Leveraging Swin Transformer for enhanced diagnosis of Alzheimer's disease using multi-shell diffusion MRI.

This study aims to support early diagnosis of Alzheimer's disease and detection of amyloid accumulation by leveraging the microstructural information available in multi-shell diffusion MRI (dMRI) data, using a vision transformer-based deep learning framework. We present a classification pipeline that employs the Swin Transformer, a hierarchical vision transformer model, on multi-shell dMRI data for the classification of Alzheimer's disease and amyloid presence. Key metrics from DTI and NODDI were extracted and projected onto 2D planes to enable transfer learning with ImageNet-pretrained models. To efficiently adapt the transformer to limited labeled neuroimaging data, we integrated Low-Rank Adaptation. We assessed the framework on diagnostic group prediction (cognitively normal, mild cognitive impairment, Alzheimer's disease dementia) and amyloid status classification. The framework achieved competitive classification results within the scope of multi-shell dMRI-based features, with the best balanced accuracy of 95.2% for distinguishing cognitively normal individuals from those with Alzheimer's disease dementia using NODDI metrics. For amyloid detection, it reached 77.2% balanced accuracy in distinguishing amyloid-positive mild cognitive impairment/Alzheimer's disease dementia subjects from amyloid-negative cognitively normal subjects, and 67.9% for identifying amyloidpositive individuals among cognitively normal subjects. Grad-CAM-based explainability analysis identified clinically relevant brain regions, including the parahippocampal gyrus and hippocampus, as key contributors to model predictions. This study demonstrates the promise of diffusion MRI and transformer-based architectures for early detection of Alzheimer's disease and amyloid pathology, supporting biomarker-driven diagnostics in data-limited biomedical settings.

Plasma and cerebrospinal fluid amyloid beta for the diagnosis of Alzheimer's disease dementia and other dementias in people with mild cognitive impairment (MCI).

According to the latest revised National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (now known as the Alzheimer's Association) (NINCDS-ADRDA) diagnostic criteria for Alzheimer's disease dementia, the confidence in diagnosing mild cognitive impairment (MCI) due to Alzheimer's disease dementia is raised with the application of imaging biomarkers. These tests, added to core clinical criteria, might increase the sensitivity or specificity of a testing strategy. However, the accuracy of biomarkers in the diagnosis of Alzheimer's disease dementia and other dementias has not yet been systematically evaluated. A formal systematic evaluation of the sensitivity, specificity, and other properties of positron emission tomography (PET) imaging with the (11)C-labelled Pittsburgh Compound-B ((11)C-PIB) ligand was performed. To determine the diagnostic accuracy of the (11)C- PIB-PET scan for detecting participants with MCI at baseline who will clinically convert to Alzheimer's disease dementia or other forms of dementia over a period of time. The most recent search for this review was performed on 12 January 2013. We searched MEDLINE (OvidSP), EMBASE (OvidSP), BIOSIS Previews (ISI Web of Knowledge), Web of Science and Conference Proceedings (ISI Web of Knowledge), PsycINFO (OvidSP), and LILACS (BIREME). We also requested a search of the Cochrane Register of Diagnostic Test Accuracy Studies (managed by the Cochrane Renal Group).No language or date restrictions were applied to the electronic searches and methodological filters were not used so as to maximise sensitivity. We selected studies that had prospectively defined cohorts with any accepted definition of MCI with baseline (11)C-PIB-PET scan. In addition, we only selected studies that applied a reference standard for Alzheimer's dementia diagnosis for example NINCDS-ADRDA or Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) criteria. We screened all titles generated by electronic database searches. Two review authors independently assessed the abstracts of all potentially relevant studies. The identified full papers were assessed for eligibility and data were extracted to create two by two tables. Two independent assessors performed quality assessment using the QUADAS 2 tool. We used the hierarchical summary receiver operating characteristic (ROC) model to produce a summary ROC curve. Conversion from MCI to Alzheimer's disease dementia was evaluated in nine studies. The quality of the evidence was limited. Of the 274 participants included in the meta-analysis, 112 developed Alzheimer's dementia. Based on the nine included studies, the median proportion converting was 34%. The studies varied markedly in how the PIB scans were done and interpreted.The sensitivities were between 83% and 100% while the specificities were between 46% and 88%. Because of the variation in thresholds and measures of (11)C-PIB amyloid retention, we did not calculate summary sensitivity and specificity. Although subject to considerable uncertainty, to illustrate the potential strengths and weaknesses of (11)C-PIB-PET scans we estimated from the fitted summary ROC curve that the sensitivity was 96% (95% confidence interval (CI) 87 to 99) at the included study median specificity of 58%. This equated to a positive likelihood ratio of 2.3 and a negative likelihood ratio of 0.07. Assuming a typical conversion rate of MCI to Alzheimer's dementia of 34%, for every 100 PIB scans one person with a negative scan would progress and 28 with a positive scan would not actually progress to Alzheimer's dementia.There were limited data for formal investigation of heterogeneity. We performed two sensitivity analyses to assess the influence of type of reference standard and the use of a pre-specified threshold. There was no effect on our findings. Although the good sensitivity achieved in some included studies is promising for the value of (11)C-PIB-PET, given the heterogeneity in the conduct and interpretation of the test and the lack of defined thresholds for determination of test positivity, we cannot recommend its routine use in clinical practice.(11)C-PIB-PET biomarker is a high cost investigation, therefore it is important to clearly demonstrate its accuracy and standardise the process of the (11)C-PIB diagnostic modality prior to it being widely used.

Diagnosis of prodromal Alzheimer's disease and Alzheimer's disease dementia in people with Down syndrome is a major challenge. The Cambridge Examination for Mental Disorders of Older People with Down's Syndrome and Others with Intellectual Disabilities (CAMDEX-DS) has been validated for diagnosing prodromal Alzheimer's disease and Alzheimer's disease dementia, but the diagnostic process lacks guidance. To derive CAMDEX-DS informant interview threshold scores to enable accurate diagnosis of prodromal Alzheimer's disease and Alzheimer's disease dementia in adults with Down syndrome. Psychiatrists classified participants with Down syndrome into no dementia, prodromal Alzheimer's disease and Alzheimer's disease dementia groups. Receiver operating characteristic analyses assessed the diagnostic accuracy of CAMDEX-DS informant interview-derived scores. Spearman partial correlations investigated associations between CAMDEX-DS scores, regional Aβ binding (positron emission tomography) and regional cortical thickness (magnetic resonance imaging). Diagnostic performance of CAMDEX-DS total scores were high for Alzheimer's disease dementia (area under the curve (AUC), 0.998; 95% CI 0.953-0.999) and prodromal Alzheimer's disease (AUC = 0.954; 95% CI 0.887-0.982) when compared with healthy adults with Down syndrome. When compared with those with mental health conditions but no Alzheimer's disease, CAMDEX-DS Section B scores, denoting memory and orientation ability, accurately diagnosed Alzheimer's disease dementia (AUC = 0.958; 95% CI 0.892-0.984), but were unable to diagnose prodromal Alzheimer's disease. CAMDEX-DS total scores exhibited moderate correlations with cortical Aβ (r ~ 0.4 to 0.6, P ≤ 0.05) and thickness (r ~ -0.4 to -0.44, P ≤ 0.05) in specific regions. CAMDEX-DS total score accurately diagnoses Alzheimer's disease dementia and prodromal Alzheimer's disease in healthy adults with Down syndrome.

Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment.

CSF tau and the CSF tau/ABeta ratio for the diagnosis of Alzheimer's disease dementia and other dementias in people with mild cognitive impairment (MCI).

Dementia is a progressive global cognitive impairment syndrome. In 2010, more than 35 million people worldwide were estimated to be living with dementia. Some people with mild cognitive impairment (MCI) will progress to dementia but others remain stable or recover full function. There is great interest in finding good predictors of dementia in people with MCI. The Mini-Mental State Examination (MMSE) is the best-known and the most often used short screening tool for providing an overall measure of cognitive impairment in clinical, research and community settings. To determine the diagnostic accuracy of the MMSE at various thresholds for detecting individuals with baseline MCI who would clinically convert to dementia in general, Alzheimer's disease dementia or other forms of dementia at follow-up. We searched ALOIS (Cochrane Dementia and Cognitive Improvement Specialized Register of diagnostic and intervention studies (inception to May 2014); MEDLINE (OvidSP) (1946 to May 2014); EMBASE (OvidSP) (1980 to May 2014); BIOSIS (Web of Science) (inception to May 2014); Web of Science Core Collection, including the Conference Proceedings Citation Index (ISI Web of Science) (inception to May 2014); PsycINFO (OvidSP) (inception to May 2014), and LILACS (BIREME) (1982 to May 2014). We also searched specialized sources of diagnostic test accuracy studies and reviews, most recently in May 2014: MEDION (Universities of Maastricht and Leuven, www.mediondatabase.nl), DARE (Database of Abstracts of Reviews of Effects, via the Cochrane Library), HTA Database (Health Technology Assessment Database, via the Cochrane Library), and ARIF (University of Birmingham, UK, www.arif.bham.ac.uk). No language or date restrictions were applied to the electronic searches and methodological filters were not used as a method to restrict the search overall so as to maximize sensitivity. We also checked reference lists of relevant studies and reviews, tracked citations in Scopus and Science Citation Index, used searches of known relevant studies in PubMed to track related articles, and contacted research groups conducting work on MMSE for dementia diagnosis to try to locate possibly relevant but unpublished data. We considered longitudinal studies in which results of the MMSE administered to MCI participants at baseline were obtained and the reference standard was obtained by follow-up over time. We included participants recruited and clinically classified as individuals with MCI under Petersen and revised Petersen criteria, Matthews criteria, or a Clinical Dementia Rating = 0.5. We used acceptable and commonly used reference standards for dementia in general, Alzheimer's dementia, Lewy body dementia, vascular dementia and frontotemporal dementia. We screened all titles generated by the electronic database searches. Two review authors independently assessed the abstracts of all potentially relevant studies. We assessed the identified full papers for eligibility and extracted data to create two by two tables for dementia in general and other dementias. Two authors independently performed quality assessment using the QUADAS-2 tool. Due to high heterogeneity and scarcity of data, we derived estimates of sensitivity at fixed values of specificity from the model we fitted to produce the summary receiver operating characteristic curve. In this review, we included 11 heterogeneous studies with a total number of 1569 MCI patients followed for conversion to dementia. Four studies assessed the role of baseline scores of the MMSE in conversion from MCI to all-cause dementia and eight studies assessed this test in conversion from MCI to Alzheimer´s disease dementia. Only one study provided information about the MMSE and conversion from MCI to vascular dementia. For conversion from MCI to dementia in general, the accuracy of baseline MMSE scores ranged from sensitivities of 23% to 76% and specificities from 40% to 94%. In relationship to conversion from MCI to Alzheimer's disease dementia, the accuracy of baseline MMSE scores ranged from sensitivities of 27% to 89% and specificities from 32% to 90%. Only one study provided information about conversion from MCI to vascular dementia, presenting a sensitivity of 36% and a specificity of 80% with an incidence of vascular dementia of 6.2%. Although we had planned to explore possible sources of heterogeneity, this was not undertaken due to the scarcity of studies included in our analysis. Our review did not find evidence supporting a substantial role of MMSE as a stand-alone single-administration test in the identification of MCI patients who could develop dementia. Clinicians could prefer to request additional and extensive tests to be sure about the management of these patients. An important aspect to assess in future updates is if conversion to dementia from MCI stages could be predicted better by MMSE changes over time instead of single measurements. It is also important to assess if a set of tests, rather than an isolated one, may be more successful in predicting conversion from MCI to dementia.

Association between L-α glycerylphosphorylcholine use and delayed dementia conversion: A nationwide longitudinal study in South Korea.

Decision letter: Stage-dependent differential influence of metabolic and structural networks on memory across Alzheimer’s disease continuum

Progression to Alzheimer's disease (AD) dementia from normal cognition (NC) can follow different trajectories, with most progressing through a recognizable mild cognitive impairment stage (NC-MCI-AD), while some individuals transition quickly from NC to AD dementia (NC-AD). We compared demographic characteristics, health factors, and cognitive and functional assessments across three time points: the first NC visit, the last NC visit, and the first AD dementia visit. The NC-MCI-AD group showed greater impairment in cognitive and functional scores at AD dementia diagnosis, despite maintaining better cognitive function during the NC stage. Analysis of yearly changes revealed negligible differences during NC. However, the yearly change during the AD dementia stage suggested potentially more rapid functional decline in the NC-AD group. These findings highlight the heterogeneity in AD disease progression and emphasize the importance of considering diverse progression patterns in AD research and clinical practice. We investigated the disease progression difference between patients who converted to Alzheimer's disease (AD) dementia from normal cognition (NC) directly or through the mild cognitive impairment (MCI) stage. We found that the NC-MCI-AD group showed greater impairment in cognitive and functional scores at AD dementia diagnosis. We discovered that the NC-AD group had rapid functional decline once patients were confirmed with AD onset.

Sleep loss is associated with reduced health and quality of life, and increased risk of Alzheimer's disease and related dementias. Up to 66% of persons with Alzheimer's disease and related dementias experience poor sleep, which can predict or accelerate the progression of cognitive decline. Exercise is a widely accessible intervention for poor sleep that can protect against functional and cognitive decline. No previous systematic reviews have investigated the effectiveness of exercise for sleep in older adults with mild cognitive impairment or Alzheimer's disease and related dementias. We systematically reviewed controlled interventional studies of exercise targeting subjectively or objectively (polysomnography/actigraphy) assessed sleep in persons with mild cognitive impairment or Alzheimer's disease and related dementias. We conducted searches in PubMed, Embase, Scopus and Cochrane-Library (n = 6745). Nineteen randomised and one non-randomised controlled interventional trials were included, representing the experiences of 3278 persons with mild cognitive impairment or Alzheimer's disease and related dementias. Ten had low-risk, nine moderate-risk, and one high-risk of bias. Six studies with subjective and eight with objective sleep outcomes were meta-analysed (random-effects model). We found moderate- to high-quality evidence for the beneficial effects of exercise on self-reported and objectively-measured sleep outcomes in persons with mild cognitive impairment or Alzheimer's disease and related dementias. However, no studies examined key potential moderators of these effects, such as sex, napping or medication use. Our results have important implications for clinical practice. Sleep may be one of the most important modifiable risk factors for a range of health conditions, including cognitive decline and the progression of Alzheimer's disease and related dementias. Given our findings, clinicians may consider adding exercise as an effective intervention or adjuvant strategy for improving sleep in older persons with mild cognitive impairment or Alzheimer's disease and related dementias.

¹⁸F-FDFG uptake by brain tissue as measured by positron emission tomography (PET) is a well-established method for assessment of brain function in people with dementia. Certain findings on brain PET scans can potentially predict the decline of mild cognitive Impairment (MCI) to Alzheimer's disease dementia or other dementias. To determine the diagnostic accuracy of the ¹⁸F-FDG PET index test for detecting people with MCI at baseline who would clinically convert to Alzheimer's disease dementia or other forms of dementia at follow-up. We searched the Cochrane Register of Diagnostic Test Accuracy Studies, MEDLINE, EMBASE, Science Citation Index, PsycINFO, BIOSIS previews, LILACS, MEDION, (Meta-analyses van Diagnostisch Onderzoek), DARE (Database of Abstracts of Reviews of Effects), HTA (Health Technology Assessment Database), ARIF (Aggressive Research Intelligence Facility) and C-EBLM (International Federation of Clinical Chemistry and Laboratory Medicine Committee for Evidence-based Laboratory Medicine) databases to January 2013. We checked the reference lists of any relevant studies and systematic reviews for additional studies. We included studies that evaluated the diagnostic accuracy of ¹⁸F-FDG PET to determine the conversion from MCI to Alzheimer's disease dementia or to other forms of dementia, i.e. any or all of vascular dementia, dementia with Lewy bodies, and fronto-temporal dementia. These studies necessarily employ delayed verification of conversion to dementia and are sometimes labelled as 'delayed verification cross-sectional studies'. Two blinded review authors independently extracted data, resolving disagreement by discussion, with the option to involve a third review author as arbiter if necessary. We extracted and summarised graphically the data for two-by-two tables. We conducted exploratory analyses by plotting estimates of sensitivity and specificity from each study on forest plots and in receiver operating characteristic (ROC) space. When studies had mixed thresholds, we derived estimates of sensitivity and likelihood ratios at fixed values (lower quartile, median and upper quartile) of specificity from the hierarchical summary ROC (HSROC) models. We included 14 studies (421 participants) in the analysis. The sensitivities for conversion from MCI to Alzheimer's disease dementia were between 25% and 100% while the specificities were between 15% and 100%. From the summary ROC curve we fitted we estimated that the sensitivity was 76% (95% confidence interval (CI): 53.8 to 89.7) at the included study median specificity of 82%. This equates to a positive likelihood ratio of 4.03 (95% CI: 2.97 to 5.47), and a negative likelihood ratio of 0.34 (95% CI: 0.15 to 0.75). Three studies recruited participants from the same Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort but only the largest ADNI study (Herholz 2011) is included in the meta-analysis. In order to demonstrate whether the choice of ADNI study or discriminating brain region (Chételat 2003) or reader assessment (Pardo 2010) make a difference to the pooled estimate, we performed five additional analyses. At the median specificity of 82%, the estimated sensitivity was between 74% and 76%. There was no impact on our findings. In addition to evaluating Alzheimer's disease dementia, five studies evaluated the accuracy of ¹⁸F-FDG PET for all types of dementia. The sensitivities were between 46% and 95% while the specificities were between 29% and 100%; however, we did not conduct a meta-analysis because of too few studies, and those studies which we had found recruited small numbers of participants. Our findings are based on studies with poor reporting, and the majority of included studies had an unclear risk of bias, mainly for the reference standard and participant selection domains. According to the assessment of Index test domain, more than 50% of studies were of poor methodological quality. It is difficult to determine to what extent the findings from the meta-analysis can be applied to clinical practice. Given the considerable variability of specificity values and lack of defined thresholds for determination of test positivity in the included studies, the current evidence does not support the routine use of ¹⁸F-FDG PET scans in clinical practice in people with MCI. The ¹⁸F-FDG PET scan is a high-cost investigation, and it is therefore important to clearly demonstrate its accuracy and to standardise the process of ¹⁸F-FDG PET diagnostic modality prior to its being widely used. Future studies with more uniform approaches to thresholds, analysis and study conduct may provide a more homogeneous estimate than the one available from the included studies we have identified.

18F PET with florbetaben for the early diagnosis of Alzheimer's disease dementia and other dementias in people with mild cognitive impairment (MCI).

Traumatic brain injury is an environmental risk factor that may accelerate the progression of Alzheimer's disease and behavioral and psychological symptoms of dementia in patients with mild cognitive impairment. To investigate whether traumatic brain injury in patients with mild cognitive impairment is associated with an increased risk of progression to Alzheimer's disease dementia and behavioral and psychological symptoms of dementia. A retrospective cohort study using the Korean National Health Insurance Service database. National-level health data covering healthcare utilization, diagnoses, prescriptions, and procedures in South Korea from January 2012 to December 2021. Patients diagnosed with mild cognitive impairment between January 1, 2013, and December 31, 2016, were followed until Alzheimer's disease dementia diagnosis, behavioral and psychological symptoms of dementia occurrence, death, or December 31, 2021. These patients were classified into two groups according to the presence of traumatic brain injury during the follow-up period. Age at the time of mild cognitive impairment diagnosis, sex, income level, the presence of several chronic conditions, presence of traumatic brain injury, progression of Alzheimer's disease dementia, and behavioral and psychological symptoms of dementia RESULTS: We assessed 452,718 patients (mean age: 67.16 years). Traumatic brain injury was significantly associated with an increased risk of Alzheimer's disease dementia progression (hazard ratio = 1.252, 95 % confidence interval: 1.206-1.301), particularly among patients aged <65 years (hazard ratio = 1.560, 95 % confidence interval: 1.391-1.749), and was linked to a higher risk of behavioral and psychological symptoms of dementia following Alzheimer's disease dementia diagnosis (hazard ratio = 1.300, 95 % confidence interval: 1.181-1.431). Our results underscore the importance of traumatic brain injury prevention in patients with mild cognitive impairment for mitigating the progression and neuropsychiatric complications of Alzheimer's disease.

Synaptic dysfunction is linked to cognitive symptoms in Alzheimer's disease. Thus, measurement of synapse proteins in cerebrospinal fluid may be useful biomarkers to monitor synaptic degeneration. Cerebrospinal fluid levels of the postsynaptic protein neurogranin are increased in Alzheimer's disease, including in the predementia stage of the disease. Here, we tested the performance of cerebrospinal fluid neurogranin to predict cognitive decline and brain injury in the Alzheimer's Disease Neuroimaging Initiative study. An in-house immunoassay was used to analyse neurogranin in cerebrospinal fluid samples from a cohort of patients who at recruitment were diagnosed as having Alzheimer's disease with dementia (n = 95) or mild cognitive impairment (n = 173), as well as in cognitively normal subjects (n = 110). Patients with mild cognitive impairment were grouped into those that remained cognitively stable for at least 2 years (stable mild cognitive impairment) and those who progressed to Alzheimer's disease dementia during follow-up (progressive mild cognitive impairment). Correlations were tested between baseline cerebrospinal fluid neurogranin levels and baseline and longitudinal cognitive impairment, brain atrophy and glucose metabolism within each diagnostic group. Cerebrospinal fluid neurogranin was increased in patients with Alzheimer's disease dementia (P < 0.001), progressive mild cognitive impairment (P < 0.001) and stable mild cognitive impairment (P < 0.05) compared with controls, and in Alzheimer's disease dementia (P < 0.01) and progressive mild cognitive impairment (P < 0.05) compared with stable mild cognitive impairment. In the mild cognitive impairment group, high baseline cerebrospinal fluid neurogranin levels predicted cognitive decline as reflected by decreased Mini-Mental State Examination (P < 0.001) and increased Alzheimer's Disease Assessment Scale-cognitive subscale (P < 0.001) scores at clinical follow-up. In addition, high baseline cerebrospinal fluid neurogranin levels in the mild cognitive impairment group correlated with longitudinal reductions in cortical glucose metabolism (P < 0.001) and hippocampal volume (P < 0.001) at clinical follow-up. Furthermore, within the progressive mild cognitive impairment group, elevated cerebrospinal fluid neurogranin levels were associated with accelerated deterioration in Alzheimer's Disease Assessment Scale-cognitive subscale (β = 0.0017, P = 0.01). These data demonstrate that cerebrospinal fluid neurogranin is increased already at the early clinical stage of Alzheimer's disease and predicts cognitive deterioration and disease-associated changes in metabolic and structural biomarkers over time.