frontal and parieto-occipital areas, alongside connecting subcortical areas such as the optic tract for visuospatial subscores and frontoparietal areas for language subscores. Brain-based mediation analysis offers a causal framework for evaluating the mediating role of brain structure on the relationship between age and cognition and provides a more nuanced understanding of cognitive aging than previously possible. By validating the applicability and effectiveness of this approach and making it openly available to the scientific community, we facilitate the exploration of causal mechanisms between variables mediated by the brain.

The Aphasia Recovery Cohort, an open-source chronic stroke repository.

Sharing neuroimaging datasets enables reproducibility, education, tool development, and new discoveries. Neuroimaging from many studies are publicly available, providing a glimpse into progressive disorders and human development. In contrast, few stroke studies are shared, and these datasets lack longitudinal sampling of functional imaging, diffusion imaging, as well as the behavioral and demographic data that encourage novel applications. This is surprising, as stroke is a leading cause of disability, and acquiring brain imaging is considered standard of care. The first release of the Aphasia Recovery Cohort includes imaging data, demographics and behavioral measures from 230 chronic stroke survivors who experienced aphasia. We also share scripts to illustrate how the imaging data can predict impairment. In conclusion, recent advances in machine learning thrive on large, diverse datasets. Clinical data sharing can contribute to improvements in automated detection of brain injury, identification of white matter hyperintensities, measures of brain health, and prognostic abilities to guide care.

Brainchop: Providing an Edge Ecosystem for Deployment of Neuroimaging Artificial Intelligence Models.

Deep learning has proven highly effective in various medical imaging scenarios, yet the lack of an efficient distribution platform hinders developers from sharing models with end-users. Here, we describe brainchop, a fully functional web application that allows users to apply deep learning models developed with Python to local neuroimaging data from within their browser. While training artificial intelligence models is computationally expensive, applying existing models to neuroimaging data can be very fast; brainchop harnesses the end user's graphics card such that brain extraction, tissue segmentation, and regional parcellation require only seconds and avoids privacy issues that impact cloud-based solutions. The integrated visualization allows users to validate the inferences, and includes tools to annotate and edit the resulting segmentations. Our pure JavaScript implementation includes optimized helper functions for conforming volumes and filtering connected components with minimal dependencies. Brainchop provides a simple mechanism for distributing models for additional image processing tasks, including registration and identification of abnormal tissue, including tumors, lesions and hyperintensities. We discuss considerations for other AI model developers to leverage this open-source resource.

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.

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.

Story Recall in Peer Conflict Resolution Discourse Task to Identify Older Adults Testing Within Range of Cognitive Impairment.

PURPOSE: The current study used behavioral measures of discourse complexity and story recall accuracy in an expository discourse task to distinguish older adults testing within range of cognitive impairment according to a standardized cognitive screening tool in a sample of self-reported healthy older adults. METHOD: Seventy-three older adults who self-identified as healthy completed an expository discourse task and neuropsychological screener. Discourse data were used to classify participants testing within range of cognitive impairment using multiple machine learning algorithms and stability analysis for identifying reliably predictive features in an effort to maximize prediction accuracy. We hypothesized that a higher rate of pronoun use and lower scores on story recall would best classify older adults testing within range of cognitive impairment. RESULTS: The highest classification accuracy exploited a single variable in a remarkably intuitive way: using 66% story recall as a cutoff for cognitive impairment. Forcing this decision tree model to use more features or increasing its complexity did not improve accuracy. Permutation testing confirmed that the 77% accuracy and 0.18 Brier skill score achieved by the model were statistically significant (p < .00001). CONCLUSIONS: These results suggest that expository discourse tasks that place demands on executive functions, such as working memory, can be used to identify aging adults who test within range of cognitive impairment. Accurate representation of story elements in working memory is critical for coherent discourse. Our simple yet highly accurate predictive model of expository discourse provides a promising assessment for easy identification of cognitive impairment in older adults. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.26543824.

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.

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.

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.

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.

Exploring the link between tooth loss, cognitive function, and brain wellness in the context of healthy aging.

AIMS: The aim of this study was to evaluate the utility of using MRI-derived tooth count, an indirect and nonspecific indicator of oral/periodontal health, and brain age gap (BAG), an MRI-based measure of premature brain aging, in predicting cognition in a population of otherwise healthy adults. METHODS: This retrospective study utilized data from 329 participants from the University of South Carolina's Aging Brain Cohort Repository. Participants underwent neuropsychological testing including the Montreal Cognitive Assessment (MoCA), completed an oral/periodontal health questionnaire, and submitted to high-resolution structural MRI imaging. The study compared variability on cognitive scores (MoCA) accounted for by MRI-derived BAG, MRI-derived total tooth count, and self-reported oral/periodontal health. RESULTS: We report a significant positive correlation between the total number of teeth and MoCA total scores after controlling for age, sex, and race, indicating a robust relationship between tooth count and cognition, r(208) = .233, p < .001. In a subsample of participants identified as being at risk for MCI (MoCA <= 25, N = 36) inclusion of MRI-based tooth count resulted in an R2 change of .192 (H0 = 0.138 → H1 = 0.330), F(1,31) = 8.86, p = .006. Notably, inclusion of BAG, a valid and reliable measure of overall brain health, did not significantly improve prediction of MoCA scores in similar linear regression models. CONCLUSIONS: Our data support the idea that inclusion of MRI-based total tooth count may enhance the ability to predict clinically meaningful differences in cognitive abilities in healthy adults. This study contributes to the growing body of evidence linking oral/periodontal health with cognitive function.

Identifying a group of factors predicting cognitive impairment among older adults.

BACKGROUND: Cognitive impairment has multiple risk factors spanning several domains, but few studies have evaluated risk factor clusters. We aimed to identify naturally occurring clusters of risk factors of poor cognition among middle-aged and older adults and evaluate associations between measures of cognition and these risk factor clusters. METHODS: We used data from the National Health and Nutrition Examination Survey (NHANES) III (training dataset, n = 4074) and the NHANES 2011-2014 (validation dataset, n = 2510). Risk factors were selected based on the literature. We used both traditional logistic models and support vector machine methods to construct a composite score of risk factor clusters. We evaluated associations between the risk score and cognitive performance using the logistic model by estimating odds ratios (OR) and 95% confidence intervals (CI). RESULTS: Using the training dataset, we developed a composite risk score that predicted undiagnosed cognitive decline based on ten selected predictive risk factors including age, waist circumference, healthy eating index, race, education, income, physical activity, diabetes, hypercholesterolemia, and annual visit to dentist. The risk score was significantly associated with poor cognitive performance both in the training dataset (OR Tertile 3 verse tertile 1 = 8.15, 95% CI: 5.36-12.4) and validation dataset (OR Tertile 3 verse tertile 1 = 4.31, 95% CI: 2.62-7.08). The area under the receiver operating characteristics curve for the predictive model was 0.74 and 0.77 for crude model and model adjusted for age, sex, and race. CONCLUSION: The model based on selected risk factors may be used to identify high risk individuals with cognitive impairment.

Improving 3D edge detection for visual inspection of MRI coregistration and alignment.

BACKGROUND: Visualizing edges is critical for neuroimaging. For example, edge maps enable quality assurance for the automatic alignment of an image from one modality (or individual) to another. NEW METHOD: We suggest that using the second derivative (difference of Gaussian, or DoG) provides robust edge detection. This method is tuned by size (which is typically known in neuroimaging) rather than intensity (which is relative). RESULTS: We demonstrate that this method performs well across a broad range of imaging modalities. The edge contours produced consistently form closed surfaces, whereas alternative methods may generate disconnected lines, introducing potential ambiguity in contiguity. COMPARISON WITH EXISTING METHODS: Current methods for computing edges are based on either the first derivative of the image (FSL), or a variation of the Canny Edge detection method (AFNI). These methods suffer from two primary limitations. First, the crucial tuning parameter for each of these methods relates to the image intensity. Unfortunately, image intensity is relative for most neuroimaging modalities making the performance of these methods unreliable. Second, these existing approaches do not necessarily generate a closed edge/surface, which can reduce the ability to determine the correspondence between a represented edge and another image. CONCLUSION: The second derivative is well suited for neuroimaging edge detection. We include this method as part of both the AFNI and FSL software packages, standalone code and online.

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.

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.

Effects of Hyperdirect Pathway Theta Burst Transcranial Magnetic Stimulation on Inhibitory Control, Craving, and Smoking in Adults With Nicotine Dependence: A Double-Blind, Randomized Crossover Trial.

BACKGROUND: Nicotine dependence is associated with dysregulated hyperdirect pathway (HDP)-mediated inhibitory control (IC). However, there are currently no evidence-based treatments that have been shown to target the HDP to improve IC and reduce cigarette cravings and smoking. METHODS: Following a baseline nonstimulation control session, this study (N = 37; female: n = 17) used a double-blind, randomized crossover design to examine the behavioral and neural effects of intermittent theta burst stimulation (iTBS) and continuous TBS (cTBS) to the right inferior frontal gyrus (rIFG)-a key cortical node of the HDP. Associations between treatment effects were also explored. RESULTS: At baseline, HDP IC task-state functional connectivity was positively associated with IC task performance, which confirmed the association between HDP circuit function and IC. Compared with iTBS, rIFG cTBS improved IC task performance. Compared with the baseline nonstimulation control session, both TBS conditions reduced cigarette craving and smoking; however, although craving and smoking were lower for cTBS, no differences were found between the two active conditions. In addition, although HDP IC task-state functional connectivity was greater following cTBS than iTBS, there was no significant difference between conditions. Finally, cTBS-induced improvement in IC task performance was associated with reduced craving, and cTBS-induced reduction in craving was associated with reduced smoking. CONCLUSIONS: These findings warrant further investigation into the effects of rIFG cTBS for increasing IC and reducing craving and smoking among individuals with nicotine dependence. Future sham-controlled cTBS studies may help further elucidate the mechanisms by which rIFG cTBS mediates IC and smoking behavior.

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.

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.