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Disorders, Cognitive

Cognitive disorders are a broad category of mental health conditions that impair an individual's ability to think, remember, learn, and problem-solve.
These disorders can range from mild forgetfulness to severe impairments in cognitive functioning, and may be caused by a variety of factors, including brain injury, neurological diseases, genetic conditions, and age-related changes.
Effective treatment and management of cognitive disorders often involve a multidisciplinary approach, including cognitive therapy, medication, and assistive technologies.
Reseachers in this field are continously working to identify the most effective protocols and products to advance the understanding and treatment of these complex conditions.

Most cited protocols related to «Disorders, Cognitive»

Defining Mild Cognitive Impairment Criteria

Cited 448 times

Different criteria for defining MCI in the general population have been proposed, and several have undergone revision.^{6 (link)} MCI criteria by Petersen et al.^{7 (link),8 (link)} require (1) a subjective complaint of cognitive decline by the patient, preferably corroborated by a reliable source, (2) minimal effect of the decline on day-to-day functioning and the absence of dementia, and (3) evidence of cognitive abnormalities that cannot be simply attributed to age. Such evidence can be based solely on clinician judgment, although formal neuropsychological testing is deemed helpful. Specific neuropsychological tests and cut-off scores are not stated. Quantitative measurements of function and activities of daily living are not required. Categorization into single-domain, multiple-domain, amnestic, and nonamnestic subtypes is based on the results of neuropsychological testing. Proposed MCI criteria recently developed by the National Institute on Aging and the Alzheimer’s Association MCI criteria committee^{6 (link)} and the DSM-5 Neurocognitive Disorders Work Group^{9 ,10 (link)} would also allow cognitive decline to be detected by health care providers as an alternative to patient or informant report.

Litvan I., Goldman J.G., Tröster A.I., Schmand B.A., Weintraub D., Petersen R.C., Mollenhauer B., Adler C.H., Marder K., Williams-Gray C.H., Aarsland D., Kulisevsky J., Rodriguez-Oroz M.C., Burn D.J., Barker R.A, & Emre M. (2012). Diagnostic Criteria for Mild Cognitive Impairment in Parkinson’s Disease: Movement Disorder Society Task Force Guidelines. Movement disorders : official journal of the Movement Disorder Society, 27(3), 349-356.

Publication 2012

Cognition Congenital Abnormality Dementia Disorders, Cognitive Neurocognitive Disorders Patients

Constructing a Frailty Index: Guidelines

Cited 322 times

A frailty index counts deficits in health. These deficits were defined as symptoms, signs, disabilities and diseases [5 ]. All health deficits, including continuous, ordinal and binary variables, were taken from the PEP survey data dictionary. Restricted activity, disability in Activities Daily Living (ADL) and Instrumental ADL, impairments in general cognition and physical performance (e.g. impaired grip strength, impaired walking), co-morbidity, self-rated health, and depression/mood were evaluated.
Variables can be included in a frailty index if they satisfy the following 5 criteria:
1) The variables must be deficits associated with health status. Attributes such as graying hair, while age-related, are attributes and therefore not included. 2) A deficit's prevalence must generally increase with age, although some clearly age-related adverse conditions can decrease in prevalence at very advanced ages due to survivor effects. 3) Similarly, the chosen deficits must not saturate too early. For instance, age-related lens changes resulting in problems with accommodation (presbyopia) are nearly universal by age 55; in other words, as a variable, presbyopia saturates too early to be considered as a deficit here. 4) When considering the candidate deficits as a group, the deficits that make up a frailty index must cover a range of systems – if all variables were related to cognition, for example, the resulting index might well describe changes in cognition over time, but would be a cognitive impairment index [18 (link)] not a frailty index. 5) If a single frailty index is to be used serially on the same people, the items that make up the frailty index need to be the same from one iteration to the next [19 (link)]. The requirement to use the same items need not apply to comparisons between samples – i.e. samples that use difference frailty indexes appear to yield similar results [5 ].
Deficits should be added until there are at least 30–40 total deficits. There needs to be a minimum number of deficits. In general, the more variables that are included in a frailty index, the more precise estimates become. Similarly, estimates are unstable when the number of deficits is small – about 10 or less. Even so, an index with 30–40 variables has been shown to be sufficiently accurate for predicting adverse outcomes [6 (link),14 (link)]. Furthermore, a frailty index can be constructed using information that is readily available in most health surveys, and is clinically tractable – i.e. it uses an amount that would be gathered in many routine health assessments of older adults [5 ].

Searle S.D., Mitnitski A., Gahbauer E.A., Gill T.M, & Rockwood K. (2008). A standard procedure for creating a frailty index. BMC Geriatrics, 8, 24.

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Publication 2008

Aged Cognition Disabled Persons Disorders, Cognitive Hair Lens, Crystalline Mood Ocular Accommodation Performance, Physical Presbyopia Survivors

Standardized Neuropsychological Battery for Alzheimer's Research

Cited 289 times

Cognitive domain and test selection were based on a combination of methods evolving from regular meetings of the CTF. A subcommittee was formed to specifically undertake the design of the neuropsychological test battery, to bring essential issues to the larger group and to interface with the ADCs. Three overriding criteria governed decisions for selecting domains and tests. The first was the mandate for the UDS to initially focus on cognitive markers of aging and of dementia associated with AD, the second was to minimize burden on the ADCs and their subjects, and the third was to accommodate the continuity of measures that ADCs have previously collected. A fourth principle that emerged after an initial set of domains and tests was identified was the need to overlap with other ADC initiatives such as the Late Onset Alzheimer’s Disease (LOAD) Genetics study and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Because of the need to focus on the cognitive continuum from aging without dementia, to MCI, to AD, cognitive domains were selected for their sensitivity to age-related change in cognition [17 (link)–29 (link)] sensitivity to the demonstrated primary cognitive impairments in AD [30 –36 (link)], ability to measure change over time and to stage AD [37 (link)], and ability to predict progression from MCI to AD [38 (link)–41 ]. Additional criteria for test selection included applicability of the measures to different educational levels, to diverse racial/ethnic minority groups and to Spanish-speaking populations. A Spanish translation of the UDS has been completed and is available on the NACC website (https://www.alz.washington.edu).
The minimization of burden, an issue of feasibility, had to figure centrally in test selection. Many ADCs have been conducting research for over 20 years. Well-established protocols and longitudinal research projects could be disrupted by the need to significantly alter assessment and enrollment methods, notwithstanding the added time burden for subjects and their study partners. Thus, with input from the ADCs, the CTF concluded that the neuropsychological battery should not add more than 30 minutes to existing protocols at each Center. One implication of this principle was that tests already in use by all or most ADCs would be high on the list of candidates for inclusion.
The CTF conducted several surveys of the ADCs to gather data about their ongoing assessment practices including, among other variables: 1) cognitive domains tested; 2) specific instruments and versions, for tests with multiple forms; 3) populations of subjects followed (i.e., disease and control groups; clinic and/or community samples); 4) frequency of subject visits. Once these data were acquired, the most commonly tested domains and the most commonly used specific measures were identified and comments and approval were solicited from the ADCs.

Weintraub S., Salmon D., Mercaldo N., Ferris S., Graff-Radford N.R., Chui H., Cummings J., DeCarli C., Foster N.L., Galasko D., Peskind E., Dietrich W., Beekly D.L., Kukull W.A, & Morris J.C. (2009). The Alzheimer’s Disease Centers’ Uniform Data Set (UDS): The Neuropsychological Test Battery. Alzheimer disease and associated disorders, 23(2), 91-101.

Publication 2009

Cognition Disease Progression Disorders, Cognitive Ethnic Minorities Hispanic or Latino Hypersensitivity Neuropsychological Tests Population Group Presenile Dementia Racial Groups SET Domain

Aging, Memory, and Neurological Evaluation

Cited 202 times

The Memory and Aging Project is funded by the National Institute on Aging and was approved by the Institutional Review Board of Rush University Medical Center. Older persons without known dementia must agree to an assessment of risk factors, blood donation, and a detailed clinical evaluation each year. Further, all participants also agree to donation of brain, the entire spinal cord, and selected nerve and muscles at the time of death.
Study participants are primarily recruited from retirement communities throughout northeastern Illinois Fig. (1). The study primarily enrolls residents of continuous care retirement communities. Several features of these facilities and the study design enhance the validity and generalizability of the study. Because the only exclusion is the inability to sign the Anatomical Gift Act, and because all clinical evaluations are performed as home visits, co-morbidities common in population-based epidemiologic studies are well represented; this reduces the “healthy volunteer effect” seen in many cohort studies [30 (link)]. The home visits reduce participant burden facilitating high rates of follow-up. Follow-up rates are further enhanced because these facilities provide all levels of care from independent living to unskilled and skilled nursing on campus. This also enhances autopsy rates as many participants die on campus and the Anatomical Gift Act allows us to work directly with facility staff and the funeral home to arrange the autopsy. Residents of continuous care retirement communities are predominantly white and tend to be more affluent. Therefore, the study also recruits from Section 8 and Section 202 housing subsidized by the Department of Housing and Urban Development, retirement homes, and through local churches and other social service agencies serving minorities and low-income elderly.
The study design allows the following types of analyses to be conducted within a single dataset Fig. (2): 1) the relation of risk factors with incident AD, incident MCI, and decline in cognitive and motor function; 2) the relation of neurobiologic indices with AD, MCI, and cognitive and motor function; and 3) modeling neurobiologic pathways linking risk factors to clinical phenotypes.

Bennett D.A., Schneider J.A., Buchman A.S., Barnes L.L., Boyle P.A, & Wilson R.S. (2012). Overview and Findings from the Rush Memory and Aging Project. Current Alzheimer research, 9(6), 646-663.

Publication 2012

Aged Autopsy Blood Donation Brain Cognition Continuity of Patient Care Dementia Disorders, Cognitive Ethics Committees, Research Healthy Volunteers Memory Minority Groups Nervousness Phenotype Spinal Cord Temporal Muscle Urban Development Vision Visit, Home

Cam-CAN Stage 2 Recruitment Protocol

Cited 198 times

The size of the study sample population required to reach 100 qualified participants per decile for Cam-CAN Stage 2 is expected to vary by age when accounting for exclusion and refusal, estimated population data, clinical based experience and estimates of individuals who may refuse to participate in neuroimaging. Numbers are adjusted for the proportion of the general population with exclusion criteria including MR safety contraindications (e.g. pacemakers), learning disability (living at home), cognitive impairment (Mini-Mental State Examination (MMSE) [8 (link)] score of 24 or less) and reduced response from individuals with limited longstanding illness or disability. Proportions are estimated based on data from the Office of National Statistics (ONS), the Medical Research Council Cognitive Function and Ageing Study (MRC-CFAS) [9 ] and the National Health Service (NHS) registrations. We assume that only 30% of the population will undertake the initial interview and of those who do, 40-50% will agree to take part in Stage 2 (age dependent). Numbers predicted to be needed for Stage 1 are shown in Table 2. The age group above age 88 are recruited to the same population proportion as the 78-87 decile, in order to enable cohort comparison with other population-based studies and investigation of the rare group of oldest old who are experiencing healthy ageing.Table 2

Estimated Stage 1 recruitment across the deciles to recruit 100 participants in each decile (age 18-87) for Stage 2

	Decile 1 (18-27 years)	Decile 2 (28-37 years)	Decile 3 (38-47 years)	Decile 4 (48-57 years)	Decile 5 (58-67 years)	Decile 6 (68-77 years)	Decile 7 (78-87 years)	Decile 8 (88+ years)
Contact	750	775	850	950	1250	1400	2850	1700
Interview	250	250	275	300	400	450	850	500

Estimates include numbers per decile to be contacted and interviewed.

The Cam-CAN structure provides sufficient sample size in each decile to separate age-related change from other sources of individual variation. A number of different comparisons can hypothetically be undertaken using this structure. All hypotheses are investigated at a power of 80% and α = 0.05: for linear regression, assuming the continuous data are standardised to a N(0,1) distribution, 100 per decile enables us to investigate i) a linear decline of ±0.04 across the age range; ii) a difference in linear regression slope of size ±0.06 between two risk factor groups with a prevalence of 50% (such as gender); iii) differences in the mean values of two groups (defined with 50% prevalence) of ±0.2; iv) for dichotomous outcomes with prevalence of 0.5 in one group to detect a difference of at least ±0.1. This sample is sufficiently large to be able to detect non-linear change with age, such as a change in rate of decline, and the required size to detect stability with age (to exclude a slope of up to ±0.03 per decile). Multiple hypotheses can also be undertaken, such that linear decline of slope 0.1 can be detected for 100 independent investigations protecting the type I error rate (false positives).

Shafto M.A., Tyler L.K., Dixon M., Taylor J.R., Rowe J.B., Cusack R., Calder A.J., Marslen-Wilson W.D., Duncan J., Dalgleish T., Henson R.N., Brayne C, & Matthews F.E. (2014). The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing. BMC Neurology, 14, 204.

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Publication 2014

Age Groups Cognition Disabled Persons Disorders, Cognitive Gender Health Services, National Learning Disabilities Mini Mental State Examination Pacemaker, Artificial Cardiac Safety

Most recents protocols related to «Disorders, Cognitive»

Cognitive Frailty and Functional Disability

Observational studies (cross-sectional or longitudinal cohort studies) were included if they reported on community-dwelling older adults aged 60 years and above. This age cutoff point was selected because studies on frailty typically included participants aged 60 years and above (27 (link)). Cross-sectional, prospective cohort studies were included due to the small number of longitudinal studies that reported on the association between cognitive frailty and disability. Cognitive frailty was defined by the presence of frailty or prefrailty, and concurrent cognitive impairment was identified using validated physical frailty and cognitive assessments. A preliminary literature search has identified that most studies on cognitive frailty have slightly modified the definition of cognitive frailty by the consensus group, defining this condition with the presence of mild cognitive impairment instead of a CDR of 0.5 with the exclusion of concurrent Alzheimer’s disease or other dementias, and physical frailty using the modified Fried frailty phenotype (28 (link)). Thus, the utilization of CDR was not compulsory for study inclusion in this review if a validated cognitive assessment tool was reported. Studies must report the association between cognitive frailty and functional disability (ADL or IADL, mobility, physical function).
Studies were excluded if they included hospitalized or institutionalized older adults or those with neurological disorders or dementia. Conference abstracts, reviews, randomized controlled trials, protocols, and studies published in other languages besides English were excluded. Study titles and abstracts were screened based on the inclusion/exclusion criteria, and full texts of relevant studies were screened for eligibility. Data were extracted using a piloted data extraction form, including study and participant characteristics, frailty assessment and classification, and corresponding disability outcomes and measurement. Data extraction was conducted by 1 reviewer (K.F.T.) and checked by the second reviewer (S.W.H.L.), with discrepancies resolved by consensus.

Tang K.F., Teh P.L, & Lee S.W. (2023). Cognitive Frailty and Functional Disability Among Community-Dwelling Older Adults: A Systematic Review. Innovation in Aging, 7(2), igad005.

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Publication 2023

Aged Alzheimer's Disease Cognition Cognition Disorders Compulsive Behavior Conferences Dementia Disabled Persons Disorders, Cognitive Eligibility Determination Nervous System Disorder Phenotype Physical Examination Presenile Dementia Range of Motion, Articular

Cognitive Frailty and Disability Outcomes

The primary outcomes were ADL, IADL, and mobility disabilities. Other outcomes include the combination of disabilities and physical function limitations. Data were synthesized narratively and summarized using a Harvest plot (29 (link),30 (link)) due to the small number of studies, heterogeneous outcome measurement, and cognitive frailty definition. Findings were reported based on fully adjusted statistical measures with a 95% confidence interval (CI). In the reporting of results, participants were categorized into 4 groups defined previously: (a) robust (absence of frailty and cognitive impairment), (b) prefrailty with cognitive impairment, (c) cognitive frailty (physical frailty and cognitive impairment), and (d) combined cognitive frailty (only for studies which grouped both frail and prefrail participants with cognitive impairment). Comparison between groups was made by using the robust group as the reference.

Tang K.F., Teh P.L, & Lee S.W. (2023). Cognitive Frailty and Functional Disability Among Community-Dwelling Older Adults: A Systematic Review. Innovation in Aging, 7(2), igad005.

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Publication 2023

Cognition Disabled Persons Disorders, Cognitive Genetic Heterogeneity Physical Examination Range of Motion, Articular

Diabetic Foot Ulcer Prevalence Study

All patients aged 18 years and above with DM type 1 and type 2, having a wound located below the ankle and attending the surgical department and/or DM clinics of selected hospitals in Uganda between November 1, 2021 and January 31, 2022 were recruited. Purposive.
consecutive sampling method was used until the desired sample size was reached. Patients provided written informed consent to participate in the study. Patients without mental capacity and those without an adult to consent for them were excluded from the study.
DFU patients with communication difficulty, such as those with severe cognitive impairment or those who could not consent, were excluded from the study.
The required sample size for the study patients with DFU was calculated using the Kish Leslie formula as cited by Singh [25 ]. Data about the prevalence of DFU in Uganda is still scanty, therefore we used the prevalence estimate of DFU in a cross-sectional study done in Egypt (8.7% among adult patients aged 18 years and above attending Alexandria University Teaching Hospital Diabetic clinic) to determine the sample size [26 (link)]. Using the prevalence estimate from Egypt, which is similar to the one in Kenya [27 ], resulted in a calculated sample size of 122 patients.

Vahwere B.M., Ssebuufu R., Namatovu A., Kyamanywa P., Ntulume I., Mugwano I., Pius T., Sikakulya F.K., Xaviour O.F., Mulumba Y., Jorge S., Agaba G, & Nasinyama G.W. (2023). Factors associated with severity and anatomical distribution of diabetic foot ulcer in Uganda: a multicenter cross-sectional study. BMC Public Health, 23, 463.

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Publication 2023

Adult Ankle Disorders, Cognitive Mentally Ill Persons Operative Surgical Procedures Patients Wounds

Recruiting Adult Outpatients for Depression

Patients between 18 and 65 years of age referred to a treatment package for single-episode depression will be recruited (Table 1) with minimal exclusion criteria to recruit representative adult outpatients who would typically receive treatment in routine practice (Table 1), of which the majority are women (71%) and aged 18–35 (68%) (S. Figure 1). Patients over 65 (approximately 0.7% of the target population) are excluded because of potential age-related cognitive decline, concomitant medical conditions, or medications that could interact with assessments or treatment (S. Figure 1). Allocation into the subcohorts is based on eligibility, e.g., MRI compatibility, scheduling, and patient willingness to participate.Table 1

Inclusion and exclusion criteria for patients

Patient inclusion criteria:
• Fulfilment of ICD-10 diagnostic criteria for a primary depressive episode (i.e., not secondary to known organic or other psychiatric disorder) • Referral to a treatment package for single-episode depression • Age between 18 and 65 years
Exclusion criteria:
• Psychosis or psychotic symptoms • History of severe head trauma involving hospitalization or unconsciousness for more than 5 min • Known, substantial structural brain abnormalities • Insufficient Danish language skills to complete questionnaires and cognitive testing
Additional exclusion criteria for subcohort I:
• Severe somatic disease • Contraindications for MRI (e.g., metal implants, claustrophobia, or back problems)
Additional exclusion criteria for subcohort I:
• Use of psychotropic drugs • Exposure to radioactivity > 10 mSv within the last year • Pregnancy or breastfeeding

The primary depressive episode, consistent with the International Statistical Classification of Diseases and Related Health Problems version 10 (ICD-10) criteria for MDD without psychotic features (F32.1, F32.2, F32.8 and F32.9), is confirmed by a specialist in psychiatry at the central diagnostic and referral centre.

Jensen K.H., Dam V.H., Ganz M., Fisher P.M., Ip C.T., Sankar A., Marstrand-Joergensen M.R., Ozenne B., Osler M., Penninx B.W., Pinborg L.H., Frokjaer V.G., Knudsen G.M, & Jørgensen M.B. (2023). Deep phenotyping towards precision psychiatry of first-episode depression — the Brain Drugs-Depression cohort. BMC Psychiatry, 23, 151.

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Publication 2023

Adult Brain Claustrophobia Cognition Congenital Abnormality Craniocerebral Trauma Diagnosis Diploid Cell Disorders, Cognitive Eligibility Determination Hospitalization Mental Disorders Metals Outpatients Patients Pharmaceutical Preparations Pregnancy Psychotic Disorders Psychotropic Drugs Radioactivity Satisfaction Target Population Woman

Comprehensive Cognitive Assessment in Psychiatric Disorders

All patients are assessed with a ~ 1-h neuropsychological test battery, including ‘cold’ (emotion-independent) cognitive tasks indexing reaction time; psychomotor speed; verbal learning and memory; working memory; and executive functions, as well as ‘hot’ (emotion-dependent) cognitive tasks from the Danish version of the EMOTICOM test-battery indexing emotion recognition; emotion detection; and moral emotions in social situations [60 (link)].
Patients in subcohorts I-II will complete an additional ~ 1 h of testing with tasks assessing mental flexibility, verbal fluency, and visuospatial learning and memory (see Additional questionnaires for the subcohort I-II only are in bold.
Table 3 for a complete overview of all cognitive tasks). In addition, patients’ subjective experiences of cognitive disturbances will be assessed by the Cognitive Complaints in Bipolar Disorder Rating Assessment (COBRA) questionnaire [36 (link)].Table 3

Cognitive testing before treatment

Cognitive Test	Cognitive Domaine
Whole Cohort
Simple Reaction Time task (SRT)	Reaction time
Trail Making Test A & B	Psychomotor speed/executive function
Symbol Digit Modality Task (SDMT)	Psychomotor speed/working memory
Letter-Number Sequence (LNS)	Working memory
D-KEFS Color-Word Interference Test (Stroop)	Executive function
Rey Auditory Verbal Learning Test (RAVLT)	Learning/memory
EMOTICOM Emotional Recognition Task (ERT)	Emotion recognition accuracy
EMOTICOM Emotional Intensity Morphing Task (IMT)	Emotion perceptual detection threshold
EMOITCOM Moral Emotions Task (MET)	Social cognition: guilt and shame
Additional testing in the subcohorts
D-KEFS Verbal Fluency	Executive function
Rey Complex Figure Test (RCFT)	Visuo-spatial learning/memory
Probabilistic Reversal Learning task	Learning within a feedback context
Screen for Cognitive Impairments in Psychiatry—Depression (SCIP-D)	Memory, working memory, vocabulary, psychomotor speed

Patients in subcohorts I-II will complete an additional ~ 1 h of testing with tasks assessing mental flexibility, verbal fluency, and visuospatial learning and memory (see Additional questionnaires for the subcohort I-II only are in bold.

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Publication 2023

Cognition Cognitive Testing Common Cold Disorders, Cognitive Emotions Executive Function Fingers Guilt Memory Memory, Short-Term Morphine Neuropsychological Tests Patients Recognition, Psychology

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What are the different types of cognitive disorders?

Cognitive disorders encompass a wide range of conditions that can affect various cognitive functions, including memory, attention, language, problem-solving, and decision-making. Some common types of cognitive disorders include Alzheimer's disease, dementia, mild cognitive impairment, traumatic brain injury, Parkinson's disease, Huntington's disease, and age-related cognitive decline. Each type of disorder may have unique symptoms, causes, and treatment approaches.

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What are some common challenges in managing cognitive disorders?

Managing cognitive disorders can be complex due to the wide range of symptoms, underlying causes, and individual variations. Some common challenges include accurately diagnosing the specific disorder, finding the right combination of therapies (e.g., cognitive therapy, medication, assistive technologies), addressing comorbid conditions, and ensuring long-term adherence to treatment plans. Researchers and clinicians are continuously working to develop more effective protocols and products to improve the understanding and treatment of these conditions.

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More about "Disorders, Cognitive"

Cognitive impairments, mental decline, neurocognitive disorders, and intellectual disabilities are all terms that fall under the broad category of cognitive disorders.
These conditions can stem from a variety of causes, including brain injuries, neurological diseases, genetic factors, and age-related changes.
Individuals with cognitive disorders may experience difficulties with memory, learning, problem-solving, and other cognitive functions.
The severity of these impairments can range from mild forgetfulness to severe deficits in cognitive abilities.
Effective management of cognitive disorders often involves a multidisciplinary approach, incorporating cognitive therapy, medication, and assistive technologies.
Researchers in this field are continuously working to identify the most effective protocols and products to advance the understanding and treatment of these complex conditions.
Tools like SAS 9.4, Stata version 14, SPSS version 22.0, and SAS software can be utilized to analyze data and inform research efforts.
By understanding the diverse range of cognitive disorders and the latest advancements in their treatment, healthcare professionals and researchers can work to improve the quality of life for those affected by these conditions.
Whether it's mild age-related memory loss or more severe neurocognitive impairments, the insights gained from this field can have a significant impact on improving cognitive health and wellbeing.