Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson's disease and schizophrenia

Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided

Influence of dexamethasone and weight loss on the regulation of serum leptin levels in obese individuals

The adipocyte hormone leptin is thought to serve as a signal to the central nervous system reflecting the status of fat stores. Serum leptin levels and adipocyte leptin messenger RNA levels are clearly increased in obesity. Nevertheless, the factors regulating leptin production are not fully understood. The aim of this study was to determine the effects of in vivo administration of the synthetic glucocorticoid dexamethasone and weight loss on serum leptin levels in two independent protocols. Twenty-five obese subjects were studied (18 women and 7 men, mean age 26.6 ± 6 years, BMI 31.1 ± 2.5 kg/m², %fat 40.3 ± 8.3) and compared at baseline to 22 healthy individuals. Serum levels of leptin, insulin, proinsulin and glucose were assessed at baseline and after ingestion of dexamethasone, 4 mg per day (2 mg, twice daily) for two consecutive days. To study the effects of weight loss on serum leptin, 17 of the obese subjects were submitted to a low-calorie dietary intervention trial for 8 weeks and again blood samples were collected. Serum leptin levels were significantly higher in the obese group compared to the control group and a high positive correlation between leptinemia and the magnitude of fat mass was found (r = 0.88, P<0.0001). After dexamethasone, there was a significant increase in serum leptin levels (22.9 ± 12.3 vs 51.4 ± 23.3 ng/ml, P<0.05). Weight loss (86.1 ± 15.1 vs 80.6 ± 14.2 kg, P<0.05) led to a reduction in leptin levels (25.13 ± 12.8 vs 15.9 ± 9.1 ng/ml, P<0.05). We conclude that serum leptin levels are primordially dependent on fat mass magnitude. Glucocorticoids at supraphysiologic levels are potent secretagogues of leptin in obese subjects and a mild fat mass reduction leads to a disproportionate decrease in serum leptin levels. This suggests that, in addition to the changes in fat mass, complex nutritional and hormonal interactions may also play an important role in the regulation of leptin levels