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Article

Signal in the Noise: Dispersion as a Marker of Post-Stroke Cognitive Impairment

by
Stefan Delmas
,
Anjali Tiwari
and
Neha Lodha
*
Department of Health and Exercise Science, Colorado State University, Fort Collins, CO 80523, USA
*
Author to whom correspondence should be addressed.
Appl. Sci. 2026, 16(1), 388; https://doi.org/10.3390/app16010388 (registering DOI)
Submission received: 30 November 2025 / Revised: 27 December 2025 / Accepted: 28 December 2025 / Published: 30 December 2025
(This article belongs to the Special Issue Advances in Physiotherapy and Neurorehabilitation)
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Abstract

Stroke often results in lasting cognitive impairments that severely reduce independence and quality of life. Traditional neuropsychological assessments rely on mean scores that provide an average estimate of overall cognitive function but neglect the fluctuations in performance. The variability in performance can be captured as inconsistency, i.e., fluctuations across multiple trials within a single task or as dispersion, i.e., fluctuations across multiple tasks. While inconsistency has been extensively studied, the impact of post-stroke cognitive impairment on cognitive dispersion is unknown. In this study, ninety-five stroke survivors (41 cognitively impaired and 54 cognitively normal) completed a neuropsychological battery that captured performance across five cognitive domains: executive function, attention, memory, language, and processing speed. We compared the stroke groups on across- and within-domain cognitive dispersion. Cognitively impaired stroke individuals showed elevated dispersion within executive function compared to cognitively normal individuals. The two groups did not differ on any other within-domain or across-domain cognitive dispersion. Post-stroke cognitive impairment increased variability within executive functioning. Incorporating cognitive dispersion into routine post-stroke assessment can advance clinical practice by identifying subtle cognitive instability, anticipate supportive needs, and tailor rehabilitation plans for improving stroke care.

1. Introduction

Stroke is a devastating cerebrovascular event, leaving thirty to sixty million individuals worldwide with persistent cognitive impairments each year [1,2,3,4,5,6,7,8]. Post-stroke cognitive impairment severely limits functional capacity and diminishes quality of life [9]. Current clinical assessments of cognitive impairment rely on average performance. However, mean performance does not capture moment-to-moment fluctuations in performance within a person. Variability in cognitive performance can serve as a sensitive indicator of cognitive decline [10] and functional abilities [11] following stroke. Despite its promise, variability in cognitive performance in stroke survivors with cognitive impairment remains understudied. Establishing the relevance of cognitive variability in post-stroke cognitive impairment may advance stroke care by prompting early targeted interventions and tracking therapeutic effectiveness.
Cognitive variability within an individual, termed intraindividual variability, is primarily characterized in two ways: inconsistency and dispersion [12]. Cognitive inconsistency refers to fluctuations in cognitive performance across trials in a single task whereas cognitive dispersion refers to fluctuations in cognitive performance across multiple tasks [12]. A substantial body of literature in cognitive aging has highlighted cognitive inconsistency as a robust indicator of neural integrity [12,13,14,15,16,17,18,19,20,21]. In contrast, cognitive dispersion has been a subject of systematic scientific inquiry only in the past decade [22,23,24,25,26,27,28,29,30]. Yet, emerging evidence in older adults demonstrates cognitive dispersion as a promising indicator of underlying neuropathology [22,25], poor grey-matter integrity [31], and reduced brain volume [25].
Cognitive dispersion is examined in two ways: across- and within-domain dispersion. Across-domain dispersion refers to dispersion across multiple cognitive domains. This metric provides a global index of cognitive integrity where high dispersion signals a distinct imbalance of cognitive abilities rather than generalized decline. Within-domain dispersion refers to dispersion across tasks within a single domain and reveals specific sources of cognitive variability. For example, someone may have overall stable cognitive performance but fluctuations only in a specific domain. Importantly, cognitive dispersion has been shown to predict cognitive decline in older adults [32], is linked to underlying neuropathology [29,33,34] and is associated with reduced functional dependence [23,32,35]. Despite the clear relevance of dispersion to cognitive and functional decline, whether post-stroke cognitive impairment impacts cognitive dispersion remains unknown.
This study investigates two questions. First, what is the impact of post-stroke cognitive impairment on across-domain cognitive dispersion? Given that cognitive dispersion is related to cognitive status in the aging population [29,32,33,34], we hypothesized that stroke survivors with cognitive impairment will show higher levels of across-domain dispersion relative to cognitively normal stroke survivors. Second, what is the impact of post-stroke cognitive impairment on within-domain cognitive dispersion? We compared dispersion within the cognitive domains of executive function, memory, attention, language and processing speed. We hypothesized that all domains would show elevated dispersion in cognitively impaired stroke survivors compared with cognitively normal stroke survivors. As a secondary, confirmatory analysis, we will compare mean cognitive performance between the two groups. We anticipate that the cognitively impaired stroke group will show lower mean cognitive performance across cognitive domains and within cognitive domains relative to the cognitively normal stroke group. To our knowledge, this is one of the first studies to systematically investigate cognitive dispersion in individuals with stroke.

2. Materials and Methods

2.1. Participants

This study included 95 stroke survivors. Inclusion criteria included: (1) age between 21–90 years and (2) diagnosis of stroke at least 9 months before study enrollment. Exclusion criteria included: (1) a history of epilepsy, traumatic brain injury, Parkinson’s disease, alcohol and substance abuse; (2) aphasia; (3) uncorrected hearing or visual impairment; and (4) untreated pain, anxiety or clinical depression.
Stroke participants were classified as cognitively normal (n = 54) or cognitively impaired (n = 41) based on consensus by two trained examiners using the National Institute of Neurological Disorders and Stroke-Canadian Stroke Network Vascular Cognitive Impairment Harmonization Standards (NINDS-CSN) [36]. A 30-min protocol assessed at least five cognitive domains (i.e., executive function, language, visuospatial abilities, and memory) via the Montreal Cognitive Assessment (MoCA) [37] and the age- and education-adjusted Dementia Rating Scale (DRS-2 AEMSS) [38]. Cognitive impairment was indicated by a MoCA score below 26 or a DRS-2 AEMSS score below 9. The Institutional Review Board of Colorado State University approved the experimental procedures; all work was carried out in accordance with the Declaration of Helsinki. We obtained written informed consent from the participants prior to study procedures.

2.2. Experimental Protocol

The experimental protocol consisted of a single baseline session lasting ~60 min during which participants completed health-related questionnaires, physical disability and strength assessments, and a comprehensive neuropsychological battery.

2.3. Sociodemographic and Stroke-Related Characteristics

We obtained sociodemographic characteristics (age, sex, ethnicity, education, side dominance) from the participants using questionnaires. The health records were used to obtain stroke-related characteristics (time since stroke, stroke type, lesion side and location, affected side). Table 1 provides group comparison on the participant characteristics.

2.4. Physical Disability and Strength

We assessed physical disability using the modified Rankin Scale (mRS) [39,40,41], and strength on grip and ankle dorsiflexion [42,43,44,45,46]. Grip strength was recorded using a digital hand dynamometer (Jamar Plus, model number 120604; Sammons Preston Inc., Bolingbrook, IL, USA). Ankle dorsiflexion strength was assessed in supine position using an electronic muscle tester (Lafayette Manual Muscle tester, model number 01165, Lafayette Instrument Company Inc., Lafayette, IN, USA). For each limb, the highest value from two trials was recorded. Relative strength was calculated as (paretic/non-paretic) × 100, with values < 100% indicating reduced strength on the paretic side.

2.5. Neuropsychological Battery

To assess functioning in specific cognitive domains, we performed assessments using an extensive neuropsychological battery of 13 tests targeting the cognitive domains of attention, processing speed, memory, language, and executive function. For attention, we used the Useful Field of Vision (UFOV) divided attention, selective attention, and complex selective attention subtests, where higher scores represent lower attentional capacity [47]. For processing speed, we used the Trail Making Test-Part A (TMT-A) [48,49], Digit Span-Forwards (DS-F) [50] and Digit Symbol Substitution Test (DSST) [51]. Larger values in TMT-A and smaller values in DS-F and DSST demonstrate slower processing speed. For memory, we used the Logical Memory Story A-delayed recall [52] and Hopkins Verbal Learning Test (HVLT)-delayed recall [53] where higher scores represent compromised memory. For language, we used the Category Fluency [54] and the Boston Naming Test (BNT) [55] where lower scores indicate poor language. Lastly, for executive function, we used Trail Making Test Part B (TMT-B) [48,49], Digit Span-Backwards (DS-B) [50], and Paced Auditory Serial Addition Test (PASAT) [56,57,58]. Larger values in TMT-B and smaller values in DS-B and PASAT demonstrate compromised executive function. Each test was specifically selected for their clinical utility, sensitivity to deficits in their respective cognitive domains, and their validity in neurological populations. To maximize performance validity, tests were administered in a fixed sequence that alternated between cognitive domains. Given that our battery utilizes multiple measures within single domains, this alternating structure was critical to avoid the priming effects and performance biases inherent in consecutive administration of similar tasks. This sequencing strategy effectively minimized practice effects and domain-specific interference.
Dispersion quantification. Dispersion was quantified using a standardized approach consistent with prior studies [24,26,34,59,60,61]. Raw scores from each neuropsychological assessment were first converted to z-scores using the mean and standard deviation pooled across the two stroke groups to place all measures on a common scale. For tests in which higher raw scores reflect poorer performance, z-scores were sign-reversed so that higher values uniformly indicated better cognitive performance across all assessments. These harmonized z-scores were then converted to standardized T-scores (mean = 50, SD = 10) to facilitate interpretability and comparability across tests. Dispersion was subsequently calculated as the standard deviation of T-scores across assessments, either across domains or within specific cognitive domains.
Across-domain dispersion was defined as the variability in cognitive performance across all administered neuropsychological assessments. Specifically, dispersion was calculated as the standard deviation of standardized T-scores (see previous paragraph) derived from all 13 tests: UFOV (1) divided attention, (2) selective attention, and (3) complex selective attention subtests, (4) TMT-A, (5) DS-F, (6) DSST, (7) logical memory, (8) HVLT, (9) category fluency, (10) BNT, (11) TMT-B, (12) DS-B, and (13) PASAT. An illustrative example of across-domain dispersion is presented in Figure 1. The across-domain dispersion metric captures the extent to which an individual’s performance varies across cognitive domains, with higher values indicating greater dispersion in performance across tests.
Within-domain dispersion was defined as the variability in performance across tests belonging to the same cognitive domain. An illustrative example of all within-domain dispersion outcomes is presented in Figure 1. Specifically, executive function dispersion was calculated as the standard deviation of standardized T-scores of (1) TMT-B, (2) DS-B, and (3) PASAT. Attention dispersion was calculated as the standard deviation of standardized T-scores of UFOV (1) divided attention, (2) selective attention, and (3) complex selective attention subtests. Memory dispersion was calculated as the standard deviation of standardized T-scores of (1) logical memory and (2) HVLT. Language dispersion was calculated as the standard deviation of standardized T-scores of (1) category fluency and (2) BNT. Lastly, processing speed dispersion was calculated as the standard deviation of standardized T-scores of (1) TMT-A, (2) DS-F, and (3) DSST.
Mean performance: We quantified across-domain mean performance as the average of standardized cognitive test scores across all assessments. Similarly, within-domain mean performance was calculated as the average of standardized test scores within each cognitive domain. Because dispersion is computed using the standard deviation, mean performance may influence dispersion estimates due to scale-related effects. To evaluate this potential dependency, we examined correlations between mean performance and dispersion outcomes. These associations were small in magnitude and negative (r < 0.3), indicating that higher overall performance was only weakly associated with lower dispersion. Thus, dispersion metrics capture variability in cognitive performance that is largely independent of mean performance.

2.6. Statistical Analysis

We compared participant characteristics between stroke groups using a Mann–Whitney U test for continuous variables and chi-square tests for categorical variables. Additionally, mRS was compared using chi-square, whereas grip and ankle strength were compared using Mann–Whitney U tests. To determine whether cognitive dispersion differentiated stroke groups, we conducted Mann–Whitney U tests for across-domain mean performance, across-domain dispersion, within-domain mean performance, and within-domain dispersion outcomes. Statistical significance was determined using a two-tailed alpha level of 0.05. Effect sizes were calculated using Hedge’s g and interpreted according to conventional thresholds (small ≈ 0.2, moderate ≈ 0.5, large ≥ 0.8). All analyses were performed in SPSS v29.0 (IBM, Armonk, NY, USA).

3. Results

3.1. Participant Characteristics

Cognitively impaired participants were significantly older (p = 0.002) and had fewer years of education (p = 0.007; Table 1) than cognitively normal participants. Most participants were White/Caucasian (>85%) and right-side dominant (>85%), with no significant group differences in sex (~45% female). Stroke characteristics were not significantly different between groups. Ischemic stroke was most common (>70%), lesions most frequently located in the left hemisphere (>39%) and supratentorial region (>48%). Time since stroke averaged ~4.5 years.
Given the observed group differences in age and education, we assessed the strength of the association between these variables and our dependent variables. Neither age nor education was associated with dispersion outcomes (r < 0.17). Age was moderately associated (r > 0.3) with mean performance outcomes. Therefore, we conducted additional univariate analysis of variance on mean performance outcomes with age as a covariate.

3.2. Physical Disability and Strength

The two groups did not significantly differ on mRS scores, with over 85% of the participants scoring ≤ 2. The two groups were also not significantly different (p > 0.20) on normalized grip (cognitively impaired: 91.2 ± 23.2%; cognitively normal: 98.4 ± 32.4%) and ankle dorsiflexion (cognitively impaired: 93.9 ± 22.5%; cognitively normal: 94.3 ± 20.7%).

3.3. Group Comparisons on Across-Domain Dispersion and Mean

Figure 2 shows group comparisons for across-domain dispersion and across-domain mean performance and raw values are shown in Table 2. The two stroke groups did not differ significantly on across-domain dispersion (p = 0.289). The cognitively impaired stroke group showed lower across-domain mean performance compared with the cognitively normal stroke group (p < 0.001). This significant effect was retained after controlling for age (p < 0.001). According to Cohen [62], the group differences in across-domain mean performance was large in effect size.

3.4. Group Comparisons on Within-Domain Dispersion and Mean

Figure 3 shows group comparisons for within-domain dispersion and within-domain mean performance and raw values are shown in Table 2. Cognitively impaired stroke participants exhibited significantly higher dispersion on executive function than cognitively normal stroke participants (p = 0.023). According to Cohen [62], the group differences in executive function dispersion was moderate in effect size. However, the two stroke groups did not significantly differ on within-domain dispersion of attention, memory, language and processing speed. Cognitively impaired stroke survivors showed lower within-domain mean performance across all cognitive domains than cognitively normal survivors (p < 0.001). These significant effects were retained after controlling for age (p < 0.010). According to Cohen [62], the group differences in within-domain mean performance was large in effect size.

4. Discussion

This study investigated the impact of post-stroke cognitive impairment on cognitive dispersion. Using an extensive neuropsychological battery of cognitive tasks spanning the domains of executive function, attention, memory, language, and processing speed, we compared across- and within-domain cognitive dispersion. Notably, we found that dispersion on executive function was significantly elevated in cognitively impaired compared with cognitively normal stroke group. Across-domain dispersion was comparable between cognitively impaired and cognitively normal stroke survivors. To our knowledge, this is the first study to demonstrate that cognitive variability in executive function is an important behavioral feature in post-stroke cognitive impairment. These findings shed new light on a behavioral marker of post-stroke cognitive health that can potentially enhance rehabilitation strategies for individuals who experience cognitive impairment post-stroke.

4.1. Variability in Executive Functioning Is Exacerbated in Individuals with Post-Stroke Cognitive Impairment

A key finding of this study is that cognitive dispersion in executive function emerged as elevated in stroke survivors with cognitive impairment. This result is notable because executive function is one of the domains most frequently affected after stroke [63,64] and is essential for coordinating complex, multi-step goals in daily life. Its reliability becomes particularly important when individuals must manage multiple goals simultaneously. Supporting this, Scott et al. (2022) showed in a large cohort of healthy older adults, individuals with mild cognitive impairment, and individuals with Alzheimer’s disease that dispersion in executive function predicts greater functional dependence five years later, beyond demographic factors, genetic risk, and vascular integrity [29]. Furthermore, the absence of group differences in the other four domains likely explains why across-domain dispersion also did not differ between groups, underscoring the value of within-domain dispersion for revealing more nuanced cognitive dysregulation. Together, these findings suggest that executive-function dispersion captures a distinct aspect of cognitive dysregulation not reflected in across-domain metrics and may serve as a promising target for understanding and monitoring post-stroke cognitive trajectories.
Notably, processing-speed dispersion did not differentiate cognitively impaired from cognitively normal stroke survivors. We highlight this result because inconsistency in processing speed (i.e., variability across trials within a single task) is a central construct in the cognitive aging literature and a well-established marker of neural integrity that increases with aging and onset of cognitive impairment [13,14,15,16,17,18]. Hultsch, MacDonald, and Dixon (2002) showed that processing speed dispersion across multiple reaction time tasks increases with age in healthy adults [12]. The discrepancy between our findings and prior work may be explained by two reasons. First, although both inconsistency and dispersion reflect within-person variability, these constructs are quantified in different ways. Inconsistency is task-specific, whereas dispersion spans multiple tasks and likely engages more complex processes. Second, our measure of dispersion relied on traditional pen-and-paper tasks rather than reaction-time paradigms typically used to assess processing speed. Because pen-and-paper tasks involve additional motor and perceptual demands, they may dilute pure processing speed variance and reduce sensitivity to group differences.

4.2. Variability Across Cognitive Domains Is Similar Among Stroke Individuals

Complex daily activities demand the coordination of multiple cognitive domains. For example, planning and cooking a new dinner recipe for guests draws on the ability to switch back and forth between attention, memory, language, and executive function. Variability in using these cognitive domains (i.e., across-domain dispersion) may subsequently interfere with the quality of the dining experience. Growing evidence links higher dispersion to functional difficulties. For example, higher across-domain dispersion is associated with functional disability [35] and self-reported detriments in activities of daily living [32] in community-dwelling older adults. Furthermore, dispersion increases with age [65], predicts cognitive decline 1 year later in older adults [32], and predicts faster atrophy in Alzheimer’s-vulnerable brain regions among cognitively normal individuals [25].
In contrast to prior findings in other populations [22], we did not observe differences in across-domain dispersion between stroke survivors with and without cognitive impairment. One possible explanation is that across-domain dispersion combines variability from multiple cognitive domains into a single summary measure. As a result, pronounced variability within a specific domain may be diluted when combined together with more uniform performance in other domains. Consequently, examining within-domain dispersion may better reveal the underlying presence of variability.

4.3. Clinical Implications

The presence of post-stroke cognitive impairment is often overshadowed in clinical assessments due to the overt motor deficits. And yet, post-stroke cognitive impairments remain highly prevalent even after significant physical recovery and contribute to difficulty in performing functional tasks [66]. Furthermore, stroke rehabilitation typically targets improvements in mean performance, often neglecting variability in performance as a worthy rehabilitation target [67]. Based on the findings in this study, we propose that cognitive dispersion could be an important consideration in stroke recovery. First, given that dispersion in executive-function dispersion may be closely linked with daily functioning [26,29,33,34,68,69,70,71], elevated dispersion could prompt targeted interventions and inform prognosis. Second, dispersion metrics leveraging existing neuropsychological assessments could provide deeper insights into patient’s prognosis without additional testing. Third, cognitive dispersion may serve as a sensitive outcome measure for evaluating success of treatments that aim stabilization of cognitive function. In summary, incorporating dispersion metrics into routine post-stroke assessment may assist clinicians to detect subtle cognitive instability, anticipate support needs, and precisely tailor rehabilitation plans.

4.4. Limitations and Further Considerations

We acknowledge several limitations of the current work. First, although the number of assessments per cognitive domain aligns with prior studies, additional tests would provide a more robust estimate of dispersion. Second, tests within the same cognitive domain varied in difficulty, which may have introduced floor or ceiling effects and potentially reduced sensitivity to detect group differences in dispersion. Future studies should incorporate measures with harmonized difficulty levels. Third, although motor function was characterized using the modified Rankin Scale (mRS) and strength measures, more comprehensive motor assessments (e.g., Fugl-Meyer or dexterity-based measures) could provide additional insight into the relationship between motor ability and cognitive dispersion. Although beyond the scope of the current study, inclusion of such measures in future work may enhance the rigor and interpretability of findings, particularly given our reliance on pen-and-paper cognitive tasks. Fourth, the absence of pre-stroke baseline cognitive data and a matched non-stroke control group limits the ability to isolate stroke-specific effects on cognitive dispersion and precludes causal inference. Thus, our findings should be interpreted as associative rather than causal and future longitudinal and case–control studies are needed to address this limitation. Fifth, findings should be interpreted cautiously as the relatively modest sample size and predominance of high-functioning (mRS ≤ 2), White/Caucasian participants limit the generalizability of these findings to more diverse stroke populations with broader ranges of functional status, racial and ethnic backgrounds, and stroke characteristics. Lastly, we did not examine how stroke characteristics (e.g., type, size, location) may interact with dispersion, despite their well-established influence on cognitive profiles and recovery trajectories.

5. Conclusions

This study provides novel evidence that cognitive dispersion, particularly within executive functioning, offers additional insight into post-stroke cognitive impairment. Although across-domain dispersion did not distinguish cognitive status, elevated executive-function dispersion among cognitively impaired stroke survivors underscores the value of examining variability within specific cognitive systems. Together, these findings position cognitive dispersion as a promising behavioral marker that may enhance prognostic evaluation and inform more targeted rehabilitation planning.

Author Contributions

Conceptualization, S.D. and N.L.; methodology, S.D. and N.L.; software, N.L.; validation, N.L.; formal analysis, S.D.; investigation, S.D. and A.T.; resources, N.L.; data curation, S.D., A.T. and N.L.; writing—original draft preparation, S.D.; writing—review and editing, A.T. and N.L.; visualization, S.D.; supervision, N.L.; project administration, N.L.; funding acquisition, N.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Institutes of Health, grant number K01 AG070327.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Colorado State University (protocol code 20-9927H v. 11/18/2020).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
BNTBoston Naming Test
CSUColorado State University
DRS-2 AEMSSDementia Rating Scale (age- and education-adjusted)
DS-BDigit Span-Backwards
DS-FDigit Span-Forwards
DSSTDigit Symbol Substitution Test
HVLTHopkins Verbal Learning Test
IRBInstitutional Review Board
MoCAMontreal Cognitive Assessment
mRSmodified Rankin Scale
NINDS-CSNNational Institute of Neurological Disorders and Stroke-Canadian Stroke Network
PASATPaced Auditory Serial Addition Test
TMT-ATrail Making Test-Part A
TMT-BTrail Making Test Part B
UFOVUseful Field of View

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Figure 1. Dispersion quantification. Illustrative T-score values (y-axis, dots) across all neuropsychological assessments (x-axis). Across-domain dispersion is quantified as the standard deviation across all assessments (red double-sided arrow). Within-domain dispersion is quantified for executive function (green), attention (blue), memory (purple), language (orange), and processing speed (gray). Dispersion on executive function (green dotted double-sided arrow) is computed as the standard deviation of Trail Making Test Part B (TMT-B), Digit Span-Backwards (DS-B), and Paced Auditory Serial Addition Test (PASAT). Dispersion on attention (blue dotted double-sided arrow) is computed as the standard deviation of the Useful Field of Vision (UFOV) divided attention, selective attention, and complex selective attention subtests. Dispersion on memory (purple dotted double-sided arrow) is computed as the standard deviation of Logical Memory Story A-delayed recall and Hopkins Verbal Learning Test (HVLT)-delayed recall. Dispersion on language (orange dotted double-sided arrow) is computed as the standard deviation of Category Fluency and the Boston Naming Test (BNT). Lastly, dispersion on processing speed (gray dotted double-sided arrow) is computed as the standard deviation of Trail Making Test-Part A (TMT-A), Digit Span-Forwards (DS-F) and Digit Symbol Substitution Test (DSST).
Figure 1. Dispersion quantification. Illustrative T-score values (y-axis, dots) across all neuropsychological assessments (x-axis). Across-domain dispersion is quantified as the standard deviation across all assessments (red double-sided arrow). Within-domain dispersion is quantified for executive function (green), attention (blue), memory (purple), language (orange), and processing speed (gray). Dispersion on executive function (green dotted double-sided arrow) is computed as the standard deviation of Trail Making Test Part B (TMT-B), Digit Span-Backwards (DS-B), and Paced Auditory Serial Addition Test (PASAT). Dispersion on attention (blue dotted double-sided arrow) is computed as the standard deviation of the Useful Field of Vision (UFOV) divided attention, selective attention, and complex selective attention subtests. Dispersion on memory (purple dotted double-sided arrow) is computed as the standard deviation of Logical Memory Story A-delayed recall and Hopkins Verbal Learning Test (HVLT)-delayed recall. Dispersion on language (orange dotted double-sided arrow) is computed as the standard deviation of Category Fluency and the Boston Naming Test (BNT). Lastly, dispersion on processing speed (gray dotted double-sided arrow) is computed as the standard deviation of Trail Making Test-Part A (TMT-A), Digit Span-Forwards (DS-F) and Digit Symbol Substitution Test (DSST).
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Figure 2. Across-domain dispersion and mean between cognitively normal and cognitively impaired stroke survivors. (A) The two stroke groups did not significantly differ on across-domain dispersion. (B) Cognitively impaired stroke participants showed lower across-domain mean performance compared with cognitively normal stroke participants.
Figure 2. Across-domain dispersion and mean between cognitively normal and cognitively impaired stroke survivors. (A) The two stroke groups did not significantly differ on across-domain dispersion. (B) Cognitively impaired stroke participants showed lower across-domain mean performance compared with cognitively normal stroke participants.
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Figure 3. Within-domain dispersion and mean between cognitively normal and cognitively impaired stroke survivors. (A) Cognitively impaired stroke survivors only showed significantly higher dispersion on executive function relative to cognitively normal survivors. The two stroke groups did not significantly differ on any other within-domain dispersion outcomes. (B) Cognitively impaired stroke participants showed lower within-domain mean performance across all domains compared with cognitively normal stroke participants.
Figure 3. Within-domain dispersion and mean between cognitively normal and cognitively impaired stroke survivors. (A) Cognitively impaired stroke survivors only showed significantly higher dispersion on executive function relative to cognitively normal survivors. The two stroke groups did not significantly differ on any other within-domain dispersion outcomes. (B) Cognitively impaired stroke participants showed lower within-domain mean performance across all domains compared with cognitively normal stroke participants.
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Table 1. Descriptive Sample Characteristics (N = 95).
Table 1. Descriptive Sample Characteristics (N = 95).
VariableCognitively NormalCognitively Impairedp
n5441
Sociodemographic characteristics
Age, years, mean ± SD62.47 ± 13.9770.53 ± 10.380.002
Years of education, mean ± SD16.42 ± 2.3815.13 ± 2.400.007
Women, %44.4448.780.675
Ethnicity, % 0.134
Non-Hispanic White98.1587.80
African American07.32
Hispanic02.44
Other1.852.44
Side Dominance, % 0.713
Right85.1987.80
Left14.8112.20
Type of stroke, % 0.086
Ischemic85.1970.73
Hemorrhagic3.7014.63
Both04.88
Unknown11.119.76
Side of the lesion, % 0.335
Right35.1926.83
Left46.3039.02
Both7.409.76
Unknown11.1124.39
Lesion location, % 0.333
Infratentorial12.9612.20
Supratentorial64.8148.78
Both5.5612.20
Unknown16.6726.83
Affected Side, % 0.341
Right38.8943.90
Left37.0443.90
Other24.0712.20
Years since stroke, mean ± SD4.56 ± 6.814.51 ± 6.670.976
modified Rankin Score 0.378
0, %24.079.76
1, %44.4446.34
2, %25.9331.71
3, %3.707.32
4, %1.854.88
Notes. SD = Standard deviation.
Table 2. Raw values for all across- and within-domain outcomes.
Table 2. Raw values for all across- and within-domain outcomes.
VariableCognitively NormalCognitively Impaired
Across-Domain
Dispersion7.08 ± 1.957.87 ± 2.48
Mean53.64 ± 4.2745.21 ± 6.18
Within-Domain
Executive Function
Dispersion5.77 ± 3.177.25 ± 3.34
Mean54.07 ± 5.3844.64 ± 7.91
Attention
Dispersion5.18 ± 2.934.97 ± 2.04
Mean54.02 ± 7.2844.71 ± 7.94
Memory
Dispersion4.82 ± 4.465.66 ± 4.40
Mean53.71 ± 6.3745.11 ± 9.12
Language
Dispersion6.17 ± 5.265.84 ± 3.63
Mean52.60 ± 7.0746.57 ± 8.92
Processing Speed
Dispersion6.64 ± 3.456.58 ± 5.57
Mean53.47 ± 5.2145.43 ± 7.81
Notes. All values are reported as mean ± standard deviation.
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Delmas, S.; Tiwari, A.; Lodha, N. Signal in the Noise: Dispersion as a Marker of Post-Stroke Cognitive Impairment. Appl. Sci. 2026, 16, 388. https://doi.org/10.3390/app16010388

AMA Style

Delmas S, Tiwari A, Lodha N. Signal in the Noise: Dispersion as a Marker of Post-Stroke Cognitive Impairment. Applied Sciences. 2026; 16(1):388. https://doi.org/10.3390/app16010388

Chicago/Turabian Style

Delmas, Stefan, Anjali Tiwari, and Neha Lodha. 2026. "Signal in the Noise: Dispersion as a Marker of Post-Stroke Cognitive Impairment" Applied Sciences 16, no. 1: 388. https://doi.org/10.3390/app16010388

APA Style

Delmas, S., Tiwari, A., & Lodha, N. (2026). Signal in the Noise: Dispersion as a Marker of Post-Stroke Cognitive Impairment. Applied Sciences, 16(1), 388. https://doi.org/10.3390/app16010388

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