Prediction of Long-term Cognitive Function After Minor Stroke Using Functional Connectivity

Efficacy Assessment of Cerebral Perfusion Augmentation through Functional Connectivity in an Acute Canine Stroke Model

BACKGROUND AND PURPOSE

Ischemic stroke disrupts functional connectivity within the brain's resting-state networks (RSNs), impacting recovery. This study evaluates the effects of norepinephrine and hydralazine (NEH), a cerebral perfusion augmentation therapy, on RSN integrity in a hyperacute canine stroke model.

MATERIALS AND METHODS

Fifteen adult purpose-bred mongrel canines, divided into treatment and control (natural history) groups, underwent endovascular induction of acute middle cerebral artery occlusion (MCAO). Postocclusion, the treatment group received intra-arterial norepinephrine (0.1-1.52 µg/kg/min, adjusted for 25-45 mm Hg above baseline mean arterial pressure) and hydralazine (20 mg). Resting-state fMRI (rs-fMRI) data were acquired with a 3T scanner by using a blood oxygen level dependent-EPI sequence (TR/TE = 1400 ms/20 ms, 2.5 mm slices, 300 temporal positions). Preprocessing included motion correction, spatial smoothing (2.5 mm full width at half maximum), and high-pass filtering (0.01 Hz cutoff). Functional connectivity within RSNs were analyzed through group-level independent component analysis and weighted whole-brain ROI-to-ROI connectome, pre- and post-MCAO.

RESULTS

NEH therapy significantly maintained connectivity post-MCAO in the higher-order visual and parietal RSNs, as evidenced by thresholded statistical mapping (threshold-free cluster enhancement P corr > .95). However, this preservation was network-dependent, with no significant (P corr < .95) changes in the primary visual and sensorimotor networks.

CONCLUSIONS

NEH demonstrates potential as a proof-of-concept therapy for maintaining RSN functional connectivity after ischemic stroke, emphasizing the therapeutic promise of perfusion augmentation. These insights reinforce the role of functional connectivity as a measurable end point for stroke intervention efficacy, suggesting clinical translatability for patients with insufficient collateral circulation.

Web-based artificial intelligence to predict cognitive impairment following stroke: A multicenter study

BACKGROUND AND PURPOSE

Post-stroke cognitive impairment (PSCI) is highly prevalent in modern society. However, there is limited study implying an accurate and explainable machine learning model to predict PSCI. The aim of this study is to develop and validate a web-based artificial intelligence (AI) tool for predicting PSCI.

METHODS

The retrospective cohort study design was conducted to develop and validate a web-based prediction model. Adults who experienced a stroke between January 1, 2004, and September 30, 2017, were enrolled, and patients with PSCI were followed up from the stroke index date until their last follow-up. The model's performance metrics, including accuracy, area under the curve (AUC), recall, precision, and F1 score, were compared.

RESULTS

A total of 3209 stroke patients were included in the study. The model demonstrated an accuracy of 0.8793, AUC of 0.9200, recall of 0.6332, precision of 0.9664, and F1 score of 0.7651. In the external validation phase, the accuracy improved to 0.9039, AUC to 0.9094, recall to 0.7457, precision to 0.9168, and F1 score to 0.8224. The final model can be accessed at https://psci-calculator.my.id/.

CONCLUSION

Our results are able to produce a user-friendly interface that is useful for health practitioners to perform early prediction on PSCI. These findings also suggest that the provided AI model is reliable and can serve as a roadmap for future studies using AI models in a clinical setting.

The Graded Incomplete Letters Test (GILT): a rapid test to detect cortical visual loss, with UK Biobank implementation

Impairments of object recognition are core features of neurodegenerative syndromes, in particular posterior cortical atrophy (PCA; the 'visual-variant Alzheimer's disease'). These impairments arise from damage to higher-level cortical visual regions and are often missed or misattributed to common ophthalmological conditions. Consequently, diagnosis can be delayed for years with considerable implications for patients. We report a new test for the rapid measurement of cortical visual loss - the Graded Incomplete Letters Test (GILT). The GILT is an optimised psychophysical variation of a test used to diagnose cortical visual impairment, which measures thresholds for recognising letters under levels of increasing visual degradation (decreasing "completeness") in a similar fashion to ophthalmic tests. The GILT was administered to UK Biobank participants (total n=2,359) and participants with neurodegenerative conditions characterised by initial cortical visual (PCA, n=18) or memory loss (typical Alzheimer's disease, n=9). UK Biobank participants, including both typical adults and those with ophthalmological conditions, were able to recognise letters under low levels of completeness. In contrast, participants with PCA consistently made errors with only modest decreases in completeness. GILT sensitivity to PCA was 83.3% for participants reaching the 80% accuracy cut-off, increasing to 88.9% using alternative cut-offs (60% or 100% accuracy). Specificity values were consistently over 94% when compared to UK Biobank participants without or with documented visual conditions, regardless of accuracy cut-off. These first-release UK Biobank and clinical verification data suggest the GILT has utility in both rapidly detecting visual perceptual losses following posterior cortical damage and differentiating perceptual losses from common eye-related conditions.

Domain-specific cognitive impairment 6 months after stroke: The value of early cognitive screening

BACKGROUND

Cognitive screening following stroke is widely recommended, yet few studies have considered the prognostic value of acute domain-specific function for longer-term cognitive outcome. Identifying which post-stroke cognitive impairments more commonly occur, recover, and persist, and which impairments hold prognostic value, could inform care planning, and resource allocation.

AIMS

This study aimed to determine the prevalence of domain-specific impairment acutely and at 6 months, assess the proportion of change in cognitive performance, and examine the prognostic value of acute domain-specific cognitive screening.

METHODS

A prospective stroke cohort completed the Oxford Cognitive Screen acutely (⩽2 weeks) and 6 months post-stroke. We determined the prevalence of acute and 6-month domain-specific impairment and proportion of change in performance from acute to 6 months. Hierarchical multivariable regression was used to predict global and domain-specific cognitive impairment at 6 months adjusted for demographic/vascular factors, stroke severity, and lesion volume.

RESULTS

A total of 430 stroke survivors (mean/SD age 73.9/12.5 years, 46.5% female, median/interquartile range (IQR) National Institute of Health Stroke Scale (NIHSS) 5/2-10) completed 6-month follow-up. Acutely, domain-specific impairments were highly prevalent ranging from 26.7% (n = 112) in praxis to 46.8% (n = 183) in attention. At 6 months, the proportion of domain-specific recovery was highest in praxis (n = 73, 71%) and lowest in language (n = 89, 46%) and memory (n = 82, 48%). Severity of 6-month cognitive impairment was best predicted by the addition of acute cognitive impairment (adj R2 = 0.298, p < 0.0001) over demographic and clinical factors alone (adj R2 = 0.105, p < 0.0001). Acute cognitive function was the strongest predictor of 6-month cognitive performance (p < 0.0001). Acute domain-specific impairments in memory (p < 0.0001), language (p < 0.0001), and praxis (p < 0.0001) significantly predicted overall severity of cognitive impairment at 6 months.

CONCLUSION

Post-stroke cognitive impairment is highly prevalent across all domains acutely, while impairments in language, memory, and attention predominate at 6 months. Early domain-specific screening can provide valuable prognostic information for longer-term cognitive outcomes.

A qualitative study investigating the views of stroke survivors and their family members on discussing post-stroke cognitive trajectories

Cognitive impairment is common early after stroke but trajectories over the long term are variable. Some stroke survivors make a full recovery, while others retain a stable impairment or decline. This study explored the perceived advantages and disadvantages of discussing potential cognitive trajectories with stroke survivors and their family members. Stroke survivors at least six-months post-stroke were purposively sampled from an existing pool of research volunteers recruited originally for the OCS-Recovery study. They were invited, alongside a family member, to participate in a semi-structured interview. Interviews were audio recorded, transcribed, and analyzed using reflexive thematic analysis. Twenty-six stroke survivors and eleven family members participated. We identified one overarching theme and three related subthemes. The overarching theme was: One size does not fit all. The subthemes were: (1) Hearing about potential cognitive trajectories helps to develop realistic expectations; (2) Discussions about cognitive trajectories may be motivating; (3) Cognitive decline and post-stroke dementia discussions may be anxiety-provoking and depressing. Healthcare professionals should adopt a person-centred approach to sharing information about post-stroke cognitive trajectories. Discussions should be tailored to individual needs and preferences, with dementia-related topics in particular addressed with the utmost selectivity and sensitivity.

Functional and structural brain reorganization in patients with ischemic stroke: a multimodality MRI fusion study

Understanding how structural and functional reorganization occurs is crucial for stroke diagnosis and prognosis. Previous magnetic resonance imaging (MRI) studies focused on the analyses of a single modality and demonstrated abnormalities in both lesion regions and their associated distal regions. However, the relationships of multimodality alterations and their associations with poststroke motor deficits are still unclear. In this study, 71 hemiplegia patients and 41 matched healthy controls (HCs) were recruited and underwent MRI examination at baseline and at 2-week follow-up sessions. A multimodal fusion approach (multimodal canonical correlation analysis + joint independent component analysis), with amplitude of low-frequency fluctuation (ALFF) and gray matter volume (GMV) as features, was used to extract the co-altered patterns of brain structure and function. Then compared the changes in patients' brain structure and function between baseline and follow-up sessions. Compared with HCs, the brain structure and function of stroke patients decreased synchronously in the local lesions and their associated distal regions. Damage to structure and function in the local lesion regions was associated with motor function. After 2 weeks, ALFF in the local lesion regions was increased, while GMV did not improve. Taken together, the brain structure and function in the local lesions and their associated distal regions were damaged synchronously after ischemic stroke, while during motor recovery, the 2 modalities were changed separately.

Reward network dysfunction is associated with cognitive impairment after stroke

Stroke survivors not only suffer from severe motor, speech and neurocognitive deficits, but in many cases also from a "lack of pleasure" and a reduced motivational level. Especially apathy and anhedonic symptoms can be linked to a dysfunction of the reward system. Rewards are considered as important co-factor for learning, so the question arises as to why and how this affects the rehabilitation of stroke patients. We investigated reward behaviour, learning ability and brain network connectivity in acute (3-7d) mild to moderate stroke patients (n = 28) and age-matched healthy controls (n = 26). Reward system activity was assessed using the Monetary Incentive Delay task (MID) during magnetoencephalography (MEG). Coherence analyses were used to demonstrate reward effects on brain functional network connectivity. The MID-task showed that stroke survivors had lower reward sensitivity and required greater monetary incentives to improve performance and showed deficits in learning improvement. MEG-analyses showed a reduced network connectivity in frontal and temporoparietal regions. All three effects (reduced reward sensitivity, reduced learning ability and altered cerebral connectivity) were found to be closely related and differed strongly from the healthy group. Our results reinforce the notion that acute stroke induces reward network dysfunction, leading to functional impairment of behavioural systems. These findings are representative of a general pattern in mild strokes and are independent of the specific lesion localisation. For stroke rehabilitation, these results represent an important point to identify the reduced learning capacity after stroke and to implement individualised recovery exercises accordingly.

Structural disconnectome mapping of cognitive function in poststroke patients

BACKGROUND AND PURPOSE

Sequalae following stroke represents a significant challenge in current rehabilitation. The location and size of focal lesions are only moderately predictive of the diverse cognitive outcome after stroke. One explanation building on recent work on brain networks proposes that the cognitive consequences of focal lesions are caused by damages to anatomically distributed brain networks supporting cognition rather than specific lesion locations.

METHODS

To investigate the association between poststroke structural disconnectivity and cognitive performance, we estimated individual level whole-brain disconnectivity probability maps based on lesion maps from 102 stroke patients using normative data from healthy controls. Cognitive performance was assessed in the whole sample using Montreal Cognitive Assessment, and a more comprehensive computerized test protocol was performed on a subset (n = 82).

RESULTS

Multivariate analysis using Partial Least Squares on the disconnectome maps revealed that higher disconnectivity in right insular and frontal operculum, superior temporal gyrus and putamen was associated with poorer MoCA performance, indicating that lesions in regions connected with these brain regions are more likely to cause cognitive impairment. Furthermore, our results indicated that disconnectivity within these clusters was associated with poorer performance across multiple cognitive domains.

CONCLUSIONS

These findings demonstrate that the extent and distribution of structural disconnectivity following stroke are sensitive to cognitive deficits and may provide important clinical information predicting poststroke cognitive sequalae.

Vascular Cognitive Impairment After Mild Stroke: Connectomic Insights, Neuroimaging, and Knowledge Translation

Contemporary stroke assessment protocols have a limited ability to detect vascular cognitive impairment (VCI), especially among those with subtle deficits. This lesser-involved categorization, termed mild stroke (MiS), can manifest compromised processing speed that negatively impacts cognition. From a neurorehabilitation perspective, research spanning neuroimaging, neuroinformatics, and cognitive neuroscience supports that processing speed is a valuable proxy for complex neurocognitive operations, insofar as inefficient neural network computation significantly affects daily task performance. This impact is particularly evident when high cognitive loads compromise network efficiency by challenging task speed, complexity, and duration. Screening for VCI using processing speed metrics can be more sensitive and specific. Further, they can inform rehabilitation approaches that enhance patient recovery, clarify the construct of MiS, support clinician-researcher symbiosis, and further clarify the occupational therapy role in targeting functional cognition. To this end, we review relationships between insult-derived connectome alterations and VCI, and discuss novel clinical approaches for identifying disruptions of neural networks and white matter connectivity. Furthermore, we will frame knowledge translation efforts to leverage insights from cutting-edge structural and functional connectomics research. Lastly, we highlight how occupational therapists can provide expertise as knowledge brokers acting within their established scope of practice to drive substantive clinical innovation.

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.

Machine Learning in Action: Stroke Diagnosis and Outcome Prediction

The application of machine learning has rapidly evolved in medicine over the past decade. In stroke, commercially available machine learning algorithms have already been incorporated into clinical application for rapid diagnosis. The creation and advancement of deep learning techniques have greatly improved clinical utilization of machine learning tools and new algorithms continue to emerge with improved accuracy in stroke diagnosis and outcome prediction. Although imaging-based feature recognition and segmentation have significantly facilitated rapid stroke diagnosis and triaging, stroke prognostication is dependent on a multitude of patient specific as well as clinical factors and hence accurate outcome prediction remains challenging. Despite its vital role in stroke diagnosis and prognostication, it is important to recognize that machine learning output is only as good as the input data and the appropriateness of algorithm applied to any specific data set. Additionally, many studies on machine learning tend to be limited by small sample size and hence concerted efforts to collate data could improve evaluation of future machine learning tools in stroke. In the present state, machine learning technology serves as a helpful and efficient tool for rapid clinical decision making while oversight from clinical experts is still required to address specific aspects not accounted for in an automated algorithm. This article provides an overview of machine learning technology and a tabulated review of pertinent machine learning studies related to stroke diagnosis and outcome prediction.

Functional connectivity drives stroke recovery: shifting the paradigm from correlation to causation

Stroke is a leading cause of disability, with deficits encompassing multiple functional domains. The heterogeneity underlying stroke poses significant challenges in the prediction of post-stroke recovery, prompting the development of neuroimaging-based biomarkers. Structural neuroimaging measurements, particularly those reflecting corticospinal tract injury, are well-documented in the literature as potential biomarker candidates of post-stroke motor recovery. Consistent with the view of stroke as a 'circuitopathy', functional neuroimaging measures probing functional connectivity may also prove informative in post-stroke recovery. An important step in the development of biomarkers based on functional neural network connectivity is the establishment of causality between connectivity and post-stroke recovery. Current evidence predominantly involves statistical correlations between connectivity measures and post-stroke behavioral status, either cross-sectionally or serially over time. However, the advancement of functional connectivity application in stroke depends on devising experiments that infer causality. In 1965, Sir Austin Bradford Hill introduced nine viewpoints to consider when determining the causality of an association: [1] Strength, [2] Consistency [3] Specificity, [4] Temporality, [5] Biological gradient, [6] Plausibility, [7] Coherence, [8] Experiment, and [9] Analogy. Collectively referred to as the Bradford Hill Criteria, these points have been widely adopted in epidemiology. In this review, we assert the value of implementing Bradford Hill's framework to stroke rehabilitation and neuroimaging. We focus on the role of neural network connectivity measurements acquired from task-oriented and resting-state functional magnetic resonance imaging, electroencephalography, magnetoencephalography, and functional near-infrared spectroscopy in describing and predicting post-stroke behavioral status and recovery. We also identify research opportunities within each Bradford Hill tenet to shift the experimental paradigm from correlation to causation.