To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
More than 50 million older people worldwide are suffering from dementia, and this number is estimated to increase to 150 million by 2050. Greater caregiver burdens and financial impacts on the healthcare system are expected as we wait for an effective treatment for dementia. Researchers are constantly exploring new therapies and screening approaches for the early detection of dementia. Artificial intelligence (AI) is widely applied in dementia research, including machine learning and deep learning methods for dementia diagnosis and progression detection. Computerized apps are also convenient tools for patients and caregivers to monitor cognitive function changes. Furthermore, social robots can potentially provide daily life support or guidance for the elderly who live alone. This review aims to provide an overview of AI applications in dementia research. We divided the applications into three categories according to different stages of cognitive impairment: (1) cognitive screening and training, (2) diagnosis and prognosis for dementia, and (3) dementia care and interventions. There are numerous studies on AI applications for dementia research. However, one challenge that remains is comparing the effectiveness of different AI methods in real clinical settings.
This study compared the level of education and tests from multiple cognitive domains as proxies for cognitive reserve.
The participants were educationally, ethnically, and cognitively diverse older adults enrolled in a longitudinal aging study. We examined independent and interactive effects of education, baseline cognitive scores, and MRI measures of cortical gray matter change on longitudinal cognitive change.
Baseline episodic memory was related to cognitive decline independent of brain and demographic variables and moderated (weakened) the impact of gray matter change. Education moderated (strengthened) the gray matter change effect. Non-memory cognitive measures did not incrementally explain cognitive decline or moderate gray matter change effects.
Episodic memory showed strong construct validity as a measure of cognitive reserve. Education effects on cognitive decline were dependent upon the rate of atrophy, indicating education effectively measures cognitive reserve only when atrophy rate is low. Results indicate that episodic memory has clinical utility as a predictor of future cognitive decline and better represents the neural basis of cognitive reserve than other cognitive abilities or static proxies like education.
Objectives: The purpose of this study was to investigate the longitudinal trajectory of self- and informant-subjective cognitive complaints (SCC), and to determine if SCC predict longitudinal changes in objective measures (OM) of cognitive function. Methods: The study included healthy and cognitively normal late middle-aged adults enriched with a family history of AD who were evaluated at up to three visits over a 4-year period. At each visit (Visit 1–3), self- and informant-SCC and OM were evaluated. Linear mixed models were used to determine if the longitudinal rate of change of self- and informant-SCC were associated with demographic variables, depressive symptoms, family history (FH), and apolipoprotein epsilon 4 (APOE4) status. The same modeling approach was used to examine the effect of Visit 1 SCC on longitudinal cognitive change after controlling for the same variables. Results: At Visit 1, more self-SCC were associated with fewer years of education and more depressive symptoms. SCC were also associated with poorer performance on cognitive measures, such that more self-SCC at Visit 1 were associated with poorer performance on memory and executive functioning measures at Visit 1, while more informant-SCC were associated with faster rate of longitudinal decline on a measure of episodic learning and memory. FH and APOE4 status were not associated with SCC. Discussion: Self- and informant-SCC showed an association with OM, albeit over different time frames in our late middle-aged sample. Additional longitudinal follow-up will likely assist in further clarifying these relationships as our sample ages and more pronounced cognitive changes eventually emerge. (JINS, 2017, 23, 617–626)
The aim of this study was to examine cross-sectionally whether higher cardiorespiratory fitness (CRF) might favorably modify amyloid-β (Aβ)-related decrements in cognition in a cohort of late-middle-aged adults at risk for Alzheimer’s disease (AD). Sixty-nine enrollees in the Wisconsin Registry for Alzheimer’s Prevention participated in this study. They completed a comprehensive neuropsychological exam, underwent 11C Pittsburgh Compound B (PiB)-PET imaging, and performed a graded treadmill exercise test to volitional exhaustion. Peak oxygen consumption (VO2peak) during the exercise test was used as the index of CRF. Forty-five participants also underwent lumbar puncture for collection of cerebrospinal fluid (CSF) samples, from which Aβ42 was immunoassayed. Covariate-adjusted regression analyses were used to test whether the association between Aβ and cognition was modified by CRF. There were significant VO2peak*PiB-PET interactions for Immediate Memory (p=.041) and Verbal Learning & Memory (p=.025). There were also significant VO2peak*CSF Aβ42 interactions for Immediate Memory (p<.001) and Verbal Learning & Memory (p<.001). Specifically, in the context of high Aβ burden, that is, increased PiB-PET binding or reduced CSF Aβ42, individuals with higher CRF exhibited significantly better cognition compared with individuals with lower CRF. In a late-middle-aged, at-risk cohort, higher CRF is associated with a diminution of Aβ-related effects on cognition. These findings suggest that exercise might play an important role in the prevention of AD. (JINS, 2015, 21, 841–850)
The relative influence of amyloid burden, neuronal structure and function, and prior cognitive performance on prospective memory decline among asymptomatic late middle-aged individuals at risk for Alzheimer's disease (AD) is currently unknown. We investigated this using longitudinal cognitive data from 122 middle-aged adults (21 “Decliners” and 101 “Stables”) enrolled in the Wisconsin Registry for Alzheimer's Prevention who underwent multimodality neuroimaging [11C-Pittsburgh Compound B (PiB), 18F-fluorodeoxyglucose (FDG), and structural/functional magnetic resonance imaging (fMRI)] 5.7 ± 1.4 years (range = 2.9–8.9) after their baseline cognitive assessment. Covariate-adjusted regression analyses revealed that the only imaging measure that significantly distinguished Decliners from Stables (p = .027) was a Neuronal Function composite derived from FDG and fMRI. In contrast, several cognitive measures, especially those that tap episodic memory, significantly distinguished the groups (p's<.05). Complementary receiver operating characteristic curve analyses identified the Brief Visuospatial Memory Test-Revised (BVMT-R) Total (.82 ± .05, p < .001), the BVMT-R Delayed Recall (.73 ± .06, p = .001), and the Reading subtest from the Wide-Range Achievement Test-III (.72 ± .06, p = .002) as the top three measures that best discriminated the groups. These findings suggest that early memory test performance might serve a more clinically pivotal role in forecasting future cognitive course than is currently presumed. (JINS, 2014, 20, 1–12)
Studies of neuropathology-cognition associations are not common and have been limited by small sample sizes, long intervals between autopsy and cognitive testing, and lack of breadth of neuropathology and cognition variables. This study examined domain-specific effects of common neuropathologies on cognition using data (N = 652) from two large cohort studies of older adults. We first identified dimensions of a battery of 17 neuropsychological tests, and regional measures of Alzheimer’s disease (AD) neuropathology. We then evaluated how cognitive factors were related to dimensions of AD and additional measures of cerebrovascular and Lewy Body disease, and also examined independent effects of brain weight. All cognitive domains had multiple neuropathology determinants that differed by domain. Neocortical neurofibrillary tangles were the strongest predictors of most domains, while medial temporal tangles showed a weaker relationship with episodic memory. Neuritic plaques had relatively strong effects on multiple domains. Lewy bodies and macroscopic infarcts were associated with all domains, while microscopic infarcts had more limited associations. Brain weight was related to all domains independent of specific neuropathologies. Results show that cognition is complexly determined by multiple disease substrates. Neuropathological variables and brain weight contributed approximately a third to half of the explained variance in different cognitive domains. (JINS, 2011, 17, 602–614).
Email your librarian or administrator to recommend adding this to your organisation's collection.