D M S Tech Blog

InfoCrypto CryptoCurrency Review- December 2018

December 15, 2018
Introduction to InfoCrypto:

InfoCrypto is one of the most prominent crypto signals providers. It’s a group of telegram crypto signals launched in 2017. The main objective of InfoCrypto is to collect the group of subscribers’ data and relevant market state information. Also, this crypto signal group is consistently working to improve their service quality. Currently, they are trying to construct a platform to reach their customers with an ease and offer them next level services. The company is based on Brazil but spreading their business signals across the world day by day.

 Features of InfoCrypto:

However, InfoCrypto may confound you at the first time, but once you know its interface you will really like its services. Here, for the clearer review, I would like to let the readers know its features, and benefits as well. Just look at a glimpse about its extraordinary and trendy marketing and technical features—

·         Info crypto first offers the screenshot of what they analyze followed by introducing the entry levels under the existing circumstances.
·         This crypto signal assures you for the advanced safety and security of your website data being copied from other external sources. It also reflects the high standard of work quality.
·         Whether you are fluent in English or Portuguese, Infocrypto is ever able to support you due to its bilingual skill.  
·         Infocrypto assists their clients with Risk Management and suggests them the next level strategies. They also help you in defining the targets and avoid losses based on their signals from their technical analysis.

  Benefits of Infocrypto:

·         The dedicated team of Infocrypto attentively focuses to examine the Bitcoin Behavior on the industry. They use a distinct channel to perform the task of analyzing the Bitcoin behavior statistical data.  
·         Infocrypto as per their technical analysis based on telegram signals is capable to display multiple sorts of charts several times a day even at the moment of rapid growth or fluctuation.
·         Its telegram signals are far distinct with the comparison to ordinary text signals used by most traders. They generate Test Analysis charts as per the collection of data and recommendations and predict the profit on coins’ percentage during a short or long-term basis.   
·         They can also analyze the risk percentage by predicting the least coin value with multiple levels of purchases. Infocrypto supports the ICO projects and provides arguments by performing the technical analysis.
·         The Infocrypto team members work efficiently assuring for the high standard grade services to their subscribers. The best part which I like most that all paid subscribers acquire similar services. However, charges may differ as per the subscription duration.

Drawbacks of Infocrypto:

While reviewing the Infocrypto, crypto signals providers, I didn’t meet any drawback. Users are consistently getting all the benefits that matter to achieve the best crypto signals, strategies as well as analysis.

Finally, after a deep review, I reach my final conclusion that Infocrypto is offering the top grade services for trading and analysis. You can receive the regular analysis on Bitcoin, Altcoins and much more. Additionally, you will get the valuable suggestions for timely investment; users can clear the doubts and analysis request as well.

InfoCrypto CryptoCurrency Review- December 2018 InfoCrypto CryptoCurrency Review- December 2018 Reviewed by arvind gupta on December 15, 2018 Rating: 5

Whale Agents Cryptocurrency Review- December 2018

December 15, 2018

Introduction to Whale Agents Cryptocurrency Trading Signals:

Whale Agents offers great trading signals via entire and deep marketing research as well as technical analysis in a professional way. The dedicated team of Whale agents is available by 24X7 hours to monitor the whole marketing trends and uneven bitcoin activity. Are you willing to buy the best trading signals for Cryptocurrency? Have you desired some guides for technical analysis, tips for trading and regular Cryptocurrency general information? If yes, then you should join the world’s largest community to acquire the proven track record for the best signals.

I am here to let you know the complete pros and cons, features of Whale Agents Trading signals provider. Hope you will know it well.

Supports of Whale Agents:

·         Whale Agents has become one of the most trusted Cryptocurrency trading signals providers across the world. Approx more than 10K community members have approved the whale agents a proven trading bot.

·         The supportive team members of experienced professionals remain to cheer up all the time while offering you the valuable guidelines.

·         Whether you’re looking for some innovative ideas to boost your portfolio or desire to maximize your investment, Whale Agents might be helpful to you for the same.

·         Once after joining this community, you will ready to have lots of elegant ideas, trading tips, and innovative technical analysis data on the basis of their telegram signals.

Features of Whale Agents:

·         Whale Agents is the widest group of the leadership team, consisting of traders and high-class contributors as well as programmers who offer the best insights to you.
·         The entire team is not merely highly dedicated but also supports their customer with extreme politeness and friendly way. They offer the extraordinary proven and trendy guidelines for consistent business growth.
·         You can join the Whale Agents without any cost either by Community Plan or Premium Plan. Even if you are a newbie, you can easily access the premium services with full assistance and support.

Pros of Whale Agents:

This section refers the readers to some of its crucial pros and cons of Whale Agents. First, let’s look at the glimpse at the list of pros—

·         No minimum amount limit to get started, unlike Forex.
·         User’s funds are always under control and protectable from brokers and unwanted middlemen.
·         The delivery signals are provided you directly via the premium telegram channel.
·         The team remains active all the time to monitor the activities of Cryptocurrency and notify the premium members at the very moment they find any marketing opportunity.

Cons of Whale Agents:

While reviewing the While Agents, crypto signals providers, I found merely a single drawback i.e. its high charges.
Their high price rates may lead financial hindrances to some small scale business owners.


After going through its world of key features and user-friendly business model, I assure my readers for its safety and best trading signals on the telegram. Besides, you will also acquire valuable ideas for timely investment; users can clear the doubts and analysis request as well.   

Whale Agents Cryptocurrency Review- December 2018 Whale Agents Cryptocurrency Review- December 2018 Reviewed by arvind gupta on December 15, 2018 Rating: 5

OPC Premium Cryptocurrency Review - December 2018

December 15, 2018
Introduction to OPC Premium:

On Point Crypto (OPC) Premium works on the basis of Telegram. The company entirely based in Germany and owns the team of international Cryptocurrency professionals. The OPC premium is successful to build a loyal group of 4000 followers from all over the globe. The best part is that they deliver their exclusive and premium services at the price affordable for all sorts of business owners. The common strategy they use is proper segmenting of followers to offer them the relevant data rather than sending the bulk of unwanted signals. This way they can effectively reach their subscribers to resolve their queries.  

Supports of OPC Premium

However the team of OPC Premium is not big, but they are showing really outstanding performance in providing their customer with the Top-niche support. All paid customers can easily get in touch with the support team members via a dedicated chat system. The team also consists of well experienced and attentive crypto traders.  

Key features of OPC Premium:

Here is the list of exclusive key features found while reviewing the OPC premium –
·         Users can get in touch with their representatives with the direct call as the main communication channel. Also, customers can use a closed chat to receive innovative ideas, Technical Analysis, and other observations as well.
·         Their business model is quite handy even for a newbie. They have established the three unpaid communication channel- Paid telegram channel, and closed chat corresponding to all paid users.
·         The main objective of OPC Premium is to collect the group of subscribers’ data and relevant market state information.

Advantages of OPC Premium:

Lots of benefits are there for a business owner after joining the OPC premium—

·         You will regularly get notification of valuable insights
·         Even with a small time, they are capable of instant revert to their customer's query. That’s why the team is extremely worth admiring.
·         If you are a paid subscriber, here is something great for you. Use a closed chatting option as a communication channel to get in touch with the administration.
·         The supportive team members of experienced professionals remain to cheer up all the time while offering you the valuable guidelines.
·         Users can buy an annual subscription plan with negligible BTC. Their quality-price ratio matches all the quality standards in the industry.

Disadvantages of OPC premium:

Merely down part of OPC premium is that they don’t offer any monthly subscription. Therefore, it might be costly for beginners and small-scale businesses. Except that, the OPC premium is really outstanding in every aspect.  


OPC Premium supports all sorts of business at every moment with their trader consultants even in case you dedicate your two to three hours a day react to all their recommendations promptly. Recently OPC premium leads the full cycle USDT-BTC-ALT-BTC-USDT both for the sake of strategy and profit. After going through its world of key features and user-friendly business model, I assure my readers for its safety and best trading signals on the telegram. Besides, you will also acquire valuable ideas for timely investment; users can clear the doubts and analysis request as well.   

OPC Premium Cryptocurrency Review - December 2018 OPC Premium Cryptocurrency Review - December 2018 Reviewed by arvind gupta on December 15, 2018 Rating: 5

Analyzing Emotions of Alzheimer Patients

December 14, 2018
Analyzing Emotions of Alzheimer Patients:

Analyzing facial emotions is one of the crucial aspects of expressing views that may be impaired in patients with Alzheimer. The team of experts tested the facial emotion matching, facial emotion labeling, and same different emotion indifference in AD patients. Healthy and elderly volunteers, as well as non-demented psychiatric outpatients, are also included in while examining. Alzheimer patients were more significantly impaired than that of both control groups on all three measures. Facial identity matching procedure was also used to analyze the emotions of AD patients.

Using facial identity matching scores as a covariate provided evidence suggesting the facial emotion processing deficit may be independent of impairment in non-emotional face processing. The victims of Alzheimer also had selective impairment in labeling facial expressions of sadness. After a complete study, we have concluded that patients with Alzheimer disease have deficits in analyzing facial emotions, which may be independent of their impairment in recognizing non-emotional features of faces.

An important aspect of interpersonal communication and a significant modulator of social behavior is facial processing. Facial expressions of emotion are capable to deliver information about another person’s emotional conditions and enable one to forecast another person’s probable actions. Humans can reliably identify Facial expressions that include anger, fear, sadness, happiness, surprise, and disgust. Deficits in the analyzing of some or all facial expressions of emotion might be a contributing factor in the significant social and behavioral impairment observed in patients with Alzheimer’s disease

Traditional studies of analyzing the emotional and non-emotional facial features in Alzheimer’s disease show the variable results. Patients with Alzheimer were significantly impaired in discriminating facial identities rather than discriminating facial emotions. Some experts also found the significant impairments in several tests of facial emotions of Alzheimer patients. They also proved that these impairments were the cause of deficits in analyzing non-emotional facial features and in verbal processing that they also observed in their Alzheimer patients. Some experts also demonstrated the impairment in some measures of facial emotion recognition in Alzheimer diseases rather than the differences in facial identity recognition.

On the other side, they also attributed the facial emotion deficits to impairment in the verbal and spatial processing in the Alzheimer patients. Experts also found the evidence for independent deficits in non-emotional as well as emotional facial recognition job in the Alzheimer disease patients. Some other experts have also found that patients with Alzheimer were generally unimpaired in both analyzing and naming the facial emotions. Although several studies have found variable evidence of impairment in the recognition of facial expressions of emotion in Alzheimer patients, only the two experts Allender and Kaszniak interpret their findings as evidence for a specific impairment in emotional processing, not just an indirect consequence of deficits in verbal, spatial, or other non-emotional skills.

The possibility of deficits in the analyzing of specific facial expressions of emotions in the AD has not been systematically investigated. However, some of the expert’s team reported the preliminary evidence suggesting selective impairment in identifying the facial expressions of fear and contempt in Alzheimer disease. Atrophy and neuropathy-logical changes in the amygdala may happen early in the course of Alzheimer Disease. Due to the lesions of the amygdala have been associated with a specific impairment in processing fearful faces, it is possible that patients with mild or moderate Alzheimer disease would demonstrate the disproportionate the impairment in their capability to detect the facial expressions of fear.

The goal of our study was to go through the ability of a group of patients with Alzheimer, compared with elderly control subjects, to accurately detect and discriminate the facial expressions of six different emotions. The team of expert doctors also identified the facial identity recognition ability as a measure of non-emotional deficits in facial processing. They also included a group of elderly psychiatric outpatients with mood and anxiety disorders as a control group in addition to a group of elderly normal volunteers. Several types of research have also shown that anxious and depressed patients may have deficits in recognizing facial expressions of emotion. Improved rates of anxiety and depression symptoms have been observed in Alzheimer patients but traditional researches of facial emotion processing in Alzheimer disease have not used psychiatric control subjects. Basically, we expected to observe deficits in the non-emotional facial processing in the Alzheimer patients relative to the control groups. Deficits of Facial emotion processing in Alzheimer as well as their relationship to cognitive decline may cause new insights into the neurobiology of Alzheimer and the social and behavioral disturbances that often accompany this illness.

Methods of Analyzing the emotions of Alzheimer Patients


·         The Normal control subjects (NC) as well as the psychiatric control subjects (PC) were selected from the Martinez Veterans Affairs (VA) Outpatient Clinic.
·         Alzheimer patients were recruited from the Alzheimer’s disease Diagnostic and Treatment Centers located at Martinez VA Outpatient Clinic, University of California–Davis and Stanford/VA Palo Alto Health Care System.

All subjects signed a standard consent form approved by the Investigational Review Boards of UC-Davis and the VA Northern California Systems of Clinics. Each and every Alzheimer patients underwent structured diagnostic interviews, formal neurological evaluation as well as neuropsychological testing to assess demographic characteristics, dementia symptoms, and impairment in cognitive functioning and activities of daily living. A team of neurologists, physicians, nurses, and neuropsychologists worked day and night to diagnose the symptoms. The diagnosis of possible Alzheimer was assigned to patients as per the criteria of the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s disease and Related Disorders Associations (NINCDS/ADRDA). Alzheimer patients were excluded if they were noticed to be unable to comprehend the task instructions. None of the Alzheimer patients were reported to have prosopagnosia. All subjects were right-handed and the one who is with the history of head trauma, profound visual or hearing deficits, alcoholism, or serious neurological disorders were excluded.

Facial Processing Assessment

Four tasks of facial processing and three other neurocognitive tasks were assigned to all subjects. The facial processing test battery encompassed the Benton Facial Recognition Test and three facial emotion recognition tasks such as Facial Emotion Matching, Facial Emotion Labeling, and Same different. All tests were administered by either a board-certified geriatric psychiatrist (R.H.) or a trained research assistant.

Benton Facial Recognition Test: 

This test is to measure the subject’s ability to recognize the identity of neutral/non-emotional faces and needs the subject to choose from a set of six 2X3-inch aligned black and white photographs the face with the same identity as the reference face. Six items require one identity recognition response, 16 items require three identity recognition responses and 22 items require a maximum score of 54 correct choices. The remaining of the test battery used standardized photographs from the Japanese and Caucasian Facial Expressions of Emotion which is a series of color photographs of faces depicting six different emotions by seven Caucasian men, seven Caucasian women as well as seven Japanese men and seven Japanese women.

The six emotional expressions identified were anger, sadness, happiness, fear, surprise, and disgust. Data from cross-cultural researched have verified that the photographs accurately depict the intended emotions and demonstrated the reliability and validity of using these facial stimuli to assess the ability of subjects to recognize facial expressions of emotion.

Facial Emotion Matching:

In this task subjects were shown a photograph of the reference face and asked to match the emotion shown on the reference face with one of six simultaneously presented. All those seven photographs (reference and six alternatives) were the faces of different people of the same gender and ethnicity. The reference face was mounted on an 11-inch cardboard mat and the six alternative photographs were horizontally mounted on a 14-inch cardboard mat in a 2X3 alignment. The whole process was repeated with four male and four female reference faces for all six separate emotions and scored 48 correct choices. Facial Emotion Matching is one of the facial emotion recognition tests. The non-emotional cognitive skills required for Facial Emotion Matching were considered to be the most similar to the skills required for the Benton Facial Recognition Test. A comparative performance on these two tasks was done as a primary test of whether Alzheimer patients have deficits in identifying the facial emotion independent of their deficits in non-emotional facial processing.

Facial Emotion Labeling:

In this task, the subject was shown a photograph of the reference face identifying one of the six possible emotions. All these six emotions’ names were printed below the photograph in a 2X3-inch horizontal alignment, with the order of these names randomized across trials. The whole process was repeated with four male and four female reference faces for each of six separate emotions, getting a maximum score of 48 correct choices.

Same–Different Emotion Differentiation: In this task, the subject was shown a pair of photographs of different people of the same sex and ethnicity mounted on an 11-inch cardboard mat. Subjects, then, were asked to state if the two photographs in the pair were the same or different emotions. The process was repeated with five pairs of male and female target faces for all six separate emotions, yielding a maximum score of 60 correct choices. The intentions behind performing these tasks were to provide additional information about facial emotion processing performance in Alzheimer Diseases.

Statistical Analysis

Socio-demographic as well as clinical variables such as age, gender, ethnicity, education, MMSE, Ham-D, and STAI scores were compared in Normal control subjects (NC), psychiatric control subjects (PC), and Alzheimer patients by using analysis of variance (ANOVA). This is followed by post hoc testing with Fisher’s protected least significant difference (PLSD) test.

Facial processing performance was quantified as the total number of correct responses and compared in NC, PC, and AD patients using a MANOVA (model followed by individual ANOVAs). Subsequent post hoc testing was done with Fisher’s PLSD test. The performance comparison was done with an analysis of covariance (MANCOVA) model on the facial emotion tasks in NC, PC, and Alzheimer patients using each subject’s score on the facial identity matching task as a covariate.

The interaction between the ability to recognize specific facial expressions of emotion (fearful, happy, etc.) and the diagnosis was tested by using ANOVA with repeated measures. Also, the relationships between MMSE scores as well as performance on facial processing operations were analyzed by using correlation coefficients. Significant differences are stated for two-tailed P 0.05 for all analyses excluding the ANCOVAs.


There were 22 Alzheimer’s disease patients, 14 NC, and 10 PC. All were right-handed. Table-1 below is describing the demographic and clinical characteristics of Alzheimer patients and control subjects. 37 Caucasians, 7 African Americans, 1 Hispanic, and 2 Asians were there also. Half of the Psychiatric control (PC) subjects received diagnoses of generalized anxiety disorder and remaining received diagnoses of bipolar mood disorder. Patients with Alzheimer Disease scores from 9 to 26 and were definitely lower than those of the PC and NC subjects. No significant variations were found between AD patients, PC subjects, and NC subjects in age, education, ethnicity, Ham-D scores, or STAI scores.

The Psychiatric control group had a definite higher proportion of male subjects. There were no significant correlations between performance on the facial emotion tasks and age, education, Ham-D score, or STAI scores. Performance on Benton Facial Recognition correlated significantly with that of the Facial Emotion Matching (r 0.68; P 0.001), Same–Different Emotion Differentiation (r 0.50; P 0.001), and Facial Emotion Labeling (r 0.39; P 0.01). No significant effect of gender on performance on any of the facial processing tasks was detected.



Age, years
74  8.8
68  6.1
70  6.1
Education, years
14.0   2.8
14.4  2.7
14.3  3.2
State Anxiety
36.3  17.3
29.7  10.3
34.4  13.3
Trait Anxiety
34.2  20.4
30.1  8.5
37.8  13.7
4.0  3.7
4.2  5.5
7.3  5.7
18.5  4.4
29.1  1.4
28.6  1.5

Gender, male:female

Race, white: nonwhite

Table 2 is describing the results of individual ANOVAs for the four facial processing tasks across the three groups. On the basis of MANOVA, the number of correct responses on the facial processing tasks correlated significantly across the groups (F 22.5, df 4,41, P 0.0001). The individual ANOVAs stated that the group significantly affected each of the four facial processing tasks. As per the post hoc testing with Fisher’s PLSD test, Alzheimer patients made fewer correct responses than NC subjects and PC patients on Facial Emotion Matching, Same–Different Emotion Differentiation, Facial Emotion Labeling, and Benton Facial Recognition.

No significant variations were found between the NC and PC groups. The AD patients’ performance on two of the four facial processing tasks was significantly correlated with the severity of dementia as measured by the MMSE.

Significant Findings

Emotion Matching
0.69  0.13
0.88  0.07
0.81  0.08
F  16.0, df  2,43, P  0.0001
AD  NC, P  0.0001

AD  PC, P  0.003
0.75  0.12
0.91  0.05
0.83  0.06
F  12.0, df  2,43, P  0.0001
AD  NC, P  0.0001

AD  PC, P  0.03
Emotion Labeling
0.64  0.13
0.80  0.09
0.76  0.11
F  9.5, df  2,43, P  0.0004
AD  NC, P  0.0002

AD  PC, P  0.009
Facial Recognition
0.54  0.10
0.80  0.09
0.73  0.11
F  31.1, df  2,43, P  0.0001
AD  NC, P  0.0001

AD  PC, P  0.0001


Our study illustrates the significant impairment in the ability to analyze the facial expressions of emotion in Alzheimer patients when compared to both normal elderly volunteers and elderly, non-demented psychiatric outpatients with mood or anxiety disorders. This impairment is proof in a Facial Emotion Matching task, a Same–Different Facial Emotion Differentiation task, and a Facial Emotion Labeling task.

We found that impairment on Facial Emotion Matching is correlated with the results of Cadieux and Greve who were the only previous investigators to use such kind of test. We found that impairment in the Same–Different Emotion task is consistent with the results of Albert et al and Cadieux and Greve but contrasts of Roudier et al. However, the several authors tested their subjects just by the four trials each with four expressions (happy, sad, angry, and indifferent). This more limited test may due to their discrepant findings. We found as similar observation of impairment on Facial Emotion Labeling as the results of Albert et al, Cadieux and Greve, and Roudier et al but contrasts with the negative observations of Lavenu et al.

A team of specialists cum researchers found that AD patients were significantly impaired in recognizing nonemotional facial expressions as per the Benton Facial Recognition Testing results. Majority of investigators have found the deficits in recognition of facial expressions of emotion in an AD are the cause of underlying deficits in verbal, spatial, and facial processing rather than the result of a primary impairment in emotional processing.

Fractal and Multifractal Analysis and Speech

Speech emotion recognition is the most vital part of the field of human-computer interaction for important applications like early detection of Alzheimer's Disease- (hereafter AD). The main intention behind this article is to introduce a new feature for emotion detection from speech via the Multi Fractal Detrended Fluctuation Analysis (MFDFA) method. We are here to state a quantitative parameter for categorizing various emotions by recognizing the non-stationary details of the speech signal dynamics, produced out of differing emotions. The resulted negative features in this parameter on Angry and Sad emotion can be used for diagnosis of Alzheimer Disease. It’s a positive step towards early detection of the AD through speech analysis.

According to Mandelbrot fractal can be considered a geometric pattern which is iterated at smaller or larger scales to generate self-similar, irregular shapes or surfaces that is too difficult or impossible with Euclidian geometry. Fractal systems can extend to insurmountable large values of their coordinates and outwards in several directions from the center.

Another crucial feature of the fractal system is its self-similarity. Smaller and bigger fragments of a system look similar but not sure that they are identical to the entire fractal system. On account of stating the self-similarity features of the large and small, a distinctive power-law i.e., a mathematical pattern having the frequency of an occurrence of a given size is inversely proportional to some power-(n) of its size.), is applied. Here n implies the scaling exponent or the fractal dimension of the system. Fractals can be classified into two types: mono-fractals and multifractals.

Mono Fractals and Multi-fractals:

Mono fractals have as same scaling properties as in the different zones of the system. Multi-fractals are more complicated and self-similar objects that encompass with differently weighted fractals with multiple noninteger dimensions. This is why their scaling characteristics are different in different zones of the systems.

Detrended Fluctuation Analysis (DFA)

DFA is the way of scaling analysis where the scaling exponent which are similar to a single scale Hurst exponent or the Fractal dimension, is used to quantify the long-range correlation of stationary and non-stationary signal. Hurst exponent or the Fractal dimension of the non-stationary signal identifies the entire properties of the signal. The current research with complex systems states that naturally evolving geometries and processes can’t be characterized by a single scaling ratio as the different parts of the system are scaled differently.

Such system characterized with the better way as in multi-fractal system. MFDFA (Multifractal Detrended Fluctuation Analysis) method has been first applied successfully to research for the multi-fractal scaling behavior of several non-stationary scales invariant time series by Kantelhardt. This is the robust tool to perform scaling analysis in case of nonlinear, non-stationary time series.

Preserved and impaired emotional memory in Alzheimer’s disease:

Alzheimer patients with early atrophy of both limbic structures included in memory and emotion processing in AD deliver a unique clinical population for detecting how emotion is able to modulate retention processes. Our entire study centrally focuses on the emotional enhancement effect (EEE), EEE can be defined as the enhancement of memory for emotional events when compared to neutral ones. The EEE assessment for different memory systems in AD stats that the EEE could be preserved under specific retrieval instructions.

The very first step of this study investigates these data in light of compelling evidence that the amygdala can modulate processes of hippocampus-dependent memory. We also kept several facts in the favor of EEE that it could be a useful paradigm to reduce impairment in episodic memory tasks.

In the second step, we stated the theoretical results of the findings in favor of an EEE. As per these results we concluded the compensatory mechanism in patients with AD solicits greater amygdala functioning or additional networks even at the same time when amygdala atrophy is present. These views emphasize the significance of investigating patients with Alzheimer Diseases to understand the relationship between emotion and memory processes.

Effects of Emotions on Preserved and Impaired Memories in Alzheimer’s disease:

Memory impairments are the basis of cognitive dysfunctions described in Alzheimer’s disease. Experts are sustained by lesions of the MTL(medial temporal lobe), particularly of the hippocampus (Jack et al., 1997; Mori et al., 1997; Simic et al., 1997; Mizuno et al., 2000). Evidence of neuroimaging stated in parallel with the development of lesions in the hippocampus, the amygdala undergoes early atrophy in Alzheimer Disease (Basso et al., 2006; Horinek et al., 2007; Poulin et al., 2011). The amygdala is strictly implicated in the context of emotional processing as well as memory, raising several questions about the most possible impaired mechanisms in Alzheimer Disease.

Although AD researches on emotional processing have sometimes described the spared emotional abilities as well as preserved physiological responsiveness to emotion, still an important issue to evaluate could be such as how emotions modulate memory performance. In the reference of normal aging, lots of researches and studies have stated that the emotional content might improve memory performance with comparison to non-emotional content. In this task, our team of experts also investigated how the EEE on memory performance evolves in healthy older adults (HOA) compared with patients with Alzheimer.

Researches on the emotion effects on memory in Alzheimer Disease stated the discrepant results. Some studies showed an EEE or a necessary effect of emotion on memory, (emotional material is more significantly recalled than neutral material). While some other researches stated no emotional advantage (emotional material is less recalled than neutral material, or equivalently recalled). Hence this report effects, along with the key factors that could modulate the EEE.

We went ahead to compare in detail these emotional effects in patients with Alzheimer Disease and HOA with respect to the variations in their overall memory performance. The literature delivers the core part of this review and was obtained by searching PubMed, PsycArticles, PsychINFO, and Psychology and Behavioral Sciences Collection databases for English language articles. It evolves the key points such as “Emotion, “Memory” and “Alzheimer”. There were no restrictions placed on the year with all articles up to May 2012 included. All sorts of relevant papers from the reference lists of identified papers were also reviewed.

In these papers experts had focused mainly on AD patients, and included only studies with samples of people with this dementia. Additionally, merely those researches were included that were considered with the diagnostic criteria used to identify AD patients which is suggested by the two—

·         The one is National Institute of Neurological and Communicative Disorders and Stroke (NINCDS)
·         The second is AD and Related Disorders Association(ADRDA)

Besides these research institutes and organizations, the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders also showed their reports. However, the two studies were excluded as the patient group did not distinguish between individuals with AD and those with mixed dementia. Obviously, the research had to include at least one explicit test of the emotional effect on memory performance means a variation of memory performance between an emotional and a neutral event. Consequently, this final criterion, the studies added in this article dealt mostly with declarative memory. Approx 22 studies concerning the EEE on memory performance in AD were capable to include in the recent review.

Emotional Invariance in Alzheimer’s disease:

AD patients usually suffer with deficits in multiple cognitive domains as well as disturbances of behavior or changes in comportment can also be associated with AD. The range of behavioral changes in AD is protean, ranging from social withdrawal to disinhibition or irritability. Apathy, a lack of feeling or emotional indifference looks like the most common neuropsychiatric disorder associated with AD. Caregivers usually state the emotional indifference-apathy as a symptom that results in degradation of quality of life It also contributes to alienation from friends and family, producing caregiver stress.

Usually, the symptoms of apathy as well as abulia are confounded as emotional experiences and the awareness of conditions which can produce emotions generates emotions. This is why, apathy results in presence of abulia. However, Abulia can occur even apathy is not present. Majority of behavioral measures as well as inventories used to assess for apathy, for instance Marin’s Apathy Evaluation Scale, constitutes queries about goal oriented behaviors. The Neuropsychiatric Inventory assesses 12 domains with apathy, but no domain was found labeled as abulia. Additionally, the query about apathy inquired, “Does the victim seem less interested in his/her usual activities?” This query is asking about the presence of abulia rather than apathy. AD Patients can have emotional communication disorders. These obstacles with emotional communication and abulia might be responsible for the impression that patients with AD are emotionally apathetic. Further, several emotional experiences are produced by perceiving stimuli and knowing the situations. Therefore, patients with AD might also appear apathetic as they do not know the circumstances that normally induce emotions.

The main aim of this study was to examine whether the AD patients have alterations in their emotional experience as per their valence ratings of emotional stimuli (International Affective Picture System). Hence, we acquired healthy subjects and AD subjects make valence judgments by showing sheets of paper that had happy faces at the proximal end of the paper and sad faces at the distal end or vice versa in a counterbalanced order.

Methods to analyze the emotional indifference in AD:


Seven right-handed subjects who are in the criteria for clinical diagnosis of AD and eight healthy comparison subjects involved in this research. The mean of Alzheimer’s disease patients’ age (four women and three men) was 72.83 and recorder SD=12.31. The mean comparison subjects’ age (seven women and one man) was 59 and recorded SD=11.08. No significant difference was found in the two age groups. However, we should mention that the age of two comparison subjects was not already known. The patients were selected from the Memory Disorder Clinic at the University of Florida, and the comparison subjects were selected from the community or were spouses of the patients.

On the basis of clinical assessments, all subjects were free of any major psychiatric disturbance such as depression or anxiety. We did not use any measure of depression with all our experimental subjects still we can repeatedly evaluate the same patients over the course of many years, and as a part of our clinical evaluation we regularly ask about symptoms or signs of depression. As per the results of these clinical evaluations and the use of the Geriatric Depression Scale ranking that were available for some AD patients, we were sure that our subjects with AD were not suffering with major depression while our study. No any neurological diseases histories were found with the subjects. The mean of score for the MMSE (Mini-Mental State Examination) was 24.71 and recorded SD was 2.62 for the subjects with AD and 29.5 and recorded SD was 0.53 for the comparison participants. The demographic information and cognitive status details of the Alzheimer’s disease population are summarized

Apparatus and Procedures:

Our institutional review board approved the study as well as all subjects provided informed consent. The subjects were queried to judge the pleasantness and unpleasantness of 20 pictures having 10 positive and 10 negative. These pictures were selected from the IAPS (International Affective Picture System) which is a commonly used emotional probe that depicts emotion-laden scenes to produce emotional experiences. Healthy subjects rated all these pictures and selected the experimental stimuli as per the normative valence as well as arousal ratings. The selected pictures portray can have the both emotional facial expressions and emotionally evocative scenes. For instance, the unpleasant pictures consisted of the vicious animals and violent acts while the pleasant set added babies, couples, and sports activities. As per the normative arousal ratings, pleasant and unpleasant pictures were similar.

These pictures’ presentation order was first randomized and then every other subject acquire the reverse order of the original set 1–30 and 30–1 to counterbalance for order. These pictures were displayed on a table directly in front of the subject.

Each score sheet was 27.9 cm in length and was blank aside from a happy face at one end and a sad face on the other end. The faces were all vertically oriented such that both faces were aligned with the subjects’ midsagittal plane, and one face was above (distal to) the other. The proximal versus distal positions of these sad and happy faces were counterbalanced. Every subject judged 5 out of 10 positive (or negative) pictures with the happy face distal and the other 5 with the happy face proximal, so that a proximal or distal spatial bias would not influence the results of the valence ratings.

All participants were instructed to first look at a picture and judge how pleasant or unpleasant they found the picture. They were then instructed to indicate their judgment by making a mark on the score sheet placed in front of them. If they found the picture pleasant, they should make their mark toward the happy face. If they found the picture unpleasant, they should make their mark toward the sad face. The more pleasant they found this picture, the closer their mark should be to the happy face, and the more unpleasant they found this picture, the closer their mark should be toward the sad face.

After each subject rated all 20 pictures, each of the subject’s marks was measured from the center of the happy or sad face that was congruent with the valence depicted in this picture (the smiling face for the positive pictures, and the sad face for the negative pictures) (XF= distance between the happy or sad face and the mark). Deviations above (distal) and below (proximal to) the actual midline were measured to the nearest 0.5 mm. For the valence ratings, the dependent measures consisted of the distances, in mm, from the appropriate face (“happy” or “sad”) to the participants’ mark (XF).

Since the main aim of this research is to know if the AD patients when compared to comparison subjects have alterations in their emotional experiences and judgments (valence) or spatial attentional biases, we conducted two separate analyses to assess each of these factors. Regarding valence, the data were analyzed using a 2 (group: Alzheimer’s disease and comparison) × 2 (valence: positive and negative) mixed factorial ANOVA, with group being the between-subjects factor and valence the repeated within-subject factor.

The results indicated no significant main effect for valence and no significant interaction between group and valence. However, the main effect of group was significant (F=5.244, df=1, 13, p=0.039), with the patients affected by Alzheimer’s disease judging the pictures’ valence as less intense (mean=5.82 mm, SD=2.02) than did the comparison subjects (mean=4.25 mm; SD=1.17).

An inconsistent response was defined as placement of mark closer to the face that expressed the emotional valence that was inconsistent (contrary) to the viewed stimulus (picture). For example, if the subject was shown a violent act and subsequently made their response mark closer to the face depicting pleasant emotions (face with a smile), this response would be graded “inconsistent.”

To examine whether the differences in valence judgments were due to impaired comprehension of the instructions or of the pictures, we conducted an analysis of the percentage of consistent responses for both groups. The proportions of consistent scores were calculated by dividing the total number of correct responses by the total number of responses. The results of a 2 (group: Alzheimer’s disease and comparison) × 2 (valence: positive and negative) mixed factorial ANOVA, with group as the between subjects factor and valence as a repeated factor, indicated no significant group × valence interaction. The main effect for valence was also not significant. However, a significant main effect for group was found (F=9.15, df=1, 13, p=0.01). Inspection of the means indicated that the proportion of correct responses for the comparison group (mean=0.98, SD=0.037) was greater than for the Alzheimer’s disease group (mean=0.87, SD=0.095), regardless of the emotional valence.


Our results indicate that when subjects with Alzheimer’s disease are asked to assess the emotional valence of pictures, they judge the pictures that portray pleasant scenes as less pleasant than do the comparison participants, and when they see unpleasant pictures they rate these as less unpleasant than do the comparison subjects. These consequences states that patients with Alzheimer’s disease have a decreased emotional experience and/or responses to seeing emotional scenes. Therefore, AD patients have a reduced emotional response is not known, however there are several other possibilities.

The mean of subjects’ age with AD was higher than that of the comparison subjects. Although it is possible that with aging there is a decrease in the intensity of emotional experiences, Grühn and Scheibe, using the International Affective Picture System (IAPS), demonstrated that older subjects perceived negative pictures as more negative and positive pictures as more positive. Thus, the age of our subject groups could not explain our results. Although we did not have the ages of two of our comparison subjects, in order for the comparison subjects to have a mean age that is greater than that of the experimental group, the two comparison subjects, whose age was not recorded, would have to have a mean age of approximately 115 years. Since this is very unlikely, we doubt that age can be the major determinant of the between-group differences. Future research, however, should address the influences of age, gender, and education level.

Depression often accompanies AD, and as per the earlier notifications, patients with depression might have a reduced emotional response. However, our subjects did not clinically manifest depression. In addition, in prior studies when subjects with and without depression were asked to report their emotional experiences to positive and negative pictures, their results revealed significant differences in response to positive images (e.g., less pleasant valence, decreased happiness, increased sadness) but no clear group differences in response to negative stimuli. We found no significant interactions between group and valence, providing additional support for the postulate that the emotional indifference displayed by our Alzheimer’s disease subjects was not a result of depression.

Since the work of William James, there have been many theories of emotional experience which suggest that alterations of arousal are a critical element of emotional experience. 26 Studies of patients and animals have revealed that there are three cortical areas that appear to be involved in the “top down” or cortical control of arousal, including the inferior parietal lobes, the frontal lobes (dorsolateral and medial), and the cingulate gyrus. The “bottom up” control of arousal is mediated by the ascending thalamic reticular system, including the intralaminar and parafascicular nuclei of the thalamus. The ascending neurotransmitter systems that appear to be important in bottom-up cortical activation include the cholinergic system that projects from the basal forebrain’s nucleus of Meynert to the cerebral cortex and the norepinephrine system which projects from the locus coeruleus to the cerebral cortex.

With AD there is usual degeneration of the nucleus of Meynert-cholinergic as well as the locus coeruleus-norepinephrine systems. In addition, there is also degeneration of the parietal-frontal-anterior cingulate network that helps to modulate arousal systems. Marshall et al. demonstrated that on post mortem examination the subjects who appeared to be apathetic when assessed by the NPI prior to death had degeneration of the anterior cingulate gyrus. This is why, hope in our AD subjects, it was degeneration of these portions of the arousal network that accounted for their reduced valence ratings. Although there have been several studies of patients with Alzheimer’s disease that have demonstrated reduced phasic alerting or phasic arousal we are not aware of an arousal responsiveness investigation to emotional pictures in AD, and future research might want to more directly test this arousal hypothesis.

Van Stegeren et al. 30 recently published an FMRI study investigating the effect of propanolol (which blocks the noradrenergic activation in the brain) or placebo on the amygdala’s activation while healthy subjects were watching neutral or highly negative pictures. Their results indicated that the propanolol selectively decreased the amygdala’s activation for the emotional pictures, providing support for the postulate that the neurotransmitter norepinephrine modulates the activity of the amygdala when processing emotional stimuli. As per results, we might investigate that norepinephrine system degradation in patients with AD might have an impact on the amygdala’s activation which causes a lower arousal and a reduced valence rating of the presented emotional pictures. Future research will have to be performed to elucidate the mechanisms for the abnormal behaviors we observed.

Each score sheet was 27.9 cm in length and was blank aside from a happy face at one end and a sad face on the other end. The faces were all vertically oriented such that both faces were aligned with the subjects’ midsagittal plane, and one face was above (distal to) the other. The proximal versus distal positions of these sad and happy faces were counterbalanced. Every subject judged 5 out of 10 positive (or negative) pictures with the happy face distal and the other 5 with the happy face proximal, so that a proximal or distal spatial bias would not influence the results of the valence ratings.

All participants were instructed to first look at a picture and judge how pleasant or unpleasant they found the picture. They were then instructed to indicate their judgment by making a mark on the score sheet placed in front of them. If they found the picture pleasant, they should make their mark toward the happy face. If they found the picture unpleasant, they should make their mark toward the sad face. The more pleasant they found this picture, the closer their mark should be to the happy face, and the more unpleasant they found this picture, the closer their mark should be toward the sad face.

After each subject rated all 20 pictures, each of the subject’s marks was measured from the center of the happy or sad face that was congruent with the valence depicted in this picture (the smiling face for the positive pictures, and the sad face for the negative pictures) (XF= distance between the happy or sad face and the mark). Deviations above (distal) and below (proximal to) the actual midline were measured to the nearest 0.5 mm. For the valence ratings, the dependent measures consisted of the distances, in mm, from the appropriate face (“happy” or “sad”) to the participants’ mark (XF).

Feeling of emotions of patients with AD long after memory has faded

As per one of the studies, patients with AD continue to feel happy as well as sad long after they have forgotten watching an emotional film, Experts stated that patients with AD are 'profoundly impacted emotionally by events that they are unable to recall' and that this constitutes several  implications for carers and family members. The worse the memory of AD patients, the longer the feeling of sadness persisted for. It was noticed while researching that being unable to remember what made you sad makes it more difficult to lift the mood.

As per one of the patient during the study: "I feel my entire emotions and feelings are rushing towards me. It’s confusing a lot and I hate such kind of feelings.”

One of the experts and researchers said that the persistence of this patient’s intense negative emotion and her inability to conjure up a logical explanation due to her feelings illustrate the bewilderment that a patient with AD may feel in the face of an apparently inexplicable feeling." This is the contrast to the fact that if you erase the painful memory then the depression will ease also. Approximately 850,000 people are found with dementia in the UK and almost two thirds of whom are suffering from AD. This is forecasted to rise to two million by 2051. During the research group for 17 patients with AD were shown films, one sad and one happy and then given tests to detect how much of the film they could remember. They also noted emotions they feel over time. The entire observations were compared to a similar group of healthy people. Some patients didn’t remember what they watched in a film at all yet still described the emotions triggered by it and the effects lasted for at least 30 minutes. All these researches were published in the journal Cognitive and Behavioural Neurology.


Due to the trend of values of the Hurst exponent and the widest range of the multi-fractal spectrum, we can assure that with the multi-fractal analysis we can clearly differentiate between fundamental emotions like anger and sadness. Unlike other neurocognitive impairments, AD can’t be detected at the very early stage. It’s due to the mildness of its early symptoms. Usually, 2 to 3 years are taken to take medical prescription after the onset of the symptoms. Moreover one of its major symptoms is the lack of cognitive and perceptual ability as speech. In this task, we propose one quantitative parameter (multi-fractal spectrum range) to capture the change in intricate speech spoken dynamics by normal subject, out of different emotion. Therefore, an aberration from the normal trend of multi-fractal spectrum widths for different emotion can be contributed towards the onset of neurocognitive impairments like the AD. So we can express this method as a non-invasive method for early detection of Alzheimer Disease. Eventually, we propose to signify the inferences through a larger database and devise a simple 'android' application for a regular checkup for elderly people for self-assessment and monitoring regarding the detection of Alzheimer Disease.

Analyzing Emotions of Alzheimer Patients Analyzing Emotions of Alzheimer Patients Reviewed by arvind gupta on December 14, 2018 Rating: 5
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