Applied Neuropsychology-Adult最新文献

Cognitive disengagement syndrome (CDS) and ADHD are considered distinct but interrelated constructs. This study aimed to investigate the prevalence of elevated CDS symptoms and increased ADHD risk in the general population, and their relationships with emotion regulation difficulty (ERD) and alexithymia. Out of 1166 participants, 142 with known psychiatric conditions were excluded, resulting in 1024 participants. Participants completed various scales including Adult ADHD Self-Report Screening Scale for DSM-5 (ASRS-5), Barkley Adult Sluggish Cognitive Tempo (SCT) Scale, Difficulties in Emotion Regulation Scale-Short Form (DERS-16), and the Toronto Alexithymia Scale (TAS-20). Four groups were formed based on Barkley and ASRS-5 scores: Group 1) No elevated CDS symptoms and Low risk of ADHD, Group 2) No elevated CDS symptoms and Increased risk of ADHD, Group 3) Elevated CDS symptoms and Low risk of ADHD, Group 4) Elevated CDS symptoms and Increased risk of ADHD. Elevated CDS symptoms was found in 10% of participants, and increased ADHD risk in 9.2%. Among probable ADHD cases, 40% had elevated CDS symptoms, while 60% of elevated CDS symptoms cases had increased ADHD risk. Group 4 (elevated CDS symptoms and increased risk of ADHD) had the highest ERD and alexithymia scores, while Group 1(no elevated CDS symptoms and low risk of ADHD) had the lowest. Regression analyses showed that CDS scores predicted ERD (47%) and alexithymia (32%) better than ADHD scores (ERD: 36%, alexithymia: 23%). CDS and ADHD appear as significant concepts that could be involved in the etiology of ERD and alexithymia.

The cognitive impairment known as dementia affects millions of individuals throughout the globe. The use of machine learning (ML) and deep learning (DL) algorithms has shown great promise as a means of early identification and treatment of dementia. Dementias such as Alzheimer's Dementia, frontotemporal dementia, Lewy body dementia, and vascular dementia are all discussed in this article, along with a literature review on using ML algorithms in their diagnosis. Different ML algorithms, such as support vector machines, artificial neural networks, decision trees, and random forests, are compared and contrasted, along with their benefits and drawbacks. As discussed in this article, accurate ML models may be achieved by carefully considering feature selection and data preparation. We also discuss how ML algorithms can predict disease progression and patient responses to therapy. However, overreliance on ML and DL technologies should be avoided without further proof. It's important to note that these technologies are meant to assist in diagnosis but should not be used as the sole criteria for a final diagnosis. The research implies that ML algorithms may help increase the precision with which dementia is diagnosed, especially in its early stages. The efficacy of ML and DL algorithms in clinical contexts must be verified, and ethical issues around the use of personal data must be addressed, but this requires more study.

Recent reports indicate that the Memory Integrated Language Test (MIL) and Making Change Test Abbreviated Index (MCT-AI), two web-based performance validity tests (PVTs), have good sensitivity and specificity when used independently. This study investigated whether using these PVTs together could improve the detection of invalid performance in a mixed neuropsychiatric sample. Participants were 129 adult outpatients who underwent a neuropsychological evaluation and were classified into valid (n = 104) or invalid (n = 25) performance groups based on several commonly used PVTs. Using cut scores of ≤41 on the MIL and ≥1.05 on the MCT-AI together enhanced classification accuracy, yielding an area under the curve of .84 (95% CI: .75, .93). As compared to using the MIL and MCT-AI independently, the combined use increased the sensitivity from .10-.31 to.70 while maintaining ≥.90 specificity. Findings also indicated that failing either the MIL or MCT-AI was associated with somewhat lower cognitive test scores, but failing both was associated with markedly lower scores. Overall, using the MIL and MCT-AI together may be an effective way to identify invalid test performance during a neuropsychological evaluation. Furthermore, pairing these tests is consistent with current practice guidelines to include multiple PVTs in a neuropsychological test battery.

Objective: Remote healthcare services is an upgrowing dynamic field that has been used to reduce potential disease spread and prevent overloading of the healthcare system during COVID-19 pandemic. The need for online interventions during the pandemic required immediate response with sometimes inadequate preparation. The aim of the present study is to investigate the effectiveness of remote healthcare services in the field of neuropsychological interventions.

Methods: A systematic literature search was conducted in the electronic databases of PubMed, PsychINFO and Google Scholar. The main search terms were "remote neuropsychological intervention or training." The included articles were RCT studies published in English, examining the effectiveness of remote healthcare services in neuropsychological interventions for adults with neurological disease diagnoses. Studies involving psychiatric disorders were excluded. Two reviewers assessed the quality of the studies and risk of bias using the PEDro Scale.

Results: A total of 10 studies with 2.221 participants were included. All studies concluded that remote healthcare intervention programs can be feasible, safe and effective in the rehabilitation process of neurological diseases.

Discussion: The present review demonstrated that the domains of neuropsychology have opportunities to forge ahead beyond traditional settings and have the ability to adapt to constantly changing environmental conditions with a view to providing patient care. Health policy plans should therefore be reformulated to include these needs in accordance with the social and cultural context of implementation.

Cognitive screening assessments for neurological deficits are critical to the initial assessment of post-stroke patients. However, most measures are not designed for post-stroke patients and in particular not for people with aphasia (PWA), because they rely on language functions. The Cognitive Assessment for Stroke Patients (CASP) is a screening test that can also be administered to PWA, and was recently adapted into Hebrew. The current study aimed to compare the performance of post-stroke patients on the Hebrew versions of the CASP and the Montreal Cognitive Assessment (MoCA). Forty medical records of post-stroke patients were retrospectively examined: Twenty participants without aphasia and 20 PWA. The data included demographics, total CASP and MoCA scores, and scores in specific cognitive domains. Correlations were found between total CASP and MoCA scores, for all participants as well as for each group separately. Comparisons between groups revealed significantly higher performance of the participants without aphasia on the MoCA, but not on the CASP. Clinically, these findings suggest that the Hebrew version of the CASP can be implemented as a formal cognitive screening test for post-stroke patients, including PWA. It can help identifying PWA's cognitive state and differentiate between language and cognitive impairments, hence, contributing in planning targeted treatment.

Recent research suggests that neurocognitive deficits in patients with schizophrenia may increase the risk of developing cognitive biases. As such, we set out to determine this predictive relationship as it pertains to the development of a first-episode psychosis. We hypothesized that poorer performance in processing speed would be associated with jumping to conclusions and an externalizing bias. Poorer performance in working memory would be associated with belief inflexibility and jumping to conclusions, and poorer performance in attention would be associated with attention to threat. We hypothesized that all cognitive biases would be associated with subsyndromal positive symptoms, and schizotypal traits would moderate these relationships. Undergraduate students (N = 130) completed the Schizotypal Personality Questionnaire, DAVOS Assessment of Cognitive Biases, Community Assessment of Psychic Experiences, and a computerized neuropsychological assessment battery. Processing speed had a small effect on externalizing bias, which in turn affected subsyndromal positive symptoms. There was no moderating effect of schizotypal traits on externalizing bias, but it was significantly associated with subsyndromal positive symptoms. Only the externalizing bias was associated with subsyndromal positive symptomatology, which might be explained by a restricted range and reduced variance in performance as a result of using a university student sample. This is one of few studies that sought to explain the mechanism responsible for the development of subsyndromal positive symptoms in a healthy sample using self-report measures.

Objectives: One of the most frequently applied paradigms in the clinical and experimental fields for assessing working memory is the simple span task, composed of forward and backward recall conditions. However, the utility of the simple span measures and their relation to working memory modalities has yet to be elucidated. The current study aimed to address which of the span measures is more sensitive to the differentiation between forward and backward recall in the tactile, verbal, and visuo-spatial modalities, and to test if working memory modalities differ in the disparity between the two conditions.

Methods: 134 healthy young adults performed the Tactual, Digit, and Visuo-spatial Span tasks, producing Total Correct scores and Longest Sequence score measures.

Results: There was an interaction effect for condition and modality type, showing better performance for the forward compared to the backward recall in both measures. However, the effect size of the Longest Sequence score was significantly higher than the Total Correct score in all tasks. Furthermore, the Visuo-spatial Span exhibited a larger difference between forward and backward recall compared to the Digit and Tactual Span, whereas no difference was found between the latter two.

Conclusions: Forward and backward recall are distinguished in all three modalities, and the Longest Sequence score is more sensitive to differentiate between storage and manipulation components of working memory than the Total Correct score. Additionally, the cognitive demand imposed by manipulation compared to storage is the greatest in the visuo-spatial modality.

Objective: The purpose of this review was to highlight the need to consider factors other than language when adapting tests across cultural groups and to offer a list of tests that have been adapted for use among patients from or descended from Latin American Countries. Despite efforts in the field through publications and workshops, the authors were aware of continued errors in cross-cultural assessment of these patients, resulting in misdiagnosis and unintentional inequitable care. Thus, we sought to reinforce the existing literature.

Method: We reviewed the literature pertaining to cross-cultural adaptation of neuropsychological measures. Relevant papers were identified by our institution's Academic Enhanced Search Option (AcESO) Database, PubMed, and Google Scholar.

Results: 102 papers, articles, and other published literature were reviewed describing issues related to the adaptation of tests for groups originally from or descended from Latin American Countries residing in the United States.

Conclusions: It is imperative that tests be appropriately developed or adapted for the target population with appropriate normative data available, and ideally administered by a fluent speaker trained in assessment. Inappropriate use of tests not adapted for a particular patient's language and country of origin can result in misdiagnosis, potentially resulting in harm to the individual.

Objective: The study presented focuses on the creation of a machine learning (ML) model that uses electrophysiological (EEG) data to identify kids with attention deficit hyperactivity disorder (ADHD) from healthy controls. The EEG signals are acquired during cognitive tasks to distinguish children with ADHD from their counterparts.

Methodology: The EEG data recorded in cognitive exercises was filtered using low pass Bessel filter and notch filters to remove artifacts, by the data set owners. To identify unique EEG patterns, we used many well-known classifiers, including Naïve Bayes (NB), Random Forest, Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), AdaBoost and Linear Discriminant Analysis (LDA), to identify distinct EEG patterns. Input features comprised EEG data from nineteen channels, individually and in combination.

Findings: Study indicates that EEG-based categorization can differentiate between individuals with ADHD and healthy individuals with accuracy of 84%. The RF classifier achieved a maximum accuracy of 0.84 when particular region combinations were used. Evaluation of classification performance utilizing hemisphere-specific EEG data yielded promising outcomes, particularly in the right hemisphere channels.

Novelty: The study goes beyond traditional methodologies by investigating the effect of regional data on categorization results. The contributions of various brain regions to these classifications are being extensively researched. Understanding the role of different brain regions in ADHD can lead to better diagnosis and treatment options for individuals with ADHD. The study of categorization ability, utilizing EEG data specific to each hemisphere, particularly channels in the right hemisphere region, provides further granularity to the findings.