Eva Alfayate

Changes in resting-state functionally connected parietofrontal networks after videogame practice

Neuroimaging studies provide evidence for organized intrinsic activity under task‐free conditions. This activity serves functionally relevant brain systems supporting cognition. Here, we analyze changes in resting‐state functional connectivity after videogame practice applying a test–retest design. Twenty young females were selected from a group of 100 participants tested on four standardized cognitive ability tests. The practice and control groups were carefully matched on their ability scores. The practice group played during two sessions per week across 4 weeks (16 h total) under strict supervision in the laboratory, showing systematic performance improvements in the game. A group independent component analysis (GICA) applying multisession temporal concatenation on test–retest resting‐state fMRI, jointly with a dual‐regression approach, was computed. Supporting the main hypothesis, the key finding reveals an increased correlated activity during rest in certain predefined resting state networks (albeit using uncorrected statistics) attributable to practice with the cognitively demanding tasks of the videogame. Observed changes were mainly concentrated on parietofrontal networks involved in heterogeneous cognitive functions. Hum Brain Mapp 34:3143–3157, 2013.

Pattern of and Risk Factors for Brain Microbleeds in Neurodegenerative Dementia

Objective: A cross-sectional study was conducted to describe the prevalence, locations, and risk factors for brain microbleeds (BMBs) in neurodegenerative dementia. Methods: The database of the Alzheimer Center Reina Sofía Foundation was searched, BMBs were described, and the potential associations of BMBs were investigated using univariate statistics. Results: A total of 148 patients (age 81.6 [standard deviation 6.7], 79.1% female) were studied. Prevalence of BMBs was 44.6%. A group of patients with unusually high (ie, ≥4) number of BMBs were identified, which displayed higher number of vascular risk factors and vascular diseases. Brain microbleeds were also associated with ischemic lesions in the basal ganglia (r = .39), clinical diagnosis of Alzheimer’s disease (AD) and cerebrovascular disease (r = .33), cortical infarction (r = .20), and antiaggregant or anticoagulant treatment duration (r = .20). Conclusions: Brain microbleeds are associated with vascular burden and AD diagnosis in old patients with neurodegenerative dementia. More research is warranted regarding the mechanisms and potential clinical implications of these results.

Cerebral Microbleeds in Advanced Dementia: Clinical and Pathological Correlates

Objective: We conducted a longitudinal study to explore the clinical and pathological correlates of cerebral microbleeds (CMBs) in institutionalized patients with dementia. Methods: Clinical and magnetic resonance imaging (MRI) data were extracted from 182 nursing home patients (mean age [standard deviation]: 81.3 [6.9], 78.0% female, and 83.4% moderate to severe dementia), which were divided according to the CMBs number and location. One-year follow-up data were obtained from 153 patients, and postmortem pathological diagnosis was available in 40 patients. Results: Cerebral microbleeds were observed in 42.9% of patients and were associated with MRI ischemic lesions (P < .0005). In the adjusted analysis, lobar CMB predicted worsening of parkinsonism (standardized β: 0.43) and gait (standardized β: 0.24). A pathological diagnosis of Alzheimer’s disease was less frequent in the brains of patients with lobar and deep CMB (33.3% vs 85.3%; P < .05). Conclusion: Cerebral microbleeds were linked to cerebrovascular disease and predicted motor deterioration in institutionalized people with advanced dementia.

Predicting Very Early Stage Mild Cognitive Impairment Based on a Voxel-wise Arterial Spin Labeling Analysis

In recent years, medical images have been increasingly used as an objective method for the diagnosis of neurodegenerative diseases. Most previous studies have been based on structural or functional magnetic resonance imaging. However, the results are not yet sufficient to identify early stages of dementia. In this paper, we present an image processing and pattern recognition strategy that allows to predict short-term conversion to Mild Cognitive Impairment (MCI) based on the analysis of Arterial Spin Labeling images. Healthy subjects, categorized as individuals at risk of dementia, were assessed annually in order to identify those that converted to MCI. After 1 − 2 years, 20 subjects were classified as non-converters and 15 as converters according Mini–Mental State Examination test and other neuropsychiatric scales. The proposed approach was able to classify converter from non-converter subjects with an accuracy of 0.88 using the leave-one-out cross-validation method.