Neuropsychiatric Symptoms as Risk Factors for Cognitive Decline in Clinically Normal Older Adults: The Cache County Study

IntroductionThere has been considerable progress in identifying early cognitive and biomarker predictors of Alzheimer's disease (AD). Neuropsychiatric symptoms (NPS) are common in AD and appear to predict progression after the onset of mild cognitive impairment or dementia.ObjectivesThe objective of the study is to examine the relationship between NPS in clinically normal older adults and subsequent cognitive decline in a population-based sample.MethodsThe Cache County Study on Memory in Aging consists of a population-based sample of 5,092 older adults. We identified 470 clinically normal adults who were followed for an average period of 5.73 years. NPS were evaluated at the baseline clinical assessment using the Neuropsychiatric Inventory (NPI). NPI domain scores were quantified as the product of frequency X severity in individual NPI domains, and then summed for the NPI-Total. Neuropsychological measures were collected at baseline and at each subsequent follow-up wave. Linear mixed-effects models assessed the association of NPI-Total, NPI-Depression, and NPI-Anxiety scores (obtained at baseline) on longitudinal change in neuropsychological performance, controlling for age, sex, and education.ResultsBaseline NPI-Total score was associated with a more rapid rate of decline in word list memory, praxis recall, and animal fluency. Baseline NPI-Depression was not associated with later decline on any of the cognitive tests, while baseline NPI-Anxiety was associated with decline in Symbol Digit Modality.ConclusionIn conclusion, among clinically normal older adults derived from this population-based study, total burden of NPS was associated with longitudinal cognitive decline. These results add to the evidence that NPS are risk factors for or clinical indicators of preclinical dementia syndrome. Our study was an exploratory study and we did not control for multiple comparisons.     

Volumetric MRI predicts rate of cognitive decline related to AD and cerebrovascular disease

OBJECTIVE: To examine volumetric MRI correlates of longitudinal cognitive decline in normal aging, AD, and subcortical cerebrovascular brain injury (SCVBI). BACKGROUND: Previous cross-sectional studies examining the relationship between cognitive impairment and dementia have shown that hippocampal and cortical gray matter atrophy are the most important predictors of cognitive impairment, even in cases with SCVBI. The authors hypothesized that hippocampal and cortical gray matter volume also would best predict rate of cognitive decline in cases with and without SCVBI. METHODS: Subjects were recruited for a multicenter study of contributions to dementia of AD and SCVBI. The sample (n = 120) included cognitively normal, cognitively impaired, and demented cases with and without lacunes identified by MRI. Cases with cortical strokes were excluded. Average length of follow-up was 3.0 years. Measures of hippocampal volume, volume of cortical gray matter, presence of subcortical lacunes, and volume of white matter hyperintensity were derived from MRI. Random effects modeling of longitudinal data was used to assess effects of baseline MRI variables on longitudinal change in a measure of global cognitive ability. RESULTS: Cortical gray matter atrophy predicted cognitive decline regardless of whether lacunes were present. Hippocampal atrophy predicted decline only in those without lacunes. Neither lacunes nor white matter hyperintensity independently predicted decline. CONCLUSIONS: Results suggest that cortical atrophy is an index of disease severity in both AD and subcortical cerebrovascular brain injury and consequently predicts faster progression. Hippocampal volume may index disease severity and predict progression in AD. The absence of this effect in cases with lacunes suggests that this group is etiologically heterogeneous and is not composed simply of cases of AD with incidental stroke.     

Molecular imaging of neuropsychiatric symptoms in Alzheimer's and Parkinson's disease

Neuropsychiatric symptoms (NPS) are very common in neurodegenerative diseases and are a major contributor to disability and caregiver burden. There is accumulating evidence that NPS may be a prodrome and/or a “risk factor” of neurodegenerative diseases. The medications used to treat these symptoms in younger patients are not very effective in patients with neurodegenerative disease and may have serious side effects. An understanding of the neurobiology of NPS is critical for the development of more effective intervention strategies. Targeting these symptoms may also have implications for prevention of cognitive or motor decline. Molecular brain imaging represents a bridge between basic and clinical observations and provides many opportunities for translation from animal models and human post-mortem studies to in vivo human studies. Molecular brain imaging studies in Alzheimer's disease (AD) and Parkinson's disease (PD) are reviewed with a primary focus on positron emission tomography studies of NPS. Future directions for the field of molecular imaging in AD and PD to understand the neurobiology of NPS will be discussed.     

Impact of behavioral subsyndromes on cognitive decline in Alzheimer’s disease: data from the ICTUS study

Behavioral and psychological symptoms of dementia (BPSD) represent common manifestations among patients affected by Alzheimer’s disease (AD). Some reports have recently classified BPSD into specific clusters/subsyndromes exploring the internal structure of the Neuropsychiatric Inventory (NPI). We evaluated whether specific behavioral subsyndromes are associated with worsening cognitive function. Mild to moderate AD patients were recruited from the cohort of the Impact of Cholinergic Treatment USe (ICTUS) study. Neuropsychiatric symptoms were classified in three subsyndromes, identified at baseline, grouping different combinations of NPI items: (1) “psychotic” (“delusions” and/or “hallucinations”); (2) “affective” (“agitation” and/or “depression” and/or “anxiety” and/or “irritability”); and (3) “behavioral” (“euphoria” and/or “apathy” and/or “disinhibition” and/or “aberrant motor behavior”). Mixed model analyses were performed to measure six-monthly changes in the ADAS-Cog score over a follow-up of 2 years, according to these subsyndromes. All analyses were stratified according to AD severity as defined by the Clinical Dementia Rating (CDR). A total of 1,375 AD subjects were recruited. No NPI cluster was found to significantly (p < 0.05) affect the rate of cognitive decline across the 3 CDR classes. Our results suggest that the cognitive course of AD is not substantially influenced by the presence of specific neuropsychiatric phenotypes. Further studies are needed to extend the present findings and identify possible biological and clinical bases for behavioral subsyndromes.     

Neuroimaging in Alzheimer disease: an evidence-based review

Current clinical criteria (DSM-IIIR and NINCDS-ADRDA) for the diagnosis of dementia and AD are reliable; however, these criteria remain to be validated by clinicians of different levels of expertise at different clinical settings. Structural neuroimaging has an important role in initial evaluation of dementia for ruling out potentially treatable causes. Although CT is the appropriate choice when brain tumors, subdural hematoma, or normal pressure hydrocephalus is suspected, MR imaging is more sensitive to the white-matter changes in vascular dementia. The diagnostic accuracy of PET, SPECT, 1H MRS, and MR volumetry of the hippocampus for distinguishing patients with AD from healthy elderly individuals is comparable to the accuracy of a pathologically confirmed clinical diagnosis. Sensitivity of PET for distinguishing patients with dementia with Lewy bodies from AD, however, is higher than that of clinical evaluation; similarly, SPECT and 1H MRS may be adjuncts to clinical evaluation for distinguishing patients with frontotemporal dementia from those with AD. Neuroimaging is valuable in predicting future development of AD in patients with MCI and in carriers of the ApoE epsilon 4 allele who are at a higher risk of developing AD than are cognitively normal elderly individuals. Quantitative MR techniques (e.g., MR volumetry, DWI, magnetization transfer MR imaging, and 1H MRS) and PET are sensitive to the structural and functional changes in the brains of patients with MCI, and hippocampal volumes on MR imaging are associated with future development of AD in these individuals. PET is also sensitive to the regional metabolic decline in the brains of carriers of the ApoE epsilon 4 allele. The longitudinal decrease of whole brain and hippocampal volumes on MR imaging, NAA levels on 1H MRS, cerebral glucose metabolism on PET, and cerebral blood flow on SPECT are associated with rate of cognitive decline in patients with AD. These neuroimaging markers may be useful for monitoring symptomatic progression in groups of patients with AD for drug trials. Furthermore, antemortem MR-based hippocampal volumes correlate with the pathologic stage of AD, and the rate of hippocampal volume loss on MR imaging correlates with clinical disease progression in the cognitive continuum from normal aging to MCI and to AD. Hence, as an in vivo correlate of pathologic involvement, structural imaging measures are potential surrogate markers for disease progression in patients with established AD and in patients with prodromal AD, who will benefit most from disease-modifying therapies underway.     

Cognitive decline in AD and mild cognitive impairment is associated with global brain damage

OBJECTIVE: To investigate the relationships between structural damage in the whole brain, the temporal lobes, and the frontal lobes and cognitive decline at old age. The authors hypothesized that widespread brain damage as quantified using magnetization transfer imaging (MTI) is related to global cognitive decline, whereas regional damage to the temporal lobes is related to memory impairment, and regional damage to the frontal lobes is related to executive dysfunctioning. METHODS: Cognitive function of 22 patients with probable AD, 13 patients with mild cognitive impairment (MCI), and 28 elderly controls was assessed using an extensive neuropsychological test battery. Structural damage in the whole brain, the temporal lobes, and the frontal lobes was estimated using volumetric MTI analysis. Associations between MTI measures and neuropsychological tests were investigated using Pearson correlation analysis. RESULTS: MTI measures of the whole brain, as well as the temporal and the frontal lobes, were strongly associated with global cognitive deterioration and impairment in memory, orientation, language, praxis, gnosis, and executive functioning. However, there were no specific cognitive correlates of regional brain damage to the temporal and frontal lobes. CONCLUSIONS: Using whole brain volumetric magnetization transfer imaging, the authors demonstrated that cognitive decline in patients with mild cognitive impairment and AD is associated with widespread structural brain damage. As there were no specific relationships between regional brain damage and impairment of specific cognitive functions, pathology in AD and mild cognitive impairment is much more generalized than was expected.     

Structural neuroimaging in Alzheimer's disease: do white matter hyperintensities matter?

The targeted brain dysfunction that accompanies aging can have a devastating effect on cognitive and intellectual abilities. A significant proportion of older adults experience precipitous cognitive decline that negatively impacts functional activities. Such individuals meet clinical diagnostic criteria for dementia, which is commonly attributed to Alzheimer''s disease (AD). Structural neuroimaging, including magnetic resonance imaging (MRI), has contributed significantly to our understanding of the morphological and pathology-related changes that may underlie normal and disease-associated cognitive change in aging. White matter hyperintensities (WMH), which are distributed patches of increased hyperintense signal on T2-weighted MRI, are among the most common structural neuroimaging findings in older adults. In recent years, WMH have emerged as robust radiological correlates of cognitive decline. Studies suggest that WMH distributed in anterior brain regions are related to decline in executive abilities that is typical of normal aging, whereas WMH distributed in more posterior brain regions are common in AD. Although epidemiological, observational, and pathological studies suggest that WMH may be ischemic in origin and caused by consistent or variable hypoperfusion, there is emerging evidence that they may also reflect vascular deposition of (β-amyloid, particularly when they are distributed in posterior areas and are present in patients with AD. Findings from the literature highlight the potential contribution of small-vessel cerebrovascular disease to the pathogenesis of AD, and suggest a mechanistic interaction, but future longitudinal studies using multiple imaging modalities are required to fully understand the complex role of WMH in AD.     

Gray matter atrophy pattern in elderly with subjective memory impairment

BackgroundIndividuals with subjective memory impairment (SMI) report worsening of memory without impairment in cognitive tests. Despite normal cognitive performance, they may be at higher risk of cognitive decline compared with individuals without SMI.MethodsWe used a discriminative function (a support vector machine) trained on an independent data set of 226 healthy control subjects and 191 patients with probable Alzheimer's disease (AD) dementia to characterize the baseline gray matter patterns of 24 individuals with SMI and 53 control subjects. We tested for associations of these gray matter patterns with SMI presence, cognitive performance at baseline, and cognitive decline at follow-up.ResultsIndividuals with SMI showed greater similarity to an AD gray matter pattern compared with control subjects without SMI. In addition, episodic memory decline was associated with an AD gray matter pattern in the SMI group.ConclusionsOur results indicate a link between the gray matter atrophy pattern of patients with AD and the presence of SMI. Furthermore, multivariate pattern recognition approaches seem to be a sensitive method for identifying subtle brain changes that correspond to future memory decline in SMI.     

Neuropsychiatric symptoms in older people with and without cognitive impairment

Neuropsychiatric symptoms (NPS) are non-cognitive disturbances such as depression. Rates of NPS have been shown to increase as cognitive ability declines and may be useful in predicting transition from mild cognitive impairment (MCI) to dementia. This community-based study reports the association between NPS and cognitive decline over two years. Participants included 873 community dwelling adults aged 70-90 years enrolled in the Sydney Memory and Ageing Study. NPS were assessed by the Neuropsychiatric Inventory (NPI). Cognitive impairment was defined by diagnosis (MCI or incident dementia) or neuropsychological test performance across five cognitive domains. Cognitive decline was defined by progression to dementia or worse neuropsychological performance. Total NPS at baseline did not differ between those without cognitive impairment (26.2%) and those with MCI (28.8%), but agitation and apathy were associated with MCI. Only baseline depression was associated with dementia at follow-up. Total NPS at baseline was cross-sectionally associated with cognitive impairment in executive function, attention, and global cognition, but did not predict cognitive decline. Depression, anxiety, agitation, anxiety, and apathy were all associated with impairment in at least one cognitive domain, but only anxiety and agitation were significantly associated with cognitive decline. Sensitivity analyses applied more stringent criteria for NPS and cognitive impairment in MCI but did not alter interpretation of results from the main analysis. Overall rates of NPS at baseline were not associated with MCI, dementia, or cognitive decline over two years. Additional follow-up is needed to further examine this association over a longer time course.     

Parallel group ICA+ICA: Joint estimation of linked functional network variability and structural covariation with application to schizophrenia

There is growing evidence that rather than using a single brain imaging modality to study its association with physiological or symptomatic features, the field is paying more attention to fusion of multimodal information. However, most current multimodal fusion approaches that incorporate functional magnetic resonance imaging (fMRI) are restricted to second‐level 3D features, rather than the original 4D fMRI data. This trade‐off is that the valuable temporal information is not utilized during the fusion step. Here we are motivated to propose a novel approach called “parallel group ICA+ICA” that incorporates temporal fMRI information from group independent component analysis (GICA) into a parallel independent component analysis (ICA) framework, aiming to enable direct fusion of first‐level fMRI features with other modalities (e.g., structural MRI), which thus can detect linked functional network variability and structural covariations. Simulation results show that the proposed method yields accurate intermodality linkage detection regardless of whether it is strong or weak. When applied to real data, we identified one pair of significantly associated fMRI‐sMRI components that show group difference between schizophrenia and controls in both modalities, and this linkage can be replicated in an independent cohort. Finally, multiple cognitive domain scores can be predicted by the features identified in the linked component pair by our proposed method. We also show these multimodal brain features can predict multiple cognitive scores in an independent cohort. Overall, results demonstrate the ability of parallel GICA+ICA to estimate joint information from 4D and 3D data without discarding much of the available information up front, and the potential for using this approach to identify imaging biomarkers to study brain disorders.     

Neuropsychiatric symptoms and brain structural alterations in Fabry disease

Background: Neuropsychiatric symptoms (NPS), mainly cognitive deficits up to dementia and depressive syndromes have been described repeatedly in Fabry disease (FD). However, examinations regarding the pattern, extent, and frequency of the NPS in FD are still lacking. Moreover, the relationship between NPS and brain structural alterations in FD is unknown. The aim of this study was 1) to characterize NPS in a relatively large cohort of adult subjects with FD, and 2) to explore the association of cognitive performance and depressive syndromes with the FD‐typical brain structural findings. Methods: Twenty‐five Fabry patients (age 36.5 ± 11.0) with mild to moderate disease involvement and 20 age, gender‐, and education‐matched healthy controls were extensively studied by neuropsychiatric assessment, structural magnetic resonance imaging, magnetic resonance angiography, and diffusion‐tensor imaging. Results: Patients with FD showed deficits only in the attention domain. Clinically relevant depressive syndromes were noted in 60% of the patients. The subgroup of patients with markedly elevated volumes of white matter lesions (not associated with actual stroke; n = 7) showed slightly more learning and memory deficits, but no higher depression rate compared to less affected patients. Conclusions: Against the prevailing assumption, Fabry patients, even those with marked brain structural alterations, showed only mild cognitive deficits. The high frequency of depression in FD is likely to be related to the burden of this chronic multiorganic hereditary disease, but not to the FD‐typical brain structural alterations. Longitudinal studies are necessary to clear, if the mild cognitive deficits in FD might precede clinically relevant cognitive decline.     

Rates of decline distinguish Alzheimer's disease and mild cognitive impairment relative to normal aging: integrating cognition and brain function

AIMS: Increasing age is the strongest risk factor for Alzheimer's disease (AD). Yet, departure from normal age-related decline for established markers of AD including memory, cognitive decline and brain function deficits, has not been quantified. METHODS: We examined the cross-sectional estimates of the "rate of decline" in cognitive performance and psychophysiological measures of brain function over age in AD, preclinical (subjective memory complaint-SMC, Mild Cognitive Impairment-MCI) and healthy groups. Correlations between memory performance and indices of brain function were also conducted. RESULTS: The rate of cognitive decline increased between groups: AD showed advanced decline, and SMC/MCI groups represented intermediate stages of decline relative to normal aging expectations. In AD, advanced EEG alterations (excessive slow-wave/reduced fast-wave EEG, decreased working memory P450 component) were observed over age, which were coupled with memory decline. By contrast, MCI group showed less severe cognitive changes but specific decreases in the working memory N300 component and slow-wave (delta) EEG, associated with decline in memory. DISCUSSION AND INTEGRATIVE SIGNIFICANCE: While the cognitive data suggests a continuum of deterioration associated with increasing symptom severity across groups, integration with brain function measures points to possible distinct compensatory strategies in MCI and AD groups. An integrative approach offers the potential for objective markers of the critical turning point, with age as a potential factor, from mild memory problems to disease.     

CSF Apo-E levels associate with cognitive decline and MRI changes

Apolipoprotein E (APOE) ε4 allele is the most important genetic risk factor for Alzheimer’s disease (AD) and it is thought to do so by modulating levels of its product, apolipoprotein E (Apo-E), and regulating amyloid-β (Aβ) clearance. However, information on clinical and biomarker correlates of Apo-E proteins is scarce. We examined the relationship of cerebrospinal fluid (CSF) and plasma Apo-E protein levels, and APOE genotype to cognition and AD biomarker changes in 311 AD neuroimaging initiative subjects with CSF Apo-E measurements and 565 subjects with plasma Apo-E measurements. At baseline, higher CSF Apo-E levels were associated with higher total and phosphorylated CSF tau levels. CSF Apo-E levels were associated with longitudinal cognitive decline, MCI conversion to dementia, and gray matter atrophy rate in total tau/Aβ_1–42 ratio and APOE genotype-adjusted analyses. In analyses stratified by APOE genotype, our results were only significant in the group without the ε4 allele. Baseline CSF Apo-E levels did not predict longitudinal CSF Aβ or tau changes. Plasma Apo-E levels show a mild correlation with CSF Apo-E levels, but were not associated with longitudinal cognitive and MRI changes. Based on our analyses, we speculate that increased CSF Apo-E2 or -E3 levels might represent a protective response to injury in AD and may have neuroprotective effects by decreasing neuronal damage independent of tau and amyloid deposition in addition to its effects on amyloid clearance.     

Longitudinal cognitive changes in patients with early Parkinson's disease and neuropsychiatric symptoms

Aims

In this study, we aimed to investigate the effect of neuropsychiatric symptoms (NPS) on the rate of cognitive decline for both global cognition and specific cognitive domains in a cohort of patients from the Parkinson's Progression Markers Initiative (PPMI).

Method

Prospectively longitudinal data were obtained from the PPMI cohort. NPS, including depression, anxiety, apathy, psychosis, impulse control disorders (ICDs), and cognition ability, were evaluated by a series of questionnaires. Linear mixed-effects models were used to investigate the relationship between NPS and the rate of cognitive decline. Generalized estimating equations (GEEs) were used to investigate the relationship between NPS and the occurrence of mild cognitive impairment (MCI).

Results

In total, 423 patients with Parkinson's disease (PD) were recruited at baseline and 395, 378, 366, 346, and 315 participants were followed up at 1, 2, 3, 4, and 5 years, respectively. Depression, anxiety, apathy, and psychosis were associated with global cognitive decline. Except for those with ICDs, patients with psychosis, depression, anxiety, and apathy were more likely to meet the criteria for MCI. Patients with depression and anxiety showed a progressive decline in four major cognitive domains. Apathy and ICDs were separately associated with a progressive decline in processing speed-attention and memory, respectively.

Conclusions

Neuropsychiatric symptoms, including psychosis, depression, anxiety, and apathy, could be used to predict future cognitive decline in patients with PD.     

Detecting cognitive changes in preclinical Alzheimer's disease: A review of its feasibility

Significant progress has been made in characterizing the biological changes occurring in preclinical Alzheimer's disease (AD). Cognitive dysfunction has been viewed, however, as a late-stage phenomenon, despite increasing evidence that changes may be detected in the decades preceding dementia. In the absence of comprehensive evidence-based guidelines for preclinical cognitive assessment, longitudinal cohort and neuroimaging studies have been reviewed to determine the temporal order and brain biomarker correlates of specific cognitive functions. Episodic memory decline was observed to be the most salient cognitive function, correlating with high levels of amyloid deposition and hypoconnectivity across large-scale brain networks. Prospective studies point to early decline in both episodic and semantic memory processing as well as executive functions in the predementia period. The cognitive tests have, however, been principally those used to diagnose dementia. New procedures are required which target more finely the medial temporal lobe subregions first affected by clinically silent AD pathology.     

The ABC of Alzheimer's disease: cognitive changes and their management in Alzheimer's disease and related dementias

Cognitive decline, commonly first recognized as memory impairment, is a typical feature of Alzheimer's disease (AD). Neuropathological changes in the cerebral cortex and limbic system lead to deficits in learning, memory, language, and visuospatial skills. The precise nature of cognitive dysfunction reflects the distribution of pathological changes in AD. These will vary along the disease severity continuum and may also depend on where the disease sits in the spectrum of dementia. For example, AD-related disorders such as Lewy body dementia (LBD) and Parkinson's disease dementia (PDD) also show symptoms of cognitive decline and share several pathological features, including degeneration of cortical cholinergic and striatal dopaminergic neurons. In vascular dementia (VaD), there is often an unequal distribution of cognitive deficit, with severe impairment in some functions and relative sparing of others. Cholinesterase (ChE) inhibitors, which help restore acetylcholine levels in the brain, are licensed for the symptomatic treatment of AD and have shown additional benefit in related dementias. Physiological correlates of cholinergic function/dysfunction in the brain include regional cerebral blood flow, glucose metabolism, and cerebrospinal fluid levels of ChE enzymes. These variables represent valuable markers of the clinical efficacy of ChE inhibitors. However, direct assessment of cognitive improvement, stabilization or decline is usually considered the key efficacy parameter in clinical studies of ChE inhibitors in AD and related dementias. Large-scale, placebo-controlled clinical studies of ChE inhibitors have demonstrated efficacy in treating the cognitive impairments associated with AD. Randomized comparative studies of ChE inhibitors are now under way to directly compare symptomatic efficacy and effects on disease progression. Clinical trial data of the cognitive effects of ChE inhibitors in AD, LBD, PDD, and VaD are discussed in detail in this article. The benefits of long-term treatment on symptomatic improvement in cognition and further potential disease-modifying effects are highlighted.     

Disrupted structural and functional brain connectomes in mild cognitive impairment and Alzheimer’s disease

Alzheimer’s disease (AD) is the most common type of dementia, comprising an estimated 60–80% of all dementia cases. It is clinically characterized by impairments of memory and other cognitive functions. Previous studies have demonstrated that these impairments are associated with abnormal structural and functional connections among brain regions, leading to a disconnection concept of AD. With the advent of a combination of non-invasive neuroimaging (structural magnetic resonance imaging (MRI), diffusion MRI, and functional MRI) and neurophysiological techniques (electroencephalography and magnetoencephalography) with graph theoretical analysis, recent studies have shown that patients with AD and mild cognitive impairment (MCI), the prodromal stage of AD, exhibit disrupted topological organization in large-scale brain networks (i.e., connectomics) and that this disruption is significantly correlated with the decline of cognitive functions. In this review, we summarize the recent progress of brain connectomics in AD and MCI, focusing on the changes in the topological organization of large-scale structural and functional brain networks using graph theoretical approaches. Based on the two different perspectives of information segregation and integration, the literature reviewed here suggests that AD and MCI are associated with disrupted segregation and integration in brain networks. Thus, these connectomics studies open up a new window for understanding the pathophysiological mechanisms of AD and demonstrate the potential to uncover imaging biomarkers for clinical diagnosis and treatment evaluation for this disease.     

Predicting cognitive decline in Alzheimer disease. Role of platelet amyloid precursor protein

An altered pattern of platelet amyloid precursor protein (APP) forms, consisting of a reduced ratio of the upper (130 kDa) to the lower (110 to 106 kDa) immunoreactivity band (APPr), is associated with Alzheimer disease (AD), although in the early stages of AD this pattern shows high variability. To explore whether APPr values at baseline may predict the rate of cognitive decline, we evaluated patients with mild AD before and after 1 year of treatment with acetylcholinesterase inhibitors. Lower APPr at baseline was the only predictor of progressive cognitive decline, suggesting that this biomarker might be a useful indicator of prognosis for patients with AD.     

Longitudinal trajectory of Amyloid‐related hippocampal subfield atrophy in nondemented elderly

Hippocampal atrophy and abnormal β‐Amyloid (Aβ) deposition are established markers of Alzheimer's disease (AD). Nonetheless, longitudinal trajectory of Aβ‐associated hippocampal subfield atrophy prior to dementia remains unclear. We hypothesized that elevated Aβ correlated with longitudinal subfield atrophy selectively in no cognitive impairment (NCI), spreading to other subfields in mild cognitive impairment (MCI). We analyzed data from two independent longitudinal cohorts of nondemented elderly, including global PET‐Aβ in AD‐vulnerable cortical regions and longitudinal subfield volumes quantified with a novel auto‐segmentation method (FreeSurfer v.6.0). Moreover, we investigated associations of Aβ‐related progressive subfield atrophy with memory decline. Across both datasets, we found a converging pattern that higher Aβ correlated with faster CA1 volume decline in NCI. This pattern spread to other hippocampal subfields in MCI group, correlating with memory decline. Our results for the first time suggest a longitudinal focal‐to‐widespread trajectory of Aβ‐associated hippocampal subfield atrophy over disease progression in nondemented elderly.     

Quantifying brain metabolism from FDG‐PET images into a probability of Alzheimer's dementia score

¹⁸F‐fluorodeoxyglucose positron emission tomography (FDG‐PET) enables in‐vivo capture of the topographic metabolism patterns in the brain. These images have shown great promise in revealing the altered metabolism patterns in Alzheimer's disease (AD). The AD pathology is progressive, and leads to structural and functional alterations that lie on a continuum. There is a need to quantify the altered metabolism patterns that exist on a continuum into a simple measure. This work proposes a 3D convolutional neural network with residual connections that generates a probability score useful for interpreting the FDG‐PET images along the continuum of AD. This network is trained and tested on images of stable normal control and stable Dementia of the Alzheimer's type (sDAT) subjects, achieving an AUC of 0.976 via repeated fivefold cross‐validation. An independent test set consisting of images in between the two extreme ends of the DAT spectrum is used to further test the generalization performance of the network. Classification performance of 0.811 AUC is achieved in the task of predicting conversion of mild cognitive impairment to DAT for conversion time of 0–3 years. The saliency and class activation maps, which highlight the regions of the brain that are most important to the classification task, implicate many known regions affected by DAT including the posterior cingulate cortex, precuneus, and hippocampus.