Hippocampal Volume Measurements Research Articles

Selective correlation of hippocampal volumes with WADA memory scores in mesial temporal sclerosis patients

ObjectiveThe WADA test is used to determine cerebral language dominance and assess the risk of postoperative amnesia following mesial temporal lobe resection. This study aims to explore the correlation between automated measures of hippocampal volume and WADA memory scores and to evaluate whether these volumetric measurements can reliably predict WADA memory scores.MethodsThis study included patients who underwent a comprehensive presurgical assessment along with bilateral WADA testing. Hippocampal volumes were measured from high-resolution brain MRIs using automated software (volBrain), which were harmonized and normalized to whole brain volume. These harmonized and normalized volumes were then correlated with ipsilateral WADA memory scores and stratified according to brain MRI findings. A similar analysis was conducted between hippocampal volume asymmetry and WADA memory score asymmetry (WMA). A Receiver Operating Characteristic (ROC) curve was generated to compare the sensitivity and specificity in predicting successful WADA outcomes based on ipsilateral harmonized normalized hippocampal volumes.ResultsIn patients with mesial temporal sclerosis (MTS), significant positive correlations were found between harmonized normalized hippocampal volumes and ipsilateral WADA memory scores, as well as between harmonized hippocampal volume asymmetries and WMA. However, no significant correlations were found in patients with epileptogenic lesions other than MTS or those with normal brain MRIs. A harmonized normalized hippocampal volume threshold of ≥ 28.94 units was identified as a predictor of a WADA memory score exceeding 50% following contralateral carotid artery injection, with a sensitivity of 62.1% and a specificity of 100%.SignificanceThis study indicates that hippocampal volumetry could potentially serve as an alternative to the WADA test in patients with MTS. Conversely, in individuals with normal MRI results or other types of epileptogenic lesions, hippocampal volumetry does not reliably predict memory deficits, necessitating the use of the WADA test or functional MRI for planning resections of mesial temporal structures in the dominant hemisphere.

Effect of epileptiform discharges and hippocampal volume on cognitive dysfunction following clipping of ruptured aneurysms in the anterior circulation.

Cognitive dysfunction after aneurysmal subarachnoid hemorrhage (aSAH) remains unclear due to various neurological impairments. This study aimed to evaluate the changes in hippocampal volume, cognitive function, and interictal epileptiform discharges after clipping in patients with aSAH of anterior circulation. Patients with modified Rankin Scale scores of 0-3 points who underwent clipping were evaluated. Aneurysmal locations were classified as middle cerebral artery (MCA), internal carotid artery (ICA), and anterior cerebral artery (ACA). Surgery was performed using the transsylvian approach or interhemispheric approach. Hippocampal volume measurement, neuropsychological assessments, and interictal electroencephalogram evaluations were performed postoperatively at the subacute phase. Epileptiform discharges were assessed using the spike index (SI). We included 60 patients (23 men, 37 women; median age, 57.4 years). Aneurysmal locations were found in the MCA, ICA, and ACA in 23, 19, and 18 patients, respectively. The postoperative hippocampal volume was significantly reduced on the clipping approach side in the MCA and ICA groups (MCA, p < .001; ICA, p < .001). There was no correlation between hippocampal volume and cognitive function. A significant difference was noted in elevated SI on the approach side of the MCA (p < .001), ICA (p < .001), and ACA (p < .001) in the transsylvian approach group. The elevated SI on the left approach side showed significant differences in some neuropsychological assessments (performance intellectual quotient, p = .028; perceptual organization, p = .045; working memory, p = .003). Cognitive dysfunction in the subacute phase after clipping for aSAH was not correlated with hippocampal volume reduction but was correlated with interictal epileptiform discharges.

Deciphering the Synergy Between Amyloid‐Beta and Tau Pathologies in Preclinical Alzheimer's Disease

AbstractBackgroundThe extracellular deposition of amyloid‐beta (Aβ) and intracellular aggregation of hyperphosphorylated tau proteins stand as histopathological hallmarks of Alzheimer's disease (AD). The spatial‐temporal relationship between the Aβ pathway and tau pathophysiology in AD, plays a crucial role in comprehending the pathogenesis and progression of AD. Understanding the interrelationship between Aβ and tau, is crucial to elucidate the failure of previous therapeutic strategies and understanding AD progression and will inform the next generation of clinical trials. We employed a robust machine learning approach to predict entorhinal tau levels, using Aβ SUVR‐identified pattern. Subsequently, we employed the identified Aβ SUVR pattern to predict long‐term hippocampal volume and Preclinical Alzheimer Cognitive Composite (PACC) cognitive measurement at five‐year follow‐up, as indicators of neurodegeneration and cognitive decline, respectively.MethodWe analyzed PET measures for amyloid and tau in a cohort of 509 participants, 233 participants (104 Aβ+, 129 Aβ−) from ADNI as training data and 276 participants (93 Aβ+, 183 Aβ−) from Harvard Aging Brain Study (HABS) as an independent test set. We employed support vector regression (SVR) as a machine learning regression method for predicting entorhinal tau levels based on Aβ SUVR pattern. Feature‐selection and cross validation were performed to build and train the model using ADNI training data and the performance of the model was tested using independent HABS data. We also tested if the trained SVR model can successfully predict hippocampal volume and PACC scores at 5‐year follow‐up using baseline Aβ SUVR pattern. We constructed separate predictive models for Aβ+ and Aβ‐ subjects.ResultThe SVR model trained on ADNI Aβ data successfully predicted entorhinal tau, future hippocampal volume and PACC scores in an independent HABS dataset with a correlation accuracy of 0.50, 0.68 and 0.55, respectively (Figure 1). Particularly, Aβ load in the medial orbitofrontal and rostral middle frontal regions contributed the most to predicting entorhinal tau and future hippocampal volume and cognitive outcomes (Figure 2).ConclusionOur study indicates that amyloid load in medial prefrontal regions may signal subsequent tau accumulation in the entorhinal cortex, and future neurodegeneration and cognitive decline, suggesting its potential role as a marker for AD progression.

Correlation Between Cognitive Impairment in Early‐Stage Amyloid‐Positive Alzheimer's Disease and CA1 Hippocampal Subfield Volume Loss Using 7T MRI

AbstractBackgroundEarly diagnosis is crucial in Alzheimer's disease (AD) for optimal treatment outcomes. Neuropsychometric assessments, particularly using the Uniform Data Set Neuropsychological Battery (UDSNB3.0) [1], provide insights into cognitive domains in early stages of Alzheimer’s disease before significant hippocampal atrophy occurs. This study leverages 7T MRI, which offers superior spatial resolution, to investigate these correlates.MethodForty‐seven participants were recruited (24 AD patients and 23 matched healthy controls [HC]; age range: 40 ‐ 76 years). Participants selected for the study met the ATN criteria [2] for symptomatic Alzheimer's disease with CSF‐Amyloid positivity. Participants with significant hippocampal atrophy (i.e. MTA score of &gt;2 on clinical MRI at 1.5/3T [3] were excluded. Imaging was carried out on a Philips Achieva 7T scanner equipped with a Nova Medical single‐channel transmit and 32‐channel receive (1Tx32Rx) head coil. The acquired images included PSIR sequences (TE/TR=3.1/6.9ms; FA=60°) with isotropic 0.55 mm resolution, as well as T2‐weighted FSE sequences (TE/TR=119/5900ms; FA=90°) with a resolution of 0.38x0.39x1.50 mm³.ResultThe reduction in the volume of CA1 hippocampal subfield in patients with AD significantly correlated with multiple cognitive domains, highlighting its central role in various aspects of cognitive function (see Table 1 and Figure 2). The change in volume was not significant for the whole brain showing that the hippocampus was changing particularly and not simply as part of general brain atrophy.A significant linear correlation was evident between the volume of CA1 and the total MoCA score (R2 = 0.47; p=0.02), indicating a particularly strong correlation with the memory category with p&lt;0.05. There were also significant correlations between CA1 and specific cognitive subdomains including executive function, attention, language, Orientation, Craft Story Immediate, Number Span Backward.ConclusionThe observed result, indicating a robust correlation between the CA1 hippocampal subfield volume and various cognitive measures, in the early stages of Alzheimer's disease, provides valuable insights into the intricate relationship between neuroanatomy and cognitive function.

Supervised clustering of MRI measures informed by incident cognitive impairment via aggregated survival model SHAP values

AbstractBackgroundAlzheimer’s disease (AD) is a complex heterogeneous neurodegenerative disease. Unsupervised clustering techniques have been used to identify disease subtypes, but such approaches are limited since subtypes may not directly be related to disease progression. Herein, we implement a novel supervised clustering approach that aims to identify MRI‐derived subtypes that are likely to experience incident cognitive impairment (ICI).MethodsWe used data from 822 cognitively normal individuals (68.4 ± 9.5 years old) with Clinical Dementia Rating® (CDR®) of 0 at baseline, from the Knight ADRC. We performed supervised clustering of eight volumetric MRI regional measures at baseline. MRI measures were adjusted for age, sex, and education. Mean aggregated time‐dependent Shapley Additive exPlanation (SHAP) values were derived from random survival forest models with an ICI outcome based on time‐to‐conversion from a CDR=0 to CDR&gt;0 over 10 years of follow‐up. K‐means clustering was then applied to SHAP values. Analysis of Variance was used to assess cluster differences and linear regression was used to understand longitudinal trajectories of MRI volumetrics and CSF biomarkers.ResultsIn the entire sample, hippocampus, entorhinal, and amygdala SHAP values were determined to be the most important MRI measures in terms of estimated contribution to the model prediction with the hippocampus differentiating from other regional measure about 24 months from baseline (Figure 1). The optimal number of clusters was determined to be four. Cluster characteristics and significant differences among them are presented in Table 1 and Figure 1. Cluster 4 is characterized by the largest mean hippocampus SHAP value with approximately 89% of participants experiencing ICI. Figure 2 shows longitudinal linear trajectories for MRI and cerebrospinal measures. Cluster 1 was the healthiest across all longitudinal biomarkers. Whereas, Cluster 4 had the smallest hippocampal volume and abnormal CSF measures both at baseline and over time.ConclusionsWe implemented a novel application of SHAP values from survival models in a supervised clustering approach to identifying MRI‐subtypes with different probabilities of incident cognitive impairment. This approach could be used to elucidate the heterogeneity inherent in the relationships among AD biomarkers and longitudinal cognitive changes.

Contribution of automated hippocampal volume measurements to radiological diagnosis in cases of mesial temporal sclerosis

Aims: Epilepsy, particularly drug-resistant epilepsy in adults, is often caused by Mesial Temporal Sclerosis (MTS), which can develop after brain injury from febrile illnesses or trauma. Magnetic Resonance Imaging (MRI) is crucial for diagnosing MTS, although challenges such as patient movement and mild or bilateral hippocampal atrophy can complicate the diagnosis. T1-weighted hippocampal volume measurements are effective in detecting MTS, with recent software advancements enabling automatic hippocampal segmentation. This study compared the hippocampal volumes and indices between adults with MTS and a control group of similar age and sex in the Turkish population. This study aimed to highlight structural differences in the hippocampus associated with MTS. Methods: This study involved a retrospective review of cranial MRIs scans from patients with MTS, confirmed through histopathological examination after epilepsy surgery. To ensure unbiased comparisons, a control group was selected using propensity score matching by age and gender. Two experienced neuroradiologists independently assessed the MRIs findings for MTS without knowledge of hippocampal volumetric data. Hippocampal volumes were measured using FreeSurfer software and standardized using the hippocampal volume index (HVI) and hippocampal asymmetry index (HAI). Results: In our study of 38 patients, MTS was found in 55.2% of patients on the right side and in 44.8% of patients on the left side, with no bilateral cases. Visual MRI analysis identified MTS in 84.2% of patients, with an area under of curve (AUC) of 0.921. Automatic volumetry detected MTS in 23 patients with an AUC of 0.791. Combining both methods, MTS was diagnosed in 33 patients, with an AUC of 0.922. Conclusion: Automated volumetric analyses have been shown to enhance the detection of hippocampal volume loss in patients with MTS.

Neuropsychiatric symptoms: Risk factor or disease marker? A study of structural imaging biomarkers of Alzheimer's disease and incident cognitive decline.

Neuropsychiatric symptoms (NPS) are risk factors for Alzheimer's disease (AD) but can also manifest secondary to AD pathology. Mild behavioral impairment (MBI) refers to later-life emergent and persistent NPS that may mark early-stage AD. To distinguish MBI from NPS that are transient or which represent psychiatric conditions (non-MBI NPS), we investigated the effect of applying MBI criteria on NPS associations with AD structural imaging biomarkers and incident cognitive decline. Data for participants (n = 1273) with normal cognition (NC) or mild cognitive impairment (MCI) in the National Alzheimer's Coordinating Center Uniform Data Set were analyzed. NPS status (MBI, non-MBI NPS) was derived from the Neuropsychiatric Inventory Questionnaire and psychiatric history. Normalized measures of bilateral hippocampal (HPC) and entorhinal cortex (EC) volume, and AD meta-region of interest (ROI) mean cortical thickness were acquired from T1-weighted magnetic resonance imaging scans. Multivariable linear and Cox regressions examined NPS associations with imaging biomarkers and incident cognitive decline, respectively. MBI was associated with lower volume and cortical thickness in all ROIs in both NC and MCI, except for EC volume in NC. Non-MBI NPS were only associated with lower HPC volume in NC. Although both of the NPS groups showed higher hazards for MCI/dementia than No NPS, MBI participants showed more rapid decline. Although both types of NPS were linked to HPC atrophy, only NPS that emerged and persisted in later-life, consistent with MBI criteria, were related to AD neurodegenerative patterns beyond the HPC. Moreover, MBI predicted faster progression to dementia than non-MBI NPS.

Combining neuropsychological assessment and structural neuroimaging to identify early Alzheimer's disease in a memory clinic cohort.

The current study examined the contributions of comprehensive neuropsychological assessment and volumetric assessment of selected mesial temporal subregions on structural magnetic resonance imaging (MRI) to identify patients with amnestic mild cognitive impairment (aMCI) and mild probable Alzheimer's disease (AD) dementia in a memory clinic cohort. Comprehensive neuropsychological assessment and automated entorhinal, transentorhinal, and hippocampal volume measurements were conducted in 40 healthy controls, 38 patients with subjective memory symptoms, 16 patients with aMCI, 16 patients with mild probable AD dementia. Multinomial logistic regression was used to compare the neuropsychological and MRI measures. Combining the neuropsychological and MRI measures improved group membership prediction over the MRI measures alone but did not improve group membership prediction over the neuropsychological measures alone. Comprehensive neuropsychological assessment was an important tool to evaluate cognitive impairment. The mesial temporal volumetric MRI measures contributed no diagnostic value over and above the determinations made through neuropsychological assessment.

Trauma-related intrusive memories and anterior hippocampus structural covariance: an ecological momentary assessment study in posttraumatic stress disorder

Trauma-related intrusive memories (TR-IMs) are hallmark symptoms of posttraumatic stress disorder (PTSD), but their neural correlates remain partly unknown. Given its role in autobiographical memory, the hippocampus may play a critical role in TR-IM neurophysiology. The anterior and posterior hippocampi are known to have partially distinct functions, including during retrieval of autobiographical memories. This study aimed to investigate the relationship between TR-IM frequency and the anterior and posterior hippocampi morphology in PTSD. Ninety-three trauma-exposed adults completed daily ecological momentary assessments for fourteen days to capture their TR-IM frequency. Participants then underwent anatomical magnetic resonance imaging to obtain measures of anterior and posterior hippocampal volumes. Partial least squares analysis was applied to identify a structural covariance network that differentiated the anterior and posterior hippocampi. Poisson regression models examined the relationship of TR-IM frequency with anterior and posterior hippocampal volumes and the resulting structural covariance network. Results revealed no significant relationship of TR-IM frequency with hippocampal volumes. However, TR-IM frequency was significantly negatively correlated with the expression of a structural covariance pattern specifically associated with the anterior hippocampus volume. This association remained significant after accounting for the severity of PTSD symptoms other than intrusion symptoms. The network included the bilateral inferior temporal gyri, superior frontal gyri, precuneus, and fusiform gyri. These novel findings indicate that higher TR-IM frequency in individuals with PTSD is associated with lower structural covariance between the anterior hippocampus and other brain regions involved in autobiographical memory, shedding light on the neural correlates underlying this core symptom of PTSD.

Exploring the Value of MRI Measurement of Hippocampal Volume for Predicting the Occurrence and Progression of Alzheimer's Disease Based on Artificial Intelligence Deep Learning Technology and Evidence-Based Medicine Meta-Analysis.

Alzheimer's disease (AD), a major dementia cause, lacks effective treatment. MRI-based hippocampal volume measurement using artificial intelligence offers new insights into early diagnosis and intervention in AD progression. This study, involving 483 AD patients, 756 patients with mild cognitive impairment (MCI), and 968 normal controls (NC), investigated the predictive capability of MRI-based hippocampus volume measurements for AD risk using artificial intelligence and evidence-based medicine. Utilizing data from ADNI and OASIS-brains databases, three convolutional neural networks (InceptionResNetv2, Densenet169, and SEResNet50) were employed for automated AD classification based on structural MRI imaging. A multitask deep learning model and a densely connected 3D convolutional network were utilized. Additionally, a systematic meta-analysis explored the value of MRI-based hippocampal volume measurement in predicting AD occurrence and progression, drawing on 23 eligible articles from PubMed and Embase databases. InceptionResNetv2 outperformed other networks, achieving 99.75% accuracy and 100% AUC for AD-NC classification and 99.16% accuracy and 100% AUC for MCI-NC classification. Notably, at a 512×512 size, InceptionResNetv2 demonstrated a classification accuracy of 94.29% and an AUC of 98% for AD-NC and 97.31% accuracy and 98% AUC for MCI-NC. The study concludes that MRI-based hippocampal volume changes effectively predict AD onset and progression, facilitating early intervention and prevention.

Comparison of deep learning convolutional neural networks method with conventional volume-based morphometry measurement of hippocampal volume in Alzheimer's disease

Dementia is a spectrum of diseases characterised by a progressive and irreversible decline in cognitive function. Appropriate tools and references are essential for evaluating individuals' cognitive levels, especially hippocampal volume, as it is the commonly used biomarker in detecting Alzheimer's disease (AD). It is important to note that while there is no cure for dementia, early intervention and support can greatly improve the lives of those affected. Our ongoing AD research is being conducted to develop new treatments and improve our understanding of the disease by using voxel-based morphometry (VBM) to compare sensitivity and specificity with the HippoDeep toolbox. We validated AD's hippocampal volume compared to age-matched healthy controls (HC) based on the HippoDeep Model by comparing it with VBM as the reference standard. Significant differences between hippocampal volume in AD and HC have been detected using VBM and HippoDeep analysis.

Toward hippocampal volume measures on ultra-high field magnetic resonance imaging: a comprehensive comparison study between deep learning and conventional approaches.

The hippocampus is a complex brain structure that plays an important role in various cognitive aspects such as memory, intelligence, executive function, and path integration. The volume of this highly plastic structure is identified as one of the most important biomarkers of specific neuropsychiatric and neurodegenerative diseases. It has also been extensively investigated in numerous aging studies. However, recent studies on aging show that the performance of conventional approaches in measuring the hippocampal volume is still far from satisfactory, especially in terms of delivering longitudinal measures from ultra-high field magnetic resonance images (MRIs), which can visualize more boundary details. The advancement of deep learning provides an alternative solution to measuring the hippocampal volume. In this work, we comprehensively compared a deep learning pipeline based on nnU-Net with several conventional approaches including Freesurfer, FSL and DARTEL, for automatically delivering hippocampal volumes: (1) Firstly, we evaluated the segmentation accuracy and precision on a public dataset through cross-validation. Results showed that the deep learning pipeline had the lowest mean (L = 1.5%, R = 1.7%) and the lowest standard deviation (L = 5.2%, R = 6.2%) in terms of volume percentage error. (2) Secondly, sub-millimeter MRIs of a group of healthy adults with test-retest 3T and 7T sessions were used to extensively assess the test-retest reliability. Results showed that the deep learning pipeline achieved very high intraclass correlation coefficients (L = 0.990, R = 0.986 for 7T; L = 0.985, R = 0.983 for 3T) and very small volume percentage differences (L = 1.2%, R = 0.9% for 7T; L = 1.3%, R = 1.3% for 3T). (3) Thirdly, a Bayesian linear mixed effect model was constructed with respect to the hippocampal volumes of two healthy adult datasets with longitudinal 7T scans and one disease-related longitudinal dataset. It was found that the deep learning pipeline detected both the subtle and disease-related changes over time with high sensitivity as well as the mild differences across subjects. Comparison results from the aforementioned three aspects showed that the deep learning pipeline significantly outperformed the conventional approaches by large margins. Results also showed that the deep learning pipeline can better accommodate longitudinal analysis purposes.

Neuropsychological and biomarker predictors of conversion to dementia in young‐onset Mild Cognitive Impairment

AbstractBackgroundMild Cognitive Impairment (MCI) is a clinical condition that captures the stage between normal aging and dementia along the continuum of cognitive impairment. While it is usually thought to be precursor of Alzheimer’s Disease, when occurring at young ages (i.e., before 65 years old) it may increase the risk of all forms of neurodegenerative dementing diseases. We studied which neuropsychological and biomarker characteristics are predictors of conversion to dementia in a sample of patients with young‐onset MCI.MethodSubjects with a diagnosis of MCI and early‐onset of cognitive symptoms (&lt; 65 yo) were recruited in a prospective longitudinal study. They underwent extended neuropsychological assessment, Magnetic Resonance Imaging with measurement of hippocampal volume, and lumbar puncture with measurements of cerebrospinal fluid (CSF) light‐chain neurofilaments (NFL), beta‐amyloid 1‐42, total‐ and phosphorylated‐tau. They were followed‐up clinically and repeated the neuropsychological evaluation at 18 months from the baseline. Statistical analyses were performed with Stata15. Each biomarker and neuropsychological variable were added in multivariate Cox regression models with age, sex, and education.Result84 young‐onset MCI patients were recruited. After 18 months, 26 converted to dementia while 58 remained stable (i.e., MCI). Preliminary analyses showed that baseline smaller bilateral hippocampal volumes, lower baseline MMSE scores, baseline worst performances in tests of language, verbal long‐term memory, and executive functions, as well as baseline elevated levels of CSF NFLs increased the risk of conversion to dementia. Among them, the higher risk was given by the Free and Cued Selective Reminding Test (FCSRT), the Corsi Supraspan, the Frontal Assessment Battery, and the CSF NFLs levels, as shown by spline regression analyses (Figure 1). Of note, the FCSRT delayed recall showed a U‐shaped relationship with the risk of conversion (i.e., not only low/bad performance but also high/good performance were both related to a higher risk of conversion) suggesting that the types of predicted conversion may be different.ConclusionOur results suggest that, in young‐onset MCI, a combination of CSF‐NFLs levels and baseline performance at long‐term memory tests may be predictive of conversion to dementia, either AD or FTD.

Detecting early Alzheimer’s disease using neuropsychological assessment and structural neuroimaging

AbstractBackgroundAlzheimer’s disease (AD) can be challenging to diagnose early as cognitive changes are mild. Neuropsychological assessment, in contrast to cognitive screening or isolated cognitive tests, are more sensitive to the mild cognitive changes in early AD. Magnetic resonance imaging (MRI) can detect early structural brain changes in AD, particularly entorhinal cortex, transentorhinal cortex, and hippocampal atrophy. Combining neuropsychological assessment with MRI examination may yield improved ability to detect early AD. The current study examined combining neuropsychological and MRI measures to detect patients with mild cognitive impairment (MCI) in a memory clinic sample.MethodNeuropsychological assessment and entorhinal cortex, transentorhinal cortex, and hippocampal volume measurement were conducted in memory clinic patients, including 38 with memory symptoms, 16 with MCI, and 16 with dementia due to AD, and in 40 healthy controls. Backward stepwise multinomial logistic regression was used to compare the neuropsychological measures and MRI measures.ResultAmong the neuropsychological measures, memory and processing speed were the most important measures that contributed to group membership prediction. All MRI measures contributed to group membership prediction. The neuropsychological measures were more accurate than the MRI measures at detecting patients with MCI. Combining the MRI measures with the neuropsychological measures did not improve group membership prediction.ConclusionNeuropsychological assessment is an important tool to support MCI diagnosis. MRI measures contribute little diagnostic value over and above neuropsychological assessment. Further advancement in deriving MRI‐based measurements is required to achieve clinical utility.

The Olfactory Stress Test does not replicate in a larger sample

AbstractBackgroundWe previously published data showing that olfactory test performance changes on the University of Pennsylvania Smell Identification Test (UPSIT) following an intranasal ‘challenge’ with the anticholinergic atropine (an ‘Olfactory Stress Test’, (OST)) were significantly correlated with measures of cognition, hippocampal volume and APOE genotype in a small (n = 56) convenience sample of elders. The results suggested that this procedure might unmask incipient Alzheimer’s disease.Here, we present data from two new studies, including an attempted replication of the above findings.MethodIn study one, the full 40 item UPSIT was administered to the left nostril to 56 individuals. Data from this study were used to determine the relationship between scores on the first 20 items (booklets 1+2) with those on the second 20 items (booklets 3+4) of the UPSIT.The second study comprised 296 elders (aged 65 or older) who underwent olfactory testing, including the OST, assessment of cognition with the Audio Recorded Cognitive Screen, APOE genotyping, and hippocampal volume (in a subsample). In this study the first 20 items of UPSIT were always administered before and the second 20 items always after intranasal atropine and regression analyses were conducted to investigate for any association of the adjusted (see below) UPSIT change score following atropine with memory or hippocampal volume.ResultIn study 1, the mean score on the first 20 items of the UPSIT was 1.53 (SD 2.07) greater than the second 20 items (t = 5.55, p&lt;.001). An appropriate adjustment factor derived from these data were used to scale the study 2 raw post atropine scores (from UPSIT booklets 3+4) into equivalent scores on booklets 1+2. These post‐atropine adjusted scores were then used in the calculation of the change in (adjusted) UPSIT following atropine.In a series of stepwise linear regression analyses, after controlling for age, gender and education, the adjusted UPSIT change score did not predict any additional variance in memory scores or hippocampal volume.ConclusionIn this larger study, we failed to replicate our earlier findings. The ’Olfactory Stress Test’ is unlikely to aid in the early detection of AD.

APOE‐dependent and ‐independent polygenic pathways determining early Alzheimer’s Disease pathological changes in CSF and MRI

AbstractBackgroundLittle is known about the genetic factors and downstream molecular pathways determining individual variability in fluid and imaging biomarkers associated with Alzheimer’s disease(AD). We studied polygenic risk scores (PRS) and pathway‐specific PRS in relationship with AD fluid and imaging biomarkers, in non‐demented individuals from the European Prevention of Alzheimer’s Dementia (EPAD) cohort.MethodEPAD inclusion criteria were age&gt;50 and Clinical Dementia Rating = 0.5 (n = 1886, Table 1). AD‐PRS was based on 85 previously identified loci, including and excluding APOE (PRSAPOE, PRSnoAPOE)[1]. Using gene‐variant and variant‐pathway mapping[2], six pathway‐specific PRSnoAPOE were identified:1) immune‐activation, 2) signal‐transduction, 3) inflammatory‐response, 4) migration, 5) amyloid‐production, and 6) clearance. Linear models were used to assess the relationship of all PRS with fluid AD biomarkers, including Aß1‐42, p‐Tau181, and t‐tau; and several imaging biomarkers, including hippocampal volume, global and lobar white matter hyperintensities (WMH) volumes, fractional anisotropy (FA) in 14 regions of interest from diffusion tensor imaging, and 10 resting‐state network connectivity from functional MRI. Models were adjusted for age, sex, site, and multiple comparisons.ResultModels’ coefficients are reported in Table 2. PRSAPOE was significantly associated with decreased Aß1‐42, and increased p‐Tau181 and t‐Tau. PRSnoAPOE showed a weaker, but still significant, negative association with Aß1‐42 levels, and a significant positive association with p‐Tau181 and t‐Tau. Aß1‐42 was only significantly associated with the migration, amyloid, and clearance pathways, while all pathways were significantly positively associated with p‐Tau181 and t‐Tau. Hippocampal volume was only significantly associated with PRSAPOE. Regarding WMH load, PRSAPOE showed a positive association with global, frontal periventricular, and parietal deep lobes. Furthermore, the clearance pathway was related to WMH load in all regions, most strongly in periventricular areas. Only the migration pathway was related to increases in FA in the splenium, body, and genu of the corpus callosum. Only the PRSAPOE was related to reduced functional connectivity (FC) in the default mode, control, and visual networks.ConclusionWe show that genetic risk beyond APOE facilitates the manifestation of AD related pathologies. Moreover, clearance and migration pathways were associated with neuroimaging measures of white matter integrity, while FC and hippocampal volume were associated with overall AD genetic risk.

APOE‐dependent and ‐independent polygenic pathways determining early Alzheimer’s Disease pathological changes in CSF and MRI

AbstractBackgroundLittle is known about the genetic factors and downstream molecular pathways determining individual variability in fluid and imaging biomarkers associated with Alzheimer’s disease(AD). We studied polygenic risk scores (PRS) and pathway‐specific PRS in relationship with AD fluid and imaging biomarkers, in non‐demented individuals from the European Prevention of Alzheimer’s Dementia (EPAD) cohort.MethodEPAD inclusion criteria were age&gt;50 and Clinical Dementia Rating≤0.5 (n = 1886, Table 1). AD‐PRS was based on 85 previously identified loci, including and excluding APOE (PRSAPOE, PRSnoAPOE)[1]. Using gene‐variant and variant‐pathway mapping[2], six pathway‐specific PRSnoAPOE were identified:1) immune‐activation, 2) signal‐transduction, 3) inflammatory‐response, 4) migration, 5) amyloid‐production, and 6) clearance. Linear models were used to assess the relationship of all PRS with fluid AD biomarkers, including Aβ1‐42, p‐Tau181, and t‐tau; and several imaging biomarkers, including hippocampal volume, global and lobar white matter hyperintensities (WMH) volumes, fractional anisotropy (FA) in 14 regions of interest from diffusion tensor imaging, and 10 resting‐state network connectivity from functional MRI. Models were adjusted for age, sex, site, and multiple comparisons.ResultModels’ coefficients are reported in Table 2. PRSAPOE was significantly associated with decreased Aβ1‐42, and increased p‐Tau181 and t‐Tau. PRSnoAPOE showed a weaker, but still significant, negative association with Aβ1‐42 levels, and a significant positive association with p‐Tau181 and t‐Tau. Aβ1‐42 was only significantly associated with the migration, amyloid, and clearance pathways, while all pathways were significantly positively associated with p‐Tau181 and t‐Tau.Hippocampal volume was only significantly associated with PRSAPOE. Regarding WMH load, PRSAPOE showed a positive association with global, frontal periventricular, and parietal deep lobes. Furthermore, the clearance pathway was related to WMH load in all regions, most strongly in periventricular areas.Only the migration pathway was related to increases in FA in the splenium, body, and genu of the corpus callosum. Only the PRSAPOE was related to reduced functional connectivity (FC) in the default mode, control, and visual networks.ConclusionWe show that genetic risk beyond APOE facilitates the manifestation of AD related pathologies. Moreover, clearance and migration pathways were associated with neuroimaging measures of white matter integrity, while FC and hippocampal volume were associated with overall AD genetic risk.

Trajectories of plasma biomarkers of Alzheimer’s disease and neurodegeneration in the general population

AbstractBackgroundPlasma biomarkers of Alzheimer’s disease (AD) and neurodegeneration have demonstrated diagnostic utility and associations with AD pathology. Our objective was to map the average trajectories over age of plasma biomarkers and more established biomarkers including cognition, amyloid PET, and hippocampal volume.MethodWe included 1731 cognitively unimpaired (CU) and 226 mild cognitive impairment (MCI) participants from the Mayo Clinic Study of Aging (mean age of 70.3 years; 46.3% female; 14.8 years of mean education) with one or more measurements of amyloid‐PET, MRI with hippocampal volume measurements adjusted for head size (HVa), and plasma biomarkers (Aβ42/40 ratio, p‐tau181, GFAP, and NfL measured using Quanterix Simoa assays). A global cognitive z‐score was created by averaging the z‐scores of memory, language, executive function, and visuospatial domains. Smoothing spline curves were calculated for the values of each variable over age and placed on the same plot. Since each variable has a different scale, the smoothed curves were centered at the predicted value at age 50 and scaled so that a value of 1 would be one standard deviation of the fitted values from ages 50‐80.ResultAt age 50, GFAP and NfL start to increase, and Aβ42/40 was observed to decrease. At age 55, amyloid‐PET and p‐tau181 had a smooth and upward‐curving increase. After age 70, the average values of p‐tau181 increased at a higher rate. Average cognition started declining after 55 years. HVa began to decline after age 60 and then showed a steeper decline at age 72.ConclusionThis study demonstrates that predicted values of plasma GFAP, NfL, and Aβ42/40 across participants begin to change at younger ages than cognitive decline or increases observed in amyloid PET. The changes observed during middle age in GFAP and NfL likely reflect non‐AD pathologies and aging processes, while Aβ42/40 may be a leading indicator of future cerebral amyloidosis. Predicted plasma neurodegenerative biomarkers (GFAP and NfL) begin to change at younger ages than a traditional biomarker of neurodegeneration (HVa). Predicted ptau181 increases were observed in tandem with amyloid PET likely reflecting current amyloid status.

Association of Pathologic and Volumetric Biomarker Changes With Cognitive Decline in Clinically Normal Adults.

Hippocampal volume (HV) atrophy is a well-known biomarker of memory impairment. However, compared with β-amyloid (Aβ) and tau imaging, it is less specific for Alzheimer disease (AD) pathology. This lack of specificity could provide indirect information about potential copathologies that cannot be observed in vivo. In this prospective cohort study, we aimed to assess the associations among Aβ, tau, HV, and cognition, measured over a 10-year follow-up period with a special focus on the contributions of HV atrophy to cognition after adjusting for Aβ and tau. We enrolled 283 older adults without dementia or overt cognitive impairment in the Harvard Aging Brain Study. In this report, we only analyzed data from individuals with available longitudinal imaging and cognition data. Serial MRI (follow-up duration 1.3-7.0 years), neocortical Aβ imaging on Pittsburgh Compound B PET scans (1.9-8.5 years), entorhinal and inferior temporal tau on flortaucipir PET scans (0.8-6.0 years), and the Preclinical Alzheimer Cognitive Composite (3.0-9.8 years) were prospectively collected. We evaluated the longitudinal associations between Aβ, tau, volume, and cognition data and investigated sequential models to test the contribution of each biomarker to cognitive decline. We analyzed data from 128 clinically normal older adults, including 72 (56%) women and 56 (44%) men; median age at inclusion was 73 years (range 63-87). Thirty-four participants (27%) exhibited an initial high-Aβ burden on PET imaging. Faster HV atrophy was correlated with faster cognitive decline (R2 = 0.28, p < 0.0001). When comparing all biomarkers, HV slope was associated with cognitive decline independently of Aβ and tau measures, uniquely accounting for 10% of the variance. Altogether, 45% of the variance in cognitive decline was explained by combining the change measures in the different imaging biomarkers. In older adults, longitudinal hippocampal atrophy is associated with cognitive decline, independently of Aβ or tau, suggesting that non-AD pathologies (e.g., TDP-43, vascular) may contribute to hippocampal-mediated cognitive decline. Serial HV measures, in addition to AD-specific biomarkers, may help evaluate the contribution of non-AD pathologies that cannot be measured otherwise in vivo.

3 Olfactory Dysfunction as a Preclinical Biomarker of AD: Psychophysical Olfactory Performance Reflects Hippocampal Integrity in Non-Demented Older Adults

Objective:One of the greatest challenges of the Alzheimer’s disease (AD) epidemic is identifying the disease prior to substantial neurological compromise. The established biomarkers of AD, such as measures of cognitive impairment, hippocampal atrophy, and CSF measures of beta amyloid and tau, used in research and drug trials are less indicative of AD pathology in preclinical, non-demented, populations. Olfactory dysfunction, a well-established sensory impairment of AD found to correlate strongly with tau burden and hippocampal volume measures, has shown to be a promising preclinical biomarker for AD progression. Several studies have found either impaired odor identification or odor memory at baseline to predict 5-year follow-up cognitive decline and conversion from MCI to AD, but less is known about how olfactory performance reflects the integrity of associated brain regions such as the hippocampus. The present analysis aims to explore the value of psychophysical olfactory assessment as biomarker measure in preclinical AD studies and drug trials by investigating its relationships with structural measures of the hippocampus.Participants and Methods:A sample consisted of non-demented older adults (age &gt;75), recruited from the UCSD Alzheimer’s Disease Research Center as part of a ongoing olfactory biomarker study. Participants completed the AD Assessment Scale-Cognitive Subscale-13 (ADAS-Cog-13), San Diego Odor Identification Test (SDOIT), tests of odor recognition memory (ORMem) and odor associative memory (OAM), and MRI derived hippocampal volumes and average hippocampal occupancy (Avg HOC). Left and right hippocampal volumes were adjusted for each participant’s estimated intracranial volume. Bivariate correlations were calculated for ADAS-Cog-13 and SDOIT total scores, performance scores for odor recognition and odor associative memory tests, and the three hippocampal measures (bilateral volumes and average occupancy).Results:ADAS-Cog-13 score did not show significant correlations with either hippocampal measure at the .05 level. SDOIT scores were significantly correlated with the measure of Avg HOC (p&lt;.05). ORMem false positive responses were significantly correlated with Avg HOC (p&lt;.01) and right hippocampal volume (p&lt;.05). ORMem miss responses and OAM errors were both correlated with left (p&lt;.05) and right (p&lt;.01) hippocampal volumes.Conclusions:These results demonstrate that psychophysical assessments of odor identification and odor memory can better reflect the integrity of the hippocampus in nondemented older adults, compared to the neuropsychological ADAS-Cog-13. This is congruent with olfactory dysfunction preceding cognitive-memory decline in AD cases and provides support for the utility of psychophysical olfactory assessment along with other established AD biomarkers in research and drug trials in preclinical populations.Acknowledgements:Supported by NIH grant # R01AG062006-04 from the National Institute on Aging to CM. Special thank you to the staff and participants of the UCSD ADRC, especially Christina Gigliotti, and Aaron Jacobson at the UCSD Center for fMRI.