APOE genotype, hippocampal volume, and cognitive reserve predict improvement by cognitive training in older adults without dementia: a randomized controlled trial.

Cognitive training (CT) programs aim to improve cognitive performance and impede its decline. Thus, defining the characteristics of individuals who can benefit from these interventions is essential. Our objectives were to assess if the cognitive reserve (CR), APOE genotype (e4 carriers/non-carriers) and/or hippocampal volume might predict the effectiveness of a CT program. Participants were older adults without dementia (n = 226), randomized into parallel experimental and control groups. The assessment consisted of a neuropsychological protocol and additional data regarding total intracranial, gray matter, left/right hippocampus volume; APOE genotype; and Cognitive Reserve (CR). The intervention involved multifactorial CT (30 sessions, 90 min each), with an evaluation pre- and post-training (at six months); the control group simply following the center's routine activities. The primary outcome measures were the change in cognitive performance and the predictors of change. The results show that APOE-e4 non-carriers (79.1%) with a larger left hippocampal volume achieved better gains in semantic verbal fluency (R2 = .19). Subjects with a larger CR and a greater gray matter volume better improved their processing speed (R2 = .18). Age was correlated with the improvement in executive functions, such that older age predicts less improvement (R2 = .07). Subjects with a larger left hippocampal volume achieved more significant gains in general cognitive performance (R2 = .087). In conclusion, besides the program itself, the effectiveness of CT depends on age, biological factors like genotype and brain volume, and CR. Thus, to achieve better results through a CT, it is essential to consider the different characteristics of the participants, including genetic factors.Trial registration: Trial retrospectively registered on January 29th, 2020-(ClinicalTrials.gov -NCT04245579).

The cognitive impact of guard shifts in physicians: a before-after study.

OBJECTIVE: We intend to evaluate the change of different cognitive functions after a guard shift in physicians. METHODS: A descriptive observational study was performed. The study population included Spanish physicians who were asked to complete before and immediately after their guard shift the following tests: Digit Span, TMT-A, TMT-B, Symbol Digit, Stroop, Free and Cued Selective Reminding, creep and formal fluency, Visual Orientation and Space Perception tests, and the Leeds Sleep Questionnaire. RESULTS: Thirty subjects were included in the study among which 43.3% were female. The mean subjective impression of the guard shift was 4.47/10 (2.57) and was correlated with the number of sleep hours during the guard shift (r = 0.72, p < 0.001). Statistically significant differences in the comparison between pre- and post-guard period results were found for the Symbol Digit test (88.27 vs. 81.47, p = 0.005), Digit Span test (7.43 vs. 7.00, p = 0.025), Free and Cued Selective Reminding Test (22.73 vs. 21.20, p = 0.002), categorical fluency (39.03 vs. 34.53, p = 0.008), and phonemic fluency (19.57 vs. 16.13, p = 0.001). CONCLUSIONS: The realization of guard shift was associated with a lower yield in several cognitive functions, especially in tasks related to executive function and attention.

Management of thunderclap headache in the emergency room: A retrospective cohort study.

INTRODUCTION: The evaluation of red flags is crucial for the accurate the diagnosis of headache disorders, especially for thunderclap headache. We analysed if secondary headache disorders were adequately ruled out in patients that presented to the emergency room with thunderclap headache. METHODS: In this retrospective cohort study, we screened all patients that visited the emergency room for headache, including those that described thunderclap headache. We measured the frequency with which secondary causes were not adequately ruled out. We analysed the order of the exams, the final diagnosis, and the time elapsed between arrival, initial request for imaging, and the completion of the imaging. RESULTS: We screened 2132 patients, and 42 (1.9%) fulfilled eligibility criteria. Mean age was 43.1 ± 17.1 years, and 57% of patients were female. For 22 (52.4%) patients, the work-up was incomplete. Vascular study was missing in 16 (38.1%) patients, cerebrospinal fluid evaluation in nine (21.4%), and magnetic resonance imaging in seven (16.7%), with multiple assessments missing in six (14.3%). There were ten different combinations in which the exams were performed, with the most frequent being the second exam's cerebral spinal fluid evaluation in 18 (52.9%) and the computed tomography angiogram in 10 (29.4%). A secondary cause of thunderclap headache was found in 16 (38.1%) patients, and four (9.5%) had a primary headache diagnosis after an adequate and complete study. CONCLUSIONS: Thunderclap onset was described in one of every 50 patients that visited the emergency room for headache. More than half of these patients were not adequately managed. More than a third of thunderclap headache patients had a secondary cause.

Validation of the Lille's Apathy Rating Scale in Very Mild to Moderate Dementia.

OBJECTIVE: Apathy is one of the most common and disabling syndromes of dementia and presents at all stages of the disease. Comprehensive and structured methods to assess apathy in dementia are still needed. Lille's Apathy Rating Scale (LARS) has shown good psychometric properties for apathy evaluation in Parkinson disease but has not been validated in dementia. The aim of this study was to validate the LARS in a cohort of patients with very mild to moderate dementia. METHODS: 101 patients with cognitive impairment (Clinical Dementia Rating ≤ 2) and 50 healthy subjects were recruited. Patient diagnoses included 43 individuals with Alzheimer disease, 41 frontotemporal dementia, and 17 primary progressive aphasia. In addition to LARS, the following assessments were administered: Clinical Dementia Rating, Interview for Deterioration in Daily Living Activities in Dementia, Functional Activities Questionnaire, Frontal Behavioral Inventory, Neuropsychiatric Inventory (NPI), and Hamilton Depression Rating Scale. RESULTS: Internal consistency for LARS (Cronbach's alpha) was 0.940. Test-retest intraclass correlation coefficient (ICC) was 0.940 and inter-rater ICC was 0.987. The correlation among LARS and NPI apathy scores (concurrent validity) was 0.834. Receiver operating characteristic analysis estimated an area under the curve of 0.987. The optimal cutoff point was -10. Although total LARS score was influenced by the presence of depression, this disorder was independent with respect to apathy. CONCLUSION: LARS is reliable and valid for detecting and quantifying apathy in patients with dementia, even in very early stages of the disease.

Decoding the Best Automated Segmentation Tools for Vascular White Matter Hyperintensities in the Aging Brain: A Clinician's Guide to Precision and Purpose.

Cerebrovascular damage from small vessel disease (SVD) occurs in healthy and pathological aging. SVD markers, such as white matter hyperintensities (WMH), are commonly found in individuals over 60 and increase in prevalence with age. WMHs are detectable on standard MRI by adhering to the STRIVE criteria. Currently, visual assessment scales are used in clinical and research scenarios but is time-consuming and has rater variability, limiting its practicality. Addressing this issue, our study aimed to determine the most precise WMH segmentation software, offering insights into methodology and usability to balance clinical precision with practical application. This study employed a dataset comprising T1, FLAIR, and DWI images from 300 cognitively healthy older adults. WMHs in this cohort were evaluated using four automated neuroimaging tools: Lesion Prediction Algorithm (LPA) and Lesion Growth Algorithm (LGA) from Lesion Segmentation Tool (LST), Sequence Adaptive Multimodal Segmentation (SAMSEG), and Brain Intensity Abnormalities Classification Algorithm (BIANCA). Additionally, clinicians manually segmented WMHs in a subsample of 45 participants to establish a gold standard. The study assessed correlations with the Fazekas scale, algorithm performance, and the influence of WMH volume on reliability. Results indicated that supervised algorithms were superior, particularly in detecting small WMHs, and can improve their consistency when used in parallel with unsupervised tools. The research also proposed a biomarker for moderate vascular damage, derived from the top 95th percentile of WMH volume in healthy individuals aged 50 to 60. This biomarker effectively differentiated subgroups within the cohort, correlating with variations in brain structure and behavior.

Intelligent digital tools for screening of brain connectivity and dementia risk estimation in people affected by mild cognitive impairment: the AI-Mind clinical study protocol.

More than 10 million Europeans show signs of mild cognitive impairment (MCI), a transitional stage between normal brain aging and dementia stage memory disorder. The path MCI takes can be divergent; while some maintain stability or even revert to cognitive norms, alarmingly, up to half of the cases progress to dementia within 5 years. Current diagnostic practice lacks the necessary screening tools to identify those at risk of progression. The European patient experience often involves a long journey from the initial signs of MCI to the eventual diagnosis of dementia. The trajectory is far from ideal. Here, we introduce the AI-Mind project, a pioneering initiative with an innovative approach to early risk assessment through the implementation of advanced artificial intelligence (AI) on multimodal data. The cutting-edge AI-based tools developed in the project aim not only to accelerate the diagnostic process but also to deliver highly accurate predictions regarding an individual's risk of developing dementia when prevention and intervention may still be possible. AI-Mind is a European Research and Innovation Action (RIA H2020-SC1-BHC-06-2020, No. 964220) financed between 2021 and 2026. First, the AI-Mind Connector identifies dysfunctional brain networks based on high-density magneto- and electroencephalography (M/EEG) recordings. Second, the AI-Mind Predictor predicts dementia risk using data from the Connector, enriched with computerized cognitive tests, genetic and protein biomarkers, as well as sociodemographic and clinical variables. AI-Mind is integrated within a network of major European initiatives, including The Virtual Brain, The Virtual Epileptic Patient, and EBRAINS AISBL service for sensitive data, HealthDataCloud, where big patient data are generated for advancing digital and virtual twin technology development. AI-Mind's innovation lies not only in its early prediction of dementia risk, but it also enables a virtual laboratory scenario for hypothesis-driven personalized intervention research. This article introduces the background of the AI-Mind project and its clinical study protocol, setting the stage for future scientific contributions.

Brain Structural and Functional Changes in Cognitive Impairment Due to Alzheimer's Disease.

Cognitive neuropsychology seeks a potential alignment between structural and functional brain features to explain physiological or pathological processes, such as Alzheimer's disease (AD). Several structural and functional brain changes occurring during the disease, including cognitive impairment, are found at the end of the patient's life, but we need to know more about what happens before its onset. In order to do that, we need earlier biomarkers at preclinical stages, defined by those biomarkers, to prevent the cognitive impairment. In this minireview, we have tried to describe the structural and functional changes found at different stages during AD, focusing on those features taking place before clinical diagnosis.

Cognitive Training Modulates Brain Hypersynchrony in a Population at Risk for Alzheimer's Disease.

BACKGROUND: Recent studies demonstrated that brain hypersynchrony is an early sign of dysfunction in Alzheimer's disease (AD) that can represent a proxy for clinical progression. Conversely, non-pharmacological interventions, such as cognitive training (COGTR), are associated with cognitive gains that may be underpinned by a neuroprotective effect on brain synchrony. OBJECTIVE: To study the potential of COGTR to modulate brain synchrony and to eventually revert the hypersynchrony phenomenon that characterizes preclinical AD. METHODS: The effect of COGTR was examined in a sample of healthy controls (HC, n = 41, 22 trained) and individuals with subjective cognitive decline (SCD, n = 49, 24 trained). Magnetoencephalographic activity and neuropsychological scores were acquired before and after a ten-week COGTR intervention aimed at improving cognitive function and daily living performance. Functional connectivity (FC) was analyzed using the phase-locking value. A mixed-effects ANOVA model with factors time (pre-intervention/post-intervention), training (trained/non-trained), and diagnosis (HC/SCD) was used to investigate significant changes in FC. RESULTS: We found an average increase in alpha-band FC over time, but the effect was different in each group (trained and non-trained). In the trained group (HC and SCD), we report a reduction in the increase in FC within temporo-parietal and temporo-occipital connections. In the trained SCD group, this reduction was stronger and showed a tentative correlation with improved performance in different cognitive tests. CONCLUSION: COGTR interventions could mitigate aberrant increases in FC in preclinical AD, promoting brain synchrony normalization in groups at a higher risk of developing dementia.

The relationship between retinal layers and brain areas in asymptomatic first-degree relatives of sporadic forms of Alzheimer's disease: an exploratory analysis.

BACKGROUND: Two main genetic risks for sporadic Alzheimer's disease (AD) are a family history and ɛ4 allele of apolipoprotein E. The brain and retina are part of the central nervous system and share pathophysiological mechanisms in AD. METHODS: We performed a cross-sectional study with 30 participants without a family history of sporadic AD (FH-) and noncarriers of ApoE ɛ4 (ApoE ɛ4-) as a control group and 34 participants with a family history of sporadic AD (FH+) and carriers of at least one ɛ4 allele (ApoE ɛ4+). We analyzed the correlations between macular volumes of retinal layers and thickness of the peripapillary retinal nerve fiber layer (pRNFL) measured by optical coherence tomography (OCT) with the brain area parameters measured by magnetic resonance imaging (MRI) in participants at high genetic risk of developing AD (FH+ ApoE ɛ4+). RESULTS: We observed a significant volume reduction in the FH+ ApoE ɛ4+ group compared with the control group in some macular areas of (i) macular RNFL (mRNFL), (ii) inner plexiform layer (IPL), (iii) inner nuclear layer (INL), and (iv) outer plexiform layer (OPL). Furthermore, in the FH+ ApoE ɛ4+ group, the retinal sectors that showed statistically significant volume decrease correlated with brain areas that are affected in the early stages of AD. In the same group, the peripapillary retinal nerve fiber layer (pRNFL) did not show statistically significant changes in thickness compared with the control group. However, correlations of these sectors with the brain areas involved in this disease were also found. CONCLUSIONS: In cognitively healthy participants at high genetic risk of developing sporadic forms of AD, there are significant correlations between retinal changes and brain areas closely related to AD such as the entorhinal cortex, the lingual gyrus, and the hippocampus.

Electrophysiological brain signatures for the classification of subjective cognitive decline: towards an individual detection in the preclinical stages of dementia.

BACKGROUND: Alzheimer's disease (AD) prevalence is rapidly growing as worldwide populations grow older. Available treatments have failed to slow down disease progression, thus increasing research focus towards early or preclinical stages of the disease. Subjective cognitive decline (SCD) is known to increase the risk of developing AD and several other negative outcomes. However, it is still very scarcely characterized and there is no neurophysiological study devoted to its individual classification which could improve targeted sample recruitment for clinical trials. METHODS: Two hundred fifty-two older adults (70 healthy controls, 91 SCD, and 91 MCI) underwent a magnetoencephalography scan. Alpha relative power in the source space was employed to train a LASSO classifier and applied to distinguish between healthy controls and SCD. Moreover, MCI participants were used to further validate the previously trained algorithm. RESULTS: The classifier was significantly associated to SCD with an AUC of 0.81 in the whole sample. After randomly splitting the sample in 2/3 for discovery and 1/3 for validation, the newly trained classifier was also able to correctly classify SCD individuals with an AUC of 0.75 in the validation sample. The regions selected by the algorithm included medial frontal, temporal, and occipital areas. The algorithm trained to select SCD individuals was also significantly associated to MCI diagnostic. CONCLUSIONS: According to our results, magnetoencephalography could be a useful tool for distinguishing individuals with SCD and healthy older adults without cognitive concerns. Furthermore, our classifier showed good external validity, being not only successful for an unseen SCD sample, but also in a different population with MCI cases. This supports its utility in the context of preclinical dementia. These findings highlight the potential applications of electrophysiological techniques to improve sample recruitment at the individual level in the context of clinical trials.

Diffusion Tensor Imaging Measures of Brain Connectivity for the Early Diagnosis of Alzheimer's Disease.

The prognostic capacity of the diffusion tensor imaging measures fractional anisotropy (FA) and mean diffusivity (MD) to detect mild cognitive impairment (MCI) progression to Alzheimer's disease (AD) was assessed in 135 MCI patients and 72 healthy subjects over a median follow-up of 40 months. Forty-nine MCI patients (36.3%) developed AD. The factors MD left hippocampus, FA left cingulate, and FA left hippocampus emerged as predictors of progression. Age (hazard ratio [HR] 1.21), delayed text recall (HR 0.89), FA left uncinate (HR 1.90), FA left hippocampus (HR 2.21), and carrying at least one ApoE4 allele (HR 2.86) were associated with a high conversion rate. FA measures revealed the greatest discriminative capacity (Harrell's C = 0.73 versus 0.65 without FA; p = 0.034). The inclusion of FA structural connectivity data in our model improved discrimination between subjects with MCI progressing or not to dementia.

Uncrossed epileptic seizures in Joubert syndrome.

Joubert syndrome and related disorders comprise a subgroup of ciliopathies defined by the presence of the 'molar tooth sign', a midbrain-hindbrain malformation identifiable by neuroimaging. Characteristically, the corticospinal tract and superior cerebellar peduncles do not decussate. Epileptic seizures are uncommon. We present a case of a 28-year-old man with a background of Leber's congenital amaurosis with nephronophthisis, requiring kidney transplantation, and mental retardation, who developed epileptic seizures consisting of a short muffled cry and involuntary shaking movements of the extremities beginning in the left upper limb; these episodes lasted several seconds and occurred in clusters. Simultaneous video-EEG recording showed an ictal pattern in the left frontal lobe. Brain MRI revealed the pathognomonic 'molar tooth sign'; diffusion tensor imaging (DTI)-tractography showed a lack of decussation of both corticospinal tracts. To the best of our knowledge, this is the first time that DTI-tractography has been used to uncover the anatomical substrate underlying the semiology of epileptic seizures.