Sage Journals: Discover world-class research

Abstract

Purpose:

Post-stroke (PS) cognitive impairment (CI) is frequent and its devastating functional and vital consequences are well known. Despite recent guidelines, they are still largely neglected. A large number of recent studies have re-examined the epidemiology, diagnosis, imaging determinants and management of PSCI. The aim of this update is to determine whether these new data answer the questions that are essential to reducing PSCI, the unmet needs, and steps still to be taken.

Methods:

Literature review of stroke unit-era studies examining key steps in the management of PSCI: epidemiology and risk factors, diagnosis (cognitive profile and assessments), imaging determinants (quantitative measures, voxelwise localization, the disconnectome and associated Alzheimer’s disease [AD]) and treatment (secondary prevention, symptomatic drugs, rehabilitation and noninvasive brain stimulation) of PSCI.

Findings:

(1) the prevalence of PSCI of approximately 50% is probably underestimated; (2) the sensitivity of screening tests should be improved to detect mild PSCI; (3) comprehensive assessment is now well-defined and should include apathy; (4) easily available factors can identify patients at high risk of PSCI; (5) key imaging determinants are the location and volume of the lesion and the resulting disconnection, associated AD and brain atrophy; WMH, ePVS, microhemorrhages, hemosiderosis, and cortical microinfarcts may contribute to cognitive impairment but are more likely to be markers of brain vulnerability or associated AD that reduce PS recovery; (6) remote and online assessment is a promising approach for selected patients; (7) secondary stroke prevention has not been proven to prevent PSCI; (8) symptomatic drugs are ineffective in treating PSCI and apathy; (9) in addition to cognitive rehabilitation, the benefits of training platforms and computerized training are yet to be documented; (10) the results and the magnitude of improvement of noninvasive brain stimulation, while very promising, need to be substantiated by large, high-quality, sham-controlled RCTs.

Discussion and conclusion:

These major advances pave the way for the reduction of PSCI. They include (1) the development of more sensitive screening tests applicable to all patients and (2) online remote assessment; crossvalidation of (3) clinical and (4) imaging factors to (5) identify patients at risk, as well as (6) factors that prompt a search for associated AD; (7) the inclusion of cognitive outcome as a secondary endpoint in acute and secondary stroke prevention trials; and (8) the validation of the benefit of noninvasive brain stimulation through high-quality, randomized, sham-controlled trials. Many of these objectives can be rapidly and easily attained.

Graphical abstract

Keywords

Stroke Alzheimer’s disease mild cognitive impairment dementia executive functions lesion symptom mapping

Introduction

Post-stroke (PS) cognitive impairment (CI) is frequent and its consequences well known, with an increased risk of disability, institutionalization,^1–3 stroke recurrence and death.^4,5 International guidelines emphasize the importance of identifying patients with PSCI, referring them for appropriate neuropsychological evaluations and initiating early treatment.^6–8 However, they are still largely overlooked.⁹ A large number of recent studies, pooled data analyses and meta-analyses have re-examined the epidemiology, diagnosis, imaging and management of PSCI. In this update based on literature review, our aim is to determine whether these new data answer the indispensable questions needed to define management that will reduce PSCI and its consequences in the near future. This update summarizes the recent advances, unmet needs, and steps yet to be taken before these disorders can be reduced.

Methods: Selection of critical points for PSCI reduction

Since the ultimate goal is to provide effective treatment/management for PSCI patients, recent advances in this area were reviewed. To achieve effective treatment/management, it is first necessary to diagnose patients with PSCI, so it is necessary to report on recent advances in diagnosis, that is, neuropsychological assessment (including screening tests). Since it is impossible (and probably inappropriate) to assess the cognitive and behavioral status of all stroke patients with sufficient accuracy, it is appropriate to review strategies for identifying patients at risk (i.e. patients who should be referred for neuropsychological assessment): we have focused on the two strategies that have been previously explored on the basis of the clinical and imaging characteristics of these patients. Despite their interest, new and interesting aspects such as vascular dysfunction and inflammation were not addressed because they do not currently have practical implications for patient management. The data used were derived from recent literature reviews, ongoing works by the authors, their opinions and discussions in various international working groups.

After a brief epidemiological review highlighting the limitations of current estimates of the prevalence of PSCI, the study was organized around the following questions: (1) can we improve diagnosis both through the use of screening tests and with (2) comprehensive batteries, (3) can we identify patients at risk by both clinical (4) and imaging factors, analyzing features that account for the emergence of PSCI such as (5) post-stroke cavity characteristics, (6) white matter hyperintensities (WMH), (7) markers of small vessel pathology, (8) the contribution of Alzheimer’s disease (AD) and Cerebral amyloid angiopathy (CAA), and (9) structural and functional disconnection. Finally, we reviewed (10) more recent and promising approaches, including (11) remote cognitive assessment and (12) PSCI management, including (13) non-invasive stimulation.

Epidemiology: The prevalence of PSCI

PCSI has been assessed in numerous studies, most focusing on major CI (i.e. dementia), and reported varying prevalence^10–12 (Table 1). A meta-analysis reported a PSCI prevalence of 53%, two-thirds corresponding to mild CI and one-third to dementia.¹¹ Heterogeneity across studies is due to several factors (Table 1) the most important of which is probably the applicability of cognitive assessment, as 4%–25% of patients are non-assessable and are at high risk of CI.^13–15

Table 1.

Epidemiology of poststroke cognitive impairment (PSCI): Facts and knowledge taken for granted versus yet to be clarified-unmet needs.

	Taken for granted	To be clarified-unmet needs
Prevalence	• Dementia: 26.5% (varies from 7% to 67%)^10,11	• Underestimation of prevalence is likely^11,14,15
	• Mild CI: 36.4% ( varies from 5% to 64%)^11,12	• Need for sensitive cognitive tool adapted to severe strokes^21,22
	• Overall PSCI: 53% with about two thirds of mild CI in initially hospitalized patients^11,14,15	• Utility of remote administration²³
	• Depends on selection at baseline,^10,13,16,17 attrition at follow-up,¹⁸ applicability of cognitive assessment,^13–15 sensitivity of cognitive evaluation, criterion of cognitive deficit^11,19 and time of assessment^11,20	• Utility of remote administration²³
Risk factors
Stroke subtype	More frequent in hemorrhage and infarct^{10,11,24–26} than in ruptured aneurysm^27–29 and cerebral venous thrombosis^27,30	Cognitive impairment in pure cerebellar lesions to be documented^31–34
Others factors	Age,^10,11,35,36 cognitive reserve and educational attainment,^6,10,35 APOE genotype,³⁷ prestroke CI, disability or frailty,³⁸ stroke severity,^35,36 recurrent stroke, multiple or large lesions,^10,35,36 left hemisphere stroke, acute complications or cognitive disorders (including delirium), low score on cognitive screening test^10,35,36 and gross imaging markers^6,10,35	Predictive model based on:
		• vascular risk factors: low predictive accuracy³⁹
		• clinical and gross imaging factors (age, stroke severity, multiple strokes, low score on screening test, extensive to confluent WMH): good to excellent sensitivity^35,36 but large validation studies needed

WMH: white matter hyperintensities.

This indicates that the prevalence of PSCI of approximately 50% among initially hospitalized patients is probably underestimated.

The diagnosis of PSCI

The diagnosis of PSCI relies on the accuracy of cognitive tools, which have evolved. The aim of this section is to examine whether we can improve the diagnosis of PSCI.

Screening tests

The use of cognitive-screening tests (Table 2) is part of the clinical assessment at the poststroke visit and sometimes before discharge from the acute stroke unit. The MiniMental State Examination⁴⁰ and Montreal Cognitive Assessment (MoCA)⁴¹ are the most frequently used but their sensitivity for PSCI is only moderate.^21,22 A meta-analysis²² suggests that acute testing yields higher sensitivity (and lower specificity), although this is still too low to use it as a substitute for comprehensive assessment. Other screening tests are being developed (e.g.,^42,43) but their superiority over existing tools still requires validation.

Table 2.

Diagnosis of poststroke cognitive impairment: Facts and knowledge taken for granted versus yet to be clarified-unmet needs.

	Taken for granted	To be clarified-unmet needs
Screening test	MMSE⁴⁰ and MoCA⁴¹: moderately good sensitivity^21,22	Need for sensitive screening tool adapted to severe strokes
Comprehensive battery	- standard battery (e.g. Harmonization standards battery^24,44) assesses:	• The usual executive tests (trail making, digit symbol coding and fluency tests) need to be supplemented for some causes of stroke such as aneurysm rupture²⁷
	• 6 major domains: processing speed, executive functions, episodic memory, language, visuoconstructive abilities and behavioral-socio-emotional changes-depression	- Crossvalidation of optimal and parsimonious combination of tests needed for hemineglect assessment
	• to be supplemented by optional modules if necessary
	• most frequently impaired domains in stroke⁴⁵
	• closely related to disability^3,46
	• executive functions and speed: the most vulnerable functions^19,24,27,47
	- Hemineglect assessment:
	• cancelation test associated if possible with line bisection, figure copying and baking tray tasks (EAN recommendation)⁴⁸
	• 3 tests (cancelation Bell test, bisection of long lines and a reading task)⁴⁹: most sensitive and parsimonious test combination to detect hemineglect⁵⁰
Predominant impairment of speed and executive functions	- slowing of processing speed:	- crossvalidation studies needed to relate patient slowing to:
	• observed in 88% [75%–95%] of PSCI in the STROKCOG cohorts²⁴	• prominent motor slowing^54,55 subsequent to disruption of frontostriatal⁵⁴ and thalamofrontal⁵⁶ tracts
	• optimal sensitivity of digit symbol coding and Trail Making tests part B²⁷	• primary attention disorders^19,24,45,57: restricted to lesions of the dorsomedial prefrontal cortex,^58–61 thalamus⁶² and right ventrolateral prefrontal cortex^54,58
	-apathy:	- crossvalidation studies needed to support 2 main mechanisms:
	• observed in approximately one third of patients^27,51,52	• “pure apathy” due to primary loss of motivation and effort estimation⁶³: lesions of ventral striatum, amygdala, mesencephalon, anterior cingulate, thalamus and inferior frontal gyrus^63,64
	• independently associated with disability^3,27,51,53	• may be secondary to sensory-motor, cognitive and depressive disorders^51,52 (i.e. secondary apathy)⁵²

MMSE: MiniMental State Examination; MoCA: Montreal Cognitive Assessment; EAN: European Academy of Neurology; PSCI: post-stroke cognitive impairment; VCI: vascular cognitive impairment; WMH: white matter hyperintensities; PET: positron emission tomography.

This indicates that the sensitivity of screening tests should be improved to detect mild PSCI in routine practice, or that an alternative strategy should be developed to select at-risk patients.

Comprehensive assessment

The comprehensive assessment^19,24,31,44 (Table 2) is the gold standard, appears to be very sensitive^{19,24,27,45,57,65} and is independently related to disability.^3,46 Assessment should include apathy because it is observed in approximately one-third of patients^27,51,52 and is independently associated with disability.^3,27 The comprehensive battery needs to be supplemented in specific situations: certain executive tests need to be added for certain causes of stroke,²⁷ as well as language assessment in aphasics⁶⁶ and hemineglect assessment in certain right hemisphere stroke patients using recent recommendations⁴⁸ and a parsimonious combination of tests⁵⁰ (Table 1).

Given that it is impossible and unnecessary to carry out a comprehensive assessment of all stroke patients, and that the sensitivity of screening tests is moderate, we suggest developing alternative strategies based on the identification of at-risk patients.

Identification of patients at risk for PSCI on the basis of clinical factors

Although CI can be observed in all patients, clinical and gross imaging risk factors have been identified. CI is more frequent in cerebral hemorrhage and infarct^11,24–26 than in ruptured aneurysm and cerebral venous thrombosis where the cerebral lesion is inconstant.^27–30 Reviews have identified a long list (Table 1) of clinical factors^10,24,45 and gross imaging markers.^6,10,35,67 Other poststroke adverse outcomes, such as fatigue⁶⁸ or obstructive sleep apnea⁶⁹ may also be associated with PSCI.

The predictive value of these factors has been examined in a few studies using initial data to determine the six-month risk. Models mainly based on vascular risk factors showed low predictive accuracy in crossvalidation studies.³⁹ The combination of five to six clinical factors (Table 1) can provide good to excellent sensitivity to predict six-month PSCI defined based on a comprehensive battery³⁵ or MoCA,³⁶ although crossvalidation is required.

This indicates that easily available factors can identify patients at high risk of PSCI, although crossvalidation studies on large cohorts are warranted.

Imaging determinants of PSCI and their use to identify patients at risk

In addition to the vascular lesions themselves, associated diseases such as Alzheimer’s disease may contribute to PSCI.^1,70–72 In terms of imaging, several features are associated with CI (Table 3): WMH, microhemorrhages and hemosiderosis, microinfarcts, enlarged perivascular spaces (ePVS), and cerebral and hippocampal atrophy.^{10,25,32,56,57,73} Quantitative measures have recently refined these imaging features (e.g. lesion volume, voxelwise analysis of lesion, brain connectivity).

Table 3.

Imaging characteristics of white matter hyperintensities and markers of small vessel disease (adapted from Strive 2⁷⁴).

	Definition
White matter hyperintensities	Signal abnormality of variable size hyperintense on T2-weighted images, such as FLAIR, without cavitation (signal different from CSF)
Microbleeds	- Small, usually up to a few mm, round low signal intensity dots on susceptibility weighted MRI sequences,⁷⁴
	- Typically result from small hemosiderin deposits that are remnants of tiny past hemorrhages⁷⁵
	- Strictly lobar cerebral microbleeds are often a manifestation of cerebral amyloid angiopathy, whereas deep and brainstem microbleeds are more likely to relate to arteriolosclerosis⁷⁵
Hemosiderosis	Thin areas of hypointensity on MRI sequences sensitive to susceptibility effects, in or overlying the superficial cortex
Cortical microinfarcts	Small lesions (size ⩽ 4 mm) strictly cortical seen on multiple MRI sequences with an appearance compatible with ischemia (hypointense on T1-weighted, hyperintense on T2-weighted or FLAIR, and isointense on T2*-weighted MRI)⁷⁶
Enlarged perivascular space	Tubular, fluid-filled spaces, following the course of small penetrating vessels through the white, and—in the brainstem and basal ganglia—also the gray matter, with signal intensity on MRI similar to CSF⁷⁷

FLAIR: fluid-attenuated inversion recovery.

Imaging characteristics responsible for PSCI occurrence

Lesion location and volume

Voxelwise lesion symptom mapping analysis showed the prominent role of left frontotemporal, thalamus, and right parietal lesions.³¹ The “strategic stroke” classically encompasses a large number of locations⁴⁵ that have been variably defined. Five strategic locations were identified using a strict definition, involving the left middle frontal gyrus, the temporoparietal junction, antero-middle thalamus, and both pyramidal tracts.³² The contribution of cerebellar lesions, especially in the crus VIII, warrants further studies.^31–33

This indicates that the location of lesions within the hemisphere should be used to identify patients at risk. A risk score based on lesion location³¹ has been developed and requires crossvalidation.

White matter hyperintensity and features of small vessel disease: Simple markers or key-players?

White matter hyperintensity

Confluent WMH promote the emergence of cognitive decline when a focal ischemic or hemorrhagic lesion has occurred^24,78–80 (Table 3). However, the underlying mechanisms remain unclear. WMH may reduce the brain resources needed for efficient recovery and promote persistent cognitive manifestations associated with a sudden ischemic or hemorrhagic lesion.^81,82 Furthermore, they may themselves contribute to a greater cognitive deficit. When extensive, they are associated with psychomotor slowing.⁸³ Finally, WMH may increase the risk of recurrent stroke that will finally result in a larger number of lesions, with subsequent cognitive decline.^83,84 In clinical practice, the degree of CI specifically associated with WMH in stroke patients remains difficult to estimate. Accumulating data in the literature suggest that the impact of WMH may largely vary depending on (1) their extent within the cerebral connectivity network,⁸⁵ (2) their exact location with a larger impact of lesions affecting anterior thalamic radiations or the forceps minor,⁸⁶ (3) the severity of the underlying tissue lesions, varying from simple water accumulation to severe neuronal and axonal loss,^87–89 (4) the age and clinical status prior the stroke event, with protective factors, such as cognitive reserve,⁹⁰ and (5) the previous cerebral status, with the presence or absence of lacunes, neurodegenerative lesions, and cerebral atrophy.⁹¹ Overall, these multiple factors explain why the exact impact of WMH is ultimately highly variable after stroke at the individual level, despite their generally pejorative predictive value.

Markers of small-vessel disease

Cerebral microbleeds, hemosiderosis, cortical microinfarcts, and ePVS constitute MRI manifestations of small-vessel disease (Table 3). There is an expanding literature on microbleeds, ePVS, and microinfarcts as predictors of PSCI.^56,92–94 Some studies found a modest association between these MRI markers and the occurrence of PSCI, whereas others did not (systematic review⁹²), likely reflecting small effect sizes. Indeed, a recent study that included a head to head comparison of these various imaging markers reported much smaller effect sizes for microbleeds and ePVS in relation to PSCI than for WMH.⁵⁶

Overall, the available data suggest that the observed associations with PSCI do not so much reflect a causative effect of these tiny lesions themselves on cognitive performance, but rather their presence indicates much more widespread microvascular disease in the brain, with a global impact on brain tissue and its resilience, thus predisposing affected individuals to a poor cognitive outcome in the event of a stroke.

The synergistic contribution of multiple imaging features

The imaging features that contribute to the assessment of cognitive performance are usually examined separately, whereas they contribute together. The few studies that have used statistical modeling of multiple imaging features suggest a dominant role for lesion location, followed by stroke volume, hippocampal and cerebral atrophy, and WMH volume, whereas the contribution of ePVS and microhemorrhages appears to be minor.^32,95–98

Pending crossvalidation studies, this indicates that, in addition to lesion location, multiple imaging features (including stroke volume, hippocampal and cerebral atrophy, and WMH volume), should be used to refine the identification of at-risk patients.

The contribution of Alzheimer disease to PSCI

Although PSCI is generally attributed solely to the vascular lesions, pioneering studies suggested that a third of PS dementia is due to associated AD.^1,70–72 The contribution of AD has been refined following amyloid positron emission tomography (PET) studies. First, PET studies refuted the promotion of amyloid deposition by stroke lesions.^99–101 Second, they showed a prevalence of amyloid positivity of approximately 15%–20% depending on the age and frequency of CI in the sample.^{99,100,102–104} Third, the largest studies showed that the amyloid burden is associated with a more severe cognitive status at baseline^99,102 and a high risk of developing severe CI at follow-up.^102,105,106 Accordingly, PET positivity was found in 30% to 38%^99,104 of PS dementia cases, thus supporting the pioneering studies.^1,70,72 This converges with the additive effect of vascular and neurodegenerative lesions on cognitive outcome.^107–109 Fourth, a high burden of posterior WMH, cortical microbleeds, centrum semiovale ePVS, and hippocampal atrophy has been shown to be suggestive of amyloïdopathy,⁹⁹ although this requires replication. Fifth, the use of cognitive profiles (based on the executive/memory contrast) to orient the cause of PSCI (i.e. pure vascular vs CI associated with AD) has been called into question by the finding of overlapping cognitive profiles in the two diseases^47,99 due to a high rate of dysexecutive disorders in AD¹¹⁰ and the predominance of the encoding-storage (also known as hippocampal) profile of vascular memory deficit.⁶⁵ Thus, only severe memory deficits (relative to executive disorders) that are not explained by stroke location should be considered an indication of associated AD. In addition to documenting the highly deleterious effect of associated AD, these data offer imaging possibilities for identifying associated AD in PSCI patients.

CAA can also be positive on amyloid PET^111,112 and is a factor associated with cognitive impairment independent of associated AD.¹¹³ Accordingly, the presence of MRI markers of CAA is associated with an increased risk of CI.²⁵ This suggests that CAA may represent a therapeutic target: a phase 2 trial targeting Aβ1-40, the major Aβ species deposited in the arterial wall in CAA, did not show the expected result.¹¹⁴

Pending more precise predictors, this indicates that association with AD should be sought, given its frequency and major prognostic impact, in at-risk patients which include age, severe memory deficits (vs executive disorders) not explained by stroke location, high posterior WMH load, cortical microbleeds, centrum semiovale ePVS, and hippocampal atrophy. Diagnostic approach (annual cognitive and imaging follow-up, CSF biomarkers, or amyloid PET) depends on patient status, the evolving availability of amyloid PET, the development of AD-modifying drugs, and the demonstration of their benefit in patients with mild to moderate stroke.

Is disconnection the key player that determines the occurrence of PSCI?

Current predictions for PSCI based on lesion location are satisfying, although limited by the accuracy of the current model of brain function.¹¹⁵ Recent evidence suggests that brain function relies more on connections within the brain than on the local contribution of regions to functions.^116–119 This can be explained by the importance of the interaction between brain regions in the achievement of brain functions, as well rapid plasticity, allowing for other connected regions to take over the additional workload.^120,121 In the context of brain damage, the contribution of disconnections to behavior has frequently shown higher statistical and explanatory power for the symptoms observed in patients.^{95,97,122–127} Hence, disconnection could be a key player in determining the occurrence of PSCI.¹²⁸ Despite the improvement provided by disconnexion, the amount of variance explained by these models is still limited.^95,123

To sum up, these data suggest that the key players are the location and volume of the lesion and the resulting disconnection, associated AD and brain atrophy. WMH, ePVS, microhemorrhages, hemosiderosis, and cortical microinfarcts may contribute to cognitive impairment but are more likely to be markers of brain vulnerability or associated AD that reduce PS recovery. Further studies are warranted to accurately determine the minimal lesion load (or disconnection) that induces PSCI and refine the modeling of imaging determinants of PSCI. Thus, additional improvement is needed before using such modeling to accurately predict patients at risk of PSCI.

Recent and promising approaches

Improving the diagnosis: Remote online cognitive assessment

Pending the development of more sensitive screening tests, comprehensive cognitive assessment is needed for patients at risk of PSCI based on clinical and imaging risk factors^8,83 (see above). The recent development of remote and online assessment may offer such an opportunity for selected patients.²³

Tools such as the Telephone Interview for Cognitive Status and telephone short MoCA⁴¹ were the first available methods for remotely measuring cognitive performance in stroke patients.¹²⁹ Recent results, obtained on a small stroke sample, showed that a self-administered tablet-based neurocognitive platform is widely acceptable and has good convergent validity.¹³⁰ The organization of a remotely accessible detailed cognitive assessment on an internet platform that can be controlled under professional supervision also appears promising. The feasibility of this procedure has already been established using a flexible, integrated system.¹³¹ Other organizational (links with professionals) and practical factors (financing of equipment, cost) must also be taken into consideration. The development of digital ecological momentary assessment presents a promising approach to monitor at-risk patients outside the hospital environment and provide access to digital therapeutics.¹³² This approach needs to be validated on a large control population before standards can be established for their clinical use. Finally, remote cognitive assessment will necessarily remain limited to patients who have the necessary computer skills, are free of major deficit and who can access a high-performance internet communication network. Professional supervision will remain crucial to ensure reproducible and high-quality data. Several studies are currently underway to document these critical points.

Another advantage of online remote assessment is that it facilitates continuous assessment. PSCI is a dynamic process that can be exacerbated by mood or sleep disturbances and external factors such as social support and environment. Therefore, continuous follow-up is needed to capture potential changes in cognition and to assess the impact of these symptoms on daily life, which is not addressed in the guidelines. The current development of digital ecological momentary assessment is a promising approach to monitor patients outside the hospital setting and provide access to digital therapeutics.

Finally, such an online assessment can feed a collaborative digital platform that offers the opportunity to optimize collaboration between physicians, including neurologists, gerontologists, and primary care physicians, speech-language pathologists, occupational therapists, neuropsychologists, nurses, and allied health professionals for optimal identification and management of cognitive problems after stroke. This indicates that remote and online assessment is a promising approach for selected patients, and their value should be clarified by ongoing studies.

Management of PSCI

Interventions (Table 4) for secondary stroke prevention including vascular risk factors (hypertension, smoking, diabetes, hyperlipidemia, obesity, obstructive sleep apnea and physical activity) are mandatory but their contribution to prevent CI remains to be proven.^6,7,133 The cognitive benefit of lacunar infarction treatment demonstrated in a recent small phase 2 trial requires validation in further studies.¹³⁴

Table 4.

Classical and promising approaches in management and brain stimulation.

	Taken for granted	To be clarified-unmet needs
Secondary stroke prevention	Interventions for hypertension, smoking, diabetes, hyperlipidemia, obesity, obstructive sleep apnea and physical activity are mandatory	But effect on cognition remains unproven^6,7,133
Pharmacological symptomatic treatments	Cholinesterase inhibitors, memantine, dopamine agonists and selective serotonin reuptake inhibitors: no significant benefit on pure vascular PSCI^7,135,136	Refine mechanisms of certain cognitive and behavioral impairments
Cognitive rehabilitation	Combination of restorative and compensatory: traditional paper and pencil-based training	Platforms and computerized training: benefit to be documented^6,7,137
Noninvasive brain stimulation (NIBS)	Change in excitability of the underlying brain cortex potentially induces long-lasting neuroplastic changes^138,139	Overall benefit and its magnitude of all NIBS methods: needs to be substantiated by large, high-quality sham-controlled randomized trials
Repeated transcranial magnetic stimulation (rTMS)	Effects on cortical activity depends on frequency:
	- high-frequency (>1 Hz) stimulation promotes local neuronal excitability,
	- low-frequency (⩽1 Hz) stimulation shows inhibitory effects¹⁴⁰
Theta-burst stimulation	rTMS technique consisting of 3 pulse bursts at 50 Hz in continuous (inhibition of local cortical excitability) or intermittent forms (facilitation of local cortical excitability)^140,141	Could induce longer duration and more intense neural activity with low-intensity, short duration stimulation than conventional rTMS¹⁴²
Transcranial direct current stimulation (tDCS)	Constant, low-intensity direct current (intensity of 0.5–2 mA) applied through two electrodes placed on the scalp; subdivided into anodal (enhances activity of superficial cortical neurons), cathodal (reduces activity of superficial cortical neurons) and dual (both anodal and cathodal)¹⁴³
Transcutaneous vagus nerve stimulation		May modulate brain neurotransmitters release and blood flow to brain areas such as hippocampus and thalamus¹⁴⁴

In addition, there are no standard pharmacological treatments for the treatment of PSCI as cholinesterase inhibitors, glutamate N-Methyl-D-aspartate receptor antagonist, dopamine agonists and selective serotonin reuptake inhibitors have failed to demonstrate a significant benefit on global cognitive function after stroke.^{7,135,136,145} The lack of efficacy of symptomatic drugs on PSCI (Table 4) may also be due to their multiple mechanisms, as recently suggested for two leading impairments in PSCI, psychomotor slowing^{45,54,57–62} and apathy^{51,52,63,64,146} (Table 1). To date, the management of PSCI mainly relies on cognitive rehabilitation, including a combination of restorative and compensatory approaches, both using traditional paper-and-pencil tasks. Recent approaches offer new opportunities to improve cognitive rehabilitation. The benefits of training platforms and computerized training are yet to be documented.^6,7,137

Noninvasive brain stimulation

New hope has arisen from the promising results of noninvasive brain stimulation (Table 4), including repeated transcranial magnetic stimulation (rTMS), theta-burst stimulation (TBS), and transcranial direct current stimulation (tDCS).¹⁴⁰ These approaches assume that, under normal circumstances, the left and right hemispheres are in a balanced state of mutual inhibition. After a stroke, the lack of inhibitory effect of the damaged hemisphere on the undamaged hemisphere causes a relative increase in excitability of the intact hemisphere, ultimately resulting in an increase in inhibition of the damaged hemisphere. In addition noninvasive brain stimulation can induce changes in the excitability of the underlying cortex and potentially induce long-lasting neuroplastic changes by promoting neurogenesis, angiogenesis, anti-inflammatory, antioxidant, and anti-apoptosis effects.¹³⁸ Different stimulation frequencies have different effects on cortical activity, with high frequency (>1 Hz) stimulation (HF-rTMS) promoting local neuronal excitability and low frequency (⩽1 Hz) stimulation (LF-rTMS) showing inhibitory effects.¹⁴⁰

Given the small size of these studies, meta-analyses have been invaluable in assessing their interest. Concerning rTMS, systematic reviews and meta-analyses of randomized controlled trials (RCT) indicate a positive effect on cognitive outcome.¹³⁸ In a systematic review of 12 RCTs involving 497 patients with PSCI, Gong et al.¹⁴⁷ reported that rTMS had a positive effect on cognitive rehabilitation. Another study¹⁴⁸ pooling data from 8 studies and 336 participants found a large effect of rTMS combined with cognitive training on global cognition, executive function, and working memory, but no effect on memory. With regards to aphasia, a large number of studies converge on its benefits,¹⁴⁹ and it is now recommended in some countries.¹⁵⁰

TBS is a novel rTMS consisting of three pulse bursts at 50 Hz. Compared with conventional rTMS, TBS can induce longer and more intense neural activity with low-intensity, short-duration stimulation. Intermittent TBS (which plays a facilitating role in local cortical excitability¹⁴⁰) applied to left dorsolateral prefrontal cortex has been reported to improve executive function and semantic comprehension.^151,152

Research findings on tDCS show contradictory effects^153–155 with a slight improvement in general cognitive performance and attention provided by anodal tDCS.¹⁵⁵ In a meta-analysis¹⁵⁵ of 15 studies (N = 820 participants) of tDCS compared with sham tDCS or control, anodal tDCS was associated with small improvements in general cognitive and attention performance, but not in memory. However, most of these studies lacked sham tDCS and safety data.

A network meta-analysis¹⁴⁰ of RCTs comparing any active noninvasive brain stimulation with sham stimulation in stroke survivors showed that high-frequency rTMS improved global cognitive function while dual-tDCS improved memory performance.

Although promising, these results and the magnitude of improvement still need to be substantiated by large, high-quality, sham-controlled RCT. They should examine the influence of the timing of stimulation, treatment frequency and duration, and stimulation parameters, particularly the stimulation site.¹⁵⁶ Indeed, the location of the lesion and its effects on connectivity can help to select the most appropriate stimulation parameters to individualize the location of the stimulation.^149,157,158 Finally, the potential benefit of combining noninvasive brain stimulation methods in the same patient should be examined, as recently suggested.¹⁵⁹

Conclusions

This update shows the major advances and provides guidance on the main issues to be resolved before reducing PSCI. This mainly includes (1) crossvalidation of factors that identify PS patients at risk of CI, (2) identification of the minimal vascular lesion that induces PSCI and (3) factors to prompt the search for associated AD; (4) development and validation of screening tests to improve the sensitivity and applicability to all patients, as well as (5) digital ecological momentary assessment and (6) cognitive assessment that are remotely administered online, and (7) used to monitor the course PSCI and (8) to provide access to digital therapeutics; (9) validation of the benefit of noninvasive brain stimulation by high-quality, individually-based, sham-controlled RCT; (10) systematic inclusion of cognitive outcome as a secondary endpoint in both acute and secondary prevention stroke trials, and (11) development of symptomatic drug trials targeting behavioral and cognitive disorders of pre-defined mechanisms. A significant number of these objectives can be easily and rapidly attained. Their findings may be incorporated into a revised version of the VASCOG criteria for vascular cognitive impairment.¹⁶⁰

Footnotes

Acknowledgements

Acknowledgments none related to this study.

Declaration of conflicting interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Olivier Godefroy: has served on scientific advisory boards (Novartis and Astra Zeneca), received funding for travel and meetings from Novartis, Lilly, Genzyme, Astrazeneca, Biogen, Teva, Pfizer, CSL-Behring, GSK, Boehringer-Ingelheim, Ipsen, Covidien, Bristol-Myers Squibb. Ardalan Aarabi: none related to this study. Yannick Béjot meeting speaker: BMS, Pfizer, Boehringher-Ingelheim, Servier, Medtronic, Amgen; Consulting: Medtronic, NovoNordisk, Novartis. Geert J Biessels: Consulting : Nestle HealthScience. Bertrand Glize: Consulting: IPSEN. Vincent MT Mok: none related to this study. Michel Thiebaut de Schotten: none related to this study. Igor Sibon: meeting speaker: BMS, Pfizer, Boehringher-Ingelheim, Servier, Medtronic, Novonordisk, Novartis, Sanofi, Astra-Zeneca; Consulting: Medtronic, NovoNordisk, Novartis, Sanofi, Astra-Zeneca. Hugues Chabriat: none related to this study. M Roussel: none related to this study.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical approval

Ethical approval was not sought for this article because it is based on literature review.

Informed consent

Informed consent was not sought for this article because it is based on literature review.

Guarantor

Contributorship

OG conceived the review and wrote the first draft of the manuscript; YB researched literature on epidemiology and wrote §; AA, GJB, HC, MTdS researched literature and wrote § on imaging; OG and VTM researched literature and wrote § on Alzheimer disease; MR and OG researched literature and wrote § on cognitive assessment; HC and IS researched literature and wrote § on remote and online assessment; OG and BG researched literature and wrote § on rehabilitation and non-invasive brain stimulation. All authors reviewed and edited the manuscript and approved the final version of the manuscript.

ORCID iDs

Olivier Godefroy

Yannick Béjot

Hugues Chabriat

References

Tatemichi

Foulkes

Mohr

, et al. Dementia in stroke survivors in the stroke data bank cohort. Prevalence, incidence, risk factors, and computed tomographic findings. Stroke 1990; 21: 858–866.

Saposnik

Cote

Rochon

, et al. Care and outcomes in patients with ischemic stroke with and without preexisting dementia. Neurology 2011; 77: 1664–1673.

Tasseel-Ponche

Barbay

Roussel

, et al. Determinants of disability at 6 months after stroke: the GRECogVASC study. Eur J Neurol 2022; 29: 1972–1982.

Moroney

Bagiella

Tatemichi

, et al. Dementia after stroke increases the risk of long-term stroke recurrence. Neurology 1997; 48: 1317–1325.

Lee

Saver

Hong

, et al. Cognitive impairment and risk of future stroke: a systematic review and meta-analysis. Can Med Assoc J 2014; 186: E536–E546.

Quinn

Richard

Teuschl

, et al. European stroke organisation and European academy of neurology joint guidelines on post-stroke cognitive impairment. Eur J Neurol 2021; 28: 3883–3920.

El Husseini

Katzan

Rost

, et al. Cognitive impairment after ischemic and hemorrhagic stroke: a scientific statement from the American heart association/American stroke association. Stroke 2023; 54: e272–e291.

Diagnostic des troubles cognitifs post-AVC: Préconisations Société Française NeuroVasculaire et Fédération des Centres Mémoires 2018. Accessed July 7, 2023. https://www.societe-francaise-neurovasculaire.fr/preconisations-sfnv

Kapoor

Lanctôt

Bayley

, et al. “good outcome” isn’t good enough: cognitive impairment, depressive symptoms, and social restrictions in physically recovered stroke patients. Stroke 2017; 48: 1688–1690.

10.

Pendlebury

Rothwell

PM.

Prevalence, incidence, and factors associated with pre-stroke and post-stroke dementia: a systematic review and meta-analysis. Lancet Neurol 2009; 8: 1006–1018.

11.

Barbay

Diouf

Roussel

, et al. Systematic review and meta-analysis of prevalence in post-stroke neurocognitive disorders in hospital-based studies. Dement Geriatr Cogn Disord 2018; 46: 322–334.

12.

Sexton

McLoughlin

Williams

, et al. Systematic review and meta-analysis of the prevalence of cognitive impairment no dementia in the first year post-stroke. Eur Stroke J 2019; 4: 160–171.

13.

Graber

Garnier

Mohr

, et al. Influence of pre-existing mild cognitive impairment and dementia on post-stroke mortality. The Dijon stroke registry. Neuroepidemiology 2020; 54: 490–497.

14.

Pendlebury

Klaus

Thomson

, et al. Methodological factors in determining risk of dementia after transient ischemic attack and stroke: (III) applicability of cognitive tests. Stroke 2015; 46: 3067–3073.

15.

Elliott

Drozdowska

Taylor-Rowan

, et al. Who is classified as untestable on brief cognitive screens in an acute stroke setting? Diagnostics 2019; 9: 95.

16.

Béjot

Duloquin

Crespy

, et al. Influence of preexisting cognitive impairment on clinical severity of ischemic stroke: the Dijon Stroke Registry. Stroke 2020; 51: 1667–1673.

17.

Pendlebury

Chen

Bull

, et al. Methodological factors in determining rates of dementia in TIA and stroke: (I) impact of baseline selection bias. Stroke 2015; 46: 641–646.

18.

Pendlebury

Chen

Welch

, et al. Methodological factors in determining risk of dementia after transient ischemic attack and stroke: (II) effect of attrition on follow-up. Stroke 2015; 46: 1494–1500.

19.

Barbay

Taillia

Nédélec-Ciceri

, et al. Prevalence of poststroke neurocognitive disorders using national institute of neurological disorders and stroke-Canadian stroke network, VASCOG criteria (vascular behavioral and cognitive disorders), and optimized criteria of cognitive deficit. Stroke 2018; 49: 1141–1147.

20.

Pendlebury

Rothwell

PM.

Incidence and prevalence of dementia associated with transient ischaemic attack and stroke: analysis of the population-based Oxford vascular study. Lancet Neurol 2019; 18: 248–258.

21.

Godefroy

Fickl

Roussel

, et al. Is the Montreal cognitive assessment superior to the mini-mental state examination to detect poststroke cognitive impairment? Stroke 2011; 42: 1712–1716.

22.

Lees

Selvarajah

Fenton

, et al. Test accuracy of cognitive screening tests for diagnosis of dementia and multidomain cognitive impairment in stroke. Stroke 2014; 45: 3008–3018.

23.

Salvadori

Pantoni

Teleneuropsychology for vascular cognitive impairment: which tools do we have?

Cereb Circ - Cogn Behav 2023; 5: 100173.

24.

Crawford

Desmond

, et al. Profile of and risk factors for poststroke cognitive impairment in diverse ethnoregional groups. Neurology 2019; 93: e2257–e2271.

25.

Moulin

Labreuche

Bombois

, et al. Dementia risk after spontaneous intracerebral haemorrhage: a prospective cohort study. Lancet Neurol 2016; 15: 820–829.

26.

Garcia

Roussel

Bugnicourt

, et al. Cognitive impairment and dementia after intracerebral hemorrhage: a cross-sectional study of a hospital-based series. J Stroke Cerebrovasc Dis 2013; 22: 80–86.

27.

Roussel

Martinaud

Hénon

, et al. The behavioral and cognitive executive disorders of stroke: the GREFEX study. PLoS One 2016; 11: e0147602.

28.

Martinaud

Perin

Gérardin

, et al. Anatomy of executive deficit following ruptured anterior communicating artery aneurysm. Eur J Neurol 2009; 16: 595–601.

29.

Buunk

Spikman

Wagemakers

, et al. The vanishing of the ACoA syndrome after aneurysmal subarachnoid haemorrhage: new era, different management, fewer problems? J Neuropsychol 2024; 18: 142–157.

30.

Bugnicourt

Guegan-Massardier

Roussel

, et al. Cognitive impairment after cerebral venous thrombosis: a two-center study. J Neurol 2013; 260: 1324–1331.

31.

Weaver

Kuijf

Aben

, et al. Strategic infarct locations for post-stroke cognitive impairment: a pooled analysis of individual patient data from 12 acute ischaemic stroke cohorts. Lancet Neurol 2021; 20: 448–459.

32.

Puy

Barbay

Roussel

, et al. Neuroimaging determinants of poststroke cognitive performance. Stroke 2018; 49: 2666–2673.

33.

Stoodley

MacMore

Makris

, et al. Location of lesion determines motor vs. cognitive consequences in patients with cerebellar stroke. NeuroImage Clin 2016; 12: 765–775.

34.

Schmahmann

Macmore

Vangel

Cerebellar stroke without motor deficit: clinical evidence for motor and non-motor domains within the human cerebellum. Neuroscience 2009; 162: 852–861.

35.

Godefroy

Yaïche

Taillia

, et al. Who should undergo a comprehensive cognitive assessment after a stroke? A cognitive risk score. Neurology 2018; 91: e1979–e1987.

36.

Dong

Ding

Cui

, et al. Development and validation of a clinical model (DREAM-LDL) for post-stroke cognitive impairment at 6 months. Aging 2021; 13: 21628–21641.

37.

Salah Khlif

Egorova-Brumley

Bird

, et al. Cortical thinning 3 years after ischaemic stroke is associated with cognitive impairment and APOE ε4. NeuroImage Clin 2022; 36: 103200.

38.

Munthe-Kaas

Aam

Saltvedt

, et al. Is frailty index a better predictor than pre-stroke modified Rankin Scale for neurocognitive outcomes 3-months post-stroke? BMC Geriatr 2022; 22: 139.

39.

Tang

EYH

Price

Robinson

, et al. Assessing the predictive validity of simple dementia risk models in harmonized stroke cohorts. Stroke 2020; 51: 2095–2102.

40.

Folstein

McHugh

PR.

“Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12: 189–198.

41.

Nasreddine

Phillips

Bédirian

, et al. The Montreal cognitive assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc 2005; 53: 695–699.

42.

Murphy

Cornford

Higginson

, et al. Oxford cognitive screen: a critical review and independent psychometric evaluation. J Neuropsychol 2023; 17: 491–504.

43.

Brookes

Hollocks

Khan

, et al. The brief memory and executive test (BMET) for detecting vascular cognitive impairment in small vessel disease: a validation study. BMC Med 2015; 13: 51.

44.

Hachinski

Iadecola

Petersen

, et al. National institute of neurological disorders and stroke-Canadian stroke network vascular cognitive impairment harmonization standards. Stroke 2006; 37: 2220–2241.

45.

Godefroy

The behavioral and cognitive neurology of stroke. Cambridge: Cambridge University Press, 2013.

46.

Nys

van Zandvoort

de Kort

, et al. The prognostic value of domain-specific cognitive abilities in acute first-ever stroke. Neurology 2005; 64: 821–827.

47.

Andriuta

Roussel

Barbay

, et al. Differentiating between Alzheimer’s disease and vascular cognitive impairment: is the “memory versus executive function” contrast still relevant? J Alzheimers Dis 2018; 63: 625–633.

48.

Moore

Milosevich

Beisteiner

, et al. Rapid screening for neglect following stroke: a systematic search and European academy of neurology recommendations. Eur J Neurol 2022; 29: 2596–2606.

49.

Azouvi

Samuel

Louis-Dreyfus

, et al. Sensitivity of clinical and behavioural tests of spatial neglect after right hemisphere stroke. J Neurol Neurosurg Psychiatry 2002; 73: 160–166.

50.

Azouvi

Rousseaux

Bartolomeo

, et al. Discriminative value of different combinations of tests to detect unilateral neglect in patients with right hemisphere damage. Eur J Neurol 2023; 30: 3332–3340.

51.

van Dalen

Moll

van

Charante

Nederkoorn

, et al. Poststroke apathy. Stroke 2013; 44: 851–860.

52.

Aubignat

Roussel

Aarabi

, et al. Poststroke apathy: major role of cognitive, depressive and neurological disorders over imaging determinants. Cortex 2023; 160: 55–66.

53.

Godefroy

Azouvi

Robert

, et al. Dysexecutive syndrome: diagnostic criteria and validation study. Ann Neurol 2010; 68: 855–864.

54.

Ouin

Roussel

Aarabi

, et al. Poststroke action slowing: motor and attentional impairments and their imaging determinants. Evidence from lesion-symptom mapping, disconnection and fMRI activation studies. Neuropsychologia 2022; 177: 108401.

55.

Godefroy

Spagnolo

Roussel

, et al. Stroke and action slowing: mechanisms, determinants and prognosis value. Cerebrovasc Dis 2010; 29: 508–514.

56.

Georgakis

Duering

Wardlaw

, et al. WMH and long-term outcomes in ischemic stroke: a systematic review and meta-analysis. Neurology 2019; 92: e1298–e1308.

57.

Chabriat

Joutel

Dichgans

, et al. Cadasil. Lancet Neurol 2009; 8: 643–653.

58.

Stuss

Alexander

Shallice

, et al. Multiple frontal systems controlling response speed. Neuropsychologia 2005; 43: 396–417.

59.

Godefroy

Lhullier

Rousseaux

Non-spatial attention disorders in patients with frontal or posterior brain damage. Brain J Neurol 1996; 119: 191–202.

60.

Godefroy

Lhullier-Lamy

Rousseaux

SRT lengthening: role of an alertness deficit in frontal damaged patients. Neuropsychologia 2002; 40: 2234–2241.

61.

Godefroy

Cabaret

Rousseaux

Vigilance and effects of fatigability, practice and motivation on simple reaction time tests in patients with lesion of the frontal lobe. Neuropsychologia 1994; 32: 983–990.

62.

Rinne

Hassan

Goniotakis

, et al. Triple dissociation of attention networks in stroke according to lesion location. Neurology 2013; 81: 812–820.

63.

Husain

Roiser

JP.

Neuroscience of apathy and anhedonia: a transdiagnostic approach. Nat Rev Neurosci 2018; 19: 470–484.

64.

Pessiglione

Vinckier

Bouret

, et al. Why not try harder? Computational approach to motivation deficits in neuro-psychiatric diseases. Brain 2018; 141: 629–650.

65.

Godefroy

Roussel

Leclerc

, et al. Deficit of episodic memory: anatomy and related patterns in stroke patients. Eur Neurol 2009; 61: 223–229.

66.

Godefroy

Dubois

Debachy

, et al. Vascular aphasias: main characteristics of patients hospitalized in acute stroke units. Stroke 2002; 33: 702–705.

67.

Drozdowska

McGill

McKay

, et al. Prognostic rules for predicting cognitive syndromes following stroke: a systematic review. Eur Stroke J 2021; 6: 18–27.

68.

Graber

Garnier

Duloquin

, et al. Association between fatigue and cognitive impairment at 6 months in patients with ischemic stroke treated with acute revascularization therapy. Front Neurol 2019; 10: 931.

69.

Zhu

Ouyang

, et al. Obstructive sleep apnea is associated with cognitive impairment in minor ischemic stroke. Sleep Breath 2022; 26: 1907–1914.

70.

Kokmen

Whisnant

O’Fallon

, et al. Dementia after ischemic stroke: a population-based study in Rochester, Minnesota (1960-1984). Neurology 1996; 46: 154–159.

71.

Pohjasvaara

Erkinjuntti

Ylikoski

, et al. Clinical determinants of poststroke dementia. Stroke 1998; 29: 75–81.

72.

Hénon

Durieu

Guerouaou

, et al. Poststroke dementia: incidence and relationship to prestroke cognitive decline. Neurology 2001; 57: 1216–1222.

73.

Charidimou

Boulouis

Frosch

, et al. The Boston criteria version 2.0 for cerebral amyloid angiopathy: a multicentre, retrospective, MRI–neuropathology diagnostic accuracy study. Lancet Neurol 2022; 21: 714–725.

74.

Duering

Biessels

Brodtmann

, et al. Neuroimaging standards for research into small vessel disease—advances since 2013. Lancet Neurol 2023; 22: 602–618.

75.

Greenberg

Vernooij

Cordonnier

, et al. Cerebral microbleeds: a guide to detection and interpretation. Lancet Neurol 2009; 8: 165–174.

76.

van Veluw

Shih

Smith

, et al. Detection, risk factors, and functional consequences of cerebral microinfarcts. Lancet Neurol 2017; 16: 730–740.

77.

Wardlaw

Benveniste

Nedergaard

, et al. Perivascular spaces in the brain: anatomy, physiology and pathology. Nat Rev Neurol 2020; 16: 137–153.

78.

Mijajlović

Pavlović

Brainin

, et al. Post-stroke dementia - a comprehensive review. BMC Med 2017; 15: 11.

79.

Pasquier

Leys

Why are stroke patients prone to develop dementia?

J Neurol 1997; 244: 135–142.

80.

Sachdev

Brodaty

Valenzuela

, et al. Clinical determinants of dementia and mild cognitive impairment following ischaemic stroke: the Sydney stroke study. Dement Geriatr Cogn Disord 2006; 21: 275–283.

81.

Kissela

Lindsell

Kleindorfer

, et al. Clinical prediction of functional outcome after ischemic stroke: the surprising importance of periventricular white matter disease and race. Stroke 2009; 40: 530–536.

82.

Arsava

Rahman

Rosand

, et al. Severity of leukoaraiosis correlates with clinical outcome after ischemic stroke. Neurology 2009; 72: 1403–1410.

83.

Verdelho

Wardlaw

Pavlovic

, et al. Cognitive impairment in patients with cerebrovascular disease: a white paper from the links between stroke ESO dementia committee. Eur Stroke J 2021; 6: 5–17.

84.

Debette

Schilling

Duperron

, et al. Clinical significance of magnetic resonance imaging markers of vascular brain injury: a systematic review and meta-analysis. JAMA Neurol 2019; 76: 81–94.

85.

Boot

Mc van Leijsen

Bergkamp

, et al. Structural network efficiency predicts cognitive decline in cerebral small vessel disease. NeuroImage Clin 2020; 27: 102325.

86.

Duering

Gonik

Malik

, et al. Identification of a strategic brain network underlying processing speed deficits in vascular cognitive impairment. Neuroimage 2013; 66: 177–183.

87.

Zhang

Ouin

Grosset

, et al. Elderly CADASIL patients with intact neurological status. Stroke 2022; 24: 352–362.

88.

De Guio

Vignaud

Chabriat

, et al. Different types of white matter hyperintensities in CADASIL: insights from 7-Tesla MRI. J Cereb Blood Flow Metab 2018; 38: 1654–1663.

89.

Duchesnay

Hadj Selem

De Guio

, et al. Different types of white matter hyperintensities in CADASIL. Front Neurol 2018; 9: 526.

90.

Zieren

Duering

Peters

, et al. Education modifies the relation of vascular pathology to cognitive function: cognitive reserve in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy. Neurobiol Aging 2013; 34: 400–407.

91.

Casolla

Caparros

Cordonnier

, et al. Biological and imaging predictors of cognitive impairment after stroke: a systematic review. J Neurol 2019; 266: 2593–2604.

92.

Ball

Shah

Ross

, et al. Predictors of post-stroke cognitive impairment using acute structural MRI neuroimaging: a systematic review and meta-analysis. Int J Stroke 2023; 18: 543–554.

93.

Sagnier

Okubo

Catheline

, et al. Chronic cortical cerebral microinfarcts slow down cognitive recovery after acute ischemic stroke. Stroke 2019; 50: 1430–1436.

94.

Oveisgharan

Dawe

, et al. Frequency and underlying pathology of pure vascular cognitive impairment. JAMA Neurol 2022; 79: 1277–1286.

95.

Talozzi

Forkel

Pacella

, et al. Latent disconnectome prediction of long-term cognitive-behavioural symptoms in stroke. Brain 2023; 146: 1963–1978.

96.

Kandiah

Chander

Lin

, et al. Cognitive impairment after mild stroke: development and validation of the SIGNAL2 risk score. J Alzheimers Dis 2016; 49: 1169–1177.

97.

Biesbroek

Weaver

Aben

, et al. Network impact score is an independent predictor of post-stroke cognitive impairment: a multicenter cohort study in 2341 patients with acute ischemic stroke. NeuroImage Clin 2022; 34: 103018.

98.

Kuceyeski

Navi

Kamel

, et al. Structural connectome disruption at baseline predicts 6-months post-stroke outcome. Hum Brain Mapp 2016; 37: 2587–2601.

99.

Godefroy

Barbay

Martin

, et al. Prevalence of amyloid cerebral deposits and cognitive outcome after stroke: the IDEA3 study. Stroke 2023; 25: 315–319.

100.

Wollenweber

Därr

Müller

, et al. Prevalence of amyloid positron emission tomographic positivity in poststroke mild cognitive impairment. Stroke 2016; 47: 2645–2648.

101.

Sahathevan

Linden

Villemagne

, et al. Positron emission tomographic imaging in stroke: cross-sectional and follow-up assessment of amyloid in ischemic stroke. Stroke 2016; 47: 113–119.

102.

Mok

VCT

Lam

BYK

Wang

, et al. Delayed-onset dementia after stroke or transient ischemic attack. Alzheimers Dement 2016; 12: 1167–1176.

103.

Koenig

McCue

Grant

, et al. Lack of association between acute stroke, post-stroke dementia, race, and β-amyloid status. NeuroImage Clin 2021; 29: 102553.

104.

Yang

Wong

Wang

, et al. Risk factors for incident dementia after stroke and transient ischemic attack. Alzheimers Dement 2015; 11: 16–23.

105.

Godefroy

Trinchard

, et al. Do amyloid cerebral deposits influence the long-term post-stroke cognitive outcome? The IDEA3 study. medRxiv. https://doi.org/10.1101/2024.07.18.24310673.

106.

Liu

Wong

, et al. Influence of amyloid-β on cognitive decline after stroke/transient ischemic attack: three-year longitudinal study. Stroke 2015; 46: 3074–3080.

107.

Andriuta

Wiener

Perron

, et al. Neuroimaging determinants of cognitive impairment in the memory clinic: how important is the vascular burden? J Neurol 2024; 271: 504–518.

108.

Snowdon

Greiner

Mortimer

, et al. Brain infarction and the clinical expression of Alzheimer disease. The nun study. JAMA 1997; 277: 813–817.

109.

Schneider

Wilson

Bienias

, et al. Cerebral infarctions and the likelihood of dementia from Alzheimer disease pathology. Neurology 2004; 62: 1148–1155.

110.

Godefroy

Martinaud

Verny

, et al. The dysexecutive syndrome of Alzheimer’s disease: the GREFEX study. J Alzheimers Dis 2014; 42: 1203–1208.

111.

Gurol

Becker

Fotiadis

, et al. Florbetapir-PET to diagnose cerebral amyloid angiopathy: a prospective study. Neurology 2016; 87: 2043–2049.

112.

Planton

Saint-Aubert

Raposo

, et al. Florbetapir regional distribution in cerebral amyloid angiopathy and Alzheimer’s disease: a PET study. J Alzheimers Dis 2020; 73: 1607–1614.

113.

Boyle

Nag

, et al. Cerebral amyloid angiopathy and cognitive outcomes in community-based older persons. Neurology 2015; 85: 1930–1936.

114.

Leurent

Goodman

Zhang

, et al. Immunotherapy with ponezumab for probable cerebral amyloid angiopathy. Ann Clin Transl Neurol 2019; 6: 795–806.

115.

Godefroy

Brain-behaviour relationships. Some models and related statistical procedures for the study of brain-damaged patients. Brain 1998; 121: 1545–1556.

116.

Thiebaut de Schotten

Foulon

Nachev

Brain disconnections link structural connectivity with function and behaviour. Nat Commun 2020; 11: 5094.

117.

Fox

MD.

Mapping symptoms to brain networks with the human connectome. New Engl J Med 2018; 379: 2237–2245.

118.

Thiebaut de Schotten

Urbanski

Batrancourt

, et al. Rostro-caudal architecture of the frontal lobes in humans. Cereb Cortex 2016. Epub ahead of print 26 July 2016. DOI: 10.1093/cercor/bhw215.

119.

Thiebaut de Schotten

Urbanski

Valabregue

, et al. Subdivision of the occipital lobes: an anatomical and functional MRI connectivity study. Cortex 2014; 56: 121–137.

120.

Carrera

Tononi

Diaschisis: past, present, future. Brain 2014; 137: 2408–2422.

121.

Monakow

CV.

Die Lokalisation im Grosshirn Und Der Funktion durch Kortikale Herde. Wiesbaden: Verlag Von J.F. Bergmann, 1914.

122.

Dulyan

Talozzi

Pacella

, et al. Longitudinal prediction of motor dysfunction after stroke: a disconnectome study. Brain Struct Funct 2022; 227: 3085–3098.

123.

Salvalaggio

De Filippo

Grazia

Zorzi

, et al. Post-stroke deficit prediction from lesion and indirect structural and functional disconnection. Brain J Neurol 2020; 143: 2173–2188.

124.

Thiebaut de Schotten

Tomaiuolo

Aiello

, et al. Damage to white matter pathways in subacute and chronic spatial neglect: a group study and 2 single-case studies with complete virtual “in vivo” tractography dissection. Cereb Cortex 2014; 24: 691–706.

125.

Pacella

Foulon

Jenkinson

, et al. Anosognosia for hemiplegia as a tripartite disconnection syndrome. eLife 2019; 8: e46075.

126.

Thiebaut de Schotten

Dell’Acqua

Ratiu

, et al. From Phineas Gage and Monsieur leborgne to H.M.: revisiting disconnection syndromes. Cereb Cortex 2015; 25: 4812–4827.

127.

Khalilian

Roussel

Godefroy

, et al. Predicting functional impairments with lesion-derived disconnectome mapping: Validation in stroke patients with motor deficits. Eur J Neurosci 2024; 59: 3074–3092.

128.

Thiebaut de Schotten

Forkel

SJ.

The emergent properties of the connected brain. Science 2022; 378: 505–510.

129.

Barber

Stott

DJ.

Validity of the telephone interview for cognitive status (TICS) in post-stroke subjects. Int J Geriatr Psychiatry 2004; 19: 75–79.

130.

Sloane

Fabian

Wright

, et al. Supervised, self-administered tablet-based cognitive assessment in neurodegenerative disorders and stroke. Dement Geriatr Cogn Disord 2023; 52: 74–82.

131.

Durisko

McCue

Doyle

, et al. A flexible and integrated system for the remote acquisition of neuropsychological data in stroke research. Telemed E Health 2016; 22: 1032–1040.

132.

Bui

Kaufman

Pham

, et al. Ecological momentary assessment of real-world functional behaviors in individuals with stroke: a longitudinal observational study. Arch Phys Med Rehabil 2022; 103: 1327–1337.

133.

Weightman

Robinson

Fallows

, et al. Improving sleep and learning in rehabilitation after stroke, part 2 (INSPIRES2): study protocol for a home-based randomised control trial of digital cognitive behavioural therapy (dCBT) for insomnia. BMJ Open 2023; 13: e071764.

134.

Wardlaw

Woodhouse

Mhlanga

, et al. Isosorbide mononitrate and cilostazol treatment in patients with symptomatic cerebral small vessel disease: the lacunar intervention trial-2 (LACI-2) randomized clinical trial. JAMA Neurol 2023; 80: 682–692.

135.

Sami

Faruqui

The effectiveness of dopamine agonists for treatment of neuropsychiatric symptoms post brain injury and stroke. Acta Neuropsychiatr 2015; 27: 317–326.

136.

Legg

Rudberg

Hua

, et al. Selective serotonin reuptake inhibitors (SSRIs) for stroke recovery. Cochrane Database Syst Rev 2021; 11: CD009286.

137.

Fava-Felix

Bonome-Vanzelli

SRC

Ribeiro

, et al. Systematic review on post-stroke computerized cognitive training: unveiling the impact of confounding factors. Front Psychol 2022; 13: 985438.

138.

Zhou

Jin

, et al. Current evidence, clinical applications, and future directions of transcranial magnetic stimulation as a treatment for ischemic stroke. Front Neurosci 2023; 17: 1177283.

139.

Wessel

Zimerman

Hummel

FC.

Non-invasive brain stimulation: an interventional tool for enhancing behavioral training after stroke. Front Hum Neurosci 2015; 9: 265.

140.

Wang

Liu

Chen

, et al. Comparative efficacy of different noninvasive brain stimulation therapies for recovery of global cognitive function, attention, memory, and executive function after stroke: a network meta-analysis of randomized controlled trials. Ther Adv Chronic Dis 2023; 14: 20406223231168754.

141.

Huang

Rothwell

Chen

, et al. The theoretical model of theta burst form of repetitive transcranial magnetic stimulation. Clin Neurophysiol 2011; 122: 1011–1018.

142.

Kricheldorff

Göke

Kiebs

, et al. Evidence of neuroplastic changes after transcranial magnetic, electric, and deep brain stimulation. Brain Sci 2022; 12: 929.

143.

Nitsche

Cohen

Wassermann

, et al. Transcranial direct current stimulation: state of the art 2008. Brain Stimul 2008; 1: 206–223.

144.

Cheng

Wang

Bai

, et al. Research advances in the application of vagus nerve electrical stimulation in ischemic stroke. Front Neurosci 2022; 16: 1043446.

145.

Hankey

Hackett

Almeida

, et al. Twelve-month outcomes of the AFFINITY trial of fluoxetine for functional recovery after acute stroke: AFFINITY trial steering committee on behalf of the AFFINITY trial collaboration. Stroke 2021; 52: 2502–2509.

146.

Narme

Roussel

Mouras

, et al. Does impaired socioemotional functioning account for behavioral dysexecutive disorders? Evidence from a transnosological study. Neuropsychol Dev Cogn B Aging Neuropsychol Cogn 2017; 24: 80–93.

147.

Gong

Peng

, et al. Therapeutic effects of repetitive transcranial magnetic stimulation on cognitive impairment in stroke patients: a systematic review and meta-analysis. Front Hum Neurosci 2023; 17: 1177594.

148.

Gao

Qiu

Yang

, et al. Repetitive transcranial magnetic stimulation combined with cognitive training for cognitive function and activities of daily living in patients with post-stroke cognitive impairment: a systematic review and meta-analysis. Ageing Res Rev 2023; 87: 101919.

149.

Arheix-Parras

Barrios

Python

, et al. A systematic review of repetitive transcranial magnetic stimulation in aphasia rehabilitation: leads for future studies. Neurosci Biobehav Rev 2021; 127: 212–241.

150.

HAS. AVC. Premières recommandations sur la rééducation à la phase chronique. Haute Autorité de Santé. 2022. Accessed November 19, 2023. https://www.has-sante.fr/jcms/p_3344372/fr/avc-premieres-recommandations-sur-la-reeducation-a-la-phase-chronique

151.

Chu

Zhang

Chen

, et al. Efficacy of intermittent theta-burst stimulation and transcranial direct current stimulation in treatment of post-stroke cognitive impairment. J Integr Neurosci 2022; 21: 130.

152.

Wen

Xie

, et al. Improvement of poststroke cognitive impairment by intermittent theta bursts: a double-blind randomized controlled trial. Brain Behav 2022; 12: e2569.

153.

Begemann

Brand

Ćurčić-Blake

, et al. Efficacy of non-invasive brain stimulation on cognitive functioning in brain disorders: a meta-analysis. Psychol Med 2020; 50: 2465–2486.

154.

Khan

Yuan

Bao

, et al. Can transcranial electrical stimulation facilitate post-stroke cognitive rehabilitation? A systematic review and meta-analysis. Front Rehabil Sci 2022; 3: 795737.

155.

Yan

Zhang

, et al. Effect of transcranial direct-current stimulation on cognitive function in stroke patients: a systematic review and meta-analysis. PLoS One 2020; 15: e0233903.

156.

Han

Liu

Tang

, et al. Effects of excitatory transcranial magnetic stimulation over the different cerebral hemispheres dorsolateral prefrontal cortex for post-stroke cognitive impairment: a systematic review and meta-analysis. Front Neurosci 2023; 17: 1102311.

157.

Zhang

Rajah

, et al. Effects of different frequencies of repetitive transcranial magnetic stimulation in stroke patients with non-fluent aphasia: a randomized, sham-controlled study. Neurol Res 2018; 40: 459–465.

158.

Biou

Cassoudesalle

Cogné

, et al. Transcranial direct current stimulation in post-stroke aphasia rehabilitation: a systematic review. Ann Phys Rehabil Med 2019; 62: 104–121.

159.

Huang

, et al. Effect of repetitive transcranial magnetic stimulation combined with transcranial direct current stimulation on post-stroke dysmnesia: a preliminary study. Clin Neurol Neurosurg 2023; 231: 107797.

160.

Sachdev

Kalaria

O’Brien

, et al. Diagnostic criteria for vascular cognitive disorders: a VASCOG statement. Alzheimer Dis Assoc Disord 2014; 28: 206–218.

Are we ready to cure post-stroke cognitive impairment? Many key prerequisites can be achieved quickly and easily

Abstract

Purpose:

Methods:

Findings:

Discussion and conclusion:

Keywords

Introduction

Methods: Selection of critical points for PSCI reduction

Epidemiology: The prevalence of PSCI

The diagnosis of PSCI

Screening tests

Comprehensive assessment

Identification of patients at risk for PSCI on the basis of clinical factors

Imaging determinants of PSCI and their use to identify patients at risk

Imaging characteristics responsible for PSCI occurrence

Lesion location and volume

White matter hyperintensity and features of small vessel disease: Simple markers or key-players?

White matter hyperintensity

Markers of small-vessel disease

The synergistic contribution of multiple imaging features

The contribution of Alzheimer disease to PSCI

Is disconnection the key player that determines the occurrence of PSCI?

Recent and promising approaches

Improving the diagnosis: Remote online cognitive assessment

Management of PSCI

Noninvasive brain stimulation

Conclusions

Footnotes

Acknowledgements

Declaration of conflicting interests

Funding

Ethical approval

Informed consent

Guarantor

Contributorship

ORCID iDs

References