[14] used regression methods in their study with the neuropsychological information \begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Clinical Features** & **LLD** & **MCI** \\ \hline \multirow{3}{*}{Diagnostic criteria} & Clinically significant alteration of mood Beginning in the elderly (\(>\)50 years) & Clinically significant CI determined by the presence of alteration of \(-\)1.5 SD below the average in one or more cognitive areas. \\ \cline{2-3}  & Can present subjective memory impairment or MCI & Can or cannot present mood disturbance, apathy, anxiety, or mild behavioral impairment \\ \hline \multirow{3}{*}{Daily life activities} & Can give up doing complex instrumental activities for motivational reasons or apathy. & Can present difficulties or have delegated complex instrumental activities to others but does not meet criteria for dementia. \\ \cline{2-3}  & Psychological factors: motivation, social isolation, stressors, etc./Biological factors: alteration of white matter, brain volume, etc. & Biological factors: vascular small vessel disease (alteration of white matter), early signs of Alzheimer’s disease, etc. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Diagnostic criteria and clinical features in late-life depression (LLD) and mild cognitive impairment (MCI). CI = cognitive impairment; SD = standard deviation. obtained. The study of [PERSON] et al. [15] aimed to examine the characteristics of cognitive dysfunction in LLD and amnestic MCI with a battery of neuropsychological tests measuring the domains of memory, executive function, processing speed, attention and visuospatial skill, and neuroimaging biomarkers using a series of statistical processes including profile analysis and multiple regression analysis. Only [PERSON] et al. [16], in their study, focused on the prediction of MCI or dementia after a one-year follow-up in LLD patients using neuropsychological scores and neuroimaging biomarkers, and employing ML techniques. In that study, MCI and dementia diagnoses were predicted in LLD patients with an accuracy of 81%. The analysis of neuropsychological and neuroimaging data with ML techniques has strongly influenced the neuroscience community. Among the ML techniques, the artificial neural networks (ANNs) are flexible, non-linear, and multidimensional mathematical systems, easily implemented and handled, and capable of solving complex functions in very diverse fields [17]. ANNs are computing paradigms, inspired by neurosciences, where the organization and storage of information are connected by computational units or nodes to allow signals to travel through the network. ANNs have been widely used in science, engineering, and medicine for solving different problems. Their ability to accurately classify and recognize patterns has encouraged researchers to employ them in solving numerous clinical problems, obtaining remarkable results. In medicine, they have been proposed as a viable computational tool in order to provide support in clinical decision-making, classification, and prediction of cognitive functioning [18]. Their clinical applications include diagnosis and outcome prediction in a wide range of diseases, such as cancer [19], diabetes [20], or acute stroke mortality [21]. Regarding other neurological disorders, ANNs have been recently applied for classifying different multiple sclerosis courses [22] and for white matter lesion segmentation [23] by using neuroimaging data. Additionally, they have been used for Parkinson's disease diagnosis [24] or for epileptic seizure detection using EEG signals [25; 26]. ANNs have also been successfully used in neurodegenerative diseases for AD prediction [27] or MCI and AD diagnosis using neuropsychological variables [18] or magnetic resonance neuroimaging biomarkers [28; 29]. As we stated before, the mechanisms underpinning the association between depression and MCI are complex and currently unclear.