64 research outputs found
Cardiovascular risk factors and cognitive decline in older people with type 2 diabetes
AIMS/HYPOTHESIS: The aim of this work was to assess the role of well-established cardiovascular risk factors in the late-life cognitive decline of patients with type 2 diabetes.
METHODS: Data from 831 participants (aged 60-75 years) attending the 4 year follow-up of the Edinburgh Type 2 Diabetes Study (ET2DS) were used. Smoking history (pack-years), BP, HbA1c, plasma glucose and cholesterol were determined at baseline clinics (single time measurements) and/or from serial data recorded on a clinical management database from diagnosis until recruitment ('historical' data). Principal component analysis derived a factor, g, of general ability from seven cognitive tests. Linear regression models of follow-up g were adjusted for baseline g to represent 4 year cognitive change. 'Accelerated late-life cognitive decline' was defined as scoring in the lowest tertile of '4 year cognitive change' regression scores. Analyses controlled for age and sex.
RESULTS: A baseline history of moderate/heavy smoking (>/= 10 pack-years) and a 1% increased historical HbA1c (equivalent to an increase by 11 mmol/mol) predicted a 64% (OR 1.64; 95% CI 1.14, 2.34; p = 0.007) and 21% (OR 1.21; 95% CI 1.00, 1.45; p = 0.046) increased risk of accelerated cognitive decline, respectively. When treated as continuous measures, higher pack-years, historical HbA1c and historical BP emerged as significant independent predictors of 4 year decline in g (standardised beta range -0.07 to -0.14; all p </= 0.05).
CONCLUSIONS/INTERPRETATION: Increased smoking and poorer glycaemic control (with relatively weaker findings for BP) during the life-course were independently associated with accelerated late-life cognitive decline. Where possible, evaluation is warranted of these risk factors as targets for intervention to reduce the burden of cognitive impairment in diabetes
Brain Structural Networks Associated with Intelligence and Visuomotor Ability
Increasing evidence indicates that multiple structures in the brain are associated with intelligence
and cognitive function at the network level. The association between the grey matter (GM) structural
network and intelligence and cognition is not well understood. We applied a multivariate approach
to identify the pattern of GM and link the structural network to intelligence and cognitive functions.
Structural magnetic resonance imaging was acquired from 92 healthy individuals. Source-based
morphometry analysis was applied to the imaging data to extract GM structural covariance. We
assessed the intelligence, verbal fluency, processing speed, and executive functioning of the
participants and further investigated the correlations of the GM structural networks with intelligence
and cognitive functions. Six GM structural networks were identified. The cerebello-parietal component
and the frontal component were significantly associated with intelligence. The parietal and frontal
regions were each distinctively associated with intelligence by maintaining structural networks with
the cerebellum and the temporal region, respectively. The cerebellar component was associated
with visuomotor ability. Our results support the parieto-frontal integration theory of intelligence by
demonstrating how each core region for intelligence works in concert with other regions. In addition,
we revealed how the cerebellum is associated with intelligence and cognitive functions
ΠΠ΅ΡΠΎΠ΄ΠΎΠ»ΠΎΠ³ΠΈΡ ΡΠΈΠ½ΡΠ΅Π·Π° Π°ΡΡ ΠΈΡΠ΅ΠΊΡΡΡΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎ-ΡΠ΅Ρ Π½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ° Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π° ΠΎΠ±ΡΡΠ°Π½ΠΎΠ²ΠΊΠΈ
ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ ΠΊ ΠΏΡΠΎΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π°ΡΡ
ΠΈΡΠ΅ΠΊΡΡΡΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎ-ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ° Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π° ΠΎΠ±ΡΡΠ°Π½ΠΎΠ²ΠΊΠΈ Π² ΡΠ΅Π°Π»ΡΠ½ΠΎΠΌ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΠΉ Π½Π° ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠ΅ΡΠ°Π΅ΠΌΡΡ
ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΡ
Π·Π°Π΄Π°Ρ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΊΠ»Π°ΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° ΠΈ Π²ΡΠ±ΡΠ°Π½Π½ΠΎΠ³ΠΎ ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²Π° ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² ΠΏΠΎΠ΄ΠΎΠ±ΠΈΡ. Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΡΠΉ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΈΠ· ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²Π° ΡΡΠ½ΠΊΡΠΈΠΉ ΡΠΈΡΡΠ΅ΠΌΡ Π²ΡΠ΄Π΅Π»ΠΈΡΡ ΠΏΠΎΠ΄ΠΎΠ±Π½ΡΠ΅ (ΠΏΠΎ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΡΠΌ ΠΏΡΠΈΠ·Π½Π°ΠΊΠ°ΠΌ) ΠΈ ΠΎΠ±ΡΠ΅Π΄ΠΈΠ½ΠΈΡΡ ΠΈΡ
Π² Π°ΡΡ
ΠΈΡΠ΅ΠΊΡΡΡΠ½ΡΠ΅ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΡ (ΡΠ½ΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½ΡΠ΅ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΠ΅ ΠΌΠΎΠ΄ΡΠ»ΠΈ).ΠΠ°ΠΏΡΠΎΠΏΠΎΠ½ΠΎΠ²Π°Π½ΠΎ ΠΏΡΠ΄Ρ
ΡΠ΄ Π΄ΠΎ ΠΏΡΠΎΠ΅ΠΊΡΡΠ²Π°Π½Π½Ρ Π°ΡΡ
ΡΡΠ΅ΠΊΡΡΡΠΈ ΡΠ΅Π½ΡΡΡ ΠΎΠ±ΡΠΎΠ±ΠΊΠΈ ΡΠ½ΡΠΎΡΠΌΠ°ΡΡΡ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΎΠ²Π°Π½ΠΎΡ ΡΠΈΡΡΠ΅ΠΌΠΈ ΠΌΠΎΠ½ΡΡΠΎΡΠΈΠ½Π³Ρ ΡΠ΅ΡΠ΅Π΄ΠΎΠ²ΠΈΡΠ° Π² ΡΠ΅Π°Π»ΡΠ½ΠΎΠΌΡ ΡΠ°ΡΡ, ΡΠΎ Π·Π°ΡΠ½ΠΎΠ²Π°Π½ΠΈΠΉ Π½Π° ΠΊΠ»Π°ΡΠΈΡΡΠΊΠ°ΡΡΡ ΡΡΠ½ΠΊΡΡΠΎΠ½Π°Π»ΡΠ½ΠΈΡ
Π·Π°Π΄Π°Ρ Π½Π° ΠΏΡΠ΄ΡΡΠ°Π²Ρ ΠΌΠ΅ΡΠΎΠ΄ΡΠ² ΠΊΠ»Π°ΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΡΠ·Ρ Ρ ΠΎΠ±ΡΠ°Π½ΠΎΡ ΠΌΠ½ΠΎΠΆΠΈΠ½ΠΈ ΠΎΠ·Π½Π°ΠΊ ΡΡ
ΠΎΠΆΠΎΡΡΡ. Π ΠΎΠ·ΡΠΎΠ±Π»Π΅Π½ΠΈΠΉ ΠΏΡΠ΄Ρ
ΡΠ΄ Π΄ΠΎΠ·Π²ΠΎΠ»ΡΡ Π²ΠΈΠ±ΡΠ°ΡΠΈ ΡΠ· ΠΌΠ½ΠΎΠΆΠΈΠ½ΠΈ ΡΡΠ½ΠΊΡΡΠΉ ΡΠΈΡΡΠ΅ΠΌΠΈ ΡΡ
ΠΎΠΆΡ (Π·Π° ΠΏΠ΅Π²Π½ΠΈΠΌΠΈ ΠΎΠ·Π½Π°ΠΊΠ°ΠΌΠΈ) Ρ ΠΏΠΎΡΠ΄Π½Π°ΡΠΈ ΡΡ
Π² Π°ΡΡ
ΡΡΠ΅ΠΊΡΡΡΠ½Ρ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΠΈ (ΡΠ½ΡΡΡΠΊΠΎΠ²Π°Π½Ρ ΡΡΠ½ΠΊΡΡΠΎΠ½Π°Π»ΡΠ½Ρ ΠΌΠΎΠ΄ΡΠ»Ρ).The approach to designing architecture of the information processing complex of the automated real time conditions monitoring system based on classification of functional tasks on the basis of methods of cluster analysis and the chosen set of similarity attributes is offered. The developed approach allows to allocate from a set of functions the systems similar (on certain attributes) and to unite them in architectural components (unified functional modules)
Cortical Microinfarcts and White Matter Connectivity in Memory Clinic Patients
Background and purpose: Cerebral microinfarcts (CMIs) are associated with cognitive impairment and dementia. CMIs might affect cognitive performance through disruption of cerebral networks. We investigated in memory clinic patients whether cortical CMIs are clustered in specific brain regions and if presence of cortical CMIs is associated with reduced white matter (WM) connectivity in tracts projecting to these regions. Methods: 164 memory clinic patients with vascular brain injury with a mean age of 72 Β± 11 years (54% male) were included. All underwent 3 tesla MRI, including a diffusion MRI and cognitive testing. Cortical CMIs were rated according to established criteria and their spatial location was marked. Diffusion imaging-based tractography was used to reconstruct WM connections and voxel based analysis (VBA) to assess integrity of WM directly below the cortex. WM connectivity and integrity were compared between patients with and without cortical CMIs for the whole brain and regions with a high CMI burden. Results: 30 patients (18%) had at least 1 cortical CMI [range 1β46]. More than 70% of the cortical CMIs were located in the superior frontal, middle frontal, and pre- and postcentral brain regions (covering 16% of the cortical surface). In these high CMI burden regions, presence of cortical CMIs was not associated with WM connectivity after correction for conventional neuroimaging markers of vascular injury. WM connectivity in the whole brain and WM voxels directly underneath the cortical surface did not differ between patients with and without cortical CMIs. Conclusion: Cortical CMIs displayed a strong local clustering in highly interconnected frontal, pre- and postcentral brain regions. Nevertheless, WM connections projecting to these regions were not disproportionally impaired in patients with compared to patients without cortical CMIs. Alternative mechanisms, such as focal disturbances in cortical structure and functioning, may better explain CMI associated cognitive impairment
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