P2‐087: A systematic review and meta‐analysis for the prevalence of depression in mild cognitive impairment

Ismail, Zahinoor; Elbayoumi, Heba; Fischer, Corinne E.; Hogan, David B.; Millikin, Colleen; Schweizer, Tom A.; Smith, Eric E.; Patten, Scott B.; Fiest, Kirsten M.

doi:10.1016/j.jalz.2015.06.624

Cited by 6 publications

(6 citation statements)

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“…Clinically, this approach can potentially assist in dementia prognostication for those presenting with cognitive complaints, as this is a common question clinicians are facing. NPS are common in prodromal dementia [20], can even present in preclinical dementia [5], and are clinically significant [21][22][23][24]. While several studies have used complex ML models to predict stages of dementia, NPS are often ignored as potentially predictive features [1,10,25].…”

Section: Discussionmentioning

confidence: 99%

Using Machine Learning to Predict Dementia from Neuropsychiatric Symptom and Neuroimaging Data

Gill

Mouchès

et al. 2020

JAD

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Background: Machine learning (ML) is a promising technique for patient-specific prediction of mild cognitive impairment (MCI) and dementia development. Neuropsychiatric symptoms (NPS) might improve the accuracy of ML models but have barely been used for this purpose. Objectives: To investigate if baseline mild behavioral impairment (MBI) status used for NPS quantification along with brain morphology features are predictive of follow-up diagnosis, median 40 months later in patients with normal cognition (NC) or MCI. Method: Baseline neuroimaging, neuropsychiatric, and clinical data from 102 individuals with NC and 239 with MCI were extracted from the Alzheimer's Disease Neuroimaging Initiative database. Neuropsychiatric inventory questionnaire items were transformed to MBI domains using a published algorithm. Diagnosis at latest follow-up was used as the outcome variable and ground truth classification. A logistic model tree classifier combined with information gain feature selection was trained to predict follow-up diagnosis. Results: In the binary classification (NC versus MCI/AD), the optimal ML model required only two features from over 200, MBI total score and left hippocampal volume. These features correctly classified participants as remaining normal or developing cognitive impairment with 84.4% accuracy (area under the receiver operating characteristics curve [ROC-AUC] = 0.86). Seven features were selected for the three-class model (NC versus MCI versus dementia) achieving an accuracy of 58.8% (ROC-AUC=0.73).

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Section: Discussionmentioning

confidence: 99%

Using Machine Learning to Predict Dementia from Neuropsychiatric Symptom and Neuroimaging Data

Gill

Mouchès

et al. 2020

JAD

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“…Thus, it is possible that the depressive symptoms of the subjects may affect the hippocampal volumes and general cognitive functions. However, depression is very common, and 30–40% of patients with AD or MCI have comorbid depression (Chi et al., ; Ismail et al., ). Therefore, our prediction model has a strength in that it can be generalized to a clinical situation.…”

Section: Discussionmentioning

confidence: 99%

Predicting neurocognitive function with hippocampal volumes and DTI metrics in patients with Alzheimer's dementia and mild cognitive impairment

et al. 2017

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IntroductionCognitive performance in patients with Alzheimer's dementia (AD) and mild cognitive impairment (MCI) has been reported to be related to hippocampal atrophy and microstructural changes in white matter (WM). We aimed to predict the neurocognitive functions of patients with MCI or AD using hippocampal volumes and diffusion tensor imaging (DTI) metrics via partial least squares regression (PLSR).MethodsA total of 148 elderly female subjects were included: AD (n = 49), MCI (n = 66), and healthy controls (n = 33). Twenty‐four hippocampal subfield volumes and the average values for fractional anisotropy (FA) and mean diffusivity (MD) of 48 WM tracts were used as predictors, CERAD‐K total scores, scores of CERAD‐K 7 cognitive subdomains and K‐GDS were used as dependent variables in PLSR.ResultsRegarding MCI patients, DTI metrics such as the MD values of the left retrolenticular part of the internal capsule and left fornix (cres)/stria terminalis were significant predictors, while hippocampal subfield volumes, like the left CA1 and hippocampal tail, were main contributors to cognitive function in AD patients, although global FA/MD values were also strong predictors. The 10‐fold cross‐validation and stricter 300‐iteration tests proved that global cognition measured by the CERAD‐K total scores and the scores of several CERAD‐K subdomains can be reliably predicted using the PLSR models.ConclusionsOur findings indicate different structural contributions to cognitive function in MCI and AD patients, implying that diffuse WM microstructural changes may precede hippocampal atrophy during the AD neurodegenerative process.

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“…NPS are common in Mild Cognitive Impairment (MCI), and are associated with poorer cognitive and psychosocial function within MCI cohorts [13, 14]. Population-based [15, 16] as well as clinic-based cohort studies [17] provide consistent evidence that NPS in MCI are associated with higher risk for incident dementia, with an estimated annual rate of progression to dementia of 25% for MCI plus NPS [17] in contrast to the rate for MCI of 10 to 15 % per year [18].…”

Section: Introductionmentioning

confidence: 99%

The Mild Behavioral Impairment Checklist (MBI-C): A Rating Scale for Neuropsychiatric Symptoms in Pre-Dementia Populations

Ismail

Agüera-Ortíz

Brodaty

et al. 2017

JAD

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Background Mild Behavioral Impairment (MBI) is a construct that describes the emergence at ≥ 50 years of age of sustained and impactful neuropsychiatric symptoms (NPS), as a precursor to cognitive decline and dementia. MBI describes NPS of any severity, which are not captured by traditional psychiatric nosology, persist for at least 6 months, and occur in advance of or in concert with Mild Cognitive Impairment (MCI). While the detection and description of MBI has been operationalized in the International Society to Advance Alzheimer’s Research and Treatment – Alzheimer’s Association (ISTAART-AA) research diagnostic criteria, there is no instrument that accurately reflects MBI as described. Objective To develop an instrument based on ISTAART-AA MBI criteria. Methods Eighteen subject matter experts participated in development using a modified Delphi process. An iterative process ensured items reflected the 5 MBI domains of 1) decreased motivation; 2) emotional dysregulation; 3) impulse dyscontrol; 4) social inappropriateness; and 5) abnormal perception or thought content. Instrument language was developed a priori to pertain to non-demented functionally independent older adults. Results We present the Mild Behavioral Impairment Checklist (MBI-C), a 34-item instrument, which can easily be completed by a patient, close informant, or clinician. Conclusion The MBI-C provides the first measure specifically developed to assess the MBI construct as explicitly described in the criteria. Its utility lies in MBI case detection, and monitoring the emergence of MBI symptoms and domains over time. Studies are required to determine the prognostic value of MBI for dementia development, and for predicting different dementia subtypes.

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P2‐087: A systematic review and meta‐analysis for the prevalence of depression in mild cognitive impairment

Cited by 6 publications

References 0 publications

Using Machine Learning to Predict Dementia from Neuropsychiatric Symptom and Neuroimaging Data

Using Machine Learning to Predict Dementia from Neuropsychiatric Symptom and Neuroimaging Data

Predicting neurocognitive function with hippocampal volumes and DTI metrics in patients with Alzheimer's dementia and mild cognitive impairment

The Mild Behavioral Impairment Checklist (MBI-C): A Rating Scale for Neuropsychiatric Symptoms in Pre-Dementia Populations

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