This study’s aim was to investigate CT (computed tomography) pattern dynamics differences within surviving and deceased adult patients with COVID-19, revealing new prognostic factors and reproducing already known data with our patients’ cohort: 635 hospitalized patients , of which 87.3% had a positive result of RT-PCR (reverse transcription-polymerase chain reaction) at admission. The number of deaths was 53 people (69.8% of them were men and 30.2% were women). In total, more than 1500 CT examinations were performed on patients, using a GE Optima CT 660 computed tomography . The study was performed at hospital admission, the frequency of repetitive scans further varied based on clinical need. The interpretation of the imaging data was carried out by 11 radiologists with filling in individual registration cards that take into account the scale of the lesion, the location, contours, and shape of the foci, the dominating types of changes, as well as the presence of additional findings and the dynamics of the process—a total of 45 parameters. Statistical analysis was performed using the software packages SPSS Statistics version 23.0  and R software version 3.3.2. For comparisons in pattern dynamics across hospitalization we used repeated measures general linear model with outcome and disease phase as factors. The crazy paving pattern, which is more common and has a greater contribution to the overall CT picture in different phases of the disease in deceased patients, has isolated prognostic significance and is probably a reflection of faster dynamics of the process with a long phase of progression of pulmonary parenchyma damage with an identical trend of changes in the scale of the lesion (as recovered) in this group of patients. Already known data on typical pulmonological CT manifestations of infection, frequency of occurrence, and the prognostic significance of the scale of the lesion were reproduced, new differences in the dynamics of the process between recovered and deceased adult patients were also found that may have prognostic significance and can be reflected in clinical practice. Coronavirus disease 2019 is a new epidemic infectious disease characterized by relatively high contagiousness for humans, the probability of developing life-threatening complications in the form of acute respiratory distress syndrome, acute respiratory and multiple organ failure, the causative agent of which is the enveloped zoonotic RNA virus SARS-CoV-2. The virus belongs to the family Coronaviridae, the genus Betacoronavirus , transmitted by airborne droplets, airborne dust, and contact ways and affects mainly ACE2R-positive cells (angiotensin-converting enzyme 2 receptor) due to its S-protein and influencing on it membrane-associated protease serine (TMPRSS2) ,7. The wThe diagnosis for COVID-19 is mainly confirmed by reverse transcription-polymerase chain reaction (PCR). Despite the presence of some disadvantages of this method, the majority of authors do not recommend using CT as a substitute for it . NeverthIn connection with the threatening epidemiological situation, many difficulties arose associated with access to clinics and hospitals, and obtaining standard and specialized diagnostic and medical care, which led to the need of reshaping patient routing, algorithms of performing manipulations . Many feThis study aimed to investigate computed tomography patterns dynamics differences within surviving and deceased adult patients with COVID-19 revealing new prognostic factors with our patients’ cohort.635 hospitalized patients , of which 87.3% had a positive result of RT-PCR at admission. In total, more than 1500 CT examinations were performed on patients.The average age of the examined patients was 59 years . The number of deaths was 53 people : 69.8% of them were men (37 people) and 30.2% were women (16 people). In 28 cases out of 53 (52.8%), the deceased patients developed ARD syndrome.CT examinations of the chest organs were performed on the maximum inspiration in the supine position with raised arms, using a GE Optima CT 660 computed tomography . The voltage value on the tube in most cases was 120 kV, with automatic current modulation of 150–350 mA (depending on the constitution and weight of the patient), with collimation of the slice thickness of 0.625 mm and a hard convolution kernel.The study was performed at hospital admission, the frequency of repetitive scans further varied based on clinical need  and by the requirements of the local regulatory guidance documents on the management of patients with COVID-19. As a summary, there were from 1 to 7 scans for every patient. The time intervals between repeated examinations were not the same in different patients. On average, a hospitalized patient spent from 18 to 27 days in the hospital (median—15 days). The results of CT studies are described depending on the time since the development of clinical symptoms.The interpretation of the imaging data was carried out by 11 radiologists with filling in individual registration cards that take into account the scale of the lesion  , the locImpact of different lung damage patterns was assessed with such method: scores of all patterns were summed and then the percentage for each pattern in the summed score of all patterns was calculated.p < 0.05. For the description of scale variables, the arithmetic means and standard deviation or median and quartiles were used , for nominal variables, the frequency and proportion (in percent) were used. The distribution of scale variables was assessed by frequency histogram evaluation. For nominal dependent variables, frequency comparison between categories of independent (grouping) variables were performed using the criterion χ2 Pearson or the exact Fischer criterion. For scale dependent variables, analysis was made using ANOVA followed by pairwise comparisons using the Dunnett method or —Kruskal–Wallis criteria with pairwise comparisons by the Mann–Whitney criterion (using Bonferroni multiplicity adjustment). For comparisons in pattern dynamics across hospitalization, we used a repeated measures general linear model with outcome and disease phase as factors.Statistical analysis was performed using the software packages SPSS Statistics version 23.0  and R software version 3.3.2. The null hypothesis was rejected at a significance level of Fifty-six patients reported smoking (8.8%). Arterial hypertension was detected in 318 patients (50.1%), myocardial infarction—in 50 patients (7.9%), 25 patients had a stroke before (3.9%), COPD was found in 41 patients (6.5%), 18 patients suffered bronchial asthma (2.8%), 32 patients were admitted with known cancer after treatment (5%), 97 patients (15.3%) suffered from well-controlled diabetes mellitus. The main results of our patients’ epidemiological risk factors analysis are shown in the The CT patterns described in the research papers of other authors  and histThe so-called ‘basic’ typical manifestations of coronavirus infection according to many (and there are currently more than 65) authors include ,36,37,38In our work, we focused exclusively on the study of the main CT manifestations of coronavirus infection and their characteristics.Area of the lung parenchyma with moderately increased density with the preservation of volume and the possibility of anatomical components of the pulmonary lobule differentiation According to our data, GGO is the most common finding, it was found on CT images in 87% of cases in recovered persons and 86.2%-in the dead. The recovered patients also showed a greater contribution of this pattern to the overall CT picture .A symptom of dilation of the terminal parts of the vessels and traction bronchiolectasis is often superimposed with this type of change .An area of the lung parenchyma intensively increased density with the preservation of volume, but inside this pattern the anatomical components of the pulmonary lobule are not distinguishable . This diConsolidation zones can be homogeneous, as well as heterogeneous .The phenomenon of “CP” corresponds to intra-and inter-lobular septal thickening on top of “GGO” changes, which is a reflection of pathological changes in both compartments: alveolar and interstitial  Figure .Often, such areas are clearly separated from the intact lung tissue and are intermediate between “GGO” changes and consolidation. The phenomenon of “CP” according to our data was registered in 42.3% of studies in those who recovered and in 72.2%—in those who died. Additionally, the contribution of this pattern to the overall picture was higher than that of the survivors .The reticular interstitial pattern suggests the presence of multiple small linear bands, in a complex resembling a network, which is a reflection of the pulmonary lobule interstitium pathology  Figure .Pleuroparenchymal bands are linear areas of consolidation, 3–5 mm thick, extending to the pleura  ; they coRC and pleuroparenchymal bands were registered in 61% and 58% of cases in recovered patients, respectively, and in 59% and 47%—in the dead. The recovered patients had a greater contribution of these changes to the overall CT picture .p < 0.05). Similar dynamics were observed in the group of recovered patients, but to a lesser extent, which is quite natural, given the increase in severity with the involvement of more and more areas of the pulmonary parenchyma.According to our data, the isolated peripheral location was found in 70.5% of recovered patients, equally peripheral and perihilar in 27.5%, an isolated perihilar location of pathological areas in 2%. In the deceased, values were 40%, 59.2%, and 0.8%, respectively. There was also a frequency increase of approximately equally peripheral and basal location of the affected areas with the course of the disease  and the dead (in 97.5% of cases) was irregular. The contours in both cases were mostly indistinct (72–75%).In those who recovered, the severity of the lesion of the pulmonary parenchyma was lower in the sum of points than in the deceased: 17.3 ± 8 versus 28.5 ± 10.5.p < 0.05).Fluid in the pleural cavity and lymphadenopathy were more often observed in deceased patients—30% and 14% versus 11% and 2% in recovered patients, respectively. At the same time, there was an increase in the frequency of occurrence of the indicated changes with the course of the disease : normal CT scan or the presence of areas of the GGO type;Progressive (5–8 days): an increase in the volume of the GGO type lesions (the frequency of isolated registration is ~40%) and the appearance of CP zones; a combination of GGO and consolidations in ~43%), GGO and RC (in ~58%); the presence of only consolidations (in ~12%) [%, GGO anPeak (9–13 days): a significant decrease in the frequency of isolated GGO type changes with a predominance of a combination of GGO and RC  ;Resolution (>14 days): an increase in the volume of the air pulmonary parenchyma with the appearance and predominant contribution of RC, parenchymal bands, and subpleural lines, a reversible halo (signs of organizing pneumonia). At the same time, there is a decrease in the contribution of the combination of GGO and consolidations to the overall CT picture (~9%), with the beginning of a downward trend in the contribution of the combination of GGO and RC (~66%). The contribution of the isolated GGO pattern remained unchanged in comparison with the previous phase (~22%)  since coWhen describing the features of CT symptoms depending on the time that has elapsed since the onset of clinical symptoms, most researchers, including us, cite the work of F. Pan et al. , in whicOur results : GGO type changes dominated in both cohorts, but almost 67% of deceased CT studies had CP pattern in this phase of disease, almost half of studies demonstrated mixed GGO-consolidation changes;Progressive (5–8 days): an increase in the volume of the GGO, appearance and increase CP , consolidations  type lesions in both cohorts, RC presented in half of the studies;Peak (9–13 days): predominance of a combination of GGO (87%), consolidations (67%), and RC (54%) in recovered patients and the combination of CP (87%), consolidations (77%) and RC (65%)—in deceased. RC demonstrated ascending trend in both cohorts, consolidations—in recovered patients, CP—in deceased, the contribution of GGO decreased in both;Resolution (>14 days): predominant contribution of consolidations and RC in both cohorts, at the same time there is a decrease in the contribution of GGO, but CP contribution decreased more slowly in deceased than in recovered patients.p < 0.005)According to our data, there is a statistically significant difference between the groups of patients with different outcomes in the average score values obtained as a result of the pulmonary parenchyma lesion assessment during CT in all phases of the disease. The average difference was ~12.5 points (p = 0.014): the maximum difference was observed between the early (0–4 days) and the progression phase (5–8 days) and amounted to ~7.7 points in a pairwise comparison, after the peak stage there was a tendency to decrease the severity of damage (p < 0.005). At the same time, the dynamics of the process in both groups were similarThere were significant changes in the average score values depending on the phase of the process  , which probably reflects a greater contribution of the ‘CP’ pattern in the group of patients who died subsequently.p = 0.017 for isolated outcome factor).The ‘GGO’ contribution was generally greater in patients who subsequently recovered  a gradual decrease in this pattern contribution was observed. However, in the initial phases of the process, there was a more pronounced and delayed increase in the ‘crazy pavement’ contribution in deceased patients, reaching a maximum in the peak phase of the disease (9–13 days). In recovered patients, the maximum of ‘crazy pavement’ contribution was observed in the progression phase (5–8 days). In this phase, the average contribution of the discussed pattern was 15.4%  and 28.7%  for recovered and deceased patients, respectively.p = 0.008 for isolated outcome factor; mean difference was 10% approximately).In addition, the ‘crazy pavement’ contribution was generally greater in patients who subsequently died (The contribution of consolidation pattern in lung damage picture showed an increase throughout disease in the group of deceased with a maximum rate between early phase (0–4 days) and progression phase (5–8 days) and between peak phase (9–13 days) and resolution phase . Significant differences were found between the recovered and deceased patients in the consolidation pattern contribution to total lung injury in the progression phase  and resolution phase , significance for the isolated outcome factor was p = 0.034.Among those who recovered, the contribution of the consolidation increased only starting from the sixth day of the disease and at a lower rate. Therefore, a statistically significant difference in the temporal dynamics was observed in groups with different outcomes of the disease , followed by an increase in their contribution to the affected lung tissue in the group of recovering patients during the peak and the resolution phases. In the resolution phase in the group of deceased patients there was a significantly lower average value of the RC contribution to the lung damage picture, due to larger impact of the consolidation and CP patterns . These findings indirectly demonstrate the role of RC as a potential marker of lung parenchyma reparation.p = 0.005, In the surviving and deceased patients, there were statistically significant differences in the linear bands’ contribution to the lung damage dynamics . Based on these results, we can confirm the role of this CT pattern as one of the markers of reparative changes in the lung parenchyma.On admission day the dominant patterns of changes  in recovered patients were patterns of GGO (48.6%), CP (16.6%), consolidation (14.8%), RC (12%) and linear bands (8%), in deceased—35%, 23%, 20.5%, 12.5%, and 9%, respectively.The frequency of the CP pattern detection in the cohort of deceased in all phases of the disease was higher than 60% of patients, reaching the maximum value (90.5%) in 0–3 days. The frequency of the consolidation pattern detection in the cohort of the deceased starting from the sixth day after symptom onset was 100%. At the same time, RC in both patients’ cohorts, starting from 3–6 days of the disease, registered in all disease phases in more than half of the cases, which also confirms the early activation and simultaneous (with damaging) course of the reparative process in the lung parenchyma.This research revealed several patterns reflecting typical CT changes and their evolution during the natural course of coronavirus infection in adult patients hospitalized in a repurposed hospital.Our results of the occurrence of a particular CT sign of coronavirus infection reproduce the results of most of the aforementioned research papers and meta-analyses ,33, exceGGO—according to our data, as well as according to the data of most authors—was the dominant type of changes in recovered patients at all phases, also one of the prevailing types of changes in deceased patients at all phases, which excludes its prognostic significance. In addition, the trend of the temporal dynamics of this type of change is identical to that of the lung damage severity. However, with the isolated detection of areas of GGO, it is possible to assume the presence of an early or resolution phase of the disease, which are described by other authors too ,36,37,38Moreover, according to Shi et al., the presence of isolated GGO was noted at the subclinical stage of the disease (before the onset of symptoms) , and S. We also noted that consolidation is an indicator of the progressive phase of the disease, it develops, as a rule at the site of GGO while most often the GGO areas themselves progress in localization and size and coexist with consolidations ,36. Besip < 0.0005 at all stages of the disease), which confirms the prognostic value of this pattern. Similar results were published in only one article by Feng Pan and co-authors [Contemporaneously, our study also revealed new patterns: we convincingly demonstrate a significant prevalence of changes in the type of CP in the cohort of deceased patients during CT examination at any time .Most authors agree that the detection of CP in combination with ‘GGO’ and (or) consolidation corresponds to the peak phase of the disease ,42,46.We confirm that CP often co-exists with the patterns of pulmonary parenchyma density increasing (GGO and consolidations) . PerhapsAccording to Feng Pan and co-authors, the areas of GGO were caused by intra-alveolar edema or protein exudate, but according to Pershina et al. , an incrThe data of Feng et al. and Pershina et al. show that the histologic substrate of the interstitial pattern is edema and lymphocytic infiltration of the interlobular and intralobular septa or deposits of collagen in them. The latter is also a symptom of later-phase DAD and probably impairs drainage of the lymph and venous blood, and also reduces the efficiency of gas diffusion, providing a mutually aggravating relationship effect.Histological substrate underlying CT-patterns, according to different studies focusing on the analysis of data from autopsy patients from COVID-19 can be three different forms: (1) diffuse alveolar damage (DAD) is usually a nonspecific response of the lung parenchyma to a multitude of injurious agents, and according to Brogna et al., aforementioned findings may reflect different phases of this form ; (2) orgThe main findings in DAD are capillary congestion, interstitial and intraalveolar edema associated with the fibrin deposition (fibromyxoid exudate), and hyaline membrane formation . HyalineOP is manifested by the formation of granulation tissue and polypoid plugs that fill the lumen of the alveolar ducts and terminal bronchioles (Masson’s body). The pattern is sensitive to corticosteroid therapy .A feature of AFOP is the presence of signs inherent in DAD and OP, with a unique feature—extensive filling of the alveolar lumen with “fibrin balls”, and not hyaline membranes, with fibroblast bodies and fibroblasts surrounding these “balls” ,57.p = 0.007), this was due to a change in the balance towards CP and consolidation. In our opinion, this may indicate a faster dynamic of the process in this group of patients.Severe patients were also less characterized by changes in the GGO type in primary lung CT  . It oftep = 0.003), while the frequency of RC did not significantly differ (p = 0.018), which in our opinion may be due to the greater sensitivity of the pattern linear strands as a marker of repair, compared with the pattern of RC. This indicates that processes of repair in deceased patients are less pronounced compared to the processes of damage to the pulmonary parenchyma. In addition, the appearance of such bands may be due not only to a violation of air patency in a separate pulmonary lobule but also to an increase in perfusion of gravity-dependent areas of the lung against the background of persistent edema and exudate, which probably did not occur in deceased patients [However, we discovered that—in the later phases of the disease—linear strands were less characteristic for the group of severe patients  and in the deceased (between progression and peak phases): the gap between the average values and CI in these cohorts during the peak phase is 15.4; 95% CI 13.3–17.5 and 28.7; 95% CI 22.5 to 34.8, respectively. This finding suggests that in the latter cohort of patients there is more extended in time progression phase, in comparison with recovered patients, which probably leads to the deterioration of the prognosis. This may be due to the long-term persistence of the virus in the lung tissue, which is probably a trigger for repeated lung damage and disease progression .p = 0.004). This may be due to different phases of the disease at the time of the last CT scan among the surviving and deceased patients; indirectly, it confirms some authors observation that depending on pulmonary parenchyma lesion severity at the beginning of the process, the time frame of the phases described above may shift. In addition, the process dynamics as showed by Yu et al. seem to be different in cohorts of patients who differ in the process severity [According to our data, the prevalence of the CP pattern is also noted in the last CT scan of deceased patients and consolidation patterns are more pronounced .According to our data in all phases of the disease, the scale of destruction of the lung parenchyma, estimated by the sum of points of the affected segments was more pronounced in severe patients .The “tropism” of pathologic changes to the peripheral and posterior parts of the lungs (which we reported above) can be explained by a more numerous and wide representation of blood and lymphatic vessels, as well as bronchioles in these parts .At the same time, there was no significant difference in the volume of damage to the right and left lungs in concordance with our data, in contradiction to reports by other authors.The dynamics of changes in the damage scale and localization revealed by us during the disease course generally corresponds to those described by other authors .Taking into account the presence of residual (not only ‘reparative’) changes on CT examinations of the lungs upon discharge from the hospital in most patients, in our opinion, further studies are also needed to assess the long-term consequences of the coronavirus infection.With regard to the clinical results of our study, it should be noted that the risk factors in our population are similar to those previously reported in other articles and reviews, with the exception of arterial hypertension. We have discussed in detail the possible reasons for this in another article on risk factors in our patient cohort Some combinations of radiological patterns were in the ‘gray’ zone, which makes it possible that certain patterns were not registered correctly, as in many other analyzed articles.The volume of lung lesion was registered visually, according to scales that are not devoid of shortcomings and subjectivity.The sample of fatal cases was relatively small, which reduced the sensitivity of the statistical analysis.This study does not include data from pathomorphological studies, which makes our arguments about the mechanisms of development of certain patterns purely hypothetical.There may have been a hidden mutual influence of factors (confounding), which may distort the results of our observational study.It should be noted that our study had several limitations that could influence the results to some extent:Thus, our research on a large enough sample of symptomatic patients hospitalized in a repurposed hospital reproduced data on typical pulmonological CT manifestations of infection, their frequency of occurrence, and the prognostic significance of the scale of the lesion. In addition, when comparing two cohorts of patients (recovered and deceased), new patterns were found and differences in the dynamics of the process were investigated: the CP pattern, which is more common and has a greater contribution to the overall CT picture in different phases of the disease in deceased patients, has isolated prognostic significance and is probably a reflection of faster dynamics of the process with a long phase of progression of pulmonary parenchyma damage with an identical trend of changes in the scale of the lesion (as recovered) in this group of patients. The revealed patterns certainly require further research and can be reflected in clinical practice.


In “Using Participatory Design Methodologies to Co-Design and Culturally Adapt the Spanish Version of the Mental Health eClinic: Qualitative Study” :e14127), the authors noted one error.In the originally published article, a value appeared incorrectly in the following sentence.A total of 7 health professionals participated in the workshops; 6 were female and their ages ranged from 22 to 24 years (median age 28 years).This has been corrected as follows:A total of 7 health professionals participated in the workshops; 6 were female and their ages ranged from 22 to 34 years (median age 28 years).The correction will appear in the online version of the paper on the JMIR Publications website on March 4, 2022, together with the publication of this correction notice. Because this was made after submission to PubMed, PubMed Central, and other full-text repositories, the corrected article has also been resubmitted to those repositories.


Callithrix jacchus). The study was primarily restricted to the nuclei of the diencephalon and amygdala. The common marmoset is a neotropical primate of the new world, and the absence of telencephalic gyrus favors the mapping of neuronal fibers. The biotinylated dextran amine was employed as an anterograde tracer. There was an evident pattern of rostrocaudal distribution of fibers within the subcortical nuclei, with medial orientation. Considering this distribution, fibers originating from the A25 cortex were found to be more clustered in the diencephalon and amygdala than those originating in the A32 cortex. Most areas of the amygdala received fibers from both cortices. In the diencephalon, all regions received projections from the A32, while the A25 fibers were restricted to the thalamus, hypothalamus, and epithalamus at different densities. Precise deposits of neuronal tracers provided here may significantly contribute to expand our understanding of specific connectivity among the medial prefrontal cortex with limbic regions and diencephalic areas, key elements to the viscerocognitive process.This study was aimed at establishing the subcorticals substrates of the cognitive and visceromotor circuits of the A32 and A25 cortices of the medial prefrontal cortex and their projections and interactions with subcortical complexes in the common marmoset monkey ( These regions are essential for maintaining the connectivity, integration, and modulation of these type of functional processes.The cognitive-behavioral activity is integrated with the emotions in the supra-modal regions of the cerebral cortex. A wide range of behaviors is coordinated mainly by the action of neurobiological processes integrated into the prefrontal cortex, establishing neuronal connections with most of the cortico-subcortical areas of the central nervous system. Although the activity of this cortex is generally associated with the most elaborate functional processes of human behavior as well as non-human primates, it is the medial region (mPFC) where major connections bridging cognitive activity with the emotions are structured. These connections through their networks with subcortical structures6. The mPFC is structurally heterogeneous. It has also been observed that the division of the cortices within the mPFC corresponds with distinct cytoarchitectonic areas, which had no evident differentiation in the granular layers II and IV, and that these areas have high cell density in layers III and V. Moreover, the supragranular layers are considerably thicker than the infragranular layers7. Thus, these areas would correspond to the A32 and A25 regions in the common marmoset, located in the mPFC caudal portion. These aspects were identified in the medial wall of the prefrontal cortex in monkeys. The A32 and A25 cortices are composed of a heterogeneous population of long-range pyramidal neurons that receive, integrate, and retransmit ascending information from subcortical origins1.The mPFC is also essential for the detection of and attention towards novelties, contextual evaluation, executive function, and objective-directed behavior. This cortex presents a significant activity that intervene in different ways in the cognitive and emotional processes, modulated by their excitement and inhibition, and is related to visceral control, mechanisms of reward and motivation, and decision making. Besides cognition and behavioral control, brain imaging studies have implicated mPFC activity in the pathophysiology of mood and anxiety disorders, including post-traumatic stress disorder and major depressive disorder9. It appears that "this region is an area of confluence of the cincture cortex, orbitofrontal region, and dorsolateral cortex"2(pp.118). Thus, it is not surprising to note that patients with mPFC lesions exhibit severe social impairment and reduced behavioral flexibility13. A partial correlation between mPFC activity and a subset of neuropsychiatric disorders related to social behavior in humans led to the hypothesis that the excitation/inhibition balance in mPFC circuits might be critical to normal social behavior15. Taken together, several studies24 illustrate the idea that various pathways of adhesion and synaptic signaling that operate in the mPFC, as well as the structure and activity of subregions in this cortex, are critical in and contribute to the initiation, maintenance and behavioral modulation/regulation, and associated effective pathology.It was described that the neuronal circuits of the mPFC are distributed mainly to the A32 and A25 regions, which are correlated by compromises in the anterior cingulate circuit and more posterior subcortical regions28. The A25 cortex has been postulated as a regulator in the generation of emotional responses. A25 cortex participates in the inhibition and processing of negative emotions in the amygdala. It is also involved in habituation behaviors, stress responses, and memories of negative affect, being an important regulatory center of behavioral adaptation32. Furthermore, data suggesting functional differences between rodents and primates have been reported. Previous work in common marmoset, areas 25 and 32 would have causal, albeit opposite, functions in the regulation of behavioral correlates of negative emotion33; their activation or inactivation would lead to variations in autonomic and behavioral activity associated with negative emotion expectancies. We suggest that these primate areas differentially regulate negative emotion and symptomatology of affective disorders.Specifically, among these mPFC subregions, the A32 cortex would be involved in the evaluation of conflict and anxiety situations, in the expression of conditioned/learned fear (but not in its acquisition), in the search for rewards, and in the development of goal-oriented strategies2. However, the specific mechanisms of the circuits mediating this activity are largely unknown. Nevertheless, one fact that could explain this opposing but coordinated functionality points to the fact that these two regions of the mPFC also establish reciprocal connections that influence the morphology and function of their global connectivity. Prominent and reciprocal projections of layers V and VI between A32 and A25 have been observed34. Specifically, the axons of projection neurons from A25 to A32, or from A32 to A25, terminate in layer VI and layer V, respectively. There is sufficient evidence to suggest that the A32 and A25 subregions have opposing but integrated influences on brain activity, particularly with activity associated with emotional processing behaviors37.In general, it has been argued that the basic distinction between A32 and A25 cortex, relates to the functions of evaluation and behavioral regulation, respectivelyIn summary, there were not data reveling the specific organization of mPFC pathways in primates. Previous studies are based on unprecise injections in the mPFC, which spread and contaminates many mPFC areas and layers, making impossible distinguish between circuits. The present work has solved this problem, highlighting these specific circuits separately in a primate specie.39. The characteristics of axonal fibers found in subcortical complexes of the common marmoset could be morphologically similar to type 2 modulating fibers , LH 7, MnPO, and AHA nuclei than the A25. Both cortices project vaguely to the SO. The fibers that reached the SHy, DM, AH, and Pe were exclusive to the A32, and those that reached the PaDC, PaLM, and JPLH were only from the A25. None of the cortices sent fibers to SCh7, sox, soxd, or SOR.The fibers originated in A25 presented a pattern of distribution and organization larger than those projected from the A32. The A32 directed the largest fiber cluster to lateral and posterior areas of the hypothalamic complex Fig. A–F, whil40 and common marmoset monkey7. Although only few fibers were observed in the nuclei in the earlier study with common marmoset7, in this study, we found that both the central MD (MDC) and medial (MDM) nuclei received fibers, especially, from the A32 and A25 cortices in sets of plexuses . This was mainly in the rostral extension, with the caudal orientation7 being denser in VAM and VAL; moreover, the posterior levels were directed dorsally to MDC and its limits with MDM and lateral MD (MDL). We also observed that the projections for MDL were denser from A32 and smaller groupings coming from A25. In both cases, the location of the plexus was predominantly in the dorsal portion. The distribution of the A25 cortex fibers , PT, and Re ; these fibers originated from the A32 cortex, while those of A25 origin were located exclusively in the Re and dorsal MD , which was consistent with the results of other studies on common marmoset7. However, in the Japanese monkey40, it has been observed that the A32 does not extend projections to these nuclei, and that like our study, it has been found that the A25 projects moderately and exclusively to the CM . Projections from the A32 to lateral and MHb nuclei were seen on the rostral surface, revealing a caudal orientation. In addition, the fiber density increases in plexus within the LHb and decreases in MHb , received dense groups of fibers in the caudal portion from the A32 Fig. H and minMHb Fig. J–L. LikeMHb Fig. B,F. Howe41), we could determine that there were a specific distribution and organization pattern of the projected fibers from the A32 , in similar pattern to medial Ce. A25 projects more to lateral Ce, and central CeC only receives projections from A327. The anterior cortical nucleus (ACo) received dense and exclusive projections from A25  in A32. These technical considerations could explain the presence of BDA labeling in the claustrum, considering that the superficial layers (2/3) could send projections to it.The studies developed in rodents , include the greatest variety of findings regarding cortical-subcortical connectivity interactions; thus, several classic and pioneering studies, describe projections from the mPFC to similar subcortical areas addressed in our study, albeit many differences could be reported. In marmosets, weak anterograde projections from A32 in the marmoset have been described in the Clastrum3. These results are consistent with findings in the marmoset. The results indicate that both mPFC cortices project to both the LH and the medial group nuclei. Although projections are dense from A32 and A25 to these nuclei, particularly A25 sends relativelymore fibers than A32 to the medial region nuclei, and A32 projects relatively more fibers than A25 to the lateral region of the hypothalamus. These results are similar to rodent connectivity. The rostral portion of the A32 and A25 projects to a major part of the rostrocaudal extent of the LH, and the caudal part of those cortices terminate in dorsomedial (DM) regions of the rat hypothalamus48. In marmoset, we identified that the A32 weakly projects to the DM, however, this nucleus did not receive projections from the injection sites in A25 cases.In rodents, it has been identified that the A32 and A25 cortices generate prominent projections to the hypothalamic nucleus LH, as well as to the medial region group of nuclei (MH)50, consistent with findings in marmoset. In rodents6 it was identified that these projections particularly reach the anterior nuclei, as well as the MPO, Pa, VMH, LH, and DM. Injections made in marmoset did not reveal projections from the A25 to the DM6. In retrograde tracing studies52, it has been identified that the LH projects to the A32 and A25 cortices, whereby it can be asserted that projections with the LH nucleus are reciprocal in rodents, which seems to be consistent with the findings in research developed in primates.Previous studies in rodents had vaguely reported that the A25 cortex has massive projections to the hypothalamus54. A fact that coincides with our results and the results in other primates studies40. In mouse3, the prevalence of projections from the A32 and A25 cortices has been described. Rostral areas of the A25 cortex distribute a higher concentration of fibers than the caudal one to the MD, as well as rostral region of the A32 cortex projects more densely  to this nucleus54, similar to the results obtained in marmoset. These connections are reciprocal; in rodents, the MD projects to the mPFC55; the MDM subdivision sends its fibers to the A25 cortex, while the MDL sends fibers to the A3253.Regarding the thalamic nuclei, the MD nucleus has been identified as the main receptor for efferences originating from the mPFC in rodents54 and marmosets.We found that the A32 and A25 cortices project densely to all subdivisions of the MD nucleus, with more abundant fibers originating from the A32 cortex. Fibers from the A32 reached preferentially the dorsal portion of the MDM, MDC, and MDL subdivisions, while the A25 distributed its fibers more laterally and medially within the subdivisions of the nucleus. Regardless of the fiber location within the MD, this nucleus received the highest fiber density projected from the mPFC cortices, both rodents59. Particularly, in rodents it was identified6 that fibers in the PV originated in the A25 cortex, contrary to this, the fibers in this nucleus (anterior region—PVA) in marmoset came from the A32 and not A25, as a result of injections in these cortices. In rats, posterior PV nucleus, and not the anterior, is the receptor of projections from the mPFC, and the AV nucleus receives projections from the A3254, as in marmosets. Curiously, the A25 cortex also projected to anterior nuclei of the thalamus, albeit not to the AV, but the AM.In marmoset, fibers were also found in the PVA, AM, IAM, Re, and the VA group. These results agree with data presented by several researchers that had rodents as experimental subject61. In our results in marmoset, fibers were identified from the A25 cortex to Re nucleus, as well as projections from the A32 cortex. In both cases, the fibers were located in the rostral portion of the Re. On the other hand, in rodent epithalamus region, it has been identified mPFC projections to the LHb nucleus6. These projections are probably originating in A32 and A25 cortices62. In marmoset, similar projections were found, although the A32 projected densely, while the A25 weakly sent its fibers to this habenular nucleus.In mouse, fibers in the Re nucleus, are projected from the A25 cortex66; describing that the BL is densely innervated by A32 and A25 cortices. In marmoset, we identified that the BL receives fibers originating from the mPFC, although not densely.Several studies covering connectivity interactions between the mPFC and the amygdala nuclei in rodents have identified the B nucleus as the main target of these cortical projections67 (Our results are opposite). The A25 of rodents provides moderate projections to BL6. From results of research that has inquired into connections originating in the amygdala, it is possible to say that the connectivity between A32 and A25 cortices to the BL nucleus is reciprocal. Rodent studies3 have reported the presence of fibers in the A25 and A32 cortices with the neuronal body within the BL area of the amygdala.Considering BL, our findings in marmoset are conflicting with data have been reported in rats. Such studies have been shown the presence of large fibers between A25-BL and smaller ones between A32-BL66. In marmoset, we revealed both A32 and A25 cortices project equally to the Me and CeM nucleus, although CeL received fibers only from A32. The A25 cortex of marmoset sends fibers moderately to AA (more than A32). Regarding the nuclei of the cortical region (even when in mice it was not specified), we observed that marmoset A25 cortex projects moderately to ACo and PCo. In addition, PCo fibers were also observed coming from A32, although poorly. The dorsal B region was the nucleus that received densest projections from A25 cortex in the amygdala, with plexuses distributed rostrocaudal, and sparsely dense projections from A32.Evidence suggests that the dorsal portion of A32 cortex, and ventral division of A32/A25, innervate several amygdala nuclei in rats 6, however, they coincide in describing A25 fibers within the Me, and although in marmoset they are weak projections, in rat they show a considerable gradient with a rostrocaudal orientation6. In marmoset the A25 cortex projects fibers to CeM and CeL, but not to the CeC subdivision, as has been described in other rodent studies6. In marmoset, the CeC subdivision only received fibers from neurons in A32 (weakly).In the marmoset, the A32 cortex projected a greater number of fibers to dorsal B, compared to A25, and a few to the ventral B. In rodents, studies do not report A25 cortex projections to the medial BIn general, these comparison data between marmoset and rodents show consistency of projections from the A32 and A25 cortices of the mPFC to the regions of the diencephalon and amygdala in both animal study models. The existence of variations in the connectivity of these cortices varies when considering the orientation and distribution of fibers within the nuclei , rather than the presence of fibers within them. Any lacunae in the empirical evidence are the result of a lack of studies, not a sustained lack of connectivity. These results only make an invitation for further research-oriented to deepen connectivity studies in primates, to compare with the abundance of results we have on rodent brain connectivity. In addition, these comparative descriptions, and the ample evidence of the connectivity of the mPFC, and its A32 and A25 cortices, provide support for the functional activity that has been attributed to these limbic cortices, as well as evidence of specific connectivity between the mPFC and subcortical regions, connections that have been described as essential for the evaluation and regulation of behavior and emotional responses.68. These could be used to understand if the established networks are linked with their functioning in animal models. Likewise, owing to the functional characteristics of the subcortical regions that are approached, our results and observations could provide crucial evidence of the neuroanatomic substrates that intervene in the autonomous and endocrine function of the hypothalamus, and their modulation in interactions with the mPFC (A32 and A25) as fibers from this cortical region were found in the main nuclei that regulate these functions. Moreover, it is viable to highlight the apparent intervention of the mPFC (A32 and A25) in the functional activity of the amygdala as a part of the limbic regions in the modulation of emotions that guide and accompany behavior regulation being mediated by previous experience, according to the functional characteristics of the A32 and A25 cortices. The A32/A25-Thalamus connections represent a wide variety of functional influences, both sensitive and motor, on the cortico-subcortical activity, considering that the different nuclei of the thalamus are involved with specific and complementary functions in a way that they act as regulators and/or activators of brain activity. This was particularly evident when one studies the modulations in the subdivisions of the mPFC, as well as by their interaction with other subcortical centers of the encephalon in the consolidation and regulation of the activity that occurs in its afferent and efferent circuits. Therefore, it was suggested that the viscerosensory and visceromotor areas in the frontal lobes are in the agranular insular cortices, A32, and A2569. Therefore, these cortices were postulated as an autonomic region in the medial prefrontal cortex.It is worth mentioning that the proximity between the anatomical analyses of neural networks in the common marmoset monkey and its macro-scale homology with the human brain preserves the relative positions of the primary sensory and motor areas, suggesting that the broad ordering and spatial relationships between networks of distributed associations might also be preserved70 and aproved by the Ethics Committee on the Use of Animals of the Federal University of Rio Grande do Norte, Brazil—CEUA/UFRN (authorization code: 024.028/2017). Also, we consulted the “Guidelines for the Euthanasia of Animals” of the American Veterinary Medical Association (AVMA)71 and the Brazilian Federal law on animal experimentation, the “Arouca law”72.All methods were carried out in accordance with relevant guidelines and regulations for animal research. All procedures were in accordance to the “ARRIVE Guidelines 2.0”Callithrix jacchus primates (common marmoset monkey). Animals from both the sexes were utilized in the study. The common marmoset monkey is a neotropical primate that is not on the list of endangered species. Micro-injections of axonal markers (Tracers) were combined for both cerebral hemispheres in the same animal to minimize the number of subjects involved. The animals were obtained from the colony of the Nucleus of Primatology of the Federal University of Rio Grande do Norte, Brazil. The absence of telencephalic gyrus in the common marmoset's brain makes it possible to conduct a systematic study of serial sections of the entire brain. There is a growing consensus for data on cortical anatomy and physiology in this species; stereotactic atlases that are accurate and detailed for the common marmoset's brain . In each animal, two experiments were performed (each hemisphere corresponds to 1 case). Using the same technique (iontophoresis), the first animal  received an injection in the A32 cortex of both hemispheres, animal 2 in the A25 cortex of both hemispheres, and animals 3 and 4 in the A25 cortex of the left hemisphere and the A32 cortex of the right hemisphere.The stereotactic coordinates were defined to reach the A32 and A25 cortices of the medial prefrontal cortex  pulsed electrical current, which enabled the exchange of ions between the two cell environments. For marking the fibers and axonal buttons , the BDA anterograde tracer  was used.The tracer deposit was made through the iontophoresis injection technique. The choice of using one or the other technique was based on the characteristics and properties of the tracer and the ease of absorption in cell membranesAt the time of taking the animals from the primatology center, they were administered a dose of Diazepam (1 mg/kg i.m.). Later, in the operating room, anesthesia was induced by inhalation of 3–4% of Isoflurane in pure oxygen. Further, a latex mask was placed on the face of the animal with a flow of 1–3% of Isoflurane in pure oxygen, which was administered to keep the animal breathing spontaneously throughout the procedure. Moreover, a 1 cc/h of saline solution was administered to the subcutaneous tissue to maintain adequate hydration.nAmp pulsed  was downloaded using a generating source  for 25–30 min. In conjunction with available reports, these procedure parameters allowed the marking of small groups of cortical cells. The micropipette was removed 5 min after the end of the iontophoretic application.The animals were positioned in the stereotactic instrument  wherein they underwent a craniotomy of the frontal bone. After the removal of the dura mater layer, a tip of the micropipette (measuring between 15–20 μm in diameter) loaded with the BDA tracer, was introduced in the cortical areas of interest, according to the specific plane selected in the stereotactic coordinates of the atlas  as well as Cafavecina , a broad-spectrum antibiotic, to confer protection against bacteria during the survival time of the animal. The time necessary to allow the proper passage of the tracer in the axon path was 15 days as calculated from the day after surgery. During this period, paracetamol was added to the water (5 mg/kg). As an anti-inflammatory drug, Ketoprofen , was administered twice a day for one week.As soon as the "injections" were finished, the dura mater was occluded with Ketamine (30 mg/kg i.p.) and Xylazine (1.5 mg/kg i.p.). The dosage of the drugs was three times to the recommended dosage for induction of anesthesia according to the protocols of the UFRN Ethics Committee-. As soon as the animals were euthanized, they were subjected to transcardiac perfusion, starting with 500 ml of saline serum , followed by 1000 ml of paraformaldehyde solution in 0.1 M phosphate buffer (pH 7.4), with 2 cc of glutaraldehyde—added minutes before the perfusion . Thereafter, 800 ml of a third solution containing paraformaldehyde in 0.1 M phosphate buffer (pH 7.4), and 15% of sucrose  was used.After 15 days of the surgical procedure, the animals were removed from the primatology nucleus. During that time, the animals received a dose of Diazepam (1 mg/kg i.m.). In the perfusion room, the animals were euthanized by using an overdose of After perfusion, the bones of the cranial cap were removed, and the exposed brain was separated stereotactically. A single section was performed at the level of the interaural plane + 12.50 and the second section at the interaural plane -1.0 (middle level). Thus, the brains were divided into three (3) blocks: block one (1) containing the frontal portion of the brain, block two (2) having the entire temporal lobe and adjacent cortex, and block three (3) the occipital part of the cerebral hemispheres and the brain stem. The blocks were removed from the cranial cavity and immersed in the same fixation solution for 24 h ; thereafter, the block was immersed in a phosphate buffer solution with 20% sucrose for three days. Subsequently, the sections were left in 30% phosphate buffer with sucrose until the moment of microtomy in the cryostat. All procedures related to surgeries and euthanasia were performed in the Neuroanatomy Laboratory of the Morphology Department at UFRN Biosciences Center.After remaining in the 30% solution of sucrose in phosphate buffer until they were submerged for cryoprotection, the blocks were positioned in the cryostat to perform the cryomicrotomy and obtain the histological sections. The equipment (Microtomy Cryostat—LEICA TEC. Ref. CM-1850) was calibrated and arranged for its operation according to its manual. Thus, it was programmed to make cuts of 50 µm. Each of the cuts was deposited in "vats" following a sequence of 1:7, in 0.1 M pH 7.4 phosphate buffer, until they were made available for the development of the corresponding histological techniques.After the microtomy, the sections of the first series were mounted on previously gelatinized, dried, and dehydrated slides. One week later, they were processed to delimit the cortical centers and subcortical nuclei of interest in the common marmoset monkey by the Nissl staining method and then covered with a cover glass. In the third series of cuts, dextran-BDA (with glucose-oxidase) was revealed; sections were first rinsed for 20 min in oxygen peroxide to quench endogenous peroxidase and subsequently incubated overnight under continuous shaking in avidin–biotin-peroxidase complex  + diaminobenzidine (DAB) + 0,1% glucose-oxidase (Sigma-Aldrich) + 1% ammonium nickel sulfate in 0.1 M phosphate buffer pH. 7.4. A week later, these tissues were subjected to the procedure of dehydration and diaphanization for intensification with Osmium. Finally, the other series were stored in an antifreeze solution for possible repetitions and data confirmation, as well as other neuronal study techniques.73 as a guide. This allowed us to have a global view of the subcortical distribution of fibers originating in the cells of the injection site. Similarly, representative sections of the content of the axons marked in the subcortical regions were identified and photographed using a lucid camera to reconstruct the subcortical pathways of axons and their distribution by area.An entire series of Sects. (1:7) with BDA were examined by optical microscopy for the location of the injection centers and the images were superimposed on the tissue with Nissl marking to determine the location of the injection coordinates. Then we proceeded to locate the marked axonal branches (fibers) while tracing on a map the areas of interest of the brain hemisphere of the common marmoset monkey. The data was produced from the alignment of the contours of the coronal sections with the atlas of Paxinos et al.76 and the tissue images were obtained in the lucid camera.The second step involved a more detailed analysis of the topographic distribution pattern of the axonal branches in each area. Similarly, the arborizations of the fibers marked with the tracer were reconstructed from contiguous coronary sections. Also, in the third step, the coronal sections were reconstructed by drawing them and the fibers found in the subcortical complexes of interest were drawn one by one. This was done in their original location. All drawings and figures were made using the CANVAS software version 12.0


Alzheimer's disease (AD) is one of the most important causes of mortality in elderly people, and it is often challenging to use traditional manual procedures when diagnosing a disease in the early stages. The successful implementation of machine learning (ML) techniques has also shown their effectiveness and its reliability as one of the better options for an early diagnosis of AD. But the heterogeneous dimensions and composition of the disease data have undoubtedly made diagnostics more difficult, needing a sufficient model choice to overcome the difficulty. Therefore, in this paper, four different 2D and 3D convolutional neural network (CNN) frameworks based on Bayesian search optimization are proposed to develop an optimized deep learning model to predict the early onset of AD binary and ternary classification on magnetic resonance imaging (MRI) scans. Moreover, certain hyperparameters such as learning rate, optimizers, and hidden units are to be set and adjusted for the performance boosting of the deep learning model. Bayesian optimization enables to leverage advantage throughout the experiments: A persistent hyperparameter space testing provides not only the output but also about the nearest conclusions. In this way, the series of experiments needed to explore space can be substantially reduced. Finally, alongside the use of Bayesian approaches, long short-term memory (LSTM) through the process of augmentation has resulted in finding the better settings of the model that too in less iterations with an relative improvement (RI) of 7.03%, 12.19%, 10.80%, and 11.99% over the four systems optimized with manual hyperparameters tuning such that hyperparameters that look more appealing from past data as well as the conventional techniques of manual selection. AD permanent neurological condition occurs rapidly and progresses continually, marked by cognitive dysfunction , modificFortunately, early diagnosis of AD offers sufferers prior knowledge about the seriousness and encourages them to take appropriate steps, e.g. dietary modifications and medication, that may significantly improve their memory and their quality of life as well . DiagnosThe acquisition of such scans is done with lesser amount of energy resulting in the low contrast and bad quality of an image with essential need of human intervention. Thus, computer-aided diagnostic systems have been developed which play a pivotal role in improving the quality of medical images highlighting the conspicuous parts more effectively . LikewisAn optimized new set of models based on Bayesian parameter optimization of the deep learning model for the classification of AD is proposedThe framework also includes comparative analysis of 2D and 3D convolutional architecture on MRI neuroimaging dataThe combination of Bayesian optimized parameters utilizing long short-term memory (LSTM) through the augmentation process has succeeded in discovering better model settings with minimal iteration and increased performanceIncreased accuracy on adoption before clinical diagnosis to anticipate conversion to Alzheimer's diseaseIn this paper, an effort has been made for early detection of AD with comparisons of modelling efficacy being conducted using the four different systems: considering the ternary classification as (a) AD, MCI, CN, and binary per mutational classification, (b) AD and MCI, (c) CN and MCI, and (d) AD and CN. Through this work, the following contributions considering the use of abovementioned classification systems have been made:These developed systems further have been comparatively evaluated utilizing multimodel DL approaches. Therefore, Bayesian methods of optimization are very effective at a very high level yet take a little longer for the optimally selecting the next hyperparameters such that the time spent for the choice of hyperparameters in reality is inconsistent with the time spent on the objective function.The remainder of the paper is organized as follows: glc interdependencies in the individual patterns corresponding to PET data of mild and moderately demented patients. Likewise, the researcher implemented multiple regressions and the process of discriminatory analysis to differentiate patients from controls and also to identify early AD trends correlated with independent memory impairment. In [Different modalities of medical imaging data include computerized tomography scans; sMRI, functional MRI, and diffusion tensor imaging have been used to diagnose AD in various ways using diverse ML algorithms –36. Azarment. In , furtherment. In  proposedment. In  to extriment. In  by extenment. In  combinedThe experimental results in  have shoConvNet or alluded to as CNN is a conventional algorithm utilized in DL with image as an input based on the significance of attributes including learnable weights, tendencies, and biases added to them alongside the differentiations existing between them. However, in correlation to other classification algorithms, the proportion of preprocessing required by a CNN is significantly less. Although primitive methods require hand-engineering of filters, yet architecture of CNN is well suitable to become familiar with these qualities reliant on enough data training. Likewise, the network is well able to capture the spatial and temporal dependencies in an MRI scans both if there should arise an occurrence of 3D and 2D relating to the clinical imaging through the cycle of reduction and viable optimization parameters .x⌋ is the floor function of x, nx is the height of the image, ny is the width of the image, nc is the number of channels, and p is for padding factor of convolution through consideration of the filter which is to be squared with an odd dimension being denoted as f allowing each pixel to be centred in the filter and consideration of all the elements around the input image dataset. Furthermore, filter slides without knowing about parameters after a certain step and pooling function is applied on the selected elements as inThe important aspect in architecture includes the definition of the convolution product which is being done after implication of basic operations including padding and stride. The convolution products are therefore evaluated as a two-dimensional matrix with the end objective that each element is related to the sum of the element of the cube (filter) and the sub cube of the image is given asFinally, there are repetitions of such convolutions followed by activation functions with definition of the pooling in the principal step as detailed in Equation  and repeCNN is well structured to accommodate a single image and to transform it into a vector representation. RNN was used to detect image as per the factor time, but in some instances, it was found unreliable in practice because of gradient explosion across long-term windows during the back propagation phase of gradients in an image .bg) inblock input  (ig) in(ii) input gate (fg) in(iii) forget gate (mg) in(iv) memory state  (Og) in(v) output gate (hg) in(vi) and hidden state   [In order to generate predictive forecasts of potential disease growth for individual patients, the paper is aimed at efficiently improving the classification system by training a deep convolution LSTM neural network on MRI-based 3D and 2D patients' neuroimages. In this work, we used the simple and follow-up visits to ADNI T1-MRI as this modality have numerous images available on ADNI. The data collection documentation is accessible on the website of the ADNI (ADNI: sc.edu/) , headed n = 365) in the Neuroimaging Informatics Technology Initiative (NiFti) format for the three different subjects: CN (n = 203), AD(n = 109), and MCI (n = 53) as baseline has been experimented and then further transformed 3D into 2D paradigm (.png extension) through the process as defined in Next, the utilization for the total number of sMRI structural scans  is utilized in classification of AD. The CDR ratings vary between 0 and 3, while 5 stages of CDR are 0, 0.5, 1, 2, and 3. For healthy controls, CDR is 0 only. On the other hand, the case is referred to as AD if CDR values are higher than 0.5 or either 1, 2, or 3. For the visualization CDR factor, as shown in 180-degree augmented training set with number of subjects 270-degree augmented training set with number of subjects In order to improve the performance of the model, further, the technique of data augmentation through rotation was applied on the training set as detailed in Finally, the performance analysis for both abovementioned augmented datasets has been performed validated against the same testing set as detailed for the use in the baseline system.For the development of the baseline classification model, we employed 2D CNN and 3D CNN architecture in the form of a feature extractor as shown in x, y, z). With the hypothesis that 3D CNN will perform better than 2D CNN, further 3D architecture of CNN has been developed which takes the 3D set of MRI scans as an input (NiFti format). Thus, the employed 3D architecture uses MRI volumes as a planar size with specific dimensions of 182 × 218 × 182 on the raw NiFti data. Thus, the schematic interpreted output is a single scalar value resulting adequate for the recognition of the disease. Moreover, the schematic illustration comprises of multiple repeated blocks of (3 × 3 × 3) convolution layer which has stride size of 1, a ReLU, (3 × 3 × 3) convolution layer which has stride size of 1, and max-pooling layer with stride size of 1. Likewise, the number of feature maps was set to eight in the first block and was doubled after each max-pooling layer to infer a sufficiently rich representation of the brain. The final recognition corresponding to each class is obtained by using a fully connected layer (dense layer), which maps the output of the last block to a single output value.However, the issues with 2D CNN are in selecting the best slice or slices and their orientation as training inputs for the network. Thus, the recommendation is the “appropriate scan” or “best multiple slices” for efficient results, which somewhat obscures the slice selection criteria. Any time, this is problematic and impractical. Thus, the concentration on the few scans and orientations around the image can lose out the crucial details as well as features required for the efficient deployment of CNN architecture. So the easiest and simplest way to ensure is to use the scans corresponding to entire brain volume, which comes with a three-dimensional pixel value, meaning a pixel value for each dimension in planar geometry extracted as  is more important from a therapeutic point of view than the issue of AD versus NC (AD vs. CN) for successful AD care. Since patients with late MCI are identified as having a very high chance of conversion to AD, and, more specifically, early MCI may be considered the starting point of AD, and MCI status is crucial in the early detection of AD. A correct and reliable diagnosis of MCI will lead to the identification of patients who are at a higher risk of developing dementia, as well as the provision of potential and regular care and the ability to prepare for the future. Developing a precise and effective computerized aided diagnosis method for the classification of CN, MCI, and AD patients could therefore be a significant step forward in the field of ageing science. As a result, this research carried out various experiments considering the same have been conducted and the prognosis for the AD detection through novel approach for optimal selection for detecting crucial biomarkers has been experimented.In this first set of experiments, an effort to evaluate for the adequate baseline system through the comparative analysis using 3D and 2D scans corresponding to both ternary (S1) and binary classification  has been made. It has been found that CONV-3D utilizing the 3D scans (NiFti format) has performed better with an average relative improvement of 3.19% and 3.79% in case of ternary AD vs. MCI vs.CN (S1) and binary classification models, respectively, as shown in For parameter optimization manually and automatically, CONV-3D models (S1 to S4) are carried forward based on their best performance out of 2D and 3D CNN.In this first set for parameter optimization, the learning rate varying at 0.0010 to 0.0030 for the four abovementioned systems S1, S2, S3, and S4 has been experimented. The varying of the learning rate at the end of each batch helps in the adequate monitoring of weight and dynamics; thus, one can make for the conclusion for the influence on the corresponding weight update. The result showcases the better performance at 0.0025 learning rate in case of ternary system S1, whereas 0.0020 for all the three binary systems S2, S3, and S4 as detailed in The other important selection is the use of algorithms or methods helpful in changing the attributes of utilized neural network such as weights and learning rate with an objective of reducing the losses. The four different optimizers including SGD, RMSprop, Adam, and Adamax have been experimented in Further parametric optimization experiments have been performed on the hidden units which have been considered an additional object in the scope of such classification learning. Likewise, the four systems S1, S2, S3, and S4 with the use of preselection of their respective learning rates and optimizers from Tables Finally, for the manual selection of the parameters, the observations as detailed in The tuning of hypermeters for the abovementioned systems S1, S2, S3, and S4 (CONV-3D) has been done by the means of Bayesian optimization such that the automatic selection will help in reduction of the time spent and even result in better generalisation on the corresponding testing dataset. The process as detailed in Finally, the experiments using rotational process of image augmentation has been performed which has served to be a handy strategy for synthetically increasing the size of the training set without having a need to acquire new images while performing experiments on CNNs. Based on the results obtained from Earlier techniques utilized by researchers obtained precision somewhere in identifying individuals with normal control subjects by different datasets of Alzheimer's disease. It is completely obvious that the intermediary stage between CN and AD is MCI and that this stage is the most difficult to identify. Also, a relatively limited number of research have finally taken account of the MCI group , and, giAn early detection of AD allows the patient to receive the best therapeutic conceivable. Numerous researchers are working on this difficult problem, and many approaches for diagnosing AD have already been created. In this work, we first trained, validated, and evaluated 2D and 3D CNN on the ADNI MRI dataset and conducted binary classification such as AD-MCI, AD-CN, and MCI-CN, as well as multiclass ternary classification involving AD-MCI-CN. As a result, the best model out of 2D and 3D CNN was carried forward for optimization based on the both manual selection and automatic selection of hyperparameters including learning rate, optimizers, and dense units by Bayesian optimization was developed. Furthermore, on the Bayesian optimized 3D CNN model, we developed a pooled hybrid CNN-LSTM technique to identify the prognosis of AD disease employing data augmentation in our proposed process. In comparison to traditional and manual selection procedures, the strategy has utilized fewer iterations, and the training for the model has also been constrained, since they aim to provide the greatest validation score in the previously stated conditions. Therefore, an overall relative improvement of 7.03%, 12.19%, 10.80%, and 11.99% was achieved, in which the best model is obtained for systems S1, S2, S3, and S4. The study can be expanded over in the future by incorporating different other frameworks, such as VGG16, VGG19, AlexNet, and DenseNet. In addition to the proper use of the transfer learning for effective AD projections, the proposed methodology for Gaussian-based Bayesian optimization of parameters can be applied. Moreover, we focused only on MRI modality in our work; in the future, this work could be expanded with the inclusion of other modalities like PET, DTI, and fMRI scans.


Different types of interfaces between the components were observed in the composites based on the magnesium–rare earth elements alloy with SiC particles, in which a chemical reaction between the components caused formation of the Re3Si2 phase. Intensive chemical reactions between the components were also observed in the composites with aluminosilicate cenospheres. Additionally, the influence of coatings created on the aluminosilicate cenospheres on the bond with the magnesium matrix was presented. A scheme of the types of interfaces between the components is proposed.In this paper, a summary of investigations of the microstructure of cast magnesium matrix composites is presented. Analyses of the interfaces between the reinforcing particles and the magnesium alloy matrices were performed. Technically pure magnesium and four various alloys with aluminum and rare earth elements (RE) were chosen as the matrix. The composites were reinforced with SiC and Ti particles, as well as hollow aluminosilicate cenospheres. Microstructure analyses were carried out by light, scanning, and transmission electron microscopy. The composites with the matrix of magnesium and magnesium–aluminum alloys with SiC and Ti particles exhibited coherent interfaces between the components. In the composites based on ternary magnesium alloy with Al and RE with Ti particles, a high-melting Al Metal matrix composites (MMCs) have been designed for many years in a variety of systems in terms of both different metal alloy matrices and various types of reinforcement phases ,14,15,16gr or SiC fibers or particles was the Al4C3 carbide, formed at the component interface [2O3, in which the reaction between Mg  and the reinforcement resulted in the formation of MgO or MgAl2O4, depending on the temperature and the volume fraction of Mg in the matrix alloy [2O3, CuAl2O4 and LiAlO2  were the products of reaction of the components, respectively [In metal matrix composites, a complete lack of reaction between the components is possible. Either the so-called mechanical bond, which occurs in many metal–ceramic composites, or a bond that allows the creation of coherent interfaces between the reinforcing phase and the matrix can be distinguished here. A different type of bond between components is the creation of new phases as an additional structural component resulting from chemical reactions between the reinforcing phase and the matrix. Intensive reactions between the components may also consequently lead to changes in the phase composition of the matrix material itself ,21,22. Rnterface ,28,30,31ix alloy . In turnThe first research works on the types of component interfaces were created in the 1960s. In 1974, Metcalfe  summarizClass I, filament and matrix mutually nonreactive and insoluble.Class II, filament and matrix mutually nonreactive but soluble.Class III, filament and matrix react to form compound(s) at interfaces.2, TiO2, Al2O3, etc.)—have been widely used, especially for carbon fibers. Techniques such as electroless deposition, physical (PVD) or chemical vapor deposition (CVD), the sol–gel process, or thermal spraying were used to form coatings on the reinforcement [Then, Metcalfe also proposed  six typeorcement ,28. It sorcement ,25 and oorcement ,31,32,33gr, Al2O3, TiC, Ti, and fly ash microspheres etc., have been designed and investigated in recent years [Due to the properties of the matrix itself, including its density, magnesium matrix composites constitute a modern group of materials, widely studied in many matrix-reinforcing phase systems. Several different magnesium matrix alloys  with various reinforcing phases (various particles or fibers), for example, SiC, Cnt years ,61,62,63The article summarizes the study of the microstructure of magnesium matrix composites in order to present the different types of component interfaces. For this purpose, various types of magnesium matrix composites were selected from among those originally designed and manufactured by casting methods. The paper shows that the use of different reinforcing particles and various magnesium alloys allowed a variety of types of bonds to be obtained between the components. The analyses of some of these composites also revealed the nucleating effect of the reinforcing particles on the structural components of the matrix alloy. Additionally, the influence of additional layers formed on the reinforcing particles  on the possibility of component interfaces is presented. In the article, the description, comparison, and extensive analysis of the phenomena occurring on the component interfaces allows the presentation of many factors occurring in these materials. On the basis of the presented results of the analyses, a scheme of the types of interfaces between the components is proposed.3P + NiO layer). These kinds of layers were described in previous works [Four different magnesium alloys were used to produce the composites in order to reveal the influence of the alloying elements on the type of component interfaces. The chemical compositions of these alloys are presented in us works ,22,49,50For presenting the different types of component interfaces, specific magnesium matrix composites were selected and prepared using casting methods, as described below.Technically pure magnesium matrix composite with SiC particles and Ti particles,AM50 magnesium matrix alloy with SiC particles,AME505 magnesium matrix alloy with Ti particles,ME3 magnesium matrix alloy with SiC particles,AZ91 magnesium matrix alloy with as-received (uncoated) aluminosilicate cenospheres,AZ91 magnesium matrix alloy with Ni–P-coated aluminosilicate cenospheres and initially heated at 773 K,AZ91 magnesium matrix alloy with Ni–P-coated aluminosilicate cenospheres.Composites 1–5 were fabricated using a casting method consisting of mechanical mixing of the molten matrix alloy followed by addition of the reinforcing particles (during mixing) and gravity casting into steel molds. The composites obtained using this method, described in detail in previous works ,22, cont3 in C2H5OH. The magnesium alloys and composite microstructures were observed with an Olympus GX51 light microscope (LM)  with differential interface contrast (DIC) and a JOEL JSM-6610LV scanning electron microscope (SEM)  with an energy-dispersive X-ray spectrometer (EDX) . The interfaces between the components were also analyzed by transmission electron microscopy (TEM) by means of a Philips CM20 FEG  and a TECNAI G2 FEC . For the TEM observations, thin foils from the composites were prepared using a Gatan dimple  and ionic Leica—EM RES 101  equipment.For the microstructure analyses, specimens from the investigated materials were prepared by standard metallographic procedures with etching in a 1% solution of HNOIn magnesium matrix composites, a kind of coherent interface of the components can occur. This type of interface is created when the reinforcing phase is wetted very well by the liquid matrix and there is no reaction between the components. It can be observed in Mg–SiC or Mg–Ti systems. Mg//[10SiC, (10Mg//(0006)SiC, Mg//(0006)SiC,  stored in a loose form may undergo natural surface oxidation at ambient temperature. Although silicon carbide is very stable in magnesium, it is very often coated with SiOxidation ,34,37,432Si compound was formed not due to the reaction of Mg with SiC, but as a result of the reaction of Mg with SiO2 originally present on the SiC particles. Silicon has a very slight (even negligible) solubility in magnesium . For this reason, during the non-equilibrium solidification of Mg2Si and practically pure Mg, a eutectic mixture was formed. The distribution of the eutectic was in turn the result of nonequilibrium solidification of the composite matrix. In this case, the sometimes-observed presence of the Mg2Si compound in the SiC particles is, therefore, the consequence of the matrix solidification process, and not the reaction between magnesium and SiC.In this case, the Mg2Si phase, materials from Mg–Si system are often called composites [2Si–SiC composite is an example of a “mixed” type composite due to the production method in which the SiC particles are introduced from the outside (ex situ), while Mg2Si is formed inside the suspension (in situ).At this point, it should also be noted that the same microstructure of the composite presented in mposites ,45,46,51Composites based on technically pure magnesium are a reference point for analysis of the influence of alloying elements on the possibilities of creating bonds between the components. Nevertheless, they are of little practical importance due to the low properties of magnesium itself. Therefore, composites are most often produced on a matrix of magnesium alloys from the Mg–Al, Mg–Zn, or Mg–RE systems . For com17Al12 intermetallic compound) formed as a result of non-equilibrium solidification. In this alloy , the presence of the Al8Mn5 intermetallic compound is also observed [The microstructure of the AM50 matrix alloy consisted of a solution of solid aluminum in magnesium α(Mg) and eutectic α + γ . 2RE phase. As the theoretical calculations show, although the Al2RE phase has a regular structure, it is possible to create coherent interfaces with titanium in the basal planes of both phases in the directions <110> of the Al2RE and <112RE phase occurs with a small volume fraction, practically not allowing observations using light microscopy or scanning electron microscopy [11RE3 intermetallic phase with acicular morphology , in materials based on the AM50 alloy matrix, nucleation of the intermetallic phase did not change the phase composition of the matrix alloy. The volume fraction of the Al8Mn5 phase was small and constant in both the Mg–Al alloys and composites on their matrix. It should be emphasized that, in the described examples, the intermetallic phases present at the component interface do not arise as a result of chemical reactions between the reinforcing phase and the matrix, but as a result of their nucleation on the reinforcement.In the composite based on the AME505 alloy with Ti particles, the Al Al11RE3 c–f. ChanReactions between components are most often detrimental due to degradation of the reinforcing phase and the formation of brittle phases at the interface. Reactions between the reinforcing phase and the matrix have very often been observed in aluminum composites ,28,30,3112RE eutectic. It should be noted that detailed microstructural analyses of the composite produced on the basis of this alloy revealed an intense reaction between the rare earth elements dissolved in magnesium and the silicon carbide particles. This reaction consequently led to the formation of what can be generally referred to as the RE3Si2 phase. This compound, formed at the interface of the components, has an acicular morphology, which is unfavorable due to the properties of composites. Additionally, the SEM and TEM micrographs of the component interface, attached in 3Si2 phase formed on the SiC particles. The intense chemical reaction between SiC and rare earth elements present in the matrix alloy led to degradation of the reinforcement. Due to the chemical reaction of the components and mechanical mixing during the production of the composites, the RE3Si2 phase also forms inside the matrix material. Thus, the introduction of SiC particles to the liquid Mg–RE alloy resulted in an intense reaction between the components, leading to the formation of a new phase in the microstructure of the composite.In magnesium matrix composites, an intense reaction between the components can be observed in materials based on Mg–RE-type alloys with SiC ,38,43. F2Si and MgO phase inside the composite material. An example of a typical structure of a magnesium composite based on the AZ91 alloy with aluminosilicate microspheres produced by gravity casting of a mechanically stirred suspension is shown in Another example of a material in which intense reactions occur between the components are composites based on magnesium with aluminosilicate cenospheres. Microspheres are most widely used as a component for aluminum matrix composites, although research has also been conducted for a Pb or Fe matrix ,14,22. N2Si type. They are also an example of a “mixed” type composite due to the production method in which the microspheres are introduced from the outside (ex situ), while MgO and Mg2Si are formed inside the suspension (in situ). Such materials are characterized, among others, by an increase in hardness or abrasion resistance; nonetheless, they do not achieve the reduction in the density of the manufactured parts, as assumed at the design stage, in relation to the matrix material.In recent years, many research works have been concerned with the production of magnesium composites with aluminosilicate microspheres, in which a different mutual volume fraction of undamaged microspheres to fractured, matrix-filled microspheres was obtained ,14,22,63A way to obtain magnesium composites with uncracked aluminosilicate cenospheres is to use layers of an additional material covering the microspheres and protecting them from direct contact with the liquid magnesium. The application of the electroless deposition method in the case of spherical fly ash seems to be the most effective. This solution enables the formation of layers, e.g., of the Ni–P type, on the aluminosilicate microspheres. Due to the provision of the most favorable conditions in the process of vacuum infiltration, the most effective is also the use of preheated microspheres with an Ni–P layer, which was described in previous study ,22,50. E3P compounds inside the layer itself, as well as NiO on its surface as a result of oxidation. The use of such coated microspheres for the production of composites by the method of vacuum infiltration facilitates the technological process due to the change in liquid magnesium wettability of the NiO layer in relation to Ni–P [3P) and an NiO layer on the border of contact with the magnesium matrix, which was described in previous works [As a result of this heat treatment performed in air atmosphere at 773 K, the amorphous Ni–P layer (formed during the electroless deposition method) crystallizes to form fine crystalline Ni and Ni to Ni–P . In the us works ,50. Thisus works ,50 did nHowever, a different kind of interface is obtained by using the aluminosilicate cenospheres described above, covered with an Ni–P layer, although not subjected to preliminary heat treatment . 2Ni compound is formed [3Ni2 phases. Both intermetallic compounds were observed at the component interface  [3 and CuAl2 phases, respectively, at the component interfaces (around the fibers). In both cases, these reaction products also prevented the carbon fiber from reacting with the aluminum matrix (reinforcement degradation and Al4C3 carbide formation). The chemical reactions between the coating and the matrix alloys, therefore, change the chemical and phase composition of the composites, resulting in the formation of new phases.Analyses of the influence of the Ni–P layer on the shaping of the microstructure of magnesium composites showed that the amorphous layer obtained in the process of electroless deposition may react with liquid magnesium, as a result of which the Mgs formed . When max alloy) ,22,49,50x alloy) . During It should also be added that, in the case of using eutectic magnesium alloys  as composite matrices with coated cenospheres (heated or nonheated), the sometimes-observed presence of eutectic near the component interfaces was the result of segregation of the alloying elements in the matrix alloy itself during its non-equilibrium solidification, and not the interaction between the components ,50.The examples of magnesium matrix composites with various particles in this article illustrate the different types of bonds between the components. They take into account the coherent interface between the components, reactions between the components, and the influence of the coating of the reinforcing phase on the creation of various interfaces with the matrix. The above-described types of interfaces between the components are presented schematically in It should also be noted that when analysing the possibilities of creating a bond between components in composites, it should be presumed that there is also the phenomenon of nucleation of the intermetallic phases that are structural components of the matrix on the coating layers formed on the reinforcing particles. Such an effect of the coating  is hypothetically possible, which is shown schematically in