Attention modulates topology and dynamics of auditory sensory gating

This work challenges the widely accepted model of sensory gating as a preattention inhibitory process by investigating whether attention directed at the second tone (S2) within a paired‐click paradigm could affect gating at the cortical level. We utilized magnetoencephalography, magnetic resonance imaging and spatio‐temporal source localization to compare the cortical dynamics underlying gating responses across two conditions (passive and attention) in 19 healthy subjects. Source localization results reaffirmed the existence of a fast processing pathway between the prefrontal cortex (PFC) and bilateral superior temporal gyri (STG) that underlies the auditory gating process. STG source dynamics comprised two gating sub‐components, Mb1 and Mb2, both of which showed significant gating suppression (>51%). The attention directed to the S2 tone changed the gating network topology by switching the prefrontal generator from a dorsolateral location, which was active in the passive condition (18/19), to a medial location, active in the attention condition (19/19). Enhanced responses to the attended stimulus caused a significant reduction in gating suppression in both STG gating components (>50%). Our results demonstrate that attention not only modulates sensory gating dynamics, but also exerts topological rerouting of information processing within the PFC. The present data, suggesting that the cortical levels of early sensory processing are subject to top‐down influences, change the current view of gating as a purely automatic bottom‐up process.     

Nonlinear biomarker interactions in conversion from mild cognitive impairment to Alzheimer's disease

Multiple biomarkers can capture different facets of Alzheimer's disease. However, statistical models of biomarkers to predict outcomes in Alzheimer's rarely model nonlinear interactions between these measures. Here, we used Gaussian Processes to address this, modelling nonlinear interactions to predict progression from mild cognitive impairment (MCI) to Alzheimer's over 3 years, using Alzheimer's Disease Neuroimaging Initiative (ADNI) data. Measures included: demographics, APOE4 genotype, CSF (amyloid‐β42, total tau, phosphorylated tau), [18^F]florbetapir, hippocampal volume and brain‐age. We examined: (a) the independent value of each biomarker; and (b) whether modelling nonlinear interactions between biomarkers improved predictions. Each measured added complementary information when predicting conversion to Alzheimer's. A linear model classifying stable from progressive MCI explained over half the variance (R² = 0.51, p < .001); the strongest independently contributing biomarker was hippocampal volume (R² = 0.13). When comparing sensitivity of different models to progressive MCI (independent biomarker models, additive models, nonlinear interaction models), we observed a significant improvement (p < .001) for various two‐way interaction models. The best performing model included an interaction between amyloid‐β‐PET and P‐tau, while accounting for hippocampal volume (sensitivity = 0.77, AUC = 0.826). Closely related biomarkers contributed uniquely to predict conversion to Alzheimer's. Nonlinear biomarker interactions were also implicated, and results showed that although for some patients adding additional biomarkers may add little value (i.e., when hippocampal volume is high), for others (i.e., with low hippocampal volume) further invasive and expensive examination may be warranted. Our framework enables visualisation of these interactions, in individual patient biomarker ‘space', providing information for personalised or stratified healthcare or clinical trial design.     

Altered resting state networks in mild cognitive impairment and mild Alzheimer's disease: an fMRI study

Activity and reactivity of the default mode network in the brain was studied using functional magnetic resonance imaging (fMRI) in 28 nondemented individuals with mild cognitive impairment (MCI), 18 patients with mild Alzheimer's disease (AD), and 41 healthy elderly controls (HC). The default mode network was interrogated by means of decreases in brain activity, termed deactivations, during a visual encoding task and during a nonspatial working memory task. Deactivation was found in the default mode network involving the anterior frontal, precuneus, and posterior cingulate cortex. MCI patients showed less deactivation than HC, but more than AD. The most pronounced differences between MCI, HC, and AD occurred in the very early phase of deactivation, reflecting the reactivity and adaptation of the network. The default mode network response in the anterior frontal cortex significantly distinguished MCI from both HC (in the medial frontal) and AD (in the anterior cingulate cortex). The response in the precuneus could only distinguish between patients and HC, not between MCI and AD. These findings may be consistent with the notion that MCI is a transitional state between healthy aging and dementia and with the proposed early changes in MCI in the posterior cingulate cortex and precuneus. These findings suggest that altered activity in the default mode network may act as an early marker for AD pathology.     

Brain structure and allelic associations in Alzheimer's disease

Background

Alzheimer's disease (AD), the most prevalent form of dementia, affects 6.5 million Americans and over 50 million people globally. Clinical, genetic, and phenotypic studies of dementia provide some insights of the observed progressive neurodegenerative processes, however, the mechanisms underlying AD onset remain enigmatic.

Aims

This paper examines late-onset dementia-related cognitive impairment utilizing neuroimaging-genetics biomarker associations.

Materials and Methods

The participants, ages 65–85, included 266 healthy controls (HC), 572 volunteers with mild cognitive impairment (MCI), and 188 Alzheimer's disease (AD) patients. Genotype dosage data for AD-associated single nucleotide polymorphisms (SNPs) were extracted from the imputed ADNI genetics archive using sample-major additive coding. Such 29 SNPs were selected, representing a subset of independent SNPs reported to be highly associated with AD in a recent AD meta-GWAS study by Jansen and colleagues.

Results

We identified the significant correlations between the 29 genomic markers (GMs) and the 200 neuroimaging markers (NIMs). The odds ratios and relative risks for AD and MCI (relative to HC) were predicted using multinomial linear models.

Discussion

In the HC and MCI cohorts, mainly cortical thickness measures were associated with GMs, whereas the AD cohort exhibited different GM-NIM relations. Network patterns within the HC and AD groups were distinct in cortical thickness, volume, and proportion of White to Gray Matter (pct), but not in the MCI cohort. Multinomial linear models of clinical diagnosis showed precisely the specific NIMs and GMs that were most impactful in discriminating between AD and HC, and between MCI and HC.

Conclusion

This study suggests that advanced analytics provide mechanisms for exploring the interrelations between morphometric indicators and GMs. The findings may facilitate further clinical investigations of phenotypic associations that support deep systematic understanding of AD pathogenesis.     

Altered circadian cortisol secretion in Alzheimer's disease: clinical and neuroradiological aspects

We determined circadian salivary cortisol levels in 18 outpatients affected by probable Alzheimer's disease (AD) and looked for a possible correlation with both cognitive impairment and brain CT scan findings. The diagnosis of probable AD was made according to the NINCDS-ADRDA criteria. The severity of cognitive impairment was quantified using the Mini Mental State Examination (MMSE) and the Global Deterioration Scale (GDS). Cortisol levels were measured on saliva samples collected at 08:00 AM and 08:00 PM. For each sample, a duplicate cortisol measurement was performed on 50 microl of saliva by means of a modified commercial radioimmunoassay kit. At the same time, 11 of the 18 AD patients enrolled also underwent a brain CT scan to estimate cerebral atrophy by using linear indexes. The mean value of cortisol levels was significantly higher in AD patients than in controls at both the morning and the evening measurements, and the circadian fluctuation of cortisol was less marked in AD patients than in controls, although this difference did not reach statistical significance. Morning cortisol levels were significantly correlated to both the MMSE and the GDS scores. A significant correlation was also found between morning cortisol levels and all the cerebral atrophy indexes. By contrast, no correlation was observed between evening cortisol levels or cortisol circadian fluctuations and either cognitive impairment or cerebral atrophy. In conclusion, despite the potential biases deriving from the small sample and the limitations of the CT scan study, our results suggest that, in AD patients, hypercortisolemia is correlated with severity of the disease.     

Down-regulation of trkA mRNA within nucleus basalis neurons in individuals with mild cognitive impairment and Alzheimer's disease

Recent studies indicate that trkA expression is reduced in end-stage Alzheimer's disease (AD). However, understanding the neuropathologic correlates of early cognitive decline, as well as the changes that underlie the transition from nondemented mild cognitive impairment (MCI) to AD, are more critical neurobiological challenges. In these regards, the present study examined the expression of trkA mRNA in individuals diagnosed with MCI and AD from a cohort of people enrolled in a Religious Orders Study. Individuals with MCI and AD displayed significant reductions in trkA mRNA relative to aged-matched controls, indicating that alterations in trkA gene expression occur early in the disease process. The magnitude of change was similar in MCI and AD cases, suggesting that further loss of trkA mRNA is not necessarily associated with the transition of individuals from nondemented MCI to AD. The loss of trkA mRNA was not associated with education, apolipoprotein E allele status, gender, Braak score, global cognitive score or Mini-Mental Status Examination. In contrast, the loss of trkA mRNA in MCI and AD was significantly correlated with function on a variety of episodic memory tests.     

The need for a consensus in the use of assessment tools for Alzheimer's disease: the Feasibility Study (assessment tools for dementia in Alzheimer Centres across Europe), a European Alzheimer's Disease Consortium's (EADC) survey

AIMS: To ensure that all Alzheimer centres across Europe are capable of using a similar method of data collection. Information about the patient assessment tools used by each participating centre was obtained and normal clinical practice in each EADC centre was documented by collecting data from routine new patient consultation. METHODS: Twenty new consecutive patients with objective memory impairment were recruited in each Alzheimer centre over 6 months. Each patient consultation was carried out according to routine clinical practice. Patient data were recorded using the anonymous patient protocol (demographic, diagnosis, MMSE score, patient assessment scales, and most prominent behavioural problem). Information about neuropsychological assessment tools used in each centre was take to account to harmonise research practice for future multicentre collaboration. RESULTS: Seven hundred and four patients from 36 memory clinics in 13 countries across Europe participated in the study. [M:F ratio 0.67. Mean age 75.4 SD 9.3 (51-102) Mean MMSE 21 SD 6 (0-30)] Five hundred and fifty-five patients had a clinical diagnosis of dementia [Alzheimer's disease (68.5%), vascular dementia (10.3%), frontal lobe dementia (5.6%), Lewy body dementia (4.1%), mixed dementia (5.6%)]. Duration of symptoms: 0-6 months 6.5%; 6-12 months 16.1%; 1-2 years 30.5%; 2-5 years 46.9%. Assessment scales used: Clinical Dementia Rating (CDR) 48.9%, Reisberg's Global Deterioration Scale (GDS) 38.6%, ADL/IADL (Lawton and Brody, 1969) 37.5%, Neuropsychological Inventory (NPI) 28.6%, Geriatric Depression Scale 22%, ADL (Katz et al., 1963) 19.2%, ADAS-Cog 14.9%, Cornell Scale for Depression 12.9%, Grober and Bushke Selective Reminding Test 11.5%, ADCS/ADL 7.7%. 64.8% of the patients experienced behavioural symptoms: apathy 13.6%; anxiety 12.8%; dysphoria 9.9%; irritability 7.8%; agitation 5.5%; hallucinations 3.6%; delusions 3.6%, sleep disorder 2.4%; desinhibition 2%. CONCLUSIONS: The most common type of cognitive decline was Alzheimer's disease followed by mild cognitive impairment and vascular dementia. CDR, GDS Reisberg, and ADL/IADL were used widely (40-50%). The NPI, geriatric depression scale and ADL (Katz, 1963) were only used in 20% of the centres. We verified large differences in the tools use in the EADC centres to evaluate patients with dementia across Europe. There is a need for a consensus in the use of assessment tools for dementia in Alzheimer's centres in Europe.