Stephanie Fountain-Zaragoza

Background/Objectives: Cognitive deficits are common after stroke and often compound motor impairments, hindering functional recovery—yet cognition remains under-addressed in stroke care. This pilot trial evaluated the feasibility and acceptability of a novel stroke telerehabilitation program—COG + OT—that delivers brief, functionally oriented cognitive rehabilitation as a primer to activity-based occupational therapy (OT). Methods: Twenty stroke survivors with arm/hand paresis participated in this single-arm pilot trial. The 8-week COG + OT program included 13 sessions across three phases: (1) cognitive rehabilitation (sessions 1–4), (2) application of cognitive strategies to task-practice OT (sessions 5–10), and (3) integration of cognitive skills into OT (sessions 11–13). Outcomes included feasibility (retention, adherence), acceptability (self-reported interest and usefulness of cognitive strategies, intervention acceptability), and engagement (digital literacy, barriers, and self-reported strategy use). Results: Retention was 95% and adherence was 99.6%. Participants reported moderate interest in cognitive strategies pre-intervention (M = 3.86/5) and found them moderately to very useful post-intervention (M = 4.42/5). Intervention acceptability and appropriateness were rated highly (M = 4.4/5). Common barriers included cognitive, environmental, and language factors; digital/technological barriers were infrequent. Self-reported use of cognitive strategies was moderate to high. Conclusions: The results of this trial strongly support the feasibility and acceptability of the COG + OT program for stroke survivors. Importantly, all participants were able to meaningfully engage in the program despite marked variability in cognitive and clinical characteristics. These findings support further investigation through randomized controlled trials to evaluate efficacy.

Background: Repetitive transcranial magnetic stimulation enhances cognition in people with mild cognitive impairment (MCI). Whereas conventional treatment requires daily sessions for 4‐6 weeks, accelerated intermittent theta burst stimulation (iTBS) shortens the treatment course to just 3 days, substantially improving feasibility of use in people with MCI. We conducted a Phase I safety and feasibility trial of iTBS in MCI, finding preliminary evidence of cognitive improvement. Here, we explore the neural mechanism of this effect by evaluating iTBS‐related changes in functional connectivity in relation to the observed cognitive change.Method: Twenty‐two patients with amnestic MCI received iTBS to left dorsolateral prefrontal cortex (l‐dlPFC) over 3 treatment days (with 8 stimulation sessions of 600 pulses per day) within 1 week. Nineteen had complete MRI and cognitive testing data. The primary cognitive outcome was the fluid cognition composite score from the NIH Toolbox Cognition Battery. We computed functional connectivity from resting‐state fMRI and calculated participation coefficient for three regions of interest. Lower participation coefficient values indicate that a region is more selectively connected to its own network and higher values indicate that it is more widely connected across networks (i.e., a “hub”). We calculated change in participation coefficient from pre‐ to post‐treatment for two regions belonging to the frontoparietal network (the l‐dlPFC iTBS target and the contralateral right dlPFC) and a negative control (primary visual; V1).Result: There was a significant, large effect‐size (d = 0.98) improvement in fluid cognition from pre‐ to post‐iTBS treatment. Improvements in cognition were significantly associated with increased participation coefficient of l‐dlPFC (r = .52, p = .022), with a marginal effect in r‐dlPFC (r = .41, p = .084) and a near zero effect in the negative control V1 (r = .03, p = .898).Conclusion: This preliminary investigation suggests that iTBS‐related cognitive improvement in MCI may be attributable to increased connectivity of the stimulated frontoparietal network. Specifically, a larger increase in “hubness” (i.e., connectivity across multiple networks) of the l‐dlPFC target region is associated with greater cognitive gains. However, as these analyses are based on a limited number of regions and small sample, future studies are needed to further evaluate the neural mechanisms of iTBS in MCI.

Objective:Repetitive transcranial magnetic stimulation (rTMS), a non-invasive neuromodulation therapy most widely used in depression, has shown evidence of secondary benefits for cognition in both neurologic and neuropsychiatric conditions. The recent development of more efficient stimulation protocols, such as accelerated high-dose intermittent theta burst (iTBS)-rTMS, has substantially reduced treatment burden by shortening the treatment course by >50%. This study aimed to establish the safety, feasibility, acceptability, and preliminary efficacy of iTBS-rTMS as a tool for bolstering cognition in individuals with amnestic mild cognitive impairment (aMCI).Participants and Methods:Twenty-four patients with aMCI were enrolled in an open-label phase I trial of iTBS-rTMS; 2 withdrew prior to initiating treatment due to personal circumstances. All participants had received a diagnosis of MCI due to possible AD from a healthcare provider (i.e., neurologist or neuropsychologist) and met actuarial neuropsychological criteria for aMCI. This sample of older adults (range: 61.5-85.2 years, M = 74.1, SD = 5.71) was predominantly White/non-Hispanic (n = 23; Black/non-Hispanic: n = 1), roughly half female (n = 13), with a college education (range: 12-20 years, M = 15.9, SD = 2.5). Participants received 24 sessions of iTBS-rTMS to the left dorsolateral prefrontal cortex over 3 days (8 sessions each, lasting roughly 2 hours per day). Participants rated their perceptions and experience of common side effects during and after each treatment session as well as retrospectively at post-treatment and 4-week follow-up. They completed structural and functional brain MRI, neuropsychiatric evaluations, and neuropsychological assessments before and after treatment and were administered a subset of these measures at 4-week follow-up. MoCA scores were used to monitor for adverse neurocognitive effects, and the fluid cognition composite score from the NIH Toolbox Cognition Battery was used to test preliminary efficacy.Results:We achieved a high retention rate (95%), with 21 of the 22 participants completing all study procedures. There were no clinically significant adverse neuroradiological, neuropsychiatric, or neurocognitive effects of treatment. Participant reports indicated high tolerability and acceptability, with a modal rating of 0 (on a scale from 0=not at all to 10=extremely) for six common side effects (i.e., headache, pain, scalp irritation, facial twitching, fatigue, fear/anxiety), assessed both during and after each treatment session. They reported very low desire to quit despite some participants rating the treatment as moderately tiring. We observed significant, large effect-size (d = 0.98) improvements in fluid cognition from pre- to post-treatment.Conclusions:Our findings support the safety, feasibility, and acceptability of iTBS-rTMS treatment in patients with aMCI. Further, although not explicitly dosed for efficacy, we provide preliminary evidence of improved fluid cognition as a function of treatment, highlighting the potential of this treatment for improving trans-domain cognitive impairment. These promising results can directly inform future trials aimed at optimizing treatment parameters, broadening the indication to other MCI subtypes, and testing the augmentation of established cognitive rehabilitation interventions when combined with rTMS.

Objective: Accumulation of cerebral amyloid-β (Aβ) is a risk factor for cognitive decline and defining feature of Alzheimer’s disease (AD). Aβ is implicated in brain network disruption, but the extent to which these changes correspond with observable cognitive deficits in preclinical AD has not been tested. This study utilized individual-specific functional parcellations to sensitively evaluate the relationship between network connectivity and cognition in adults with and without Aβ deposition. Participants and Methods: Cognitively unimpaired adults ages 45-85 completed amyloid PET, resting-state-fMRI, and neuropsychological tests of episodic memory and executive function. Participants in the upper tertile of mSUVr were considered Aβ+ (n = 50) while others were Aβ- (n = 99). Individualized functional network parcellations were generated from resting-state fMRI data. We examined the effects of group, network, and group-by-network interactions on memory and executive function. Results: We observed several interactions such that within the Aβ+ group, preserved network integrity (i.e., greater connectivity within specific networks) was associated with better cognition, whereas network desegregation (i.e., greater connectivity between relative to within networks) was associated with worse cognition. This dissociation was most apparent for cognitive networks (frontoparietal, dorsal and ventral attention, limbic, and default mode), with connectivity relating to executive function in the Aβ+ group specifically. Conclusions: Using an innovative approach to constructing individual-specified resting-state functional connectomes, we were able to detect differences in brain-cognition associations in preclinical AD. Our findings provide novel insight into specific functional network alterations occurring in the presence of Aβ that relate to cognitive function in asymptomatic individuals.

Introduction: Individuals with multiple sclerosis (MS) are vulnerable to deficits in working memory (WM), but the search for neural correlates of WM within circumscribed areas has been inconclusive. Given the widespread neural alterations observed in MS, predictive modeling approaches that capitalize on whole-brain connectivity may better capture individual differences in WM. Materials and Methods: We applied connectome-based predictive modeling to functional magnetic resonance imaging data from WM tasks in two independent samples with relapsing-remitting MS. In the internal sample ( n _internal = 36), cross-validation was used to train a model to predict accuracy on the Paced Visual Serial Addition Test from functional connectivity. We hypothesized that this MS-specific model would successfully predict performance on the N-back task in the validation cohort ( n _validation = 36). In addition, we assessed the generalizability of existing WM networks derived in healthy young adults to these samples, and we explored anatomical differences between the healthy and MS networks. Results: We successfully derived an MS-specific predictive model of WM in the internal sample (full: r _s = 0.47, permuted p = 0.011), but the predictions were not significant in the validation cohort ( r _s = −0.047; p = 0.78, mean squared error [MSE] = 0.006, R ² = −2.21%). In contrast, the healthy networks successfully predicted WM in both MS samples (internal: r _s = 0.33 p = 0.049, MSE = 0.009, R ² = 13.4%; validation cohort: r _s = 0.46, p = 0.005, MSE = 0.005, R ² = 16.9%), demonstrating their translational potential. Discussion: Functional networks identified in a large sample of healthy individuals predicted significant variance in WM in MS. Networks derived in small samples of people with MS may have limited generalizability, potentially due to disease-related heterogeneity. The robustness of models derived in large clinical samples warrants further investigation. ClinicalTrials.gov ID: NCT03244696.

Evidence-based practice in neuropsychology involves the use of validated tests, cutoff scores, and interpretive algorithms to identify clinically significant cognitive deficits. Recently, actuarial...

Background: Individuals with multiple sclerosis (MS) are vulnerable to deficits in working memory, and the search for neural correlates of working memory in circumscribed areas has yielded inconclusive findings. Given the widespread neural alterations observed in MS, predictive modeling approaches that capitalize on whole-brain connectivity may better capture individual-level working memory in this population. Methods: Here, we applied connectome-based predictive modeling to functional MRI data from working memory tasks in two independent samples with relapsing-remitting MS. In the internal validation sample ( n _internal = 36), functional connectivity data were used to train a model through cross-validation to predict accuracy on the Paced Visual Serial Addition Test, a gold-standard measure of working memory in MS. We then tested its ability to predict performance on the N-back working memory task in the external validation sample ( n _external = 36). Results: The resulting model successfully predicted working memory in the internal validation sample but did not extend to the external sample. We also tested the generalizability of an existing model of working memory derived in healthy young adults to people with MS. It showed successful prediction in both MS samples, demonstrating its translational potential. We qualitatively explored differences between the healthy and MS models in intra- and inter-network connectivity amongst canonical networks. Discussion: These findings suggest that connectome-based predictive models derived in people with MS may have limited generalizability. Instead, models identified in healthy individuals may offer superior generalizability to clinical samples, such as MS, and may serve as more useful targets for intervention. Impact Statement: Working memory deficits in people with multiple sclerosis have important consequence for employment, leisure, and daily living activities. Identifying a functional connectivity-based marker that accurately captures individual differences in working memory may offer a useful target for cognitive rehabilitation. Manglani et al. demonstrate machine learning can be applied to whole-brain functional connectivity data to identify networks that predict individual-level working memory in people with multiple sclerosis. However, existing network-based models of working memory derived in healthy adults outperform those identified in multiple sclerosis, suggesting translational potential of brain networks derived in large, healthy samples for predicting cognition in multiple sclerosis.

With advancing age, declines in the executive control of attention are accompanied by shifts in the functional topology of brain networks. However, there is increasing recognition of the considerable individual variability in the extent and types of attentional deficits that older adults exhibit, with results from neuroimaging investigations paralleling behavioral heterogeneity. Emerging computational methods leverage whole-brain functional connectivity to predict individual-level behaviors. These approaches are well-suited to the cognitive aging context, as they may elucidate configurations of functional connections that best explain group- and individual-level differences across older adults. Two independent samples of neurologically and psychiatrically healthy older adults were used to separately derive a predictive model of attentional control and test the model’s external validity. Here we show that despite challenges posed by heterogeneity in these aging samples, select functional connections carried meaningful variance, allowing for successful prediction of attention in a novel sample of older individuals.

This review examines longitudinal studies of changes in components of attention following mindfulness training. A total of 57 retreat studies, non-randomized trials, and randomized controlled trials were identified. Employing the classical taxonomy proposed by Posner and Petersen (Annual Review of Neuroscience, 13(1), 25–42, 1990), outcome measures were broadly categorized based on whether they involved maintenance of an aroused state (alerting), selective prioritization of attention to target items (orienting), or assessed conflict monitoring (executive attention). Although many non-randomized and retreat studies provide promising evidence of gains in both alerting and conflict monitoring following mindfulness training, evidence from randomized controlled trials, especially those involving active control comparison groups, is more mixed. This review calls attention to the urgent need in our field of contemplative sciences to adopt the methodological rigor necessary for establishing mindfulness meditation as an effective cognitive rehabilitation tool. Although studies including wait-listed control comparisons were fruitful in providing initial feasibility data and pre-post effect sizes, there is a pressing need to employ standards that have been heavily advocated for in the broader cognitive and physical training literatures. Critically, inclusion of active comparison groups and explicit attention to the reduction of demand characteristics are needed to disentangle the effects of placebo from treatment. Further, detailed protocols for mindfulness and control groups and examination of theoretically guided outcome variables with established metrics for reliability and validity are key ingredients in the systematic study of mindfulness meditation. Adoption of such methodological rigor will allow for causal claims supporting mindfulness training as an efficacious treatment modality for cognitive rehabilitation and enhancement.