Applied Neuropsychology-Adult最新文献

Individuals recovering from COVID-19 may experience persistent impairment in verbal memory performance, potentially due to illness-related hippocampal injury. Although verbal memory dysfunction is central to schizophrenia, the interactions between this vulnerability and COVID-19 remain unclear, with no imaging studies addressing the issue to-date. To explore this gap and generate hypotheses for future research, we adopted a multiple case study approach. Two pairs of individuals with an ICD-10 diagnosis of schizophrenia were selected, each consisting of one case with a positive COVID-19 anamnesis and one without. We calculated the Reliable Change Index to estimate the clinical significance of verbal memory performance changes, with annualized change rates in hippocampal volumes assessed against normative data. Compared to their matches, COVID-19 positive cases did not show mutually consistent changes in verbal memory performance: one case experienced a significant decline in verbal memory and learning, while the other showed a general normalization of test scores. Left hippocampal volumes showed a comparatively slowed increase, while the right hippocampi decreased in volume, although these atrophy rates did not exceed those expected in general population samples. Based on these findings, we hypothesize that COVID-19 alone does not lead to verbal memory decline in schizophrenia. Instead, the relationship between the diseases may depend on additional factors. Our case pairs differed in body mass index, systolic blood pressure, sex, phase of illness, and whole grey matter volume trajectories, leading us to hypothesize that these variables represent additional predictors or moderators of this relationship.

Background: Perceived purpose in life (PIL) is linked with many vital health outcomes, including Major Depressive Disorder (MDD).

Methods: In this study, biomarkers associated with depression and PIL were investigated using Brain Network Activation (BNA) based quantitative electroencephalography (QEEG) and event-related potential (ERP) measures in a sample of individuals with MDD. Data were analyzed before and after a 36-session, Deep transcranial magnetic stimulation (TMS) program.

Results: At baseline, the study observed that increased slow-frequency resting-state activity in the frontal and temporal regions correlated with higher levels of depression and reduced PIL. Additionally, a reduced P3b amplitude was found to predict elevated depressive symptoms. However, with the application of Deep TMS treatment notable improvements were observed in both depression (p < .001. d = 2.15) and PIL (p < .001, d = 1.59) levels. The treatment also successfully regulated resting-state QEEG (delta/beta) and ERP characteristics (P200 latency), bringing them closer to healthy levels.

Conclusions: This attenuation of brain activity patterns relating to depression is encouraging as it suggests that effects were robust and are more likely to be sustained over time. This study represents the first exploration of the effects of Deep TMS on PIL and relevant QEEG and ERP biomarkers. The initial evidence suggests that Deep TMS holds promise in enhancing both PIL and neurophysiological health. Future investigations should continue exploring the utility of Deep TMS in targeting a wide range of neuropsychological and physical health conditions, leveraging objective biomarkers such as QEEG and ERP.

This study investigated Nonword Repetition (NWR) tasks in individuals with Multiple Sclerosis (MS) compared to healthy controls (HC), focusing on phonological working memory (WM_P). Significant differences were found in NWR acurracy (NWR_acc) score between MS subgroups and HC (H = 48.2, p < 0.001). NWR_acc decreased as the number of syllables increased in both groups, indicating increased cognitive load. All MS subtypes showed lower NWR_acc compared to HC across varying syllable lengths (Mann Whitney U Test: two syllables U = 64.5, p < 0.001; three syllables U = 183, p < 0.001; four syllables U = 248, p < 0.001; five syllables U = 283.5, p < 0.001). However, no significant differences were found within MS subtypes based on syllable length. NWR_acc did not differ between mild and severe MS groups. Overall, the NWR test effectively assessed WM_P in MS, highlighting its utility in diagnosing and addressing language-cognitive challenges in individuals with MS. This underscores the importance of tailored intervention strategies to mitigate these challenges.

A new model of reserve, the Sensory, Emotional, and Cognitive Reserve (SEC) model, has been recently proposed, but so far this model has not been operationalized in instruments to evaluate the different domains of the reserve. This study introduces the SEC reserve inventory (SECri) along with preliminary data obtained from a study involving 57 adults, aged 35 and older. The SECri assesses the SEC domains using specific proxies: (a) sensory reserve (SR) through sensory acuity and sensory perception proxies; (b) emotional reserve (ER) through life events, resilience, and emotional regulation proxies; and (c) cognitive reserve (CR) through education, occupation, socioeconomic status, bilingualism, leisure activities, and personality traits proxies. Key features of SECri include self- and informant-report forms, fine-grained response scales, and the evaluation of reserve development across the lifespan. Findings on the acceptability, convergent validity between SECri domains and validated tests for the same constructs, internal consistency of each domain, and predictive validity of Montreal Cognitive Assessment scores support further research with this inventory. Future studies should consider determining SECri's psychometric properties in clinical and subclinical conditions to evaluate its prognostic value in cases of neurocognitive decline.

The results of previous research on the relationship between neuropsychiatric symptoms and cognition in multiple sclerosis (MS) have been mixed. The aim of this systematic review was to examine the evidence on the relationship between neuropsychiatric symptoms and different cognitive domains in adult (≥18 years) MS patients. A literature search was conducted in the Ovid Medline, PsycInfo, Scopus, and Web of Science databases. A total of 4,216 nonduplicate records were identified, and after screening, 37 studies met the inclusion criteria and were included in the systematic review. Higher levels of depressive symptoms were related to deficits in processing speed, verbal memory, executive functions, visuospatial functions, and attention in MS patients. Symptoms of anxiety were not consistently related to any of the cognitive functions, but the relationship to deficits in visual memory received a minimal amount of support. Higher levels of apathy were most clearly associated with impairment in executive functions, but the association with deficits in visuospatial functions, visual memory, working memory, and processing speed was also supported. The results indicate that more neuropsychiatric symptoms, especially depressive symptoms and apathy, are associated with cognitive dysfunction in MS patients. These results can be utilized in the clinical examination and treatment planning of MS patients.

Objective: The study was designed to evaluate the performance validity module of Advanced Clinical Solutions (ACS) against external criterion measures and compare two alternative aggregation methods for its five components.

Method: The ACS was evaluated against psychometrically defined criterion groups in a sample of 93 outpatients with TBI. In addition to the default method, the component performance validity tests (PVTs) were either dichotomized along a single cutoff (VI-ACS) or recoded to capture various degrees of failure (EI-ACS).

Results: The standard ACS model correctly classified 75-83% of the sample. The alternative aggregation methods produced superior overall correct classification: 80-91% (VI-ACS) and 86-91% (EI-ACS). Mild TBI was associated with higher failure rates than moderate/severe TBI. Failing just one of the five ACS components resulted in a 3- to 8-fold increase in the likelihood of failing criterion PVTs.

Conclusions: Results support the use of the standard PVT module for ACS: it is an effective measure of performance validity that is robust to moderate-to-severe TBI. Post-publication research on individual ACS components and methodological advances in PVT research provide an opportunity to enhance the overall classification accuracy of the ACS model. Passing stringent multivariate PVT cutoffs does not indicate valid performance.

Objective: Demonstrate how patient-level traumatic brain injury (TBI) data from studies in the Federal Interagency Traumatic Brain Injury Research (FITBIR) Informatics System can be harmonized and pooled to examine relationships between TBI and cognitive functioning.

Method: We harmonized and pooled data across studies and analyzed rates of probable cognitive functioning deficits by TBI history and severity.

Results: Four publicly available FITBIR studies with 3,445 participants included data on cognitive dysfunction, though only one included comparison groups (mild TBI vs. no history of TBI) and could be used in the final comparative analyses. Of the 1,539 participants, 82% had a history of mild TBI and 67% had data suggesting the presence of cognitive dysfunction. Participants with a history of mild TBI were mostly male (87%), 25-39 years old (53%), and Non-Hispanic White (60%). Conclusions: One publicly available FITBIR study reported cognitive dysfunction data as of January 2021, though findings were similar to prior research and supported an association between mild TBI and cognitive dysfunction. This proof-of-concept study shared newly developed methods including harmonization, analysis syntax, and meta-data via the FITBIR website to encourage dissemination of these TBI data resources in line with FAIR data goals.

Using diffusion tensor imaging (DTI), we assessed the extent to which fractional anisotropy values in the dorsal (i.e., arcuate fasciculus; AF) versus ventral (i.e., inferior fronto-occipital fasciculus; IFOF) distinction of structural white matter pathways associated with selected reading processes, could be replicated in skilled adult readers (N = 17) and extended to adults with reading impairments (N = 13). In addition to the AF and IFOF, motor-based tracts (i.e., posterior limb of the internal capsule (PLIC) and the frontal aslant tract (FAT)) were isolated to explore their role in reading performance. Several interesting relationships with reading performance emerged. First, orthographic awareness was related to the left IFOF in skilled readers, whereas orthographic awareness was related to left PLIC for impaired readers. Morphological awareness was related to left FAT for skilled readers, whereas morphological awareness was related to right AF, right IFOF and left PLIC for impaired readers. Overall, these findings support the notion that adult reading performance (both skilled and impaired) is related to the structural properties of the ventral white matter pathways. More consideration should be paid to motor pathways, particularly the PLIC, and their role in compensatory reading strategies in individuals with reading impairments.

Objectives: Neuropsychological tests (NPTs) are standard tools for assessing cognitive function. These tools can evaluate the cognitive status of a subject, which can be time-consuming and expensive for interpretation. Therefore, this paper aimed to optimize the systematic NPTs by machine learning and develop new classification models for differentiating healthy controls (HC), mild cognitive impairment, and Alzheimer's disease dementia (ADD) among groups of subjects.

Patients and methods: A total dataset of 14,926 subjects was obtained from the formal 46 NPTs based on the Seoul Neuropsychological Screening Battery (SNSB). The statistical values of the dataset included an age of 70.18 ± 7.13 with an education level of 8.18 ± 5.50 and a diagnosis group of three; HC, MCI, and ADD. The dataset was preprocessed and classified in two- and three-way machine-learning classification from scikit-learn (www.scikit-learn.org) to differentiate between HC versus MCI, HC versus ADD, HC versus Cognitive Impairment (CI) (MCI + ADD), and HC versus MCI versus ADD. We compared the performance of seven machine learning algorithms, including Naïve Bayes (NB), random forest (RF), decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), AdaBoost, and linear discriminant analysis (LDA). The accuracy, sensitivity, specificity, positive predicted value (PPV), negative predictive value (NPV), area under the curve (AUC), confusion matrixes, and receiver operating characteristic (ROC) were obtained from each model based on the test dataset.

Results: The trained models based on 29 best-selected NPT features were evaluated, the model with the RF algorithm yielded the best accuracy, sensitivity, specificity, PPV, NPV, and AUC in all four models: HC versus MCI was 98%, 98%, 97%, 98%, 97%, and 99%; HC versus ADD was 98%, 99%, 96%, 97%, 98%, and 99%; HC versus CI was 97%, 99%, 92%, 97%, 97%, and 99% and HC versus MCI versus ADD was 97%, 96%, 98%, 97%, 98%, and 99%, respectively, in predicting of cognitive impairment among subjects.

Conclusion: According to the results, the RF algorithm was the best classification model for both two- and three-way classification among the seven algorithms trained on an imbalanced NPTs SNSB dataset. The trained models proved useful for diagnosing MCI and ADD in patients with normal NPTs. These models can optimize cognitive evaluation, enhance diagnostic accuracy, and reduce missed diagnoses.

Patients with aphasia (PWA), particularly those with agrammatic aphasia, experience problems in sentence comprehension. Studies have found that Mapping Therapy (MT) can improve sentence processing in PWA. This paper aims to review the literature on therapeutic studies using MT for the treatment of sentence processing in PWA. All studies on the treatment of sentence comprehension using MT were found by searching Cochrane Library, ISI Web of Knowledge, Google Scholar, Pubmed, and Scopus from 1986 until December 2023, with the combination of these search keywords: 'aphasia, sentence, comprehension, mapping therapy, treatment, rehabilitation'. All studies (single-subject or group design) on the treatment of sentence comprehension using MT in PWA were reviewed. An adaptation of the Cochrane Collaboration's risk of bias (RoB) tool was used to assess the risk of bias (RoB) in the reviewed studies. A total of 14 studies on 81 participants were selected and reviewed. All studies (13 studies) had employed a single-subject design, except for one study that had used a group design. Twelve studies (86%) showed that MT is effective in the remediation of sentence comprehension in PWA. Generalization to untrained sentences similar to the trained structure was also observed in 12 studies (86%). Generalization to untrained structures (usually passive sentences) was limited. In addition, cross-modal improvement in sentence production was observed in 8 studies (57%). This review highlights the need for a more detailed investigation of the effect of MT on cross-modal generalization using elicited production of the sentence types trained during comprehension treatment.