Disruption of resting-state functional connectivity in acute ischemic stroke: comparisons between right and left hemispheric insults

doi:10.21203/rs.3.rs-2215093/v1

Download PDF

Research Article

Disruption of resting-state functional connectivity in acute ischemic stroke: comparisons between right and left hemispheric insults

https://doi.org/10.21203/rs.3.rs-2215093/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 01 Feb, 2024

Read the published version in Brain Topography →

You are reading this latest preprint version

Background: Few resting-state functional magnetic resonance imaging (RS-fMRI) studies evaluated the impact of acute ischemic changes on cerebral functional connectivity (FC) and its relationship with functional outcomes after acute ischemic stroke (AIS), considering the side and size of lesions.

Objective: To characterize alterations of FC of patients with AIS by analyzing 12 large-scale brain networks (NWs) with RS-fMRI. Additionally, we evaluated the impact of side (right (RH) or left (LH) hemisphere) and size (lacunar or non-lacunar) of insult on the disruption of brain NWs.

Materials and Methods: 38 patients diagnosed with AIS (19 RH and 19 LH) who performed 3T MRI scans up to 72 hours after stroke were compared to 44 healthy controls. Images were processed and analyzed with the software toolbox UF²C with SPM12. For the first level, we generated individual matrices based on the time series extraction from 70 regions of interest (ROIs) from 12 functional NWs, constructing Pearson’s cross-correlation; the second-level analysis included an analysis of covariance (ANCOVA) to investigate differences between groups. The statistical significance was determined with p<0.05, after correction for multiple comparisons with false discovery rate (FDR) correction.

Results: Overall, individual with LH insults developed poorer six months clinical outcomes. A high degree of FC disruption was observed in LH insults (widespread pattern), mainly in non-lacunar lesions. Changes in FC for RH insults did not survive FDR corrections.

Conclusion: Our findings demonstrated that LH stroke causes severe FC alterations in the network topological properties, presumably related to impairment in their long-term recovery.

Acute Ischemic Stroke

Functional Resonance Magnetic Imaging

Resting State.

Ischemic stroke is a leading worldwide cause of mortality; new perspectives on clinical intervention (implementation of stroke units, mechanical thrombectomy, and improved hypertension control) have increased the survival rate in the last years ³³. Although some predictors of the outcome of stroke have been identified (such as the National Institute of Health Score Scale [NIHSS]²⁹, such as infarct characteristics ²⁹, age, uncontrolled hypertension³⁰, hyperglycemia and inflammation)⁴⁴ the relationship between lateralization and the functional outcome is still unclear. Until now, some of the studies considered the right hemisphere (RH) lesion had a more unsatisfactory evolution when compared to the left-sided. Given cerebral hemispheric asymmetry and specific functions on each side, clinical changes in the presence of cortical dysfunction can influence rehabilitation and outcome differently. Aphasia from the left hemisphere (LH) stroke and hemispatial or unilateral neglect in RH stroke can result in structural and functional alterations involving ipsilateral and contralateral brain regions ^32,43.

Recently, resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used to investigate brain connectivity. It provides information about the functional integrity of brain networks (NWs) ¹² characterized by a pairwise temporal integration between brain regions promoting integrative information processing, being a practical approach to detecting Functional Connectivity (FC) alterations after stroke ¹². This technique can examine brain connectivity without subjecting individuals to cognitive tasks ⁵; this fundamental characteristic has facilitated the design of studies for patients in various clinical conditions ^11,18.

Some rs-fMRI studies performed during the chronic phase of the stroke have been applied to estimate functional impairment and predict recovery in stroke survivors ^22,46. Unfortunately, few rs-fMRI have been conducted during the acute stage ²⁰, providing little evidence of whether the dysfunction between local and remote cortical regions can start during this period and follows a distinct pattern in AIS patients ^10,40. While intuitively, we hypothesize that larger infarcts associate with severe FC disruption, it is unclear what is the impact of the side of ischemic insult on FC.

Dynamic changes of FC have been reported according to the stroke evolution: in the acute phase of stroke (less than 24 hours), the FC between bilateral motor cortices decreases; in other stages (between 7 and 90 days after stroke, when motor function start to recover), the FC increases reaching near normality ²⁰. These data are similar to a previous RS fMRI study, which included mild-to-moderate motor deficits in the subacute phase (up to four weeks since stroke) ⁷. On the other hand, Puig ⁴⁰ scanned patients on day three after stroke onset. Using an ROI-Based analysis, they showed that the severely injured brain presented decreased interhemispheric connectivity associated with an increase of ipsilateral FC, involving cortico-subcortical connections and crossed connections (right and left cerebellum). One recent study ⁶ demonstrated that severely stroke patients (scanned in the first two weeks from stroke onset) had significantly reduced connectivity between cortical sensorimotor components, suggesting that severe injury may promote excessive disruption of cortical motor areas. In contrast (and contrary to expectation), moderately affected patients presented more remarkable alterations of FC ⁶.

Due to la ack of studies about rs-fMRI in the acute stage of AIS, we investigated the impact of an early brain insult on acute resting state Functional Connectivity and the relationship with the late functional outcome. For more detailed analyses, we separately evaluated the effects of side and size lesions on the disruption of 12 large-scale brain resting-state networks.

Subjects

We recruited a total of 50 consecutive subjects with acute ischemic stroke (25 women, median of 63 years, range [33–96]) evaluated at the Emergency Department of the University of Campinas who performed a 3T MRI protocol (detailed below) including RS-fMRI scan (with 3D-T1-weighted) up to 72 hours after the stroke event, between March 2017 and May 2019. We had to exclude 12 individuals due to movement artifacts during MRI acquisition. The comparisons of clinical characteristics between groups of included and excluded subjects are presented in Supplemental table 1. Inclusion criteria included: aged > 18 years, admitted in the acute stage (less than 72 hours) with an anterior circulation stroke, presenting a single new lesion in the structural MRI. We excluded patients with contraindications to MRI (pacemakers, metallic prostheses, or claustrophobia), psychiatric disorders, cognitive impairment, previous chronic infarction, and other neurological disorders. Some ischemic stroke (IS) factors were also considered exclusion criteria, such as acute posterior circulation stroke, wake-up stroke (or ischemic event with more than 72h), non-collaborative patients, and those who refused to participate. The clinical characteristics of the 38 included subjects (21 women; median of 63 years, range [33–89]) are described in Table 1. They were divided into two groups according to the affected hemisphere (Right Hemisphere, RH = 19; Left Hemisphere, LH = 19) and compared to 44 controls (never presented stroke, other neurological or psychiatric dysfunction) (22 women; the median age of 61 years, range [50–80]); patients and controls were balanced for age (p = 0.4) and sex (p = 0.8). The group of controls included individuals with known risk factors for acute ischemic stroke; however, we could not obtain information about associated risk factors from 12 subjects in this group.

After classifying the patients according to Trial of Org 10172 in Acute Stroke Treatment (TOAST)¹, we separated them into two groups: Lacunar (deep and small infarcts most involving basal ganglia or other white matter region supplied by penetrating arteries, not measuring more than 15mm)¹, and Non-Lacunar AIS (larger lesions resulting from cardioembolic etiology, large artery atherosclerosis and other causes)¹. As we considered separately the side of the lesion (left and right), we had four groups: Lacunar (left and right) and non-Lacunar (left and right). With these groups, we analyzed both the effect of lateralization and the lesion size.

We registered demographic data from all patients (including sex, age, and comorbidities) and stroke data (age at onset, etiologic classification, and functionality measures [Rankin Score]). Additionally, after six months from the acute event, we applied the modified Rankin Scale (mRS, a measurement of recovery of motor function after stroke), which comprehends seven levels covering functional outcomes from ‘no symptoms = 0’ to ‘death = 6’, assessing the effectiveness and ineffectiveness of acute stroke therapies. ²³

Ethics Approval

The Research Ethics Committee of the University of Campinas previously approved this study (Certificate of Ethical Appreciation Presentation - CAAE 68052316.4.0000.5404), and we obtained written informed consent from all subjects or families.

MRI Acquisition

All images were acquired at the University of Campinas’ Clinical Hospital using a 3T Philips Achieva MRI scanner, including two image protocols:

The Neurovascular Protocol includes volumetric/structural acquisitions in T1, T2, FLAIR, SWI, DWI, ADC, and Magnetic Resonance Angiography (MRA) with “time of flight” (TOF). The parameters were: three-dimensional T1 weighted turbo field echo images (WI) (180 slices, 1 mm³ isotropic voxels, acquired on the sagittal plane, 1mm thickness, flip angle = 8^o, TR = 7.1ms, echo time (TE) 3.2 milliseconds, matrix = 240 × 240, field of view (FOV) = 240 × 240); T2 weighted image (1.5 mm³ isotropic voxels, 180 axial slices, no gap, TR = 1.8ms, TE = 340ms, FOV = 230 × 230mm²); fluid-attenuated inversion recovery (FLAIR) image (voxel size = 1.2 × 1.2 × 0.6mm³, FOV = 250 × 250 mm², TE = 276ms, TR = 4.8ms, TI = 1.650ms) ⁴². Additionally, we applied a resting-state protocol acquired with echo-planar imaging sequence, with isotropic voxels of 27mm³, on axial plane, with 40 axial slices, no gap, TR = 2 seconds, TE = 30 milliseconds, flip angle = 90^o, resulting in 180 dynamics in a 6 minutes scan with FOV = 240x240 mm² ¹⁴. Patients were instructed to rest with closed eyes, not think about anything in particular, and not fall asleep. Subjects were not sedated and did not receive contrast ¹⁸.

Image Preprocessing and Analysis

Images were analyzed with the software toolbox (UF²C, http://www.lniunicamp.com/uf2c/) running on MATLAB (2019b, The MathWorks, Inc. USA) with SPM12 (www.fil.ion.ucl.ac.uk/MATLAB2019b)³. The protocol includes the following steps of pre-processing according to UF²C standard protocol¹⁴.

All images were pre-processed according to the standard UF²C toolbox pipeline ¹⁴, based on dynamic realignments of fMRI images, spatial normalization to the MNI- space (Montreal Neurologic Institute Standard template), co-registration of fMRI mean image with volumetric T1-weighted image of each subject, framewise displacement (FD) estimation, and smoothing with 6x6x6 mm³ (FWHM) smoothing kernel. Besides, the structural images were segmented in different tissues (grey matter (GM), white matter (WM) and cerebral spinal fluid (CSF)), and normalized to the MNI space. The segmented GM maps were interpolated to match the functional images and used to mask the analysis, removing non-GM regions. We also regressed for six head movement parameters (three rotational and three translational), censoring vectors (framewise displacement (FD) > 0.5mm), and the average of the cerebrospinal fluid and white matter signals. Furthermore, we detrended (removed linear trends) the time series and filtered them with band-pass (0.008-0.1 Hz) ¹³.

We performed an exploratory investigation of functional connectivity (FC) dysfunction in the acute stage of ischemic stroke, including 12 functional resting-state networks (Anterior and Posterior Salience, Basal Ganglia, Dorsal and Ventral Default Mode, Left and Right Executive Control Network, Auditory, Visual, Language, Sensorimotor and Visuospatial/Dorsal Attention). These 70 ROIs (regions of interest) were selected to evaluate FC in widespread brain areas, considering relevant functional NWs.^3,14.

Statistical Analysis

Demographic group variables and stroke specificities were analyzed with SPSS23. We used Chi-Square tests to analyze categorical variables (sex, co-morbidities, and stroke specificities) and Mann-Whitney tests to compare continuous variables with non-normal distribution (Table 1).

The statistical analyses of functional connectivity were performed with the UF²C toolbox¹⁴, which allowed the analysis of resting-state parameters in two levels: at the first level, individual matrices were generated based on the extraction of time series from the 70 ROIs. These time series were used for constructing Pearson’s Cross-Correlation matrices; in other words, every pair of ROIs (ROI-ROI analysis) were included in the connectivity matrix of each subject and converted to z-score using Fisher z-transformation. The second-level analyses were performed with comparisons between patients and controls (group comparisons of ROI-to-ROI FC), using analysis of covariance (ANCOVA) tests with age and sex as covariates. Statistical significance was determined with p < 0.05, corrected for multiple comparisons using FDR (False Discovery Rate) procedure.⁴

Demographic and Clinical Data

Thirty-eight patients (19 RH-AIS and 19 LH-AIS) were included and compared to forty-four controls without previous AIS. The three groups were balanced in terms of age, sex, and co-morbidities, as seen in Table 1.

Table 1

Demographic and Clinical Data from Patients and Controls
Data	RH Stroke (19)	LH Stroke (19)	Controls* (44)	P-value
Male/Female	9 /10	8 /11	22 /22	0.85
Median Age (range) 95% CI [lower-upper limits]	63 (33–89) [54–73]	63 (46–85) [51–81]	61 (50–80) [60–63]	0.69
Hypertension (yes/no)	12/7	12 /7	18/14	0.84
Smoking (yes/no)	4 /15	5 /14	2/30	0.12
Atrial Fibrillation (yes/no)	3 /16	1 /18	2/30	0.42
Diabetes (yes/no)	7 /12	8 /11	6/26	0.16
Hyperlipidemia (yes/no)	8 /11	9 /12	8/24	0.22
Data presented as median (range); CI: confidence interval *We were unable to recover information about comorbidities for 12 volunteers

Stroke Data

Information about the stroke was collected through medical records, and structural MRIs were visually inspected by a neurovascular physician (to determine the classification of the stroke -lacunar or non-lacunar); telephone contact was performed after six months (to classify the outcome). All stroke data (size, treatment, and outcome) is exposed in Table 2.

Table 2

Stroke Data
	RH Stroke (19)	LH Stroke (19)	P-value
Non-lacunar/Lacunar	9 /10	13 /6	0.32
NIHSS at admission Median (range) 95% CI [lower-upper limits]	8 (1–23) [6–14]	10 (3–20) [6–16]	0.56
Treated with rTPA (yes/no)	7/12	8/11	1
Modified Rankin after 6 months Median (range) 95% CI [lower-upper limits]	1 (0–5) [0–1]	3 (0–6) [1–5]	0.006
0–2(Good Outcome)/3–6(Poor Outcome)	17 / 2	8 / 11	0.006
RH: right hemisphere; LH: left hemisphere; CI: confidence interval

Although the size of stroke (lacunar/non-lacunar, p = 0.32) and proportion of thrombolysis (p = 1) were balanced between RH and LH groups, the two groups (LH/RH) differ in terms of the outcome. While 58% (n = 11) of the LH-AIS group presented a poor outcome (considering mRs 3 to 6), only 11% (n = 2) of the RH-AIS group evolved with a poor outcome (p = 0.006). The separate analysis clarified that the poor outcome in the LH group was most related to individuals with non-lacunar lesions. Figure 1.

Functional connectivity analysis

We initially performed the analysis based on the side of the stroke. Subsequently, we investigated each group (LH and RH) according to the size of the stroke lesion.

1) According to the side of the stroke (RH/LH):

While the whole LH-AIS exhibited bilateral and widespread abnormal interactions among all 12 networks (including executive control, salience, basal-ganglia, and others) (p < 0.05, FDR corrected) (Fig. 2A, Supplemental tables 2 and 3), the alterations in the RH-AIS group (compared to controls) did not survive correction for multiple comparisons (Supplemental Fig. 1A).

2) Analyses according to the type of lesion (Lacunar or Non-lacunar) and side (RH/LH):

After the primary analyses, we performed additional tests to disentangle the interaction between size (Lacunar × Non-Lacunar Lesions) and side (RH × LH) since we suspected non-lacunar lesions could result in a widespread pattern of FC disruption.

The LH-AIS group presented significant abnormalities in both the lacunar and non-lacunar subgroups. While the Non-lacunar subgroup exhibited a bilateral and widespread abnormal FC pattern, disrupted intra and interhemispheric interactions among all 12 NWs (including executive control, salience, basal-ganglia, and others) (Fig. 2B, Supplemental Tables 2 and 4), the Lacunar subgroup presented decreased FC in four RS NWs (Anterior and Posterior Salience, Right and Left ECN) (Fig. 2C, Supplemental tables 2 and 5) (p < 0.05, corrected with FDR).

The analyses of the RH-AIS group according to the size did not survive the correction for multiple comparisons, consistent with the findings of the whole group analysis. These results are displayed in Supplemental Figs. 1B and C.

In our sample, considering the homogeneity of clinical factors between the two groups (patients and controls), we observed a high degree of FC disruption associated with a poor outcome when the lesion is on the left hemisphere, displaying a bilateral and widespread pattern of FC disruption proportional to the lesion size. Given the higher number of patients with extensive injuries on the left side, we decided to proceed with a counterproof by subdividing them according to the side and size of the lesion (Lacunar Right and Left, Non-Lacunar Right and Left). These sub-analyses confirmed the importance of laterality in the FC disruption in the acute phase of stroke, being the degree of disturbance proportional to the lesion's size.

Many fMRI studies have been performed in the scope of rehabilitation, involving the FC on chronic stroke patients´ recovery. Most of these investigated the complex process related to cerebral structural and functional reorganization.^{34,39,41,46,47} However, fewer evaluated the impact of lateralization and type of lesion in the FC of the stroke´s hyperacute phase. Lately, RS-fMRI studies with stroke patients demonstrated disturbances in interhemispheric connectivity.^7,20 Previous studies identified a correlation between severe motor symptoms and a significant reduction of connectivity between cortical sensorimotor components. Thus, interhemispheric connectivity in stroke patients is considered an important indicator of recovery ^41,46. Meanwhile, the majority included patients in the subacute phase^6,8 and chronic phase ^26,36,45; the few which enrolled patients in the hyperacute phase evaluated FC related to prognosis^10,40 and reperfusion treatment.⁹, without emphasizing the role of the side of ischemic insult.

There is no consensus in the literature about which hemisphere affected by stroke is related to the worst outcome, and evidence is still inconclusive. A series of studies investigate laterality influence (addressing specific symptoms of each hemisphere, such as hemispatial neglect and aphasia, in addition to NIHSS) on the outcome and motor recovery of these patients, without homogeneous results, possibly due to the heterogeneity of the population.^17,19,28,43

Considering both RH and LH were balanced for pre-stroke comorbidities, we inferred that the disruption of FC was associated with the poor outcome (58%) in the LH-AIS group compared to the RH-AIS outcome (11%). Although we do not have complete explanations for such findings, we speculate that differences may be partially explained by the relation between inter and intra-hemisphere connectivity and brain reorganization in post-stroke patients ^21,37. LH stroke has been described in the literature as more frequent and more severe (based on higher NIHSS scores on admission and more severe neurological deficits) ^16,24 with a worse outcome (and higher mortality) ^24,32. It is mainly justified by the incidence of large vessel ischemic stroke in the territory of the left middle cerebral artery ²⁴ and the symptom of aphasia related negatively to the outcome ³². In contrast, some authors considered the right-sided hemisphere insult had a more unsatisfactory outcome when compared to left-sided. There may be some role of unilateral neglect on functional recovery and the time when the patient arrives at the hospital (later than left-sided stroke), which delays the start of treatment^16,17,35.

After subdividing the groups according to the type/size of stroke (Lacunar RH and LH-AIS and Non-lacunar RH and LH-AIS), we demonstrated that the left hemisphere lesion yields a more severe pattern of FC disruption, proportionally to the size of the lesion. Given that our samples’ Left side Stroke presented a worse outcome, we speculate a possible relationship between the initial disruption and poor long-term outcome. There is clear evidence of structural and functional brain asymmetry since fetal stages, sedimented later by language acquisition ²⁵; additionally, a higher number of large pyramidal cells are localized in language regions on the left side ²⁷. One study with children up to two years suggested that the LH has a higher metabolic demand which may leave this hemisphere more vulnerable to reduced blood flow; this demand may eventually impact poststroke neuroplasticity (in adults)². We speculate that the dominant LH (for language and handedness) is metabolically demanding, presents more bilateral stronger connections, and is, therefore, more susceptible to insults that cause widespread disruption of functional connectivity.

Contrary to some studies ^15,38, our results showed that the lacunar stroke infarct was not always correlated to a better outcome compared to infarcts involving the main territory. In other words, lacunar lesions located in the LH were also related to significant disruption of functional connectivity. The importance of lesion in the LH has been investigated, and some studies reported aphasia – an impairment of language comprehension and expression (a major consequence of an LH lesion) – as an independent factor associated with functional status, increased length of hospital stay, and complication during acute stroke admission ³¹.

Some limitations should be considered when interpreting the results. First, it is a cross-sectional study that can only provide preliminary evidence and not the relationship's causality. Second, our sample size was relatively small, considering the difficulties of performing MRI scans in a clinical emergency setup. Some patients could not perform MRIs within our time window due to the need for critical assistance (medications in continuous infusion or frame instability). Third, the ROI-ROI approach gives us a hypothesis-based investigation of brain areas but determines a statistical correction for the multiplicity of comparisons that may reject more minor effects (true positives). Finally, considering our small sample size, the requirements for a robust statistical analysis may have underpowered our comparison.

The abnormalities in the network connectivity presented in the LH group can imply that LH stroke causes alterations in the whole brain’s networks’ topological properties, presumably related to impairment in their recovery. Rs-fMRI emerges as a promising tool in the AIS scenario as it can be used in a wide range of patients with different levels of cognitive capability or dysfunction since it does not require the ability to perform a particular task. Further studies with larger samples may contribute to better understand the physiopathology of the impact of ischemic insult on brain connectivity and eventually provide biomarkers related to future diagnostic and therapeutic strategies for an individualized rehabilitation plan.

Acute Ischemic Stroke (AIS); Functional Resonance Magnetic Imaging (fMRI); Resting State (RS).

Author Contribution

Study concept and design: CLY, MK, FC, WMA
acquisition of data: MK, BMC, WMA
analysis and interpretation: MK, CLY, LSS, FC, WMA, BMC
critical revision of the manuscript for important intellectual content: MK, CLY, LSS, FC, WMA, BMC
study supervision CLY

Funding: CNPQ (315953/2021-7; scholarship for MK: 140790/2017-9;), CEPID-BRAIN (FAPESP 2013-07559/3); CAPES (88887.505625/2020-00).

Conflicting interests: WA receives speaker fees from Boehringer Ingelheim, Pfizer and Daiichi Sankyo; these are not related to the content of the present study.

Data availability: data collected and analyzed for this study will be available at the university database and will be available upon reasonable request.

Adams, H. P. et al.(1993) Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. Stroke 24, 35-41, doi:10.1161/01.str.24.1.35.
Arditi, H., Feldman, R., Hammerman, C. & Eidelman, A. I.(2007) Cerebral blood flow velocity asymmetry, neurobehavioral maturation, and the cognitive development of premature infants across the first two years. J Dev Behav Pediatr 28, 362-368, doi:10.1097/DBP.0b013e318114315d.
Ashburner, J.(2009) Computational anatomy with the SPM software. Magn Reson Imaging 27, 1163-1174, doi:10.1016/j.mri.2009.01.006.
Benjamini, Y. & Hochberg, Y.(1995) Controlling the False Discovery Rate: A Practical and Powerful Approach 	to Multiple Testing. Journal of the Royal Statistical Society Series B (Methodological) 57, 289-300, doi:http://dx.doi.org/10.2307/2346101.
Biswal, B., Yetkin, F. Z., Haughton, V. M. & Hyde, J. S.(1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34, 537-541, doi:10.1002/mrm.1910340409.
Bonkhoff, A. K. et al.(2020) Acute ischaemic stroke alters the brain's preference for distinct dynamic connectivity states. Brain 143, 1525-1540, doi:10.1093/brain/awaa101.
Carter, A. R. et al.(2010) Resting interhemispheric functional magnetic resonance imaging connectivity predicts performance after stroke. Ann Neurol 67, 365-375, doi:10.1002/ana.21905.
Chen, J. et al.(2018) Dynamic Alterations in Spontaneous Neural Activity in Multiple Brain Networks in Subacute Stroke Patients: A Resting-State fMRI Study. Front Neurosci 12, 994, doi:10.3389/fnins.2018.00994.
Chen, Q. et al.(2019) One-step analysis of brain perfusion and function for acute stroke patients after reperfusion: A resting-state fMRI study. J Magn Reson Imaging 50, 221-229, doi:10.1002/jmri.26571.
Chi, N. F. et al.(2018) Cerebral Motor Functional Connectivity at the Acute Stage: An Outcome Predictor of Ischemic Stroke. Sci Rep 8, 16803, doi:10.1038/s41598-018-35192-y.
Crofts, A., Kelly, M. E. & Gibson, C. L.(2020) Imaging Functional Recovery Following Ischemic Stroke: Clinical and Preclinical fMRI Studies. J Neuroimaging 30, 5-14, doi:10.1111/jon.12668.
Dacosta-Aguayo, R. et al.(2015) Impairment of functional integration of the default mode network correlates with cognitive outcome at three months after stroke. Hum Brain Mapp 36, 577-590, doi:10.1002/hbm.22648.
de Campos, B. M., Casseb, R. F. & Cendes, F.(2020) UF2C — User-Friendly Functional Connectivity: A neuroimaging toolbox for fMRI processing and analyses. SoftwareX 11, doi:10.1016/j.softx.2020.100434.
de Campos, B. M., Coan, A. C., Lin Yasuda, C., Casseb, R. F. & Cendes, F.(2016) Large-scale brain networks are distinctly affected in right and left mesial temporal lobe epilepsy. Hum Brain Mapp 37, 3137-3152, doi:10.1002/hbm.23231.
Del Bene, A. et al.(2012) Progressive lacunar stroke: review of mechanisms, prognostic features, and putative treatments. Int J Stroke 7, 321-329, doi:10.1111/j.1747-4949.2012.00789.x.
Di Legge, S., Saposnik, G., Nilanont, Y. & Hachinski, V.(2006) Neglecting the difference: does right or left matter in stroke outcome after thrombolysis? Stroke 37, 2066-2069, doi:10.1161/01.STR.0000229899.66019.62.
Fink, J. N., Frampton, C. M., Lyden, P., Lees, K. R. & Investigators, V. I. S. T. A.(2008) Does hemispheric lateralization influence functional and cardiovascular outcomes after stroke?: an analysis of placebo-treated patients from prospective acute stroke trials. Stroke 39, 3335-3340, doi:10.1161/STROKEAHA.108.523365.
Fox, M. D. & Raichle, M. E.(2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 8, 700-711, doi:10.1038/nrn2201.
Gialanella, B. & Ferlucci, C.(2010) Functional outcome after stroke in patients with aphasia and neglect: assessment by the motor and cognitive functional independence measure instrument. Cerebrovasc Dis 30, 440-447, doi:10.1159/000317080.
Golestani, A. M., Tymchuk, S., Demchuk, A., Goodyear, B. G. & Group, V.-S.(2013) Longitudinal evaluation of resting-state FMRI after acute stroke with hemiparesis. Neurorehabil Neural Repair 27, 153-163, doi:10.1177/1545968312457827.
Grefkes, C. & Fink, G. R.(2011) Reorganization of cerebral networks after stroke: new insights from neuroimaging with connectivity approaches. Brain 134, 1264-1276, doi:10.1093/brain/awr033.
Grefkes, C. & Fink, G. R.(2014) Connectivity-based approaches in stroke and recovery of function. Lancet Neurol 13, 206-216, doi:10.1016/S1474-4422(13)70264-3.
Harrison, J. K., McArthur, K. S. & Quinn, T. J.(2013) Assessment scales in stroke: clinimetric and clinical considerations. Clin Interv Aging 8, 201-211, doi:10.2147/CIA.S32405.
Hedna, V. S. et al.(2013) Hemispheric differences in ischemic stroke: is left-hemisphere stroke more common? J Clin Neurol 9, 97-102, doi:10.3988/jcn.2013.9.2.97.
Hepper, P. G.(2013) The developmental origins of laterality: fetal handedness. Developmental psychobiology 55, 588-595, doi:10.1002/dev.21119.
Hordacre, B. et al.(2020) Resting State Functional Connectivity Is Associated With Motor Pathway Integrity and Upper-Limb Behavior in Chronic Stroke. Neurorehabil Neural Repair 34, 547-557, doi:10.1177/1545968320921824.
Hutsler, J. J.(2003) The specialized structure of human language cortex: pyramidal cell size asymmetries within auditory and language-associated regions of the temporal lobes. Brain and language 86, 226-242
Ito, H., Kano, O. & Ikeda, K.(2008) Different variables between patients with left and right hemispheric ischemic stroke. J Stroke Cerebrovasc Dis 17, 35-38, doi:10.1016/j.jstrokecerebrovasdis.2007.11.002.
Kerr, D. M., Fulton, R. L., Lees, K. R. & Collaborators, V.(2012) Seven-day NIHSS is a sensitive outcome measure for exploratory clinical trials in acute stroke: evidence from the Virtual International Stroke Trials Archive. Stroke 43, 1401-1403, doi:10.1161/STROKEAHA.111.644484.
Khatri, P., Conaway, M. R., Johnston, K. C. & Investigators, A. S. A. P. S. A.(2012) Ninety-day outcome rates of a prospective cohort of consecutive patients with mild ischemic stroke. Stroke 43, 560-562, doi:10.1161/STROKEAHA.110.593897.
Kim, G., Min, D., Lee, E. O. & Kang, E. K.(2016) Impact of Co-occurring Dysarthria and Aphasia on Functional Recovery in Post-stroke Patients. Ann Rehabil Med 40, 1010-1017, doi:10.5535/arm.2016.40.6.1010.
Kongsawasdi, S., Klaphajone, J., Watcharasaksilp, K. & Wivatvongvana, P.(2018) Prognostic Factors of Functional Recovery from Left Hemispheric Stroke. ScientificWorldJournal 2018, 4708230, doi:10.1155/2018/4708230.
Lackland, D. T. et al.(2014) Factors influencing the decline in stroke mortality: a statement from the American Heart Association/American Stroke Association. Stroke 45, 315-353, doi:10.1161/01.str.0000437068.30550.cf.
Lassalle-Lagadec, S. et al.(2012) Subacute default mode network dysfunction in the prediction of post-stroke depression severity. Radiology 264, 218-224, doi:10.1148/radiol.12111718.
Laufer, Y., Sivan, D., Schwarzmann, R. & Sprecher, E.(2003) Standing balance and functional recovery of patients with right and left hemiparesis in the early stages of rehabilitation. Neurorehabil Neural Repair 17, 207-213, doi:10.1177/0888439003259169.
Liu, H., Cai, W., Xu, L., Li, W. & Qin, W.(2019) Differential Reorganization of SMA Subregions After Stroke: A Subregional Level Resting-State Functional Connectivity Study. Front Hum Neurosci 13, 468, doi:10.3389/fnhum.2019.00468.
Nair, V. A. et al.(2015) Functional connectivity changes in the language network during stroke recovery. Ann Clin Transl Neurol 2, 185-195, doi:10.1002/acn3.165.
Ntaios, G. et al.(2016) Small-vessel occlusion versus large-artery atherosclerotic strokes in diabetics: Patient characteristics, outcomes, and predictors of stroke mechanism. Eur Stroke J 1, 108-113, doi:10.1177/2396987316647856.
Ovadia-Caro, S. et al.(2013) Longitudinal effects of lesions on functional networks after stroke. J Cereb Blood Flow Metab 33, 1279-1285, doi:10.1038/jcbfm.2013.80.
Puig, J. et al.(2018) Resting-State Functional Connectivity Magnetic Resonance Imaging and Outcome After Acute Stroke. Stroke 49, 2353-2360, doi:10.1161/STROKEAHA.118.021319.
Rehme, A. K. & Grefkes, C.(2013) Cerebral network disorders after stroke: evidence from imaging-based connectivity analyses of active and resting brain states in humans. J Physiol 591, 17-31, doi:10.1113/jphysiol.2012.243469.
Silva, D. S. et al.(2018) Default Mode Network Disruption in Stroke-Free Patients with Atrial Fibrillation. Cerebrovasc Dis 45, 78-84, doi:10.1159/000486689.
Stein, M. S., Kilbride, C. & Reynolds, F. A.(2016) What are the functional outcomes of right hemisphere stroke patients with or without hemi-inattention complications? A critical narrative review and suggestions for further research. Disabil Rehabil 38, 315-328, doi:10.3109/09638288.2015.1037865.
Tuttolomondo, A. et al.(2009) Inflammation as a therapeutic target in acute ischemic stroke treatment. Curr Top Med Chem 9, 1240-1260, doi:10.2174/156802609789869619.
Vicentini, J. E. et al.(2021) Subacute functional connectivity correlates with cognitive recovery six months after stroke. Neuroimage Clin 29, 102538, doi:10.1016/j.nicl.2020.102538.
Wang, L. et al.(2010) Dynamic functional reorganization of the motor execution network after stroke. Brain 133, 1224-1238, doi:10.1093/brain/awq043.
Zhang, J. et al.(2014) Structural damage and functional reorganization in ipsilesional m1 in well-recovered patients with subcortical stroke. Stroke 45, 788-793, doi:10.1161/STROKEAHA.113.003425.

No competing interests reported.

Download PDF

Journal Publication

published 01 Feb, 2024

Read the published version in Brain Topography →

Editorial decision: Major revision
31 Jul, 2023
Reviews received at journal
24 Jul, 2023
Reviewers agreed at journal
22 Jan, 2023
Reviews received at journal
14 Nov, 2022
Reviewers agreed at journal
09 Nov, 2022
Reviewers invited by journal
03 Nov, 2022
Editor assigned by journal
02 Nov, 2022
Submission checks completed at journal
01 Nov, 2022
First submitted to journal
28 Oct, 2022

You are reading this latest preprint version

Disruption of resting-state functional connectivity in acute ischemic stroke: comparisons between right and left hemispheric insults

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Materials And Methods

Subjects

Ethics Approval

MRI Acquisition

Image Preprocessing and Analysis

Statistical Analysis

Results

Demographic and Clinical Data

Stroke Data

Functional connectivity analysis

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1