Cognitive impairment after focal brain lesions is better predicted by damage to structural than functional network hubs.

Hubs are highly connected brain regions important for coordinating processing in brain networks. It is unclear, however, which measures of network "hubness" are most useful in identifying brain regions critical to human cognition. We tested how closely two measures of hubness-edge density and participation coefficient, derived from white and gray matter, respectively-were associated with general cognitive impairment after brain damage in two large cohorts of patients with focal brain lesions (N = 402 and 102, respectively) using cognitive tests spanning multiple cognitive domains. Lesions disrupting white matter regions with high edge density were associated with cognitive impairment, whereas lesions damaging gray matter regions with high participation coefficient had a weaker, less consistent association with cognitive outcomes. Similar results were observed with six other gray matter hubness measures. This suggests that damage to densely connected white matter regions is more cognitively impairing than similar damage to gray matter hubs, helping to explain interindividual differences in cognitive outcomes after brain damage.

The role of gender in cognitive outcomes from stroke.

OBJECTIVE: Stroke can cause cognitive impairment, which can lead to challenges returning to day-to-day activities. Knowing what factors are associated with cognitive impairment post-stroke can be useful for predicting outcomes and guiding rehabilitation. One such factor is gender: previous studies are inconclusive as to whether gender influences cognitive outcomes post-stroke. Accounting for key variables, we examined whether there are gender differences in cognitive outcomes after stroke. METHOD: We analyzed data from neuropsychological assessments of 237 individuals tested in the chronic epoch (≥ 3 months) following ischemic stroke. Using ANCOVA and linear mixed modeling, we examined gender as a predictor of cognition as measured by general cognitive ability (g), Full-Scale IQ, and 18 cognitive tests, controlling for age at stroke onset, education, premorbid intelligence, and lesion volume. RESULTS: There were no significant gender differences in overall cognitive outcomes as measured by g (p = .887) or Full-Scale IQ (p = .801). There were some significant gender differences on specific cognitive tests, with women outperforming men on scores from the Rey Auditory Verbal Learning Test (ps < .01) and men outperforming women on the Wechsler Adult Intelligence Scale Arithmetic and Information subtests (ps < .01). CONCLUSIONS: Our findings indicate that men and women have similar overall cognitive outcomes after stroke, when demographic and lesion factors are accounted for. Although men and women differed in their performance on some individual cognitive tests, neither gender performed systematically better or worse. However, for learning, working memory, and verbal knowledge/comprehension, gender may be an important predictor of outcome post-stroke.

Post-stroke outcomes predicted from multivariate lesion-behaviour and lesion network mapping.

Clinicians and scientists alike have long sought to predict the course and severity of chronic post-stroke cognitive and motor outcomes, as the ability to do so would inform treatment and rehabilitation strategies. However, it remains difficult to make accurate predictions about chronic post-stroke outcomes due, in large part, to high inter-individual variability in recovery and a reliance on clinical heuristics rather than empirical methods. The neuroanatomical location of a stroke is a key variable associated with long-term outcomes, and because lesion location can be derived from routinely collected clinical neuroimaging data there is an opportunity to use this information to make empirically based predictions about post-stroke deficits. For example, lesion location can be compared to statistically weighted multivariate lesion-behaviour maps of neuroanatomical regions that, when damaged, are associated with specific deficits based on aggregated outcome data from large cohorts. Here, our goal was to evaluate whether we can leverage lesion-behaviour maps based on data from two large cohorts of individuals with focal brain lesions to make predictions of 12-month cognitive and motor outcomes in an independent sample of stroke patients. Further, we evaluated whether we could augment these predictions by estimating the structural and functional networks disrupted in association with each lesion-behaviour map through the use of structural and functional lesion network mapping, which use normative structural and functional connectivity data from neurologically healthy individuals to elucidate lesion-associated networks. We derived these brain network maps using the anatomical regions with the strongest association with impairment for each cognitive and motor outcome based on lesion-behaviour map results. These peak regional findings became the 'seeds' to generate networks, an approach that offers potentially greater precision compared to previously used single-lesion approaches. Next, in an independent sample, we quantified the overlap of each lesion location with the lesion-behaviour maps and structural and functional lesion network mapping and evaluated how much variance each could explain in 12-month behavioural outcomes using a latent growth curve statistical model. We found that each lesion-deficit mapping modality was able to predict a statistically significant amount of variance in cognitive and motor outcomes. Both structural and functional lesion network maps were able to predict variance in 12-month outcomes beyond lesion-behaviour mapping. Functional lesion network mapping performed best for the prediction of language deficits, and structural lesion network mapping performed best for the prediction of motor deficits. Altogether, these results support the notion that lesion location and lesion network mapping can be combined to improve the prediction of post-stroke deficits at 12-months.

Multivariate Lesion-Behavior Mapping of General Cognitive Ability and Its Psychometric Constituents.

General cognitive ability, or general intelligence (g), is central to cognitive science, yet the processes that constitute it remain unknown, in good part because most prior work has relied on correlational methods. Large-scale behavioral and neuroanatomical data from neurologic patients with focal brain lesions can be leveraged to advance our understanding of the key mechanisms of g, as this approach allows inference on the independence of cognitive processes along with elucidation of their respective neuroanatomical substrates. We analyzed behavioral and neuroanatomical data from 402 humans (212 males; 190 females) with chronic, focal brain lesions. Structural equation models (SEMs) demonstrated a psychometric isomorphism between g and working memory in our sample (which we refer to as g/Gwm), but not between g and other cognitive abilities. Multivariate lesion-behavior mapping analyses indicated that g and working memory localize most critically to a site of converging white matter tracts deep to the left temporo-parietal junction. Tractography analyses demonstrated that the regions in the lesion-behavior map of g/Gwm were primarily associated with the arcuate fasciculus. The anatomic findings were validated in an independent cohort of acute stroke patients (n = 101) using model-based predictions of cognitive deficits generated from the Iowa cohort lesion-behavior maps. The neuroanatomical localization of g/Gwm provided the strongest prediction of observed g in the new cohort (r = 0.42, p < 0.001), supporting the anatomic specificity of our findings. These results provide converging behavioral and anatomic evidence that working memory is a key mechanism contributing to domain-general cognition.SIGNIFICANCE STATEMENT General cognitive ability (g) is thought to play an important role in individual differences in adaptive behavior, yet its core processes remain unknown, in large part because of difficulties in making causal inferences from correlated data. Using data from patients with focal brain damage, we demonstrate that there is a strong psychometric correspondence between g and working memory - the ability to maintain and control mental information, and that the critical neuroanatomical substrates of g and working memory include the arcuate fasciculus. This work provides converging behavioral and neuroanatomical evidence that working memory is a key mechanism contributing to domain-general cognition.

Thalamic strokes that severely impair arousal extend into the brainstem.

In this study, we evaluate the role of the thalamus in the neural circuitry of arousal. Level of consciousness within the first 12 hours of a thalamic stroke is assessed with lesion symptom mapping. Impaired arousal correlates with lesions in the paramedian posterior thalamus near the centromedian and parafascicular nuclei, posterior hypothalamus, and midbrain tegmentum. All patients with severely impaired arousal (coma, stupor) had lesion extension into the midbrain and/or pontine tegmentum, whereas purely thalamic lesions did not severely impair arousal. These results are consistent with growing evidence that pathways most critical for human arousal lie outside the thalamus. Ann Neurol 2018;84:926-930.

Data-driven biomarkers better associate with stroke motor outcomes than theory-based biomarkers.

Chronic motor impairments are a leading cause of disability after stroke. Previous studies have associated motor outcomes with the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to model chronic motor outcomes after stroke and compares the accuracy of these associations to previously-identified theory-based biomarkers. Using a cross-validation framework, regression models were trained using lesion masks and motor outcomes data from 789 stroke patients from the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA) Stroke Recovery Working Group. Using the explained variance metric to measure the strength of the association between chronic motor outcomes and imaging biomarkers, we compared theory-based biomarkers, like lesion load to known motor tracts, to three data-driven biomarkers: lesion load of lesion-behaviour maps, lesion load of structural networks associated with lesion-behaviour maps, and measures of regional structural disconnection. In general, data-driven biomarkers had stronger associations with chronic motor outcomes accuracy than theory-based biomarkers. Data-driven models of regional structural disconnection performed the best of all models tested (R 2 = 0.210, P < 0.001), performing significantly better than the theory-based biomarkers of lesion load of the corticospinal tract (R 2 = 0.132, P < 0.001) and of multiple descending motor tracts (R 2 = 0.180, P < 0.001). They also performed slightly, but significantly, better than other data-driven biomarkers including lesion load of lesion-behaviour maps (R 2 = 0.200, P < 0.001) and lesion load of structural networks associated with lesion-behaviour maps (R 2 = 0.167, P < 0.001). Ensemble models - combining basic demographic variables like age, sex, and time since stroke - improved the strength of associations for theory-based and data-driven biomarkers. Combining both theory-based and data-driven biomarkers with demographic variables improved predictions, and the best ensemble model achieved R 2 = 0.241, P < 0.001. Overall, these results demonstrate that out-of-sample associations between chronic motor outcomes and data-driven imaging features, particularly when lesion data is represented in terms of structural disconnection, are stronger than associations between chronic motor outcomes and theory-based biomarkers. However, combining both theory-based and data-driven models provides the most robust associations.

Data-driven biomarkers outperform theory-based biomarkers in predicting stroke motor outcomes.

Chronic motor impairments are a leading cause of disability after stroke. Previous studies have predicted motor outcomes based on the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to predict chronic motor outcomes after stroke and compares the accuracy of these predictions to previously-identified theory-based biomarkers. Using a cross-validation framework, regression models were trained using lesion masks and motor outcomes data from 789 stroke patients (293 female/496 male) from the ENIGMA Stroke Recovery Working Group (age 64.9±18.0 years; time since stroke 12.2±0.2 months; normalised motor score 0.7±0.5 (range [0,1]). The out-of-sample prediction accuracy of two theory-based biomarkers was assessed: lesion load of the corticospinal tract, and lesion load of multiple descending motor tracts. These theory-based prediction accuracies were compared to the prediction accuracy from three data-driven biomarkers: lesion load of lesion-behaviour maps, lesion load of structural networks associated with lesion-behaviour maps, and measures of regional structural disconnection. In general, data-driven biomarkers had better prediction accuracy - as measured by higher explained variance in chronic motor outcomes - than theory-based biomarkers. Data-driven models of regional structural disconnection performed the best of all models tested (R2 = 0.210, p < 0.001), performing significantly better than predictions using the theory-based biomarkers of lesion load of the corticospinal tract (R2 = 0.132, p< 0.001) and of multiple descending motor tracts (R2 = 0.180, p < 0.001). They also performed slightly, but significantly, better than other data-driven biomarkers including lesion load of lesion-behaviour maps (R2 =0.200, p < 0.001) and lesion load of structural networks associated with lesion-behaviour maps (R2 =0.167, p < 0.001). Ensemble models - combining basic demographic variables like age, sex, and time since stroke - improved prediction accuracy for theory-based and data-driven biomarkers. Finally, combining both theory-based and data-driven biomarkers with demographic variables improved predictions, and the best ensemble model achieved R2 = 0.241, p < 0.001. Overall, these results demonstrate that models that predict chronic motor outcomes using data-driven features, particularly when lesion data is represented in terms of structural disconnection, perform better than models that predict chronic motor outcomes using theory-based features from the motor system. However, combining both theory-based and data-driven models provides the best predictions.

Does brain damage caused by stroke versus trauma have different neuropsychological outcomes? A lesion-matched multiple case study.

Traumatic brain injury (TBI) and stroke both have the potential to cause significant damage to the brain, with resultant neuropsychological impairments. How these different mechanisms of injury influence cognitive and behavioral changes associated with brain damage, however, is not well understood. Moreover, previous research directly comparing TBI and stroke has not accounted carefully for lesion location and size. Here, using a detailed lesion-matching approach that was used previously to compare neuropsychological outcomes in stroke versus tumor, we compared the neuropsychological profiles of 14 patients with focal lesions caused by TBI to those of 27 lesion-matched patients with stroke. Each patient with TBI was matched to two patients with stroke, based on lesion location and size (except 1 TBI case where only 1 stroke match was available). Demographic attributes (age, gender, handedness, education) were also matched in the TBI: stroke triplets, as much as possible. The patients with TBI versus stroke had similar performances across all cognitive and behavioral measures, with no significant or clinically meaningful differences. A supplemental analysis on developmental- versus adult-onset TBI cases (with their respective stroke matches) also yielded non-significant results, with TBI and stroke groups being statistically indistinguishable. Our results suggest that focal lesions caused by TBI versus stroke have similar neuropsychological outcomes in the chronic recovery phase, when location and size of lesion are comparable across TBI versus stroke mechanisms of injury.

Neural correlates of recognition and naming of famous persons and landmarks: A special role for the left anterior temporal lobe.

The anterior temporal lobes (ATLs) have been shown to be crucial for recognition and naming of unique entities such as persons and places. In this chapter, we review previous research that identified the neural underpinnings of these processes, and discuss the convergence zone theory of conceptual knowledge and proper name retrieval. Lesion-deficit and neuroimaging studies have found that the temporal poles are essential for recognition and naming of unique persons and places. Research has shown laterality, in that the right anterior temporal pole is specialized for recognition and the left for naming. Here, we analyzed recognition and naming of persons and landmarks in a large neurologic sample (N=244) using the Iowa Famous Faces and Famous Landmarks tests. For both categories, education had a significant effect on recognition and naming performances, but age and gender did not. Lesion-symptom maps revealed lower naming scores for both Faces and Landmarks associated with lesions to the anterior and mesial left temporal lobe. Lower recognition scores were also linked to left temporal lobe damage, possibly due to the method we used for measuring recognition (verbally based). Overall, the results demonstrate the importance of the temporal lobes for recognition and naming of unique persons and places.

Right inferior frontal gyrus damage is associated with impaired initiation of inhibitory control, but not its implementation.

Inhibitory control is one of the most important control functions in the human brain. Much of our understanding of its neural basis comes from seminal work showing that lesions to the right inferior frontal gyrus (rIFG) increase stop-signal reaction time (SSRT), a latent variable that expresses the speed of inhibitory control. However, recent work has identified substantial limitations of the SSRT method. Notably, SSRT is confounded by trigger failures: stop-signal trials in which inhibitory control was never initiated. Such trials inflate SSRT, but are typically indicative of attentional, rather than inhibitory deficits. Here, we used hierarchical Bayesian modeling to identify stop-signal trigger failures in human rIFG lesion patients, non-rIFG lesion patients, and healthy comparisons. Furthermore, we measured scalp-EEG to detect ß-bursts, a neurophysiological index of inhibitory control. rIFG lesion patients showed a more than fivefold increase in trigger failure trials and did not exhibit the typical increase of stop-related frontal ß-bursts. However, on trials in which such ß-bursts did occur, rIFG patients showed the typical subsequent upregulation of ß over sensorimotor areas, indicating that their ability to implement inhibitory control, once triggered, remains intact. These findings suggest that the role of rIFG in inhibitory control has to be fundamentally reinterpreted.

Academic skills after brain injury: A lifespan perspective.

OBJECTIVES: This study investigated academic skills outcomes after brain injury and identified the influence of age and injury factors across the lifespan. METHOD: Our sample included 651 participants with focal brain lesions. Math, reading, and spelling data from the Wide Range Achievement Test (WRAT) were used as the academic skills outcomes. Age of lesion onset ranged from 0 to 85 years old. Linear regressions were conducted to identify the relation between age and injury factors and academic skills outcomes. Lesion-symptom mapping was conducted to identify the brain areas that, when lesioned, were associated with deficits in academic skills. RESULTS: A quadratic model of age of lesion onset significantly predicted math (R² = .28, p < .001), reading (R² = .29, p < .001), and spelling outcomes (R² = .32, p < .001), while accounting for various covariates. Education, sex, lesion size and laterality, etiology, and seizure history were additional reliable predictors of academic skills outcomes across the lifespan. Academic skill deficits were associated with damage to various brain areas across the left-hemisphere frontal, temporal, and parietal lobes, the insular area, and left- and right-hemisphere white matter. CONCLUSIONS: This study supports age of lesion onset as a relevant predictor of academic skills after brain injury in a lifespan sample. Several other variables (e.g., education, sex, lesion characteristics, and seizure history) are notable in the prediction of outcomes across the lifespan. Future work could investigate more diverse samples and emphasize recruitment of early onset injuries to examine generalizability and potential critical periods for academic skills. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Choosing spouses and houses: Impaired congruence between preference and choice following damage to the ventromedial prefrontal cortex.

OBJECTIVE: A well-documented effect of focal ventromedial prefrontal cortex (vmPFC) damage is a deficit in real-world decision making. An important aspect of this deficit may be a deficiency in "internal consistency" during social decision making-that is, impaired congruence between expressed preferences versus actual behavioral choices. An example of low internal consistency would be if one expressed the desire to marry someone with impeccable moral character, yet proceeded to marry someone convicted of multiple felonies. Here, we used a neuropsychological approach to investigate neural correlates of internal consistency in complex decision making. METHOD: Sixteen individuals with focal vmPFC lesions, 16 brain damage comparison individuals, and 16 normal comparison individuals completed a 3-option forced-choice preference task in which choices were made using attribute sets. Participants also completed visual-analogue preference ratings to indicate how much they liked each option, and rated the influence of each attribute on their decision making. Options were either social (potential spouses) or nonsocial (potential houses). Internal consistency for a trial was defined as agreement between the choice and the most positively rated option. RESULTS: A mixed design analysis of variance revealed that internal consistency between choices and preferences derived from summed attribute ratings was significantly lower for the vmPFC group relative to comparison participants, but only in the social condition (pη2 = .09), 95% CI [.002, .163]. CONCLUSIONS: Internal consistency during social decisions may be deficient in patients with vmPFC damage, leading to a discrepancy between preferences and choices. The vmPFC may provide an important neural mechanism for aligning behavioral choices with expressed preferences. (PsycINFO Database Record