Formal Neuropsychological Assessment Research Articles

Spectrum of neuropsychological challenges in Turner syndrome.

Turner syndrome (TS) is associated with significant neuropsychological challenges, and screening is recommended at key transition stages. Our goal was to describe the institutional experience of formal neuropsychological assessments in TS and assess differences by karyotype. Data were abstracted by retrospective chart review of completed assessments between January 1, 2019, and October 31, 2022, referred from the newly established multidisciplinary clinic, and descriptive statistical analyses were presented (SAS V9.4). Of 114 patients, 38 (33%) had completed neuropsychological assessment at a median age of 11.3 years (IQR 6.5-14.9). Median full-scale IQ (FSIQ) was lower in those with a 45,X karyotype compared with other karyotypes (p = 0.027) but did not meet statistical significance at the adjusted significance level for multiple comparisons. Lower median non-verbal IQ [performance intelligence quotient (PIQ)] relative to verbal IQ (VIQ) was observed. Diagnoses of attention-deficit hyperactivity disorder (ADHD) (26%) and anxiety disorder (26%) were common followed by specific learning disorder (mathematics; 18%) and autism spectrum disorder (16%). The prevalence of neuropsychological abnormalities in our diverse clinic underscores the importance of early and routine neuropsychological testing in TS.

Impact of Neuropsychological Assessment on Subjective Memory Complaints in Patients With Mild Cognitive Impairment.

Patients with Alzheimer's disease (AD), namely at an initial stage like amnestic cognitive impairment (aMCI), typically present with memory complaints. They also have difficulties regarding self-knowledge about their cognitive deficits. In clinical practice, a formal neuropsychological assessment is often done. The present study aimed to understand whether patients with aMCI retain the ability to monitor the success or failure in their performance during the neuropsychological assessment and adjust the report of memory complaints accordingly, as compared to healthy controls. Participants were patients with aMCI and healthy controls who were questioned about their own memory abilities using the Subjective Memory Complaints (SMC) scale, applied before and after the neuropsychological assessment protocol. A repeated measures General Linear Model was performed to analyze changes in SMC (within-subjects effects) after the neuropsychological assessment, in patients with aMCI and healthy controls (between-subjects effects). Eighty volunteers, 40 patients with aMCI and 40 healthy controls, participated in the study. Patients with aMCI showed lower MMSE scores, more depressive symptoms, and deficits in memory and learning, language and executive domains. Patients with aMCI had higher SMC scores [9.4(3.6)] than healthy controls [4.4(2.3)] before the neuropsychological assessment. A statistically significant interaction was found between the SMC and the diagnostic group, meaning that healthy controls decreased SMC [3.4(1.9)] after the neuropsychological assessment, whereas patients with aMCI kept high levels of SMC [9.6(3.9)]. In patients with aMCI, an inverse correlation between logical memory and the change in SMC was found, so that patients with lower scores in the logical memory test tended to increase their memory complaints after the assessment. Both patients with aMCI and healthy controls can monitor and update the impression about their memory abilities following a formal neuropsychological assessment. Patients with aMCI maintain a high SMC level, which is inversely associated with their memory performance. In practical terms, SMC should be measured consistently at a particular moment in time, preferably preceding the objective neuropsychological assessment.

Automatic detection of cognitive impairment in patients with white matter hyperintensity and causal analysis of related factors using artificial intelligence of MRI

PurposeWhite matter hyperintensity (WMH) is a common feature of brain aging, often linked with cognitive decline and dementia. This study aimed to employ deep learning and radiomics to develop models for detecting cognitive impairment in WMH patients and to analyze the causal relationships among cognitive impairment and related factors. Materials and methodsA total of 79 WMH patients from hospital 1 were randomly divided into a training set (62 patients) and a testing set (17 patients). Additionally, 29 patients from hospital 2 were included as an independent testing set. All participants underwent formal neuropsychological assessments to determine cognitive status. Automated identification and segmentation of WMH were conducted using VB-net, with extraction of radiomics features from cortex, white matter, and nuclei. Four machine learning classifiers were trained on the training set and validated on the testing set to detect cognitive impairment. Model performances were evaluated and compared. Causal analyses were conducted among cortex, white matter, nuclei alterations, and cognitive impairment. ResultsAmong the models, the logistic regression (LR) model based on white matter features demonstrated the highest performance, achieving an AUC of 0.819 in the external test dataset. Causal analyses indicated that age, education level, alterations in cortex, white matter, and nuclei were causal factors of cognitive impairment. ConclusionThe LR model based on white matter features exhibited high accuracy in detecting cognitive impairment in WMH patients. Furthermore, the possible causal relationships among alterations in cortex, white matter, nuclei, and cognitive impairment were elucidated.

Preclinical Brain Network Abnormalities in Patients with Subjective Cognitive Decline.

Alzheimer's disease (AD) is the most common form of dementia worldwide. Currently there are no disease modifying treatments available. Detecting subjects with increased risk to develop dementia is essential for future clinical trials. Subjective cognitive decline (SCD) is a condition defining individuals who perceive a decrease in their own cognitive functioning in the absence of any detectable deficit on neuropsychological testing. SCD individuals show AD-related biomarkers abnormalities in cerebrospinal fluid. The aim of the present study was to assess brain functional connectivity (FC) changes in SCD individuals. 23 SCD and 33 healthy subjects (HS) underwent an extensive neuropsychological assessment and 3T-MRI scanning including a T1-w volume and resting-state fMRI (RS-fMRI) to assess brain atrophy and brain FC. No between-group differences in grey matter volumes were detected. SCD subjects compared to HS showed both increased and decreased FC in the executive and parietal networks. Associations between cognitive measures, mainly assessing working memory, and FC within brain networks were found both in SCD and HS separately. SCD individuals showed FC abnormalities in networks involving fronto-parietal areas that may account for their lower visuo-spatial working memory performances. Dysfunctions in executive-frontal networks may be responsible for the cognitive decline subjectively experienced by SCD individuals despite the normal scores observed by formal neuropsychological assessment. The present study contributes to consider SCD individuals in an early AD stage with an increased risk of developing the disease in the long term.

Translation, cross-cultural adaptation, and validation of the Persian version of Everyday Memory Questionnaire-Revised (EMQ-R) in patients with multiple sclerosis

Purpose The present research translated and validated the Persian version of the Everyday Memory Questionnaire-Revised (EMQ-R) in patients with multiple sclerosis (MS). Methods A two-step study was performed in the current work. First, the scale was translated and culturally adopted to Persian. In the second step, the translated questionnaire was presented to 150 patients with MS and 50 individuals in the control group. Then, construct validity (factor analysis and clinical validity) and reliability measures (test–retest reliability and internal consistency) were computed for this questionnaire. Results Patients with MS obtained higher scores in EMQ-R than the control group (p < .001). The findings of the Kaiser-Meyer-Olkin and Bartlett test approved the sampling adequacy for computing the factor analysis (p < .001). The accuracy of the three-dimensional structure was confirmed by confirmatory factor analysis (CFA). Findings of test–retest (ICC = .95, 95%CI .91–.98, p < .001) and internal consistency revealed a satisfactory value (α = .95, p < .001). Conclusions Satisfactory findings for construct validity and high values for reliability revealed that the Persian version of EMQ-R is a reliable and valid scale to measure the everyday memory of patients with MS in the cognitive assessments of this group. IMPLICATIONS FOR REHABILITATION Persian EMQ-R is a valid, reliable, fast, and easy to administer tool for evaluating the beliefs and insights of patients suffering from MS or other clinical conditions about their cognitive dysfunctions, in day-to-day lives with some differentiation between memory and attentional difficulties. This questionnaire can be a practical clinical tool for the assessment of the cognitive deficits, which might not be detected via formal neuropsychological assessments, and could be a valuable scale to measure the effects of treatment approaches to level up memory function in a way that could be generalized to daily life performance.

Baseline Predictors of Longitudinal Cognitive Outcomes in Persons with Mild Cognitive Impairment

Introduction: The study aimed to explore longitudinal cognitive outcomes and to ascertain predictors of conversion to dementia in a hospital-based mild cognitive impairment (MCI) cohort classified according to the neuropsychological phenotype at baseline. Materials and Methods: Subjects aged >55 years who had a clinical diagnosis of MCI at initial visit between 2010 and 2018, with at least one formal neuropsychological assessment at baseline and follow-up of a minimum of 2 years were included. The prospective study was completed based on evaluation at last follow-up to gauge conversion to dementia, quantification of performance on activities of daily living and when available, longitudinal neuropsychological test scores. Results: Ninety-five patients with MCI met the inclusion criteria with a mean age of 68.4 ± 6.4 years at baseline and a mean duration of follow-up for 6.4 ± 3.2 years. The cumulative conversion rate to dementia was 22.2% (21/95) and the annualized conversion rate was 3.3% per year of follow-up. The majority of subjects who had converted had multidomain MCI (66%). Only white matter changes on MRI brain revealed correlation with baseline neuropsychology tests. The multivariate logistic regression analysis revealed the utility of lower baseline list recognition (adjusted odds ratio: 0.735 [95% confidence interval: 0.589–0.916]; p 0.006), lower immediate logical memory (0.885 [0.790–0.990]; p 0.03), and high perseverative error scores on set shifting (3.116 [1.425–6.817]; p 0.004) as predictors of conversion. A model score of +2.615 could predict conversion with sensitivity of 72% and specificity of 98% over 6.4 years follow-up. Conclusion: There was a higher risk of conversion associated with multidomain MCI. Logistic regression-based estimations of dementia risk utilizing domain-based neuropsychology test scores in MCI have high specificity for diagnosis at baseline.

The conundrum of neuropsychiatric systemic lupus erythematosus: Current and novel approaches to diagnosis.

Recognising neuropsychiatric involvement by systemic lupus erythematosus (SLE) is of growing importance, however many barriers to this exist at multiple levels of our currently available diagnostic algorithms that may ultimately delay its diagnosis and subsequent treatment. The heterogeneous and non-specific clinical syndromes, serological and cerebrospinal fluid (CSF) markers and neuroimaging findings that often do not mirror disease activity, highlight important research gaps in the diagnosis of neuropsychiatric SLE (NPSLE). Formal neuropsychological assessments or the more accessible screening metrics may also help improve objective recognition of cognitive or mood disorders. Novel serum and CSF markers, including autoantibodies, cytokines and chemokines have also shown increasing utility as part of diagnosis and monitoring, as well as in distinguishing NPSLE from SLE patients without SLE-related neuropsychiatric manifestations. Novel neuroimaging studies also expand upon our existing strategy by quantifying parameters that indicate microarchitectural integrity or provide an assessment of neuronal function. Some of these novel markers have shown associations with specific neuropsychiatric syndromes, suggesting that future research move away from considering NPSLE as a single entity but rather into its individually recognized neuropsychiatric manifestations. Nevertheless, it is likely that a composite panel of these investigations will be needed to better address the gaps impeding recognition of neuropsychiatric involvement by SLE.

Fetal demise and Wernicke–Korsakoff syndrome in a patient with hyperemesis gravidarum: a case report

BackgroundWernicke–Korsakoff syndrome is a neuropsychiatric disorder caused by thiamine deficiency composed of two related disorders accounting for an acute presentation and chronic progression. Hyperemesis gravidarum presents a significant risk factor for Wernicke–Korsakoff syndrome as symptoms may rapidly progress in the setting of pregnancy. We present the first-reported case of hyperemesis-gravidarum-associated Wernicke encephalopathy in a patient in the first half of pregnancy in which a missed diagnosis led to septic shock, fetal demise, and eventual profound Korsakoff syndrome.Case presentationWe present the case of a 33-year-old primigravid African American woman at 15 weeks gestational age who initially presented at a community emergency department with nausea and vomiting that ultimately progressed to severe hyperemesis-gravidarum-associated Wernicke–Korsakoff syndrome, fetal demise, and septic shock. The patient received a total of 6 weeks of high-dose parenteral thiamine. Magnetic resonance imaging of the head and formal neuropsychological assessment following treatment plateau confirmed the diagnosis of Wernicke–Korsakoff syndrome.ConclusionsThe multisystem complications seen in severe thiamine deficiency can delay timely administration of high-dose thiamine, particularly in pregnancy, in which the classic triad of Wernicke–Korsakoff syndrome may not raise clinical suspicion due to rapid progression of neurological sequelae in this population. We advise a low threshold for parenteral thiamine repletion in pregnant women with persistent vomiting as hyperemesis gravidarum-induced severe thiamine deficiency can result in Wernicke–Korsakoff syndrome, sepsis, and fetal demise.

Neuropsychological test using machine learning for cognitive impairment screening

Objectives Neuropsychological tests (NPTs) are widely used tools to evaluate cognitive functioning. The interpretation of these tests can be time-consuming and requires a specialized clinician. For this reason, we trained machine learning models that detect normal controls (NC), cognitive impairment (CI), and dementia among subjects. Patients and methods A total number of 14,927 subject datasets were collected from the formal neuropsychological assessments Seoul Neuropsychological Screening Battery (SNSB) by well-qualified neuropsychologists. The dataset included 44 NPTs of SNSB, age, education level, and diagnosis of each participant. The dataset was preprocessed and classified according to three different classes NC, CI, and dementia. We trained machine-learning with a supervised machine learning classifier algorithm support vector machine (SVM) 30 times with classification from scikit-learn (https://scikit-learn.org/stable/) to distinguish the prediction accuracy, sensitivity, and specificity of the models; NC vs. CI, NC vs. dementia, and NC vs. CI vs. dementia. Confusion matrixes were plotted using the testing dataset for each model. Results The trained model's 30 times mean accuracies for predicting cognitive states were as follows; NC vs. CI model was 88.61 ± 1.44%, NC vs. dementia model was 97.74 ± 5.78%, and NC vs. CI vs. dementia model was 83.85 ± 4.33%. NC vs. dementia showed the highest accuracy, sensitivity, and specificity of 97.74 ± 5.78, 97.99 ± 5.78, and 96.08 ± 4.33% in predicting dementia among subjects, respectively. Conclusion Based on the results, the SVM algorithm is more appropriate in training models on an imbalanced dataset for a good prediction accuracy compared to natural network and logistic regression algorithms. The NC vs. dementia machine-learning trained model with SVM based on NPTs SNSB dataset could assist neuropsychologists in classifying the cognitive function of subjects.

“A fog that impacts everything”: a qualitative study of health-related quality of life in people living with HIV who have cognitive impairment

BackgroundCognitive impairment (CI) in people living with HIV (PLWH) is an important health concern in the context of an ageing HIV population. Impacting 14–28% of PLWH, CI is associated with lower health-related quality of life (HRQoL), however, evaluation of the illness-specific factors comprising HRQoL in PLWH with CI have not been assessed.ObjectiveWe sought to contribute evidence toward an understanding of HRQoL and identify domains of HRQoL in PLWH with CI.MethodsQualitative interviews with 25 PLWH with objective CI related to HIV disease were conducted with participants attending HIV clinics in the UK. Clinically significant CI was defined based on The European AIDS Clinical Society guidelines, requiring: (i) subjective reporting of cognitive symptoms; (ii) symptoms to be related to HIV (e.g. potentially confounding non-HIV related conditions have been excluded or are being optimally managed) and; (iii) formal neuropsychological assessment confirming CI. Median age was 56 years (range 35–80); 18 participants were men (72%); 11 (44%) were white British and 8 (32%) were Black African; 14 (56%) were men that have sex with men and 10 (40%) were heterosexual; median number of years living with HIV was 17 (range 1–34); and all participants were on combination antiretroviral therapy. Analyses employed techniques from grounded theory, underpinned by an inductive, collaborative team-based approach.ResultsFindings revealed seven interrelated domains comprising HRQoL experiences were identified: Physical function, Cognition, Social connectedness, Physical and mental health, Stigma, Self-concept, and Control and acceptance, and each was defined by specific descriptive components.ConclusionThis study provides valuable insights on the factors that drive HRQoL in PLWH with CI and contribute to a body of evidence which provides targets for the development of targeted interventions to maintain or improve quality of life.

Comparison of RCF scoring system to clinical decision for the Rey complex figure using machine‐learning algorithm

AbstractBackgroundThe interpreting of RCF scoring requires the standard RCF scoring system and the clinical decision, based on clinical dementia rate (CDR) by clinicians. The interpretation of RCF using clinical decision might not be accurate in the detection of mild cognitive impairment (MCI), or dementia patients in comparison to the RCF scoring system. For this reason, a machine‐learning algorithm was used to demonstrate that scoring RCF using clinical decision was not as accurate as the RCF scoring system in predicting MCI or dementia patients from healthy subjects.MethodThe RCF dataset, which consisted of 2,232 subjects, was collected from formal neuropsychological assessments at Chung‐Ang University Hospital. The RCF dataset was classified according to different criteria, where the first dataset had normal and abnormal subjects. They were grouped depending on the threshold cutoff point of 16%, based on the RCF scoring system. The second dataset had normal, MCI, and dementia subjects, classified depending on the clinical decision from patient CDR scores. The models were trained using a convolutional neural network (CNN) for machine learning, and an artificial neural network algorithm Tensorflow was implemented to distinguish the prediction accuracies of cognitive states of two models: the first model (Normal, Abnormal), and the second model (Normal, MCI, and Dementia).ResultThe trained model's accuracy for predicting cognitive states was as following; 96% and 88% were from the first and second models, respectively. The first model had the sensitivity for detecting abnormal of 85% with an area under the receiver operating characteristic (ROC) curve of 0.847, and the second model had the sensitivity for detecting MCI or Dementia of 78% with an area under the ROC curve of 0.778.ConclusionThe first model, trained using a machine learning algorithm for predicting abnormal patients, was more accurate in comparison to the second model for predicting MCI or Dementia. Based on these results, the RCF scoring system has the potential of presenting better accurate criteria in distinguishing cognitive impairment among patients in comparison to the clinical decision.

Cognitive and Psychological Functioning in Chiari Malformation Type I Before and After Surgical Decompression - A Prospective Cohort Study.

Chiari Malformation Type I (CM-I) is defined as cerebellar tonsil displacement more than 5mm below the foramen magnum. This displacement can alter cerebrospinal fluid flow at the cervicomedullary junction resulting in Valsalva-induced headaches and syringomyelia and compress the brainstem resulting in bulbar symptoms. However, little is known about cognitive and psychological changes in CM-I. To prospectively assess cognitive and psychological performance in CM-I and determine whether changes occur after surgical decompression. Blinded evaluators assessed symptomatic CM-I patients ages ≥18 with a battery of neuropsychological and psychological tests. Testing was conducted preoperatively and 6 to 18 mo postoperatively. Data were converted to Z-scores based on normative data, and t-tests were used to analyze pre-post changes. A total of 26 patients were included, with 19 completing both pre- and post-op cognitive assessments. All patients had resolution of Valsalva-induced headaches and there was improvement in swallowing dysfunction (P<.0001), ataxia (P=.008), and sleep apnea (P=.021). Baseline performances in visual perception and construction (z = -1.11, P=.001) and visuospatial memory (z = -0.93, P=.002) were below average. Pre-post comparisons showed that CM-I patients had stable cognitive and psychological functioning after surgery, without significant changes from preoperative levels. CM-I patients had below average performance in visuospatial and visuoconstructional abilities preoperatively. Prospective longitudinal data following surgery demonstrated improved neurologic status without any decline in cognition or psychological functioning. Routine pre- and postoperative formal neuropsychological assessment in CM-I patients help quantify cognitive and behavioral changes associated with surgical decompression.

Naturalistic smartphone keyboard typing reflects processing speed and executive function.

ObjectiveThe increase in smartphone usage has enabled the possibility of more accessible ways to conduct neuropsychological evaluations. The objective of this study was to determine the feasibility of using smartphone typing dynamics with mood scores to supplement cognitive assessment through trail making tests.MethodsUsing a custom‐built keyboard, naturalistic keypress dynamics were unobtrusively recorded in individuals with bipolar disorder (n = 11) and nonbipolar controls (n = 8) on an Android smartphone. Keypresses were matched to digital trail making tests part B (dTMT‐B) administered daily in two periods and weekly mood assessments. Following comparison of dTMT‐Bs to the pencil‐and‐paper equivalent, longitudinal mixed‐effects models were used to analyze daily dTMT‐B performance as a function of typing and mood.ResultsComparison of the first dTMT‐B to paper TMT‐B showed adequate reliability (intraclass correlations = 0.74). In our model, we observed that participants who typed slower took longer to complete dTMT‐B (b = 0.189, p < .001). This trend was also seen in individual fluctuations in typing speed and dTMT‐B performance (b = 0.032, p = .004). Moreover, participants who were more depressed completed the dTMT‐B slower than less depressed participants (b = 0.189, p < .001). A practice effect was observed for the dTMT‐Bs.ConclusionTyping speed in combination with depression scores has the potential to infer aspects of cognition (visual attention, processing speed, and task switching) in people's natural environment to complement formal in‐person neuropsychological assessments that commonly include the trail making test.

The Leuven late life depression (L3D) study: PET-MRI biomarkers of pathological brain ageing in late-life depression: study protocol

BackgroundMajor depressive disorders rank in the top ten causes of ill health in all but four countries worldwide and are the leading cause of years lived with disability in Europe (WHO). Recent research suggests that neurodegenerative pathology may contribute to the development of late-life depression (LLD) in a sub-group of patients and represent a target for prevention and early diagnosis. In parallel, electroconvulsive therapy (ECT), which is the most effective treatment for severe LLD, has been associated with significant brain structural changes. In both LLD and ECT hippocampal volume change plays a central role; however, the neurobiological mechanism underlying it and its relevance for clinical outcomes remain unresolved.MethodsThis is a monocentric, clinical cohort study with a cross-sectional arm evaluating PET-MR imaging and behavioural measures in 64 patients with LLD compared to 64 healthy controls, and a longitudinal arm evaluating the same imaging and behavioural measures after 10 ECT sessions in 20 patients receiving ECT as part of their normal clinical management. Triple tracer PET-MRI data will be used to measure: hippocampal volume (high resolution MRI), synaptic density using [11C]UCB-J, which targets the Synaptic Vesicle Glycoprotein 2A receptor, tau pathology using [18F]MK-6240, and cerebral amyloid using [18F]-Flutemetamol, which targets beta-amyloid neuritic plaques in the brain. Additional MRI measures and ultrasound will assess cerebral vascular structure and brain connectivity. Formal clinical and neuropsychological assessments will be conducted alongside experience sampling and physiological monitoring to assess mood, stress, cognition and psychomotor function.DiscussionThe main aim of the study is to identify the origin and consequences of hippocampal volume differences in LLD by investigating how biomarkers of pathological ageing contribute to medial temporal lobe pathology. Studying how synaptic density, tau, amyloid and vascular pathology relate to neuropsychological, psychomotor function, stress and ECT, will increase our pathophysiological understanding of the in vivo molecular, structural and functional alterations occurring in depression and what effect this has on clinical outcome. It may also lead to improvements in the differential diagnosis of depression and dementia yielding earlier, more optimal, cost-effective clinical management. Finally, it will improve our understanding of the neurobiological mechanism of ECT.Trial registrationClinicalTrials.gov Identifier: NCT03849417, 21/2/2019.

Comparison of RCF Scoring System to Clinical Decision for the Rey Complex Figure Using Machine-Learning Algorithm.

Background and PurposeInterpreting the Rey complex figure (RCF) requires a standard RCF scoring system and clinical decision by clinicians. The interpretation of RCF using clinical decision by clinicians might not be accurate in the diagnosing of mild cognitive impairment (MCI) or dementia patients in comparison with the RCF scoring system. For this reason, a machine-learning algorithm was used to demonstrate that scoring RCF using clinical decision is not as accurate as of the RCF scoring system in predicting MCI or mild dementia patients from normal subjects.MethodsThe RCF dataset consisted of 2,232 subjects with formal neuropsychological assessments. The RCF dataset was classified into 2 datasets. The first dataset was to compare normal vs. abnormal and the second dataset was to compare normal vs. MCI vs. mild dementia. Models were trained using a convolutional neural network for machine learning. Receiver operating characteristic curves were used to compare the sensitivity, specificity, and area under the curve (AUC) of models.ResultsThe trained model's accuracy for predicting cognitive states was 96% with the first dataset (normal vs. abnormal) and 88% with the second dataset (normal vs. MCI vs. mild dementia). The model had a sensitivity of 85% for detecting abnormal with an AUC of 0.847 with the first dataset. It had a sensitivity of 78% for detecting MCI or mild dementia with an AUC of 0.778 with the second dataset.ConclusionsBased on this study, the RCF scoring system has the potential to present more accurate criteria than the clinical decision for distinguishing cognitive impairment among patients.

QOL-14. A BIOPSYCHOSOCIAL APPROACH TO BRAIN INJURY REHABILITATION FOLLOWING TREATMENT FOR PAEDIATRIC BRAIN TUMOURS: CAN PHARMACOTHERAPY AID NEUROPSYCHOLOGICAL OUTCOME?

Long term survival following paediatric brain tumours has vastly improved in recent decades. Consequently there is a drive towards improved quality of survivorship. Brain tumours, surgical resection and adjuvant therapies represent mechanisms for brain injury and can therefore negatively impact a child’s neuropsychological trajectory; affecting cognition, behaviour, emotional and adaptive functioning and educational/occupational outcomes. A biopsychosocial approach to rehabilitation should target each of these domains through supported remediation, environmental modification and psychoeducation for young people and the key systems around them (e.g. families, education). There is a growing evidence base for the role of concordant psychopharmacologies to improve neuropsychological outcome. Since 2015 children treated at RHSC Edinburgh for brain tumours have been offered pharmacotherapy alongside usual rehabilitation approaches if they demonstrate significant difficulties with Attention, Processing Speed and/or Executive Function on formal neuropsychological assessment. Patients are referred to a Consultant Psychiatrist or Paediatrician (as per local protocol) for medication selection, titration and monitoring. A short case series (N=14) is presented outlining brain tumour pathologies, treatment modalities, neuropsychological profile and rationale for recommending pharmacotherapy. Approximately 50% of patients took up the offer. The treatment/s offered and self or parents reported outcomes is summarised. Pharmacotherapy was broadly effective; “it’s been like night and day”, although for one case (N=1) the side effects outweighed any benefit; “she became even more emotional”. Findings indicate that pharmacotherapy should be considered alongside conventional neurorehabilitation techniques for CYP with specific cognitive difficulties following treatment for paediatric brain tumours.

Cognitive profiles in amyloid‐PET‐eligible patients: A retrospective study

AbstractBackgroundThe early cognitive profile of Alzheimer’s Disease (AD) is typically characterised by predominant impairment in episodic memory, reflecting medial temporal lobe dysfunction. However, little is known about the cognitive profile associated with AD pathology in those patients who meet Appropriate Use Criteria (AUC, Johnson et al., 2013) for Amyloid PET Imaging (API). In this group, AD pathology is frequently associated with an atypical clinical course or presentation. Here, we systematically evaluate the cognitive profiles of AUC‐meeting patients in a real‐world clinical setting, combining the information provided by the neuropsychological assessment with the results of Amyloid PET Imaging.MethodFrom a larger sample of 396 patients who underwent Amyloid PET at the Imperial Memory Centre between December 2013 and June 2019, we included those individuals who also had at least one formal neuropsychological assessment (minimum of 4 tests) within 18 months of API (n=124). Both these were solely conducted for clinical purposes, and API was requested after multidisciplinary team discussion. Cognitive measures in amyloid positive (Aβ+) and negative (Aβ‐) patients were compared using non‐parametric tests.ResultsNeuropsychological assessment preceded Amyloid PET Imaging in most cases, for both amyloid‐positive (n=51, mean age 67.24±9.32 years, 58.8% female) and amyloid‐negative (n=73, mean age 67.9±9.64 years, 41.1% female) groups. Cognitive performance in the amyloid‐positive group was significantly worse in 6 of the 14 cognitive measures taken into account: visuo‐spatial functioning, executive functioning, working memory, verbal learning, verbal delayed recall, and confrontation naming (Figure 1). Verbal learning, verbal delayed recall and confrontation naming were the most frequently impaired measures in the amyloid‐positive group, with around 50% of patients performing below 2 standard deviations. Notably, self‐reported symptoms of depression were significantly higher in the amyloid‐negative (7.3±4.38) than in the amyloid‐positive group (4.68±3.7) (p=.002) (Figure 2).ConclusionsIn a sample of patients attending a tertiary Behavioural Neurology clinic, neuropsychological evaluation revealed worse performance in positive API across a range of domains, particularly verbal memory and confrontation naming, with higher rates of symptoms of depression being reported more frequently in the amyloid negative group.

A Place for Psychological Testing in the Assessment of Adult ADHD

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by symptoms of inattention, impulsivity, and hyperactivity that interfere with social, occupational, or academic functioning. Clinical communities differ regarding whether or not formal psychological or neuropsychological assessment is necessary for diagnosing ADHD. A comprehensive assessment model that emphasizes differential clinical diagnosis is recommended. This model provides practice guidelines that ensure accurate diagnosis when faced with potential complicating factors, such as symptom overlap, substance abuse, and symptom feigning.

Alterations of white matter integrity associated with cognitive deficits in patients with glioma.

ObjectiveThis study aimed to investigate the characteristic of brain structural connections in glioma patients and further evaluate the relationship between changes in the white matter tracts and cognitive decline.MethodsThis retrospective study included a total of 35 subjects with glioma and 14 demographically matched healthy controls, who underwent diffusion tensor imaging scans and formal neuropsychological assessment tests. Fractional anisotropy (FA) values of white matter tracts were derived from atlas‐based analysis to compare group differences. Furthermore, subgroup‐level analysis was performed to differentiate the effects of tumor location on white matter tracts. Partial correlation analysis was used to examine the associations between neurocognitive assessments and the integrity of tracts. Region of interest‐based network analysis was performed to validate the alteration of structural brain network in subjects with glioma.ResultsCompared with controls, subjects with glioma exhibited reduced FA values in the right uncinate fasciculus. Besides, subjects with glioma exhibited worse performance in several cognitive assessments. Partial correlation analysis indicated that the FA value in the right superior longitudinal fasciculus temporal part was significantly positively correlated with scores of visual–spatial abilities in subjects with glioma in the right temporal lobe (r = .932, p = .002). Region of interest‐based network analysis revealed that subjects with glioma exhibited reduced FA, fiber length (FL), and fiber number (FN) between specific brain regions compared with controls.ConclusionThe present study demonstrated the reduced integrity of white matter tracts and altered structural connectivity in brain networks in patients with glioma. Notably, white matter tracts in the right hemisphere might be vulnerable to the effects of a frontal or temporal lesion and might be associated with deficient cognitive function.

Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data

BackgroundNeuropsychological tests (NPTs) are important tools for informing diagnoses of cognitive impairment (CI). However, interpreting NPTs requires specialists and is thus time-consuming. To streamline the application of NPTs in clinical settings, we developed and evaluated the accuracy of a machine learning algorithm using multi-center NPT data.MethodsMulti-center data were obtained from 14,926 formal neuropsychological assessments (Seoul Neuropsychological Screening Battery), which were classified into normal cognition (NC), mild cognitive impairment (MCI) and Alzheimer’s disease dementia (ADD). We trained a machine learning model with artificial neural network algorithm using TensorFlow (https://www.tensorflow.org) to distinguish cognitive state with the 46-variable data and measured prediction accuracies from 10 randomly selected datasets. The features of the NPT were listed in order of their contribution to the outcome using Recursive Feature Elimination.ResultsThe ten times mean accuracies of identifying CI (MCI and ADD) achieved by 96.66 ± 0.52% of the balanced dataset and 97.23 ± 0.32% of the clinic-based dataset, and the accuracies for predicting cognitive states (NC, MCI or ADD) were 95.49 ± 0.53 and 96.34 ± 1.03%. The sensitivity to the detection CI and MCI in the balanced dataset were 96.0 and 96.0%, and the specificity were 96.8 and 97.4%, respectively. The ‘time orientation’ and ‘3-word recall’ score of MMSE were highly ranked features in predicting CI and cognitive state. The twelve features reduced from 46 variable of NPTs with age and education had contributed to more than 90% accuracy in predicting cognitive impairment.ConclusionsThe machine learning algorithm for NPTs has suggested potential use as a reference in differentiating cognitive impairment in the clinical setting.