Olfaction Modulates Inter-Subject Correlation of Neural Responses

Odors can be powerful stimulants. It is well-established that odors provide strong cues for recall of locations, people and events. The effects of specific scents on other cognitive functions are less well-established. We hypothesized that scents with different odor qualities will have a different effect on attention. To assess attention, we used Inter-Subject Correlation of the EEG because this metric is strongly modulated by attentional engagement with natural audiovisual stimuli.We predicted that scents known to be “energizing” would increase Inter-Subject Correlation during watching of videos as compared to “calming” scents. In a first experiment, we confirmed this for eucalyptol and linalool while participants watched animated autobiographical narratives. The result was replicated in a second experiment, but did not generalize to limonene, also considered an “energizing” odorant. In a third, double-blind experiment, we tested a battery of scents including single molecules, as well as mixtures, as participants watched various short video clips. We found a varying effect of odor on Inter-Subject Correlation across the various scents. This study provides a basis for reliably and reproducibly assessing effects of odors on brain activity. Future research is needed to further explore the effect of scent-based up-modulation in engagement on learning and memory performance. Educators, product developers and fragrance brands might also benefit from such objective neurophysiological measures

Functional Deficits in Patients with Mild Cognitive Impairment: Prediction of Ad

Objective: To evaluate the predictive utility of self-reported and informant-reported functional deficits in patients with mild cognitive impairment (MCI) for the follow-up diagnosis of probable AD. Methods: The Pfeffer Functional Activities Questionnaire (FAQ) and Lawton Instrumental Activities of Daily Living (IADL) Scale were administered at baseline. Patients were followed at 6-month intervals, and matched normal control subjects (NC) were followed annually. Results: Self-reported deficits were higher for patients with MCI than for NC. At baseline, self- and informant-reported functional deficits were significantly greater for patients who converted to AD on follow-up evaluation than for patients who did not convert, even after controlling for age, education, and modified Mini-Mental State Examination scores. While converters showed significantly more informant- than self-reported deficits at baseline, nonconverters showed the reverse pattern. Survival analyses further revealed that informant-reported deficits (but not self-reported deficits) and a discrepancy score indicating greater informant- than self-reported functional deficits significantly predicted the development of AD. The discrepancy index showed high specificity and sensitivity for progression to AD within 2 years. Conclusions: These findings indicate that in patients with MCI, the patient's lack of awareness of functional deficits identified by informants strongly predicts a future diagnosis of AD. If replicated, these findings suggest that clinicians evaluating MCI patients should obtain both self-reports and informant reports of functional deficits to help in prediction of long-term outcome

PET Network Abnormalities and Cognitive Decline in Patients with Mild Cognitive Impairment

Temporoparietal and posterior cingulate metabolism deficits characterize patients with Alzheimer's disease (AD). A H(2)(15)O resting PET scan covariance pattern, derived by using multivariate techniques, was previously shown to discriminate 17 mild AD patients from 16 healthy controls. This AD covariance pattern revealed hypoperfusion in bilateral inferior parietal lobule and cingulate; and left middle frontal, inferior frontal, precentral, and supramarginal gyri. The AD pattern also revealed hyperperfusion in bilateral insula, lingual gyri, and cuneus; left fusiform and superior occipital gyri; and right parahippocampal gyrus and pulvinar. In an independent sample of 23 outpatients with mild cognitive impairment (MCI) followed at 6-month intervals, the AD pattern score was evaluated as a predictor of cognitive decline. In this MCI sample, an H2(15)O resting PET scan was carried out at baseline. Mean duration of follow-up was 48.8 (SD 15.5) months, during which time six of 23 MCI patients converted to AD. In generalized estimating equations (GEE) analyses, controlling for age, sex, education, and baseline neuropsychological scores, increased AD pattern score was associated with greater decline in each neuropsychological test score over time (Mini Mental State Exam, Selective Reminding Test delayed recall, Animal Naming, WAIS-R digit symbol; Ps<0.01-0.001). In summary, a resting PET covariance pattern previously reported to discriminate AD patients from control subjects was applied prospectively to an independent sample of MCI patients and found to predict cognitive decline. Independent replication in larger samples is needed before clinical application can be considere

Covariance PET Patterns in Early Alzheimer's Disease and Subjects with Cognitive Impairment but No Dementia: Utility in Group Discrimination and Correlations with Functional Performance

Although multivariate analytic techniques might identify diagnostic patterns that are not captured by univariate methods, they have rarely been used to study the neural correlates of Alzheimer's disease (AD) or cognitive impairment. Nonquantitative H2(15)O PET scans were acquired during rest in 17 probable AD subjects selected for mild severity [mean-modified Mini Mental Status Examination (mMMS) 46/57; SD 5.1], 16 control subjects (mMMS 54; SD 2.5) and 23 subjects with minimal to mild cognitive impairment but no dementia (mMMS 53; SD 2.8). Expert clinical reading had low success in discriminating AD and controls. There were no significant mean flow differences among groups in traditional univariate SPM Noxel-wise analyses or region of interest (ROI) analyses. A covariance pattern was identified whose mean expression was significantly higher in the AD as compared to controls (P = 0.03; sensitivity 76-94%; specificity 63-81%). Sites of increased concomitant flow included insula, cuneus, pulvinar, lingual, fusiform, superior occipital and parahippocampal gyri, whereas decreased concomitant flow was found in cingulate, inferior parietal lobule, middle and inferior frontal, supramarginal and precentral gyri. The covariance analysis-derived pattern was then prospectively applied to the cognitively impaired subjects: as compared to subjects with Clinical Dementia Rating (CDR) = 0, subjects with CDR = 0.5 had significantly higher mean covariance pattern expression (P = 0.009). Expression of this pattern correlated inversely with Selective Reminding Test total recall (r = -0.401, P = 0.002), delayed recall (r = -0.351, P = 0.008) and mMMS scores (r = -0.401, P = 0.002) in all three groups combined. We conclude that patients with AD may differentially express resting cerebral blood flow covariance patterns even at very early disease stages. Significant alterations in expression of resting flow covariance patterns occur even for subjects with cognitive impairment. Expression of covariance patterns correlates with cognitive and functional performance measures, holding promise for meaningful associations with underlying biopathological processes

A 10-item smell identification scale related to risk for Alzheimer's disease

University of Pennsylvania Smell Identification Test data from control subjects (n = 63), patients with mild cognitive impairment (n = 147), and patients with Alzheimer's disease (n = 100) were analyzed to derive an optimal subset of items related to risk for Alzheimer's disease (ie, healthy through mild cognitive impairment to early and moderate disease stages). The derived 10-item scale performed comparably with the University of Pennsylvania Smell Identification Test in classifying subjects, and it strongly predicted conversion to Alzheimer's disease on follow-up evaluation in patients with mild cognitive impairment. Independent replication is needed to validate these findings

The impact of anxiety on conversion from mild cognitive impairment to Alzheimer's disease

Objective—To compare state and trait anxiety in mild cognitive impairment (MCI) patients and matched control subjects, and to assess the impact of these variables in predicting conversion to Alzheimer’s disease. Methods—One hundred and forty-eight patients with MCI, broadly defined, were assessed and followed systematically. Baseline predictors for follow-up conversion to AD (entire sample: 39/148 converted to Alzheimer’s disease (AD)) included the Spielberger State-Trait Anxiety Inventory (STAI). Results—At baseline evaluation, MCI patients had higher levels of state and trait anxiety than controls, with no differences between future AD converters (n = 39) and non-converters. In agestratified Cox proportional hazards model analyses, STAI State was not a significant predictor of conversion to AD (STAI State ≤30 vs. > 30 risk ratio, 1.68; 95% CI, 0.75, 3.77; p = 0.21), but higher Trait scores indicated a lower risk of conversion when STAI State, education, the Folstein MiniMental State Examination and HAM-D (depression score) were also included in the model (STAI Trait ≤30 vs. > 30 risk ratio, 0.36; 95% CI, 0.16, 0.82; p = 0.015). Conclusions—In contrast to two other recent studies that showed anxiety predicted cognitive decline or conversion to AD, in this clinic-based sample, state anxiety was not a significant predictor. However, higher Trait anxiety predicted a lower risk of future conversion to AD. Further research with systematic long-term follow-up in larger samples is needed to clarify the role of state and trait anxiety in predicting MCI conversion to AD

Validation and Optimization of Statistical Approaches for Modeling Odorant-Induced fMRI Signal Changes in Olfactory-Related Brain Areas

Recent neuroimaging studies have converged to show that odorant-induced responses to prolonged stimulation in primary olfactory cortex (POC) are characterized by a rapidly habituating time course. Different statistical approaches have effectively modeled this time course. One approach explicitly modeled rapid habituation using an exponentially decaying reference waveform that decreased to baseline levels within 30 to 40 s. A second approach modeled an early transient response by simply shortening the odorant ‘ON’ period to be less than the actual stimulation period (i.e., 9 of 40 s). The goal of the current study was to validate, compare, and optimize these methodological approaches by applying them to an olfactory fMRI block-design dataset from 10 healthy young subjects presented with odorants for 12 s (ON), alternating with 30 s of clear air (OFF). Both approaches significantly improved sensitivity to odorant-induced signal changes in POC relative to a square-wave model based on the actual stimulation period. Our findings further demonstrate that the ‘optimal’ model fit to the data was achieved by shortening the odorant ‘ON’ period to approximately 6 s. These results suggest that sensitivity to odorant- induced POC activity in block-design experiments can be optimized by modeling an early phasic response followed by a precipitous rather than specific exponential decrease to baseline levels. Notably, whole brain voxel-wise analyses further established that modeling rapid habituation in this way is not only sensitive, but also highly specific to odorant-induced activation in a well-established network of olfactory- related brain areas

Combining Early Markers Strongly Predicts Conversion from Mild Cognitive Impairment to Alzheimer's Disease

Background—The utility of combining early markers to predict conversion from mild cognitive impairment (MCI) to Alzheimer’s Disease (AD) remains uncertain. Methods—148 outpatients with MCI, broadly defined, were followed at 6-month intervals. Hypothesized baseline predictors for follow-up conversion to AD (entire sample: 39/148 converters) were cognitive test performance, informant report of functional impairment, apolipoprotein E genotype, olfactory identification deficit, MRI hippocampal and entorhinal cortex volumes. Results—In the 3-year follow-up patient sample (33/126 converters), five of eight hypothesized predictors were selected by backward and stepwise logistic regression: FAQ (informant report of functioning), UPSIT (olfactory identification), SRT immediate recall (verbal memory), MRI hippocampal volume, MRI entorhinal cortex volume. For 10% false positives (90% specificity), this five-predictor combination showed 85.2% sensitivity, combining age and MMSE showed 39.4% sensitivity, and combining age, MMSE, and the three clinical predictors (SRT immediate recall, FAQ, and UPSIT) showed 81.3% sensitivity. Area under ROC curve was greater for the fivepredictor combination (0.948) than age plus MMSE (0.821; p =.0009), and remained high in subsamples with MMSE ≥ 27/30 and amnestic MCI. For the entire patient sample, based on dichotomizing estimated risk at 0.5, positive likelihood ratio was 16.8 (95% CI 6.4, 44.3) and negative likelihood ratio was 0.2 (95% CI 0.1, 0.4). Conclusions—The five-predictor combination strongly predicted conversion to AD and was markedly superior to combining age and MMSE. Combining only clinically administered measures also led to strong predictive accuracy. If independently replicated, the findings have potential utility for early detection of AD

Predictive Utility of Apolipoprotein E Genotype for Alzheimer Disease in Outpatients With Mild Cognitive Impairment

Background: In cognitively impaired patients without dementia, the utility of apolipoprotein E (APOE) genotyping is unclear. Objective: To evaluate the predictive utility of the APOE ε4 genotype for conversion to probable Alzheimer disease (AD). Design: Naturalistic, longitudinal study. Setting: Memory disorders outpatient clinic. Patients: A total of 136 patients with memory complaints were determined to have mild cognitive impairment and were evaluated every 6 months. Fifty-seven age- and sex-matched healthy controls were evaluated annually. Main Outcome Measures: Primary outcome measures included conversion to AD. Secondary outcome measures included change over time in Mini-Mental State Examination (MMSE) score and Selective Reminding Test (SRT) delayed recall score. Results: The APOE ε4 allele was present in 25% of patients and 21% of healthy controls. During a mean ± SD follow-up of 35.2 ± 24.3 months, 35 of 136 patients converted to AD. APOE ε4 carrier status did not differ between converters (31%) and nonconverters to AD (23%, P = .3) and did not affect the time trend in MMSE or SRT scores in the entire sample. Four of 5 APOE ε4 homozygotes converted to AD compared with 7 of 29 heterozygotes (P = .02). In a Cox proportional hazards model stratified by age quartiles, after controlling for sex, education, MMSE score, and SRT delayed recall score, APOE ε4 increased the risk of AD in patients 70 to 85 years old (n = 57; risk ratio, 2.77; 95% confidence interval, 1.1-7.3; P = .03) but not in patients 55 to 69 years old (n = 79; P = .7). Conclusions: APOE ε4 carrier status was associated with conversion to AD in older outpatients after controlling for known demographic and clinical risk factors, and APOE ε4 homozygosity was associated with increased risk of conversion to AD. However, APOE ε4 carrier status by itself did not predict cognitive decline or conversion to AD, indicating that APOE genotyping in patients with mild cognitive impairment may have limited clinical applicability for prediction of outcome