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INTRODUCTION Identifying individuals at risk of developing dementia is crucial for early intervention. Mild cognitive impairment (MCI) and subjective memory complaints (SMCs) are considered its preceding stages. This study aimed to assess the utility of functional near‐infrared spectroscopy (fNIRS) in identifying individuals with MCI and SMC. METHODS One hundred fifty‐one participants were categorized into normal cognition (NC); amnestic MCI (aMCI); non‐amnestic MCI (naMCI); and mild, moderate, and severe SMC groups. Task‐related prefrontal hemodynamics were measured using fNIRS during a visual memory span task. RESULTS Results showed significantly lower oxyhemoglobin (HbO) levels in aMCI, but not in naMCI, compared to the NC. In addition, severe SMC had lower HbO levels than the NC, mild, and moderate SMC. Receiver operating characteristic analysis demonstrated 69.23% and 69.70% accuracy in differentiating aMCI and severe SMC from NC, respectively. DISCUSSION FNIRS may serve as a potential non‐invasive biomarker for early detection of dementia. Highlights Only amnestic mild cognitive impairment (aMCI), but not non‐amnestic MCI, showed lower oxyhemoglobin (HbO) than normal individuals. Reduced HbO was observed in those with severe subjective memory complaints (SMCs) compared to normal cognition (NC), mild, and moderate SMCs. Functional near‐infrared spectroscopy measures were associated with performance in memory assessments. Prefrontal hemodynamics could distinguish aMCI and severe SMC from NC.

Alzheimer’s disease (AD) is a progressive disease leading to cognitive decline, and to prevent it, researchers seek to diagnose mild cognitive impairment (MCI) early. Particularly, non-amnestic MCI (naMCI) is often mistaken for normal aging as the representative symptom of AD, memory decline, is absent. Subjective cognitive decline (SCD), an intermediate step between normal aging and MCI, is crucial for prediction or early detection of MCI, which determines the presence of AD spectrum pathology. We developed a computer-based cognitive task to classify the presence or absence of AD pathology and stage within the AD spectrum, and attempted to perform multi-stage classification through electroencephalography (EEG) during resting and memory encoding state. The resting and memory-encoding states of 58 patients (20 with SCD, 10 with naMCI, 18 with aMCI, and 10 with AD) were measured and classified into four groups. We extracted features that could reflect the phase, spectral, and temporal characteristics of the resting and memory-encoding states. For the classification, we compared nine machine learning models and three deep learning models using Leave-one-subject-out strategy. Significant correlations were found between the existing neurophysiological test scores and performance of our computer-based cognitive task for all cognitive domains. In all models used, the memory-encoding states realized a higher classification performance than resting states. The best model for the 4-class classification was cKNN. The highest accuracy using resting state data was 67.24%, while it was 93.10% using memory encoding state data. This study involving participants with SCD, naMCI, aMCI, and AD focused on early Alzheimer’s diagnosis. The research used EEG data during resting and memory encoding states to classify these groups, demonstrating the significance of cognitive process-related brain waves for diagnosis. The computer-based cognitive task introduced in the study offers a time-efficient alternative to traditional neuropsychological tests, showing a strong correlation with their results and serving as a valuable tool to assess cognitive impairment with reduced bias.

ABSTRACTObjectivesDementia assessment includes cognitive and behavioral testing with informant verification. Conventional testing is resource-intensive, with uneven access. Online unsupervised assessments could reduce barriers to risk assessment. The aim of this study was to assess the relationship between informant-rated behavioral changes and participant-completed neuropsychological test performance in older adults, both measured remotely via an online unsupervised platform, the Brain Health Registry (BHR). DesignObservational cohort study. SettingCommunity-dwelling older adults participating in the online BHR. Informant reports were obtained using the BHR Study Partner Portal. ParticipantsThe final sample included 499 participant–informant dyads. MeasurementsParticipants completed online unsupervised neuropsychological assessment including Forward Memory Span, Reverse Memory Span, Trail Making B, and Go/No-Go tests. Informants completed the Mild Behavioral Impairment Checklist (MBI-C) via the BHR Study Partner portal. Cognitive performance was evaluated in MBI+/− individuals, as was the association between cognitive scores and MBI symptom severity. ResultsMean age of the 499 participants was 67, of which 308/499 were females (61%). MBI + status was associated with significantly lower memory and executive function test scores, measured using Forward and Reverse Memory Span, Trail Making Errors and Trail Making Speed. Further, significant associations were found between poorer objectively measured cognitive performance, in the domains of memory and executive function, and MBI symptom severity. ConclusionThese findings support the feasibility of remote, informant-reported behavioral assessment utilizing the MBI-C, supporting its validity by demonstrating a relationship to online unsupervised neuropsychological test performance, using a previously validated platform capable of assessing early dementia risk markers.

Mild behavioral impairment (MBI) describes later life acquired, sustained neuropsychiatric symptoms (NPS) in cognitively normal individuals or those with mild cognitive impairment (MCI), as an at-risk state for incident cognitive decline and dementia. We developed an operational definition of MBI and tested whether the presence of MBI was related to caregiver burden in patients with subjective cognitive decline (SCD) or MCI assessed at a memory clinic. MBI was assessed in 282 consecutive memory clinic patients with SCD (n = 119) or MCI (n = 163) in accordance with the International Society to Advance Alzheimer's Research and Treatment – Alzheimer's Association (ISTAART–AA) research diagnostic criteria. We operationalized a definition of MBI using the Neuropsychiatric Inventory Questionnaire (NPI-Q). Caregiver burden was assessed using the Zarit caregiver burden scale. Generalized linear regression was used to model the effect of MBI domains on caregiver burden. While MBI was more prevalent in MCI (85.3%) than in SCD (76.5%), this difference was not statistically significant (p = 0.06). Prevalence estimates across MBI domains were affective dysregulation (77.8%); impulse control (64.4%); decreased motivation (51.7%); social inappropriateness (27.8%); and abnormal perception or thought content (8.7%). Affective dysregulation (p = 0.03) and decreased motivation (p=0.01) were more prevalent in MCI than SCD patients. Caregiver burden was 3.35 times higher when MBI was present after controlling for age, education, sex, and MCI (p < 0.0001). MBI was common in memory clinic patients without dementia and was associated with greater caregiver burden. These data show that MBI is a common and clinically relevant syndrome.

BackgroundThis is the first national audit of neurology-led cognitive clinics. Previous audits have focused on community memory assessment services.1–3 AimTo complete a UK-wide audit of cognitive neurology services to compare staffing, diagnostic practice and research and treatment capability.MethodsData was collected using an online platform4 dementiadata.com covering January-December 2023. All institutions providing NHS Cognitive Neurology Services were invited to participate. A committee designed questions comparable to previous memory service audits.Results20 centres submitted data. Per service there were 3.4[0.1-20] FTE Doctors, 1.3[0-7] FTE Nurses, 0.9 FTE Allied Health Professionals. Modal average patient age was 60-65 years. The commonest diagnoses seen were Alzheimer’s Disease (30%), Functional memory impairment (14%) and Mild Cognitive Impairment (11%) but a large range of cognitive disorders were seen.All centres offered genetic testing, CSF biomarkers, MRI and DAT scans but fewer have access to PET and SPECT. <50% offered blood-based biomarkers.68% of centres undertook research studies, but only half participated in clinical trials.OutcomeThere is significant variation in capacity, diagnostic practice and access to diagnostics and research. This audit allows cognitive neurology services to benchmark their practice for the first time and identify quality improvement opportunities.ReferencesThe 2019 National Memory Service Audit. March 2020.National Audit of Dementia Memory Assessment Services Spotlight Audit 2021.Memory Services Spotlight Audit 2023-24.Godfrey A, Argyle IT Consulting.katherinestockton@nhs.net

BackgroundRecent evidence supports the use of disease-modifying therapies (DMTs) in early Alzheimer’s disease (AD). Lecanemab and donanemab have received FDA approval and could be introduced into UK clinical practice pending NICE approval. This would require an assessment of eligibility and an estimation of the capacity needed for the safe delivery of these therapies at clinical sites. This study aimed to estimate the proportion of patients seen in our neurology-led clinics in Nottingham who might be eligible for DMTs.MethodsThe CogNID study (IRAS250525) focuses on patients with Mild Cognitive Impairment (MCI) who are referred to neurology-led clinics at Nottingham University Hospitals NHS Trust for further investigation. We analysed clinical, cognitive, neuroimaging and CSF biomarker data collected from MCI patients to estimate the proportion potentially eligible for DMTs.ResultsAmong the 429 MCI patients who participated in CogNID study, 111 (25.9%) were diagnosed with possible clinical diagnosis of AD. Of these, 70 (63.1%) met cognitive criteria for DMT eligibility, with one excluded based on MRI criteria. Thus, 69 patients (16.1%) were potentially eligible for DMTs (mean age:63 ± 8 years; 64%:male;ethnicity: 59% White British).ConclusionsApproximately 16% of patients seen in our neurology-led tertiary referral clinics may be eligible for DMTs.priya.sharma15@nhs.net

•We present a new EEG data set and evaluation tasks with well-formed annotations.•We propose a novel, fully end-to-end deep model (CEEDNet) for screening EEG signals.•CEEDNet aims to bring all functions for EEG analysis in a seamless learnable fashion.•The proposed CEEDNet significantly improves accuracy compared to existing methods.•Extensive experiments and analyses provide the in-depth property of our CEEDNet. Based on the Chung-Ang University Hospital EEG (CAUEEG) dataset, this paper presents a new fully end-to-end deep learning approach for screening EEG signals, called the CAUEEG End-to-end Deep neural Network (CEEDNet). The core idea of CEEDNet is to combine all the functional elements used to analyze EEG signals in a seamless learnable fashion. CEEDNet pursues to utilize the domain characteristics of EEG signals while minimizing unnecessary human intervention. On the CAUEEG-Dementia and CAUEEG-Abnormal evaluation tasks, CEEDNet produced a significant im provement in accuracy and other metrics compared with existing methods. [Display omitted] For automatic EEG diagnosis, this paper presents a new EEG data set with well-organized clinical annotations called Chung-Ang University Hospital EEG (CAUEEG), which has event history, patient’s age, and corresponding diagnosis labels. We also designed two reliable evaluation tasks for the low-cost, non-invasive diagnosis to detect brain disorders: i) CAUEEG-Dementia with normal, mci, and dementia diagnostic labels and ii) CAUEEG-Abnormal with normal and abnormal. Based on the CAUEEG dataset, this paper proposes a new fully end-to-end deep learning model, called the CAUEEG End-to-end Deep neural Network (CEEDNet). CEEDNet pursues to bring all the functional elements for the EEG analysis in a seamless learnable fashion while restraining non-essential human intervention. Extensive experiments showed that our CEEDNet significantly improves the accuracy compared with existing methods, such as machine learning methods and Ieracitano-CNN (Ieracitano et al., 2019), due to taking full advantage of end-to-end learning. The high ROC-AUC scores of 0.9 on CAUEEG-Dementia and 0.86 on CAUEEG-Abnormal recorded by our CEEDNet models demonstrate that our method can lead potential patients to early diagnosis through automatic screening.

INTRODUCTION Gantenerumab was investigated in Japanese participants with mild cognitive impairment due to Alzheimer's disease (AD) or mild AD in two global phase 3 trials (GRADUATE I/II), and a phase 2 trial in Japan (JP40959). METHODS Of 1965 participants randomized in GRADUATE I/II (global‐GRADUATE), 132 participants were enrolled from Japan (Japanese‐GRADUATE) and 67 Japanese participants were randomized 2:1:1 to high‐, low‐dose gantenerumab, and placebo in JP40959. RESULTS Slowing of cognitive and functional decline, and amyloid reduction in gantenerumab group compared to placebo group were greater in Japanese‐GRADUATE than in the global‐GRADUATE and JP40959. Plasma gantenerumab concentrations in the Japanese‐GRADUATE were slightly higher than in the global‐GRADUATE and comparable to JP40959. Gantenerumab was well tolerated in the Japanese‐GRADUATE and JP40959, matching the safety profile of the global‐GRADUATE. DISCUSSION Differences in results across the populations studied could be related to imbalances in baseline body weight, amyloid load, and disease severity. TRIAL REGISTRATION NUMBER ClinicalTrials.gov ID: NCT03444870, NCT03443973; Japan Registry for Clinical Trials ID: jRCT2080224569. Highlights Gantenerumab was evaluated in Japanese participants with Alzheimer's disease (AD) in two global phase 3 trials and a phase 2 trial in Japan. Relative reduction in Clinical Dementia Rating Sum of Boxes (CDR‐SB) deterioration favored gantenerumab in Japanese‐GRADUATE (42%) more than in global‐GRADUATE (9%) and JP40959 (–24%). Amyloid reduction in Japanese‐GRADUATE was greater than in global‐GRADUATE and JP40959. Overall, 72.7% and 27.5% of Japanese‐ and global‐GRADUATE, respectively, achieved an amyloid‐negative status. Cognitive and functional decline, and amyloid reduction could be related to baseline body weight and disease severity.

Our aim is to review the most recent evidence on novel antibody therapies for Alzheimer's disease directed against amyloid‐β. This is a joint statement of the European Association of Neurology and the European Psychiatric Association. After numerous unsuccessful endeavors to create a disease‐modifying therapy for Alzheimer's disease, substantial and consistent evidence supporting the clinical effectiveness of monoclonal antibodies aimed at amyloid‐β is finally emerging. The latest trials not only achieved their primary objective of slowing the progression of the disease over several months but also demonstrated positive secondary clinical outcomes and a decrease in amyloid‐β levels as observed through positron emission tomography scans. Taken as a whole, these findings mark a significant breakthrough by substantiating that reducing amyloid‐β yields tangible clinical benefits, beyond mere changes in biomarkers. Concurrently, the regular utilization of the new generation of drugs will determine whether statistical efficacy translates into clinically meaningful improvements. This may well signify the dawning of a new era in the development of drugs for Alzheimer's disease.