Cognitive dedifferentiation as a function of cognitive impairment in the ADNI and MemClin cohorts.

The cause of cognitive dedifferentiation has been suggested as specific to late-life abnormal cognitive decline rather than a general feature of aging. This hypothesis was tested in two large cohorts with different characteristics. Individuals (n = 2710) were identified in the Alzheimer's Disease Neuroimaging Initiative (ADNI) research database (n = 1282) in North America, and in the naturalistic multi-site MemClin Project database (n = 1223), the latter recruiting from 9 out of 10 memory clinics in the greater Stockholm catchment area in Sweden. Comprehensive neuropsychological testing informed diagnosis of dementia, mild cognitive impairment (MCI), or subjective cognitive impairment (SCI). Diagnosis was further collapsed into cognitive impairment (CI: MCI or dementia) vs no cognitive impairment (NCI). After matching, loadings on the first principal component were higher in the CI vs NCI group in both ADNI (53.1% versus 38.3%) and MemClin (33.3% vs 30.8%). Correlations of all paired combinations of individual tests by diagnostic group were also stronger in the CI group in both ADNI (mean inter-test r = 0.51 vs r = 0.33, p < 0.001) and MemClin (r = 0.31 vs r = 0.27, p = 0.042). Dedifferentiation was explained by cognitive impairment when controlling for age, sex, and education. This finding replicated across two separate, large cohorts of older individuals. Knowledge that the structure of human cognition becomes less diversified and more dependent on general intelligence as a function of cognitive impairment should inform clinical assessment and care for these patients as their neurodegeneration progresses.

Brain Atrophy Subtypes and the ATN Classification Scheme in Alzheimer's Disease.

INTRODUCTION: We investigated the association between atrophy subtypes of Alzheimer's disease (AD), the ATN classification scheme, and key demographic and clinical factors in 2 cohorts with different source characteristics (a highly selective research-oriented cohort, the Alzheimer's Disease Neuroimaging Initiative [ADNI]; and a naturalistic heterogeneous clinically oriented cohort, Karolinska Imaging Dementia Study [KIDS]). METHODS: A total of 382 AD patients were included. Factorial analysis of mixed data was used to investigate associations between AD subtypes based on brain atrophy patterns, ATN profiles based on cerebrospinal fluid biomarkers, and age, sex, Mini Mental State Examination (MMSE), cerebrovascular disease (burden of white matter signal abnormalities, WMSAs), and APOE genotype. RESULTS: Older patients with high WMSA burden, belonging to the typical AD subtype and showing A+T+N+ or A+T+N- profiles clustered together and were mainly from ADNI. Younger patients with low WMSA burden, limbic-predominant or minimal atrophy AD subtypes, and A+T-N- or A+T-N+ profiles clustered together and were mainly from KIDS. APOE ε4 carriers more frequently showed the A+T-N- and A+T+N- profiles. CONCLUSIONS: Our findings align with the recent framework for biological subtypes of AD: the combination of risk factors, protective factors, and brain pathologies determines belonging of AD patients to distinct subtypes.

Using Functional Magnetic Resonance Imaging to Detect Chronic Fatigue in Patients With Previous Traumatic Brain Injury: Changes Linked to Altered Striato-Thalamic-Cortical Functioning.

OBJECTIVE: To investigate whether functional magnetic resonance imaging (fMRI) can be used to detect fatigue after traumatic brain injury (TBI). SETTING: Neurorehabilitation clinic. PARTICIPANTS: Patients with TBI (n = 57) and self-experienced fatigue more than 1 year postinjury, and age- and gender-matched healthy controls (n = 27). MAIN MEASURES: Self-assessment scales of fatigue, a neuropsychological test battery, and fMRI scanning during performance of a fatiguing 27-minute attention task. RESULTS: During testing within the fMRI scanner, patients showed a higher increase in self-reported fatigue than controls from before to after completing the task (P < .001). The patients also showed lower activity in several regions, including bilateral caudate, thalamus, and anterior insula (all P < .05). Furthermore, the patients failed to display decreased activation over time in regions of interest: the bilateral caudate and anterior thalamus (all P < .01). Left caudate activity correctly identified 91% of patients and 81% of controls, resulting in a positive predictive value of 91%. CONCLUSION: The results suggest that chronic fatigue after TBI is associated with altered striato-thalamic-cortical functioning. It would be of interest to study whether fMRI can be used to support the diagnosis of chronic fatigue in future studies.