Cerebrovascular Reactivity Has Negligible Contribution to Hemodynamic Lag After Stroke: Implications for Functional Magnetic Resonance Imaging Studies.

BACKGROUND: Functional magnetic resonance imaging (fMRI) is ubiquitously used to study poststroke recovery. However, the fMRI-derived hemodynamic responses are vulnerable to vascular insult which can result in reduced magnitude and temporal delays (lag) in the hemodynamic response function (HRF). The cause of HRF lag remains controversial, and a better understanding of it is required to ensure accurate interpretation of poststroke fMRI studies. In this longitudinal study, we investigate the relationship between hemodynamic lag and cerebrovascular reactivity (CVR) following stroke. METHODS: Voxel-wise lag maps were calculated relative to a mean gray matter reference signal for 27 healthy controls and 59 patients with stroke across 2 time points (≈2 weeks and ≈4 months poststroke) and 2 conditions: resting-state and breath-holding. The breath-holding condition was additionally used to calculate CVR in response to hypercapnia. HRF lag was computed for both conditions across tissue compartments: lesion, perilesional tissue, unaffected tissue of the lesioned hemisphere, and their homolog regions in the unaffected hemisphere. CVR and lag maps were correlated. Group, condition, and time effects were assessed using ANOVA analyses. RESULTS: Compared with the average gray matter signal, a relative hemodynamic lead was observed in the primary sensorimotor cortices in resting-state and bilateral inferior parietal cortices in the breath-holding condition. Whole-brain hemodynamic lag was significantly correlated across conditions irrespective of group, with regional differences across conditions suggestive of a neural network pattern. Patients showed relative lag in the lesioned hemisphere which significantly reduced over time. Breath-hold derived lag and CVR had no significant voxel-wise correlation in controls, or patients within the lesioned hemisphere or the homologous regions of the lesion and perilesional tissue in the right hemisphere (mean r<0.1). CONCLUSIONS: The contribution of altered CVR to HRF lag was negligible. We suggest that HRF lag is largely independent of CVR, and could partly reflect intrinsic neural network dynamics among other factors.

The multidimensional nature of aphasia recovery post-stroke.

Language is not a single function, but instead results from interactions between neural representations and computations that can be damaged independently of each other. Although there is now clear evidence that the language profile in post-stroke aphasia reflects graded variations along multiple underlying dimensions ('components'), it is still entirely unknown if these distinct language components have different recovery trajectories and rely on the same, or different, neural regions during aphasia recovery. Accordingly, this study examined whether language components in the subacute stage: (i) mirror those observed in the chronic stage; (ii) recover together in a homogeneous manner; and (iii) have recovery trajectories that relate to changing activation in distinct or overlapping underlying brain regions. We analysed longitudinal data from 26 individuals with mild-moderate aphasia following left hemispheric infarct who underwent functional MRI and behavioural testing at ∼2 weeks and ∼4 months post-stroke. The language profiles in early post-stroke aphasia reflected three orthogonal principal components consisting of fluency, semantic/executive function and phonology. These components did not recover in a singular, homogeneous manner; rather, their longitudinal trajectories were uncorrelated, suggesting that aphasia recovery is heterogeneous and multidimensional. Mean regional brain activation during overt speech production in unlesioned areas was compared with patient scores on the three principal components of language at both the early and late time points. In addition, the change in brain activation over time was compared with the change on each of the principal component scores, both before and after controlling for baseline scores. We found that different language components were associated with changing activation in multiple, non-overlapping bilateral brain regions during aphasia recovery. Specifically, fluency recovery was associated with increasing activation in bilateral middle frontal gyri and right temporo-occipital middle temporal gyrus; semantic/executive recovery was associated with reducing activation in bilateral anterior temporal lobes; while phonology recovery was associated with reducing activation in bilateral precentral gyri, dorso-medial frontal poles and the precuneus. Overlapping clusters in the ventromedial prefrontal cortex were positively associated with fluency recovery but negatively associated with semantic/executive and phonology recovery. This combination of detailed behavioural and functional MRI data provides novel insights into the neural basis of aphasia recovery. Because different aspects of language seem to rely on different neural regions for recovery, treatment strategies that target the same neural region in all stroke survivors with aphasia might be entirely ineffective or even impair recovery, depending on the specific language profile of each individual patient.

A Bayesian optimization approach for rapidly mapping residual network function in stroke.

Post-stroke cognitive and linguistic impairments are debilitating conditions, with limited therapeutic options. Domain-general brain networks play an important role in stroke recovery and characterizing their residual function with functional MRI has the potential to yield biomarkers capable of guiding patient-specific rehabilitation. However, this is challenging as such detailed characterization requires testing patients on multitudes of cognitive tasks in the scanner, rendering experimental sessions unfeasibly lengthy. Thus, the current status quo in clinical neuroimaging research involves testing patients on a very limited number of tasks, in the hope that it will reveal a useful neuroimaging biomarker for the whole cohort. Given the great heterogeneity among stroke patients and the volume of possible tasks this approach is unsustainable. Advancing task-based functional MRI biomarker discovery requires a paradigm shift in order to be able to swiftly characterize residual network activity in individual patients using a diverse range of cognitive tasks. Here, we overcome this problem by leveraging neuroadaptive Bayesian optimization, an approach combining real-time functional MRI with machine-learning, by intelligently searching across many tasks, this approach rapidly maps out patient-specific profiles of residual domain-general network function. We used this technique in a cross-sectional study with 11 left-hemispheric stroke patients with chronic aphasia (four female, age ± standard deviation: 59 ± 10.9 years) and 14 healthy, age-matched control subjects (eight female, age ± standard deviation: 55.6 ± 6.8 years). To assess intra-subject reliability of the functional profiles obtained, we conducted two independent runs per subject, for which the algorithm was entirely reinitialized. Our results demonstrate that this technique is both feasible and robust, yielding reliable patient-specific functional profiles. Moreover, we show that group-level results are not representative of patient-specific results. Whereas controls have highly similar profiles, patients show idiosyncratic profiles of network abnormalities that are associated with behavioural performance. In summary, our study highlights the importance of moving beyond traditional 'one-size-fits-all' approaches where patients are treated as one group and single tasks are used. Our approach can be extended to diverse brain networks and combined with brain stimulation or other therapeutics, thereby opening new avenues for precision medicine targeting a diverse range of neurological and psychiatric conditions.

Stimulating Multiple-Demand Cortex Enhances Vocabulary Learning.

It is well established that networks within multiple-demand cortex (MDC) become active when diverse skills and behaviors are being learnt. However, their causal role in learning remains to be established. In the present study, we first performed functional magnetic resonance imaging on healthy female and male human participants to confirm that MDC was most active in the initial stages of learning a novel vocabulary, consisting of pronounceable nonwords (pseudowords), each associated with a picture of a real object. We then examined, in healthy female and male human participants, whether repetitive transcranial magnetic stimulation of a frontal midline node of the cingulo-opercular MDC affected learning rates specifically during the initial stages of learning. We report that stimulation of this node, but not a control brain region, substantially improved both accuracy and response times during the earliest stage of learning pseudoword-object associations. This stimulation had no effect on the processing of established vocabulary, tested by the accuracy and response times when participants decided whether a real word was accurately paired with a picture of an object. These results provide evidence that noninvasive stimulation to MDC nodes can enhance learning rates, thereby demonstrating their causal role in the learning process. We propose that this causal role makes MDC candidate target for experimental therapeutics; for example, in stroke patients with aphasia attempting to reacquire a vocabulary.SIGNIFICANCE STATEMENT Learning a task involves the brain system within which that specific task becomes established. Therefore, successfully learning a new vocabulary establishes the novel words in the language system. However, there is evidence that in the early stages of learning, networks within multiple-demand cortex (MDC), which control higher cognitive functions, such as working memory, attention, and monitoring of performance, become active. This activity declines once the task is learnt. The present study demonstrated that a node within MDC, located in midline frontal cortex, becomes active during the early stage of learning a novel vocabulary. Importantly, noninvasive brain stimulation of this node improved performance during this stage of learning. This observation demonstrated that MDC activity is important for learning.

Domain-general subregions of the medial prefrontal cortex contribute to recovery of language after stroke.

We hypothesized that the recovery of speech production after left hemisphere stroke not only depends on the integrity of language-specialized brain systems, but also on 'domain-general' brain systems that have much broader functional roles. The presupplementary motor area/dorsal anterior cingulate forms part of the cingular-opercular network, which has a broad role in cognition and learning. Consequently, we have previously suggested that variability in the recovery of speech production after aphasic stroke may relate in part to differences in patients' abilities to engage this domain-general brain region. To test our hypothesis, 27 patients (aged 59 ± 11 years) with a left hemisphere stroke performed behavioural assessments and event-related functional magnetic resonance imaging tasks at two time points; first in the early phase (∼2 weeks) and then ∼4 months after the ictus. The functional magnetic resonance imaging tasks were designed to differentiate between activation related to language production (sentential overt speech production-Speech task) and activation related to cognitive processing (non-verbal decision making). Simple rest and counting conditions were also included in the design. Task-evoked regional brain activations during the early and late phases were compared with a longitudinal measure of recovery of language production. In accordance with a role in cognitive processing, substantial activity was observed within the presupplementary motor area/dorsal anterior cingulate during the decision-making task. Critically, the level of activation within this region during speech production correlated positively with the longitudinal recovery of speech production across the two time points (as measured by the in-scanner performance in the Speech task). This relationship was observed for activation in both the early phase (r = 0.363, P = 0.03 one-tailed) and the late phase (r = 0.538, P = 0.004). Furthermore, presupplementary motor area/dorsal anterior cingulate activity was a predictor of both language recovery over time and language outcome at ∼4 months, over and above that predicted by lesion volume, age and the initial language impairment (general linear model overall significant at P < 0.0001; ExpB 1.01, P = 0.02). The particularly prominent relationship of the presupplementary motor area/dorsal anterior cingulate region with recovery of language was confirmed in voxel-wise correlation analysis, conducted unconstrained for the whole brain volume. These results accord with the hypothesis that the functionality of the presupplementary motor area/dorsal anterior cingulate contributes to language recovery after stroke. Given that this brain region is often spared in aphasic stroke, we propose that it is a sensible target for future research into rehabilitative treatments. More broadly, baseline assessment of domain-general systems could help provide a better prediction of language recovery.

Overlapping networks engaged during spoken language production and its cognitive control.

Spoken language production is a complex brain function that relies on large-scale networks. These include domain-specific networks that mediate language-specific processes, as well as domain-general networks mediating top-down and bottom-up attentional control. Language control is thought to involve a left-lateralized fronto-temporal-parietal (FTP) system. However, these regions do not always activate for language tasks and similar regions have been implicated in nonlinguistic cognitive processes. These inconsistent findings suggest that either the left FTP is involved in multidomain cognitive control or that there are multiple spatially overlapping FTP systems. We present evidence from an fMRI study using multivariate analysis to identify spatiotemporal networks involved in spoken language production in humans. We compared spoken language production (Speech) with multiple baselines, counting (Count), nonverbal decision (Decision), and "rest," to pull apart the multiple partially overlapping networks that are involved in speech production. A left-lateralized FTP network was activated during Speech and deactivated during Count and nonverbal Decision trials, implicating it in cognitive control specific to sentential spoken language production. A mirror right-lateralized FTP network was activated in the Count and Decision trials, but not Speech. Importantly, a second overlapping left FTP network showed relative deactivation in Speech. These three networks, with distinct time courses, overlapped in the left parietal lobe. Contrary to the standard model of the left FTP as being dominant for speech, we revealed a more complex pattern within the left FTP, including at least two left FTP networks with competing functional roles, only one of which was activated in speech production.

Measuring vascular reactivity with breath-holds after stroke: a method to aid interpretation of group-level BOLD signal changes in longitudinal fMRI studies.

Blood oxygenation level-dependent (BOLD) contrast functional magnetic resonance imaging (fMRI) is a widely used technique to map brain function, and to monitor its recovery after stroke. Since stroke has a vascular etiology, the neurovascular coupling between cerebral blood flow and neural activity may be altered, resulting in uncertainties when interpreting longitudinal BOLD signal changes. The purpose of this study was to demonstrate the feasibility of using a recently validated breath-hold task in patients with stroke, both to assess group level changes in cerebrovascular reactivity (CVR) and to determine if alterations in regional CVR over time will adversely affect interpretation of task-related BOLD signal changes. Three methods of analyzing the breath-hold data were evaluated. The CVR measures were compared over healthy tissue, infarcted tissue and the peri-infarct tissue, both sub-acutely (∼2 weeks) and chronically (∼4 months). In this cohort, a lack of CVR differences in healthy tissue between the patients and controls indicates that any group level BOLD signal change observed in these regions over time is unlikely to be related to vascular alterations. CVR was reduced in the peri-infarct tissue but remained unchanged over time. Therefore, although a lack of activation in this region compared with the controls may be confounded by a reduced CVR, longitudinal group-level BOLD changes may be more confidently attributed to neural activity changes in this cohort. By including this breath-hold-based CVR assessment protocol in future studies of stroke recovery, researchers can be more assured that longitudinal changes in BOLD signal reflect true alterations in neural activity.

Task-induced brain activity in aphasic stroke patients: what is driving recovery?

The estimated prevalence of aphasia in the UK and the USA is 250 000 and 1 000 000, respectively. The commonest aetiology is stroke. The impairment may improve with behavioural therapy, and trials using cortical stimulation or pharmacotherapy are undergoing proof-of-principle investigation, but with mixed results. Aphasia is a heterogeneous syndrome, and the simple classifications according to the Broca-Wernicke-Lichtheim model inadequately describe the diverse communication difficulties with which patients may present. Greater knowledge of how intact neural networks promote recovery after aphasic stroke, either spontaneously or in response to interventions, will result in clearer hypotheses about how to improve the treatment of aphasia. Twenty-five years ago, a pioneering study on healthy participants heralded the introduction of functional neuroimaging to the study of mechanisms of recovery from aphasia. Over the ensuing decades, such studies have been interpreted as supporting one of three hypotheses, which are not mutually exclusive. The first two predate the introduction of functional neuroimaging: that recovery is the consequence of the reconstitution of domain-specific language systems in tissue around the lesion (the 'perilesional' hypothesis), or by homotopic cortex in the contralateral hemisphere (the 'laterality-shift' hypothesis). The third is that loss of transcallosal inhibition to contralateral homotopic cortex hinders recovery (the 'disinhibition' hypothesis). These different hypotheses at times give conflicting views about rehabilitative intervention; for example, should one attempt to activate or inhibit a contralateral homotopic region with cortical stimulation techniques to promote recovery? This review proposes that although the functional imaging data are statistically valid in most cases, their interpretation has often favoured one explanation while ignoring plausible alternatives. In our view, this is particularly evident when recovery is attributed to activity in 'language networks' occupying sites not observed in healthy participants. In this review we will argue that much of the distribution of what has often been interpreted as language-specific activity, particularly in midline and contralateral cortical regions, is an upregulation of activity in intact domain-general systems for cognitive control and attention, responding in a task-dependent manner to the increased 'effort' when damaged downstream domain-specific language networks are impaired. We further propose that it is an inability fully to activate these systems that may result in sub optimal recovery in some patients. Interpretation of the data in terms of activity in domain-general networks affords insights into novel approaches to rehabilitation.

Cognitive control and its impact on recovery from aphasic stroke.

Aphasic deficits are usually only interpreted in terms of domain-specific language processes. However, effective human communication and tests that probe this complex cognitive skill are also dependent on domain-general processes. In the clinical context, it is a pragmatic observation that impaired attention and executive functions interfere with the rehabilitation of aphasia. One system that is important in cognitive control is the salience network, which includes dorsal anterior cingulate cortex and adjacent cortex in the superior frontal gyrus (midline frontal cortex). This functional imaging study assessed domain-general activity in the midline frontal cortex, which was remote from the infarct, in relation to performance on a standard test of spoken language in 16 chronic aphasic patients both before and after a rehabilitation programme. During scanning, participants heard simple sentences, with each listening trial followed immediately by a trial in which they repeated back the previous sentence. Listening to sentences in the context of a listen-repeat task was expected to activate regions involved in both language-specific processes (speech perception and comprehension, verbal working memory and pre-articulatory rehearsal) and a number of task-specific processes (including attention to utterances and attempts to overcome pre-response conflict and decision uncertainty during impaired speech perception). To visualize the same system in healthy participants, sentences were presented to them as three-channel noise-vocoded speech, thereby impairing speech perception and assessing whether this evokes domain general cognitive systems. As expected, contrasting the more difficult task of perceiving and preparing to repeat noise-vocoded speech with the same task on clear speech demonstrated increased activity in the midline frontal cortex in the healthy participants. The same region was activated in the aphasic patients as they listened to standard (undistorted) sentences. Using a region of interest defined from the data on the healthy participants, data from the midline frontal cortex was obtained from the patients. Across the group and across different scanning sessions, activity correlated significantly with the patients' communicative abilities. This correlation was not influenced by the sizes of the lesion or the patients' chronological ages. This is the first study that has directly correlated activity in a domain general system, specifically the salience network, with residual language performance in post-stroke aphasia. It provides direct evidence in support of the clinical intuition that domain-general cognitive control is an essential factor contributing to the potential for recovery from aphasic stroke.

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.

IC3 protocol: a longitudinal observational study of cognition after stroke using novel digital health technology.

INTRODUCTION: Stroke is a major cause of death and disability worldwide, frequently resulting in persistent cognitive deficits among survivors. These deficits negatively impact recovery and therapy engagement, and their treatment is consistently rated as high priority by stakeholders and clinicians. Although clinical guidelines endorse cognitive screening for poststroke management, there is currently no gold-standard approach for identifying cognitive deficits after stroke, and clinical stroke services lack the capacity for long-term cognitive monitoring and care. Currently, available assessment tools are either not stroke-specific, not in-depth or lack scalability, leading to heterogeneity in patient assessments. METHODS AND ANALYSIS: To address these challenges, a cost-effective, scalable and comprehensive screening tool is needed to provide a stroke-specific assessment of cognition. The current study presents such a novel digital tool, the Imperial Comprehensive Cognitive Assessment in Cerebrovascular Disease (IC3), designed to detect both domain-general and domain-specific cognitive deficits in patients after stroke with minimal input from a health professional. To ensure its reliability, we will use multiple validation approaches, and aim to recruit a large normative sample of age-matched, gender-matched and education-matched UK-based controls. Moreover, the IC3 assessment will be integrated within a larger prospective observational longitudinal clinical trial, where poststroke cognition will be examined in tandem with brain imaging and blood biomarkers to identify novel multimodal biomarkers of recovery after stroke. This study will enable deeper cognitive phenotyping of patients at a large scale, while identifying those with highest risk of progressive cognitive decline, as well as those with greatest potential for recovery. ETHICS AND DISSEMINATION: This study has been approved by South West-Frenchay Research Ethics Committee (IRAS 299333) and authorised by the UK's Health Research Authority. Results from the study will be disseminated at conferences and within peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT05885295. Stage: Pre-results.

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.

Association of Brain Age, Lesion Volume, and Functional Outcome in Patients With Stroke.

BACKGROUND AND OBJECTIVES: Functional outcomes after stroke are strongly related to focal injury measures. However, the role of global brain health is less clear. In this study, we examined the impact of brain age, a measure of neurobiological aging derived from whole-brain structural neuroimaging, on poststroke outcomes, with a focus on sensorimotor performance. We hypothesized that more lesion damage would result in older brain age, which would in turn be associated with poorer outcomes. Related, we expected that brain age would mediate the relationship between lesion damage and outcomes. Finally, we hypothesized that structural brain resilience, which we define in the context of stroke as younger brain age given matched lesion damage, would differentiate people with good vs poor outcomes. METHODS: We conducted a cross-sectional observational study using a multisite dataset of 3-dimensional brain structural MRIs and clinical measures from the ENIGMA Stroke Recovery. Brain age was calculated from 77 neuroanatomical features using a ridge regression model trained and validated on 4,314 healthy controls. We performed a 3-step mediation analysis with robust mixed-effects linear regression models to examine relationships between brain age, lesion damage, and stroke outcomes. We used propensity score matching and logistic regression to examine whether brain resilience predicts good vs poor outcomes in patients with matched lesion damage. RESULTS: We examined 963 patients across 38 cohorts. Greater lesion damage was associated with older brain age (ß = 0.21; 95% CI 0.04-0.38, p = 0.015), which in turn was associated with poorer outcomes, both in the sensorimotor domain (ß = -0.28; 95% CI -0.41 to -0.15, p < 0.001) and across multiple domains of function (ß = -0.14; 95% CI -0.22 to -0.06, p < 0.001). Brain age mediated 15% of the impact of lesion damage on sensorimotor performance (95% CI 3%-58%, p = 0.01). Greater brain resilience explained why people have better outcomes, given matched lesion damage (odds ratio 1.04, 95% CI 1.01-1.08, p = 0.004). DISCUSSION: We provide evidence that younger brain age is associated with superior poststroke outcomes and modifies the impact of focal damage. The inclusion of imaging-based assessments of brain age and brain resilience may improve the prediction of poststroke outcomes compared with focal injury measures alone, opening new possibilities for potential therapeutic targets.

A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms.

Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in stroke research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires neuroanatomical expertise. We previously released an open-source dataset of stroke T1w MRIs and manually-segmented lesion masks (ATLAS v1.2, N = 304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability (hidden MRIs and masks, n = 316) datasets. Algorithm development using this larger sample should lead to more robust solutions; the hidden datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke research.

Chronic Stroke Sensorimotor Impairment Is Related to Smaller Hippocampal Volumes: An ENIGMA Analysis.

Background Persistent sensorimotor impairments after stroke can negatively impact quality of life. The hippocampus is vulnerable to poststroke secondary degeneration and is involved in sensorimotor behavior but has not been widely studied within the context of poststroke upper-limb sensorimotor impairment. We investigated associations between non-lesioned hippocampal volume and upper limb sensorimotor impairment in people with chronic stroke, hypothesizing that smaller ipsilesional hippocampal volumes would be associated with greater sensorimotor impairment. Methods and Results Cross-sectional T1-weighted magnetic resonance images of the brain were pooled from 357 participants with chronic stroke from 18 research cohorts of the ENIGMA (Enhancing NeuoImaging Genetics through Meta-Analysis) Stroke Recovery Working Group. Sensorimotor impairment was estimated from the FMA-UE (Fugl-Meyer Assessment of Upper Extremity). Robust mixed-effects linear models were used to test associations between poststroke sensorimotor impairment and hippocampal volumes (ipsilesional and contralesional separately; Bonferroni-corrected, P<0.025), controlling for age, sex, lesion volume, and lesioned hemisphere. In exploratory analyses, we tested for a sensorimotor impairment and sex interaction and relationships between lesion volume, sensorimotor damage, and hippocampal volume. Greater sensorimotor impairment was significantly associated with ipsilesional (P=0.005; ß=0.16) but not contralesional (P=0.96; ß=0.003) hippocampal volume, independent of lesion volume and other covariates (P=0.001; ß=0.26). Women showed progressively worsening sensorimotor impairment with smaller ipsilesional (P=0.008; ß=-0.26) and contralesional (P=0.006; ß=-0.27) hippocampal volumes compared with men. Hippocampal volume was associated with lesion size (P<0.001; ß=-0.21) and extent of sensorimotor damage (P=0.003; ß=-0.15). Conclusions The present study identifies novel associations between chronic poststroke sensorimotor impairment and ipsilesional hippocampal volume that are not caused by lesion size and may be stronger in women.

Impact of UK academic foundation programmes on aspirations to pursue a career in academia.

OBJECTIVES: This study aimed to determine the role played by academic foundation programmes in influencing junior doctors' desire to pursue a career in academic medicine. METHODS: We conducted an online questionnaire-based study of doctors who were enrolled on or had completed academic foundation programmes in the UK. There were 92 respondents (44 men, 48 women). Of these, 32 (35%) possessed a higher degree and 73 (79%) had undertaken a 4-month academic placement during Foundation Year 2. Outcomes were measured using Likert scale-based ordinal response data. RESULTS: From a cohort of 115 academic foundation trainees directly contacted, 46 replies were obtained (40% response rate). A further 46 responses were obtained via indirect notification through local programme directors. From the combined responses, the majority (77%) wished to pursue a career in academia at the end of the academic Foundation Year (acFY) programme. Feeling well informed about academic careers (odds ratio [OR] 16.9, p=0.005) and possessing a higher degree (OR 31.1, p=0.013) were independently associated with an increased desire to continue in academia. Concern about reduced clinical experience whilst in academic training dissuaded from continuing in academia (OR 0.15, p=0.026). Many respondents expressed concerns about autonomy, the organisation of the programme and the quantity and quality of academic teaching received. However, choice of work carried out during the academic block was the only variable independently associated with increasing the desire of respondents to pursue a career in academia following their experiences in the acFY programme (OR 6.3, p=0.007). CONCLUSIONS: The results support the provision of well-organised academic training programmes that assist junior clinical academics in achieving clinical competencies whilst providing protected academic time, information about further academic training pathways and autonomy in their choice of academic work.

Active Acquisition for multimodal neuroimaging.

In many clinical and scientific situations the optimal neuroimaging sequence may not be known prior to scanning and may differ for each individual being scanned, depending on the exact nature and location of abnormalities. Despite this, the standard approach to data acquisition, in such situations, is to specify the sequence of neuroimaging scans prior to data acquisition and to apply the same scans to all individuals. In this paper, we propose and illustrate an alternative approach, in which data would be analysed as it is acquired and used to choose the future scanning sequence: Active Acquisition. We propose three Active Acquisition scenarios based around multiple MRI modalities. In Scenario 1, we propose a simple use of near-real time analysis to decide whether to acquire more or higher resolution data, or acquire data with a different field -of -view. In Scenario 2, we simulate how multimodal MR data could be actively acquired and combined with a decision tree to classify a known outcome variable (in the simple example here, age). In Scenario 3, we simulate using Bayesian optimisation to actively search across multiple MRI modalities to find those which are most abnormal. These simulations suggest that by actively acquiring data, the scanning sequence can be adapted to each individual. We also consider the many outstanding practical and technical challenges involving normative data acquisition, MR physics, statistical modelling and clinical relevance. Despite these, we argue that  Active Acquisition allows for potentially far more powerful, sensitive or rapid data acquisition, and may open up different perspectives on individual differences, clinical conditions, and biomarker discovery.