Serial MRI studies over 12 months using manual and atlas-based region of interest in patients with amyotrophic lateral sclerosis

Graph network measures reveal distinct white matter abnormalities in motor and extra-motor brain regions of two UMN-predominant ALS subtypes

BACKGROUND

Routine clinical magnetic resonance imaging (MRI) shows bilateral corticospinal tract (CST) hyperintensity in some patients with upper motor neuron (UMN)-predominant ALS (ALS-CST+) but not in others (ALS-CST-). Although, similar in their UMN features, the ALS-CST+ patient group is significantly younger in age, has faster disease progression and shorter survival than the ALS-CST- patient group. Reasons for the differences are unclear.

METHOD

In order to evaluate more objective MRI measures of these ALS subgroups, we used diffusion tensor images (DTI) obtained using single shot echo planar imaging sequence from 1.5 T Siemens MRI Scanner. We performed an exploratory whole brain white matter (WM) network analysis using graph theory approach on 45 ALS patients (ALS-CST+) (n = 21), and (ALS-CST-) (n = 24) and neurological controls (n = 14).

RESULTS

Significant (p < 0.05) differences in nodal degree measure between ALS patients and controls were observed in motor and extra motor regions, supplementary motor area, subcortical WM regions, cerebellum and vermis. Importantly, WM network abnormalities were significantly (p < 0.05) different between ALS-CST+ and ALS-CST- subgroups. Compared to neurologic controls, both ALS subgroups showed hubs in the right superior occipital gyrus and cuneus as well as significantly (p < 0.05) reduced small worldness supportive of WM network damage.

CONCLUSIONS

Significant differences between ALS-CST+ and ALS-CST- subgroups of WM network abnormalities, age of onset, symptom duration prior to MRI, and progression rate suggest these patients represent distinct clinical phenotypes and possibly pathophysiologic mechanisms of ALS.

Personalized Healthcare for Dementia

Dementia is one of the most common health problems affecting older adults, and the population with dementia is growing. Dementia refers to a comprehensive syndrome rather than a specific disease and is characterized by the loss of cognitive abilities. Many factors are related to dementia, such as aging, genetic profile, systemic vascular disease, unhealthy diet, and physical inactivity. As the causes and types of dementia are diverse, personalized healthcare is required. In this review, we first summarize various diagnostic approaches associated with dementia. Particularly, clinical diagnosis methods, biomarkers, neuroimaging, and digital biomarkers based on advances in data science and wearable devices are comprehensively reviewed. We then discuss three effective approaches to treating dementia, including engineering design, exercise, and diet. In the engineering design section, recent advances in monitoring and drug delivery systems for dementia are introduced. Additionally, we describe the effects of exercise on the treatment of dementia, especially focusing on the effects of aerobic and resistance training on cognitive function, and the effects of diets such as the Mediterranean diet and ketogenic diet on dementia.