Abnormal Dynamic Functional Networks in Subjective Cognitive Decline and Alzheimer's Disease

Disrupted morphological brain network organization in subjective cognitive decline and mild cognitive impairment

We aim to investigate the alterations in gray matter for subjective cognitive decline (SCD) and mild cognitive impairment (MCI) from the perspective of the human connectome. High-resolution T1-weighted images were acquired from 54 patients with SCD, 95 patients with MCI, and 65 healthy controls (HC). Morphological brain networks (MBN) were constructed using similarities in the distribution of gray matter volumes between regions. The strength of morphological connections and topographic metrics derived from the graph-theoretical analysis were compared. Furthermore, we assessed the relationship between the observed morphological abnormalities and disease severity. According to the results, we found a significantly decreased morphological connection between the somatomotor network and ventral attention network in SCD compared to HC and MCI compared to SCD. The graph-theoretic analysis illustrated disruptions in the whole network organization, where the normalized shortest path increased and the global efficiency (Eg) decreased in MCI compared to SCD. In addition, Montreal Cognitive Assessment scores of SCD patients had a significantly negative correlation with Eg. The primary limitations of the present study include the cross-sectional design, no enrolled AD patients, no assessment of amyloidosis, and the need for more comprehensive neuropsychological tests. Our findings indicate the abnormalities of morphological networks at early stages in the AD continuum, which could be interpreted as compensatory changes to retain a normal level of cognitive function. The present study could provide new insight into the mechanism of AD.

Multimodal magnetic resonance imaging on brain structure and function changes in subjective cognitive decline: a mini-review

Subjective cognitive decline (SCD) is the initial stage of Alzheimer's disease (AD). Early identification of SCD and its risk factors is of great importance for targeted interventions and for delaying the onset of AD. We reviewed the relevant literature on structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), functional magnetic resonance imaging (fMRI), and other techniques regarding SCD research in recent years. This study applied sMRI and fMRI techniques to explore abnormal brain structures and functions, which may help provide a basis for SCD diagnosis.

IMOVE: Protocol for a randomized, controlled 2x2 factorial trial of improvisational movement and social engagement interventions in older adults with early Alzheimer's disease

Background

In addition to cognitive impairment, people with Alzheimer's disease (PWAD) experience neuropsychiatric symptoms (e.g., apathy, depression), altered gait, and poor balance that further diminish their quality of life (QoL). Here, we describe a unique, randomized, controlled trial to test the hypothesis that both movement and social engagement aspects of a group dance intervention alter the connectivity of key brain networks involved in motor and social-emotional functioning and lead to improved QoL in PWAD.

Methods

IMOVE (NCT03333837) was a single-center, randomized, controlled 2x2 factorial trial that assigned PWAD/caregiver dyads to one of 4 study conditions (Movement Group, Movement Alone, Social Group, or Usual Care control). The Movement Group participated in twice-weekly group improvisational dance (IMPROVment® Method) classes for 12 weeks. The Movement Alone intervention captured the same dance movement and auditory stimuli as the group class without social interaction, and the Social Group used improvisational party games to recapitulate the fun and playfulness of the Movement Group without the movement. The primary outcome was change in QoL among PWAD. Key secondary outcomes were functional brain network measures assessed using graph-theory analysis of resting-state functional magnetic resonance imaging scans, as well as neuropsychiatric symptoms, gait, and balance.

Results

A total of 111 dyads were randomized; 89 completed the study, despite interruption and modification of the protocol due to COVID-19 restrictions (see companion paper by Fanning et al.). The data are being analyzed and will be submitted for publication in 2023.

Use of machine learning to identify functional connectivity changes in a clinical cohort of patients at risk for dementia

Objective

Progressive conditions characterized by cognitive decline, including mild cognitive impairment (MCI) and subjective cognitive decline (SCD) are clinical conditions representing a major risk factor to develop dementia, however, the diagnosis of these pre-dementia conditions remains a challenge given the heterogeneity in clinical trajectories. Earlier diagnosis requires data-driven approaches for improved and targeted treatment modalities.

Methods

Neuropsychological tests, baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI (rsfMRI), and diffusion weighted scans were obtained from 35 patients with SCD, 19 with MCI, and 36 age-matched healthy controls (HC). A recently developed machine learning technique, Hollow Tree Super (HoTS) was utilized to classify subjects into diagnostic categories based on their FC, and derive network and parcel-based FC features contributing to each model. The same approach was used to identify features associated with performance in a range of neuropsychological tests. We concluded our analysis by looking at changes in PageRank centrality (a measure of node hubness) between the diagnostic groups.

Results

Subjects were classified into diagnostic categories with a high area under the receiver operating characteristic curve (AUC-ROC), ranging from 0.73 to 0.84. The language networks were most notably associated with classification. Several central networks and sensory brain regions were predictors of poor performance in neuropsychological tests, suggesting maladaptive compensation. PageRank analysis highlighted that basal and limbic deep brain region, along with the frontal operculum demonstrated a reduction in centrality in both SCD and MCI patients compared to controls.

Conclusion

Our methods highlight the potential to explore the underlying neural networks contributing to the cognitive changes and neuroplastic responses in prodromal dementia.