Predicting changes in brain metabolism and progression from mild cognitive impairment to dementia using multitask Deep Learning models and explainable AI.

BACKGROUND: The prediction of Alzheimer's disease (AD) progression from its early stages is a research priority. In this context, the use of Artificial Intelligence (AI) in AD has experienced a notable surge in recent years. However, existing investigations predominantly concentrate on distinguishing clinical phenotypes through cross-sectional approaches. This study aims to investigate the potential of modeling additional dimensions of the disease, such as variations in brain metabolism assessed via [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET), and utilize this information to identify patients with mild cognitive impairment (MCI) who will progress to dementia (pMCI). METHODS: We analyzed data from 1,617 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had undergone at least one FDG-PET scan. We identified the brain regions with the most significant hypometabolism in AD and used Deep Learning (DL) models to predict future changes in brain metabolism. The best-performing model was then adapted under a multi-task learning framework to identify pMCI individuals. Finally, this model underwent further analysis using eXplainable AI (XAI) techniques. RESULTS: Our results confirm a strong association between hypometabolism, disease progression, and cognitive decline. Furthermore, we demonstrated that integrating data on changes in brain metabolism during training enhanced the models' ability to detect pMCI individuals (sensitivity=88.4%, specificity=86.9%). Lastly, the application of XAI techniques enabled us to delve into the brain regions with the most significant impact on model predictions, highlighting the importance of the hippocampus, cingulate cortex, and some subcortical structures. CONCLUSION: This study introduces a novel dimension to predictive modeling in AD, emphasizing the importance of projecting variations in brain metabolism under a multi-task learning paradigm.

Multimodal neuroimaging in post-COVID syndrome and correlation with cognition.

Brain changes have been reported in the first weeks after SARS-CoV-2 infection. However, limited literature exists about brain alterations in post-COVID syndrome, a condition increasingly associated with cognitive impairment. The present study aimed to evaluate brain functional and structural alterations in patients with post-COVID syndrome, and assess whether these brain alterations were related to cognitive dysfunction. Eighty-six patients with post-COVID syndrome and 36 healthy controls were recruited and underwent neuroimaging acquisition and a comprehensive neuropsychological assessment. Cognitive and neuroimaging examinations were performed 11 months after the first symptoms of SARS-CoV-2. Whole-brain functional connectivity analysis was performed. Voxel-based morphometry was performed to evaluate grey matter volume, and diffusion tensor imaging was carried out to analyse white-matter alterations. Correlations between cognition and brain changes were conducted and Bonferroni corrected. Post-COVID syndrome patients presented with functional connectivity changes, characterized by hypoconnectivity between left and right parahippocampal areas, and between bilateral orbitofrontal and cerebellar areas compared to controls. These alterations were accompanied by reduced grey matter volume in cortical, limbic and cerebellar areas, and alterations in white matter axial and mean diffusivity. Grey matter volume loss showed significant associations with cognitive dysfunction. These cognitive and brain alterations were more pronounced in hospitalized patients compared to non-hospitalized patients. No associations with vaccination status were found. The present study shows persistent structural and functional brain abnormalities 11 months after the acute infection. These changes are associated with cognitive dysfunction and contribute to a better understanding of the pathophysiology of the post-COVID syndrome.

Coronary microvascular dysfunction is associated with impaired cognitive function: the Cerebral-Coronary Connection study (C3 study).

BACKGROUND: It remains unknown whether the presence of coronary microcirculatory dysfunction (CMD) correlates with its equivalent condition in the brain, cerebral small vessel disease (CSVD). The cerebral-coronary connection (C3), a prospective blinded study, investigated the prevalence of CMD in patients with coronary artery disease (CAD) and its association with CSVD and cognitive function. METHODS AND RESULTS: Patients with documented CAD fulfilling inclusion criteria underwent physiological assessment of epicardial vessels and the microcirculation using intracoronary pressure and Doppler. Coronary microcirculation-related indices included coronary flow reserve (CFR) and hyperaemic microvascular resistance. Brain magnetic resonance imaging, transcranial Doppler (TCD), and neurocognitive examination were performed. Overall, 67 patients were included in the study (mean age 66 years, 73% female). Patients with abnormal CFR (<2.0) (55.2%) showed higher burden of white-matter hyperintensities: 43.2 vs. 20.0% (P = 0.044). After statistical adjustment, low CFR was associated with lower grey matter volume (P = 0.024) and with parameters of white-matter microstructural damage in diffusion-tensor imaging (lower fractional anisotropy and higher mean diffusivity, P = 0.029 and P = 0.032, respectively). Low CFR was associated with higher resistive (P = 0.027) and pulsatility (P = 0.043) values on TCD, and worse neurocognitive test scores (lower mini mental state examination, P = 0.025, and slower Trail Making Test A, P = 0.034). CONCLUSIONS: Coronary microcirculatory dysfunction is frequent in patients with CAD and correlates with CSVD, abnormal cerebral flow haemodynamics, and significant cognitive impairment. These findings support the hypothesis that microvascular dysfunction in the heart and the brain are part of a single pathological process affecting microcirculation in patients with CAD. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov NCT04131075.

A proposed new taxonomy of cognitive phenotypes in multiple sclerosis: The International Classification of Cognitive Disorders in MS (IC-CoDiMS).

BACKGROUND: Characterization of cognitive impairment (CI) in multiple sclerosis into distinct phenotypes holds promise for individualized treatments and biomarker exploration. OBJECTIVE: Apply a previously validated, neuropsychologically driven diagnostic algorithm to identify a taxonomy of the type of cognitive phenotypes in multiple sclerosis. METHODS: An algorithm developed and validated in other neurological diseases was applied to a cohort of 1281 people with multiple sclerosis who underwent clinical neuropsychological evaluation across three multiple sclerosis centers. A domain was marked impaired if scores on two tests within the domain fell below one of the two thresholds of interest (compared to controls; -1.0 SD and -1.5 SD below the mean). Results were then tabulated for each participant to determine the type of impairments across the sample. RESULTS: At -1 SD threshold, 48.7% were intact, 21.6% had single-domain, 14.3% bi-domain, and 15.4% multi-domain impairment. At -1.5 SD threshold, 72.9% were intact, 14.0% had single-domain, 8.2% bi-domain, and 5.0% multi-domain impairment. Processing speed was the most frequent single-domain impairment, followed by executive function and memory. CONCLUSIONS: These findings advance the taxonomy of cognitive phenotypes in multiple sclerosis and clarify the type and distribution of possible cognitive diagnoses, pave the way for further investigation of associated biomarkers, and provide clinically meaningful information to guide individualized treatment and rehabilitation.

Abnormal functional connectivity in radiologically isolated syndrome: A resting-state fMRI study.

BACKGROUND: Radiologically isolated syndrome (RIS) patients might have psychiatric and cognitive deficits, which suggests an involvement of major resting-state functional networks. Notwithstanding, very little is known about the neural networks involved in RIS. OBJECTIVE: To examine functional connectivity differences between RIS and healthy controls using resting-state functional magnetic resonance imaging (fMRI). METHODS: Resting-state fMRI data in 25 RIS patients and 28 healthy controls were analyzed using an independent component analysis; in addition, seed-based correlation analysis was used to obtain more information about specific differences in the functional connectivity of resting-state networks. Participants also underwent neuropsychological testing. RESULTS: RIS patients did not differ from the healthy controls regarding age, sex, and years of education. However, in memory (verbal and visuospatial) and executive functions, RIS patients' cognitive performance was significantly worse than the healthy controls. In addition, fluid intelligence was also affected. Twelve out of 25 (48%) RIS patients failed at least one cognitive test, and six (24.0%) had cognitive impairment. Compared to healthy controls, RIS patients showed higher functional connectivity between the default mode network and the right middle and superior frontal gyri and between the central executive network and the right thalamus (pFDR < 0.05; corrected). In addition, the seed-based correlation analysis revealed that RIS patients presented higher functional connectivity between the posterior cingulate cortex, an important hub in neural networks, and the right precuneus. CONCLUSION: RIS patients had abnormal brain connectivity in major resting-state neural networks and worse performance in neurocognitive tests. This entity should be considered not an "incidental finding" but an exclusively non-motor (neurocognitive) variant of multiple sclerosis.

Unraveling brain fog in post-COVID syndrome: Relationship between subjective cognitive complaints and cognitive function, fatigue, and neuropsychiatric symptoms.

BACKGROUND AND PURPOSE: "Brain fog" is a frequent and disabling symptom that can occur after SARS-CoV-2 infection. However, its clinical characteristics and the relationships among brain fog and objective cognitive function, fatigue, and neuropsychiatric symptoms (depression, anxiety) are still unclear. In this study, we aimed to examine the characteristics of brain fog and to understand how fatigue, cognitive performance, and neuropsychiatric symptoms and the mutual relationships among these variables influence subjective cognitive complaints. METHODS: A total of 170 patients with cognitive complaints in the context of post-COVID syndrome were evaluated using a comprehensive neuropsychological protocol. The FLEI scale was used to characterize subjective cognitive complaints. Correlation analysis, regression machine-learning algorithms, and mediation analysis were calculated. RESULTS: Cognitive complaints were mainly attention and episodic memory symptoms, while executive functions (planning) issues were less often reported. The FLEI scale, a mental ability questionnaire, showed high correlations with a fatigue scale and moderate correlations with the Stroop test, and anxiety and depressive symptoms. Random forest algorithms showed an R2 value of 0.409 for the prediction of FLEI score, with several cognitive tests, fatigue and depression being the best variables used in the prediction. Mediation analysis showed that fatigue was the main mediator between objective and subjective cognition, while the effect of depression was indirect and mediated through fatigue. CONCLUSIONS: Brain fog associated with COVID-19 is mainly characterized by attention and episodic memory, and fatigue, which is the main mediator between objective and subjective cognition. Our findings contribute to understanding the pathophysiology of brain fog and emphasize the need to unravel the main mechanisms underlying brain fog, considering several aspects.

Gaps in clinical research in frontotemporal dementia: A call for diversity and disparities-focused research.

Frontotemporal dementia (FTD) is one of the leading causes of dementia before age 65 and often manifests as abnormal behavior (in behavioral variant FTD) or language impairment (in primary progressive aphasia). FTD's exact clinical presentation varies by culture, language, education, social norms, and other socioeconomic factors; current research and clinical practice, however, is mainly based on studies conducted in North America and Western Europe. Changes in diagnostic criteria and procedures as well as new or adapted cognitive tests are likely needed to take into consideration global diversity. This perspective paper by two professional interest areas of the Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment examines how increasing global diversity impacts the clinical presentation, screening, assessment, and diagnosis of FTD and its treatment and care. It subsequently provides recommendations to address immediate needs to advance global FTD research and clinical practice.

Design and Verbal Fluency in Alzheimer's Disease and Frontotemporal Dementia: Clinical and Metabolic Correlates.

OBJECTIVE: Cognitive processes underlying verbal and design fluency, and their neural correlates in patients with Alzheimer's disease (AD) and behavioural variant Frontotemporal Dementia (bvFTD) remain unclear. We hypothesised that verbal and design fluency may be associated with distinct neuropsychological processes in AD and FTD, showing different patterns of impairment and neural basis. METHODS: We enrolled 142 participants including patients with AD (n = 80, mean age = 74.71), bvFTD (n = 34, mean age = 68.18), and healthy controls (HCs) (n = 28, mean age = 71.14), that underwent cognitive assessment and 18F-fluorodeoxyglucose positron emission tomography imaging. RESULTS: Semantic and phonemic fluency showed the largest effect sizes between groups, showing lower scores in bvFTD than AD and HCs, and lower scores in AD than HC. Both AD and bvFTD showed a lower number of unique designs in design fluency in comparison to HC. Semantic fluency was correlated with left frontotemporal lobe in AD, and with left frontal, caudate, and thalamus in bvFTD. Percentage of unique designs in design fluency was associated with the metabolism of the bilateral fronto-temporo-parietal cortex in AD, and the bilateral frontal cortex with right predominance in bvFTD. Repetitions in AD were correlated with bilateral frontal, temporal, and parietal lobes, and with left prefrontal cortex in bvFTD. CONCLUSIONS: Our findings demonstrate differential underlying cognitive processes in verbal and design fluency in AD and bvFTD. While memory and executive functioning associated with fronto-temporo-parietal regions were key in AD, attention and executive functions correlated with the frontal cortex and played a more significant role in bvFTD during fluency tasks.

Persistent olfactory dysfunction after COVID-19 is associated with reduced perfusion in the frontal lobe.

BACKGROUND: Olfactory dysfunction is common during SARS-CoV-2 infection. The pathophysiology of the persistence of this symptom and the potential relationship with central nervous system involvement is unknown. AIM OF THE STUDY: To evaluate the neural correlates of persistent olfactory dysfunction in a series of patients with post-COVID syndrome. METHODS: Eighty-two patients with post-COVID syndrome were assessed with the Brief Smell Identification Test and a multimodal MRI study including 3D-T1, T2-FLAIR, diffusion-tensor imaging, and arterial spin labeling. Olfactory and neuroimaging examinations were performed 11.18 ± 3.78 months after the acute infection. Voxel-based brain mapping analyses were conducted to correlate the olfactory test with brain volumes, white matter microstructure, and brain perfusion. RESULTS: Olfactory dysfunction was associated with lower tissue perfusion in the orbital and medial frontal regions in the arterial spin labeling sequence. Conversely, no statistically significant findings were detected in brain volumes and diffusion-tensor imaging. Mild changes in paranasal sinuses and nasal cavities were detected in 9.75% of cases, with no association with olfactory deficits. CONCLUSIONS: We provide new insights regarding the pathophysiology of persistent olfactory dysfunction after COVID-19, involving the main brain regions associated with the olfactory system.

Chitosan-Hydroxycinnamic Acids Conjugates: Emerging Biomaterials with Rising Applications in Biomedicine.

Over the past thirty years, research has shown the huge potential of chitosan in biomedical applications such as drug delivery, tissue engineering and regeneration, cancer therapy, and antimicrobial treatments, among others. One of the major advantages of this interesting polysaccharide is its modifiability, which facilitates its use in tailor-made applications. In this way, the molecular structure of chitosan has been conjugated with multiple molecules to modify its mechanical, biological, or chemical properties. Here, we review the conjugation of chitosan with some bioactive molecules: hydroxycinnamic acids (HCAs); since these derivatives have been probed to enhance some of the biological effects of chitosan and to fine-tune its characteristics for its application in the biomedical field. First, the main characteristics of chitosan and HCAs are presented; then, the currently employed conjugation strategies between chitosan and HCAs are described; and, finally, the studied biomedical applications of these derivatives are discussed to present their limitations and advantages, which could lead to proximal therapeutic uses.

Clinical exacerbation of SARS-CoV2 infection after fingolimod withdrawal.

The role of disease-modifying therapies in patients with autoimmune disorders during severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) infection is controversial. Immunocompromised patients could have a more severe coronavirus disease-2019 (COVID-19) due to the absence of an adequate immune response against the SARS-CoV-2. However, therapies that act on immune response could play a protective role by dampening the cytokine-release syndrome. Fingolimod is a drug used for immune therapy in patients with multiple sclerosis (MS) through the sequestration of activated lymphocytes in the lymph nodes. We report the case of a 57-year-old man with relapsing-remitting MS treated with fingolimod that showed a reactivation of COVID-19 with signs of hyperinflammation syndrome after fingolimod withdrawal. Our case suggests that discontinuation of fingolimod during COVID-19 could imply a worsening of SARS-CoV2 infection.

ACE2, TMPRSS2, and Furin variants and SARS-CoV-2 infection in Madrid, Spain.

It has been suggested that some individuals may present genetic susceptibility to SARS-CoV-2 infection, with particular research interest in variants of the ACE2 and TMPRSS2 genes, involved in viral penetration into cells, in different populations and geographic regions, although insufficient information is currently available. This study addresses the apparently reasonable hypothesis that variants of these genes may modulate viral infectivity, making some individuals more vulnerable than others. Through whole-exome sequencing, the frequency of exonic variants of the ACE2, TMPRSS2, and Furin genes was analyzed in relation to presence or absence of SARS-CoV-2 infection in a familial multiple sclerosis cohort including 120 individuals from Madrid. The ACE2 gene showed a low level of polymorphism, and none variant was significantly associated with SARS-CoV-2 infection. These variants have previously been detected in Italy. While TMPRSS2 is highly polymorphic, the variants found do not coincide with those described in other studies, with the exception of rs75603675, which may be associated with SARS-CoV-2 infection. The synonymous variants rs61735792 and rs61735794 showed a significant association with infection. Despite the limited number of patients with SARS-CoV-2 infection, some variants, especially in TMPRSS2, may be associated with COVID-19.

Diagnosis of Alzheimer's disease and behavioural variant frontotemporal dementia with machine learning-aided neuropsychological assessment using feature engineering and genetic algorithms.

BACKGROUND: Neuropsychological assessment is considered a valid tool in the diagnosis of neurodegenerative disorders. However, there is an important overlap in cognitive profiles between Alzheimer's disease (AD) and behavioural variant frontotemporal dementia (bvFTD), and the usefulness in diagnosis is uncertain. We aimed to develop machine learning-based models for the diagnosis using cognitive tests. METHODS: Three hundred and twenty-nine participants (170 AD, 72 bvFTD, 87 healthy control [HC]) were enrolled. Evolutionary algorithms, inspired by the process of natural selection, were applied for both mono-objective and multi-objective classification and feature selection. Classical algorithms (NativeBayes, Support Vector Machines, among others) were also used, and a meta-model strategy. RESULTS: Accuracies for the diagnosis of AD, bvFTD and the differential diagnosis between them were higher than 84%. Algorithms were able to significantly reduce the number of tests and scores needed. Free and Cued Selective Reminding Test, verbal fluency and Addenbrooke's Cognitive Examination were amongst the most meaningful tests. CONCLUSIONS: Our study found high levels of accuracy for diagnosis using exclusively neuropsychological tests, which supports the usefulness of cognitive assessment in diagnosis. Machine learning may have a role in improving the interpretation and test selection.

Sera from Patients with NMOSD Reduce the Differentiation Capacity of Precursor Cells in the Central Nervous System.

INTRODUCTION: AQP4 (aquaporin-4)-immunoglobulin G (IgG)-mediated neuromyelitis optica spectrum disorder (NMOSD) is an inflammatory demyelinating disease that affects the central nervous system, particularly the spinal cord and optic nerve; remyelination capacity in neuromyelitis optica is yet to be determined, as is the role of AQP4-IgG in cell differentiation. MATERIAL AND METHODS: We included three groups-a group of patients with AQP4-IgG-positive neuromyelitis optica, a healthy group, and a sham group. We analyzed differentiation capacity in cultures of neurospheres from the subventricular zone of mice by adding serum at two different times: early and advanced stages of differentiation. We also analyzed differentiation into different cell lines. RESULTS AND CONCLUSIONS: The effect of sera from patients with NMOSD on precursor cells differs according to the degree of differentiation, and probably affects oligodendrocyte progenitor cells from NG2 cells to a lesser extent than cells from the subventricular zone; however, the resulting oligodendrocytes may be compromised in terms of maturation and possibly limited in their ability to generate myelin. Furthermore, these cells decrease in number with age. It is very unlikely that the use of drugs favoring the migration and differentiation of oligodendrocyte progenitor cells in multiple sclerosis would be effective in the context of neuromyelitis optica, but cell therapy with oligodendrocyte progenitor cells seems to be a potential alternative.

Intranasal Administration of Undifferentiated Oligodendrocyte Lineage Cells as a Potential Approach to Deliver Oligodendrocyte Precursor Cells into Brain.

Oligodendrocyte precursor cell (OPC) migration is a mechanism involved in remyelination; these cells migrate from niches in the adult CNS. However, age and disease reduce the pool of OPCs; as a result, the remyelination capacity of the CNS decreases over time. Several experimental studies have introduced OPCs to the brain via direct injection or intrathecal administration. In this study, we used the nose-to brain pathway to deliver oligodendrocyte lineage cells (human oligodendroglioma (HOG) cells), which behave similarly to OPCs in vitro. To this end, we administered GFP-labelled HOG cells intranasally to experimental animals, which were subsequently euthanised at 30 or 60 days. Our results show that the intranasal route is a viable route to the CNS and that HOG cells administered intranasally migrate preferentially to niches of OPCs (clusters created during embryonic development and adult life). Our study provides evidence, albeit limited, that HOG cells either form clusters or adhere to clusters of OPCs in the brains of experimental animals.

Spectrum of Headaches Associated With SARS-CoV-2 Infection: Study of Healthcare Professionals.

BACKGROUND: Series of patients with SARS-CoV-2 infection report headache in 6%-15% of cases, although some data suggest that the actual frequency is higher, and that headache is not associated with fever. No study published to date has analyzed the characteristics of headache in these patients. OBJECTIVE: To analyze the characteristics of COVID-19 related headaches. METHODS: We conducted a survey of Spaniard healthcare professionals who have been infected by SARS-CoV-2 and presented headache during the course of the disease. The survey addressed respondents' medical history and headache characteristics, and we analyzed the association between both. RESULTS: We analyzed the responses of a sample of 112 healthcare professionals. History of migraine was reported by 20/112 (17.9%) of respondents, history of tension-type headache by 8/112 (7.1%), and history of cluster headache was reported by a single respondent; 82/112(73.2%) of respondents had no history of headache. Headache presented independently of fever, around the third day after symptom onset. The previous history of migraine was associated with a higher frequency of pulsating headache (20% in patients with previous migraine vs 4.3% in those with no history of migraine, P = .013). CONCLUSION: Headache is often holocranial, hemicranial, or occipital, pressing, and worsens with physical activity or head movements. Because the characteristics of the headache and the associated symptoms are heterogeneous in our survey, we suggest that several patterns with specific pathophysiological mechanisms may underlie the headache associated with COVID-19.

An application of machine learning with feature selection to improve diagnosis and classification of neurodegenerative disorders.

BACKGROUND: The analysis of health and medical data is crucial for improving the diagnosis precision, treatments and prevention. In this field, machine learning techniques play a key role. However, the amount of health data acquired from digital machines has high dimensionality and not all data acquired from digital machines are relevant for a particular disease. Primary Progressive Aphasia (PPA) is a neurodegenerative syndrome including several specific diseases, and it is a good model to implement machine learning analyses. In this work, we applied five feature selection algorithms to identify the set of relevant features from 18F-fluorodeoxyglucose positron emission tomography images of the main areas affected by PPA from patient records. On the other hand, we carried out classification and clustering algorithms before and after the feature selection process to contrast both results with those obtained in a previous work. We aimed to find the best classifier and the more relevant features from the WEKA tool to propose further a framework for automatic help on diagnosis. Dataset contains data from 150 FDG-PET imaging studies of 91 patients with a clinic prognosis of PPA, which were examined twice, and 28 controls. Our method comprises six different stages: (i) feature extraction, (ii) expertise knowledge supervision (iii) classification process, (iv) comparing classification results for feature selection, (v) clustering process after feature selection, and (vi) comparing clustering results with those obtained in a previous work. RESULTS: Experimental tests confirmed clustering results from a previous work. Although classification results for some algorithms are not decisive for reducing features precisely, Principal Components Analisys (PCA) results exhibited similar or even better performances when compared to those obtained with all features. CONCLUSIONS: Although reducing the dimensionality does not means a general improvement, the set of features is almost halved and results are better or quite similar. Finally, it is interesting how these results expose a finer grain classification of patients according to the neuroanatomy of their disease.

What is the meaning of PASAT rejection in multiple sclerosis?

BACKGROUND: Paced Auditory Serial Addition Test (PASAT) is one of the most used neuropsychological tests in multiple sclerosis (MS), specially for screening. However, the applicability of the test is limited because of the rejection of the test completion in a proportion of patients. We aimed to investigate the clinical, neuropsychological, and MRI findings associated to PASAT rejection. METHODS: Cross-sectional and observational study. A total of 343 patients with MS underwent neuropsychological testing and structural MRI. RESULTS: One hundred and twenty-one (35.3%) of patients declined the administration of the test. Among those patients that declined the administration, rejection occurred before the onset of test in 35.5%, during or after the practice in 43%, and during the test administration in 21.5%. Rejection of the test was associated to a worse performance in all cognitive tests administered, but not to depression or baseline fatigue scales. In regression analysis, education, cognitive impairment, EDSS, and white matter lesion load were independently associated to rejection of the test. CONCLUSIONS: Paced Auditory Serial Addition Test rejection is associated with a higher probability of cognitive impairment in MS. This suggests that patients that reject the administration of PASAT should be further examined with a neuropsychological battery to evaluate the possibility of cognitive dysfunction.