Alzheimer’s disease (AD) is a brain-related disease in which the condition of the patient gets worse with time. AD is not a curable disease by any medication. It is impossible to halt the death of brain cells, but with the help of medication, the effects of AD can be delayed. As not all MCI patients will suffer from AD, it is required to accurately diagnose whether a mild cognitive impaired (MCI) patient will convert to AD (namely MCI converter MCI-C) or not (namely MCI non-converter MCI-NC), during early diagnosis. There are two modalities, positron emission tomography (PET) and magnetic resonance image (MRI), used by a physician for the diagnosis of Alzheimer’s disease. Machine learning and deep learning perform exceptionally well in the field of computer vision where there is a requirement to extract information from high-dimensional data. Researchers use deep learning models in the field of medicine for diagnosis, prognosis, and even to predict the future health of the patient under medication. This study is a systematic review of publications using machine learning and deep learning methods for early classification of normal cognitive (NC) and Alzheimer’s disease (AD).This study is an effort to provide the details of the two most commonly used modalities PET and MRI for the identification of AD, and to evaluate the performance of both modalities while working with different classifiers.

Transcranial direct current stimulation (tDCS) is currently under investigation as a promising technique for enhancement of athletic performance through modulating cortical excitability. Through consecutive randomization, 12 experienced bodybuilders were randomly assigned to two arms receiving either sham or real tDCS over the primary motor cortex (leg area) and left temporal cortex (T3) for 13 minutes in the first session. After 72 hours, both groups received the inverse stimulation. After the brain stimulation, cerebral hemodynamic response (using frontopolar hemoencephalography) was examined upon taking three computer-based cognitive tasks i.e. reasoning, memory and verbal ability using the Cambridge Brain Science-Cognitive Platform. Subsequently, the bodybuilders performed knee extension exercise while performance indicators including one-repetition maximum (1RM), muscular endurance (SEI), heart rate (ECG), motivation (VAS), surface electromyography over quadriceps femoris muscle (sEMG) and perceived exertion (RPE) were evaluated. The real tDCS vs. sham group showed decreased RPE and HR mean scores by 14.2% and 4.9%, respectively. Regarding muscular strength, endurance, and electrical activity, the 1RM, SEI, and sEMG factors improved by 4.4%, 16.9%, and % 5.8, respectively. Meanwhile, compared to sham, real tDCS did not affect the athletes' motivation. Incidentally, it turned out that subjects who underwent T3 anodal stimulation outperformed in memory (p = 0.02) and verbal functions (0.02) as well as their corresponding frontopolar hemodynamic response [(memory HEG (p = 0.001) and verbal HEG (p = 0.003)]. Our findings suggest that simultaneous tDCS-induced excitation over the M1 leg area and left temporal area may potentially improve the overall athletic performance in experienced bodybuilders (Trial registration: IRCT20181104041543N1, Registered on 4 Nov. 2018, retrospectively registered).