1. Introduction

• lack of interaction environments;

• illnesses;

• psychological vulnerability and fragility.

• a dramatic annulment of the person’s future perspective.

• associations, statistically treated, both between symptoms and between life themes. The analysis of these associations is relevant when it is conducted at patient level. These associations must be analyzed in close connection with the similarities/ dissimilarities of the symptoms/life themes. For example, strong and significant positive associations between dissimilar symptoms can indicate the presence of syndromes.
  Using the concept of transitivity from mathematics (if A & B and B & C, then A & C), we can identify dysfunctional cycles of symptoms/life themes. These cycles signal simultaneous and multiple associations between symptoms or between life themes.

Figure 1. Block diagram of the research

Figure 1. Block diagram of the research

2. Materials and methods

2.2. Research design

2.2.1. Type and subtype of research

Our research is quantitative, non-experimental, exploratory, retrospective and descriptive.

2.2.2. Research questions

The questions that the research aims to answer are:

• What symptoms and life themes are present in middle-aged adults diagnosed with major recurrent depression?

• What relationships are there between symptoms and life themes?

• What patterns of association manifest both between symptoms and between life themes, and what are the significances of these patterns?

To address these questions, we conducted the data collection procedure presented below.

2.2.3. Data collection procedure

To identify the symptoms and life themes, we asked each patient to concisely present, within a maximum of one page, the happiest, unhappiest and some daily events. What was the premise of this request?

The absence of future perspective experienced in MD, engages the patient in a costly strategy of comparing an unfavourable present with an unaffected past by this absence. The reason behind such a strategy may lie in the need to sustain a state of seeking answers that justify and enable the acceptance of the traumatic event. In our opinion, the mood of patients is maintained by the comparison between the unhappiness and happiness themes, as well as by the presence of a daily theme encompassing activities, concerns and so on, necessary for managing the current psychological state.

At every moment in life, every person experiences a combination of psychological states. However, he/she is only aware of one of these states, probably the most relevant one. In the approach presented below, we have attempted to obtain relevant symptoms from the patients using their reports.

The happiest, unhappiest and daily events presented by each patient have been grouped into the theme of happiness, the theme of unhappiness, and the theme of everyday life. Each of the three themes was divided into life subthemes. Each life subtheme (Ti) was divided into groups of meaningful words. Each group was described based on the symptom that the patient experienced at the time of the request. For this purpose, each patient was asked to select one of the following symptoms (Sis): anxiety, trust, apathy, zest for life, fury, tranquility, regret, gratitude, low self-esteem, high self-esteem, fatigue, euphoria, sadness, happiness. Previously, all these symptoms were explained to the patients. To ensure that the patient accurately chose Si, we asked him to justify his choice.

Thus, for each patient, we obtained the matrices presented in Figure A1−Figure A2. By aggregating the eleven matrices, we obtained the matrix of Sis and Tis at the level of the group of patients (see Table 4).

2.2.4. Data processing methods

To identify and analyze the relationships between Sis and Tis, as well as the patterns of association of Sis and Tis recorded at both group and patient levels, Spearman correlation and latent semantic indexing (LSI) technique were used.

LSI is a frequently used method in the analysis of relationships between words (rows) and the documents that contain them (columns). LSI uses the method of singular value decomposition (SVD) to reduce the number of rows, identifying irrelevant words, without altering the similarity structure between documents [35]. Applying this method allows for the identification of the structure and importance of words in each document, the assessment of similarities between words, documents, the assessment of the similarities between documents and selected keywords, the identification of hidden correlations between words and between documents, and so on.

Thus, in the context of the work, the matrices that have been processed using LSI are shown in Table 4 and Figure A1-Figure A2. These matrices describe the distributions of Sis (rows) along Tis (columns), and have been processed to obtain the importance of each Si in each Tj (the matrix of tfidf (term frequency-inverse document frequency) scores), the approximated matrices of the matrices of tfidf scores, the matrices of similarity of Sis and Tis, and the correlation matrices of Sis and Tis. Some of these calculations are performed at both group and patient levels. All these results are interpreted and discussed in section 3 and 4.

The block diagram of the research calculations is presented in Figure 2.

Figure 2. Block diagram of research calculations

Figure 2. Block diagram of research calculations

Some of the mathematical preliminaries used in LSI are presented in appendix A1. Calculations were done in Matlab. The formulas and MATLAB commands used to obtain the aforementioned results are:

Formulas:

$tfidf(i,j)=(1+log2(f(i,j))\times (log2(N/df(i\left)\right))$ [36], where tfidf(i,j) is the tfidf score of the score positioned at row i and column j, f(i,j) is the frequency of Si in Tj, N is the number of Tis, df(i) is the number of occurences of Si in all Tj;

cosine similarity($\overrightarrow{C},\overrightarrow{D}$)=$\frac{\overrightarrow{C}·\overrightarrow{D}}{\left|\overrightarrow{C}\right|\left|\overrightarrow{D}\right|}$ for cosine similarity calculation between the vectors $\overrightarrow{C}$ and $\overrightarrow{D}$, -1≤cosine similarity($\overrightarrow{C},\overrightarrow{D}$)$\le 1$. To calculate the similarities of Tis and Sis, we wrote and ran a computer program;

$\rho =1-6\frac{\sum d_i^2}{n^3-n}$, where $\rho$ is Spearman correlation coefficient between two vectors, $d_i$ is the difference between two ranks and n is the number of observations, -1$\le \rho \le$1.

MATLAB commands:

$<[U,S,V]=svd\left(A\right)>$ for the singular value decomposition of the matrix A;

To identify k relevant symptoms, we used the following set of Matlab commands:

From $\frac{{\sum }_{i=1}^k{\sigma }_i^2}{{\sum }_{i=1}^N{\sigma }_i^2}\approx 0,9$ we get k, where ${\sigma }_i$ are singular values of the matrix A;

$<P=inv\left(S_k\right)\times U_k^T>$, where $S_k=S(1:k,1:k)$, $U_k^T=transpose\left(U(:,1:k)\right)$;

$<bar(X,P(l,:\left)\right)>$ we extract from line l, with $1\le l\le k$, the symptom with the highest absolute value;

$<B=U(:,1:k)\times S(1:k,1:k)\times V^T(1:k,:)>$ used to obtain the approximation of the matrix A by reducing its dimensionality (to k rows);

To represent Tis in the semantic space {S6,S2,S5}, we used the command

$<S(1:3,1:3)\times V{(1:3,1:26)}^T>$.

To obtain the ranking of the similarities of Tis in relation to Si or groups of Sis, we used the following MATLAB command set:

First of all we establish the criterion, for example S6, based on which we rank Tis. Then,

$<qs6=[0,0,0,0,0,(1+log2\left(1\right))·(log\left(2\right(N/df\left(6\right)\left)\right),0]>$

$<qS6=P\times transpose\left(qs6\right)>$

$<E=inv\left(S(1:k,1:k)\right)\times transpose\left(U_k\right)\times tfidf>$

$<sims6=dot\left(transpose\right(qS6),E)>$

$<normkfactors=\left[norm\right(E(:,1)),....,norm(E(:,26)\left)\right]>$

$<simS6=sims6./\left(norm\right(qS6)·normkfactors)>$

To calculate the Spearman correlations between Tis we used the command

$<corr\left(C{,}^{\prime }Typ{e}^{\prime }{,}^{\prime }Spearma{n}^{\prime }\right)>$, where C is the approximated matrix of the matrix of tfidf scores. To calculate the Spearman correlations between Sis, we used the command $<corr\left(transpose\left(C\right){,}^{\prime }Typ{e}^{\prime }{,}^{\prime }Spearma{n}^{\prime }\right)>$.

3. Results

Table 1. Aligning the results with the research questions

3.1. Symptoms and life themes/subthemes

Out of the fourteen Sis proposed for selection to describe the meaning of the groups of words that covered the themes of happiness, unhappiness and everyday life, the patients have only selected seven: anxiety, apathy, fury, regret, low self-esteem, fatigue, sadness. This is a severe homogenization of their life histories in terms of morbid experiences. The procedure described in section 2.2.3 led to twenty six Tis and seven Sis, which were coded in Table 2:

Table 2. Coding of Sis and Tis

Table 2. Coding of Sis and Tis

Sis	Code	Tis	Code
Anxiety	S1	Personal achievement	T1
Apathy	S2	Achievement of others	T2
Fury	S3	Personal loss	T3
Regret	S4	Loss of others	T4
Low self-esteem	S5	Devaluation of places	T5
Fatigue	S6	Failure	T6
Sadness	S7	Escape from failure	T7
		Danger	T8
		Personal chance	T9
		Child’s chance	T10
		Getting close to loved ones	T11
		Away from loved ones	T12
		Communication	T13
		Suffering	T14
		Childhood	T15
		Meditation	T16
		Regret of own existence	T17
		Care	T18
		Meaning of places	T19
		Center of attention	T20
		Utility	T21
		Help	T22
		Envy	T23
		Attachment	T24
		New	T25
		Humiliation	T26

Belonging of Tis to life themes is presented in Table 3.

Table 3. Belonging of Tis to life themes

Table 3. Belonging of Tis to life themes

Tis	Happiness theme		Unhappiness theme		Daily theme
T1	x
T2			x		x
T3	x		x
T4					x
T5	x
T6	x
T7			x
T8			x
T9	x
T10	x
T11			x
T12					x
T13	x				x
T14			x
T15	x
T16					x
T17			x
T18					x
T19	x
T20	x
T21					x
T22			x
T23			x
T24					x
T25					x
T26			x		x

3.2. Matrix of Sis and Tis at the level of the group of patients

The synthesis of the information regarding the Tis of the eleven patients led to the matrix presented in Table 4, where the numbers in the matrix indicate how many times Si has been mentioned in Tj.

The most common Si, in terms of occurrences in all Tj is S7 (21 out of 26 possible occurrences). S7 is followed by S1, S3 and S4 (18 out of 26 possible occurrences), S5 (16 out of 26 possible occurrences), S6 (15 out of 26 possible occurrences) and S2 (12 out of 26 possible occurrences).

T3 is the most mentioned in S1, S2, S3, S4 and S7, T1 is the most mentioned in S6, and T18 is the most mentioned in S5.

Table 4. Si − Tj matrix.

Table 4. Si − Tj matrix.

S/T	T1	T2	T3	T4	T5	T6	T7	T8	T9	T10	T11	T12	T13	T14	T15	T16	T17	T18	T19	T20	T21	T22	T23	T24	T25	T26
S1	26	2	30			1	2	9	2	3	2		1	7	3	2	1	4	3					4	11
S2	6	4	20	2									4	1	3	1	1	7			1		3
S3	15	2	45	1	2		3	7		1				5	2	3	12	8	2	4			1	5		2
S4	28	2	38	1	4	4	6	6		1			5	3	9	3	1	25	3	1		3
S5	5	1	4		1	2		1			3		1	3	6	2	5	13	5	3	2
S6	23	4	20		1	1		9			2	2		3	6	2	6	6	6						12
S7	52	7	61		2		12	16	3		3	8	5	1	5	10	6	2	2		3	1	3	5	3

3.3. Matrix of the importance of Sis on each Tj at the level of the group of patients

The matrix of the importance of each Si on each Tj is presented in Table 5. The zero scores correspond to the cells in Table 4 where Sis are not reported. The other scores within the matrix were calculated using the formula of tfidf scores presented in section 2.2.4. Let us compare the importance of S1 and S7 in T1. How can we explain the fact that S1 is more important than S7 despite the higher frequency of S7? The answer lies in the number of occurrences of S1 in all Tis (df(1)), which is lower than the number of occurrences of S7 in all Tis (df(7)). tfidf scores are significantly influenced by df(i). The lower the df(i), the higher the tfidf scores, and vice versa. A Si occurring in all Tis becomes irrelevant, even though it indicates a constant presence. A Ti is important by what sets it apart from other Tis. We cannot differentiate Tis based on omnipresent Sis.

Table 5. Matrix of tfidf scores.

Table 5. Matrix of tfidf scores.

S/T	T1	T2	T3	T4	T5	T6	T7	T8	T9	T10	T11	T12	T13	T14	T15	T16	T17	T18	T19	T20	T21	T22	T23	T24	T25	T26
S1	3.02	1.06	3.13	0	0	0.53	1.06	2.21	1.06	1.37	1.06	0	0.53	2.02	1.37	1.06	0.53	1.59	1.37	0	0	0	0	1.59	2.37	0
S2	4.00	3.35	5.94	2.23	0	0	0	0	0	0	0	0	3.35	1.12	2.88	1.12	1.12	4.25	0	0	1.12	0	2.88	0	0	0
S3	2.60	1.06	3.44	0.53	1.06	0	1.37	2.02	0	0.53	0	0	0	1.76	1.06	1.37	2.43	2.12	1.06	1.59	0	0	0.53	1.76	0	1.06
S4	3.08	1.06	3.31	0.53	1.59	1.59	1.90	1.90	0	0.53	0	0	1.76	1.37	2.21	1.37	0.53	2.99	1.37	0.53	0	1.37	0	0	0	0
S5	2.33	0.70	2.10	0	0.70	1.40	0	0.70	0	0	1.81	0	0.70	1.81	2.51	1.40	2.33	3.29	2.33	1.81	1.40	0	0	0	0	0
S6	4.38	2.38	4.22	0	0.79	0.79	0	3.31	0	0	1.59	1.59	0	2.05	2.84	1.59	2.84	2.84	2.84	0	0	0	0	0	3.64	0
S7	2.06	1.17	2.14	0	0.62	0	1.41	1.54	0.80	0	0.80	1.23	1.02	0.31	1.02	1.33	1.10	0.62	0.62	0	0.80	0.31	0.80	1.02	0.80	0

In the following we will present the ranking of the importance of Sis for each Tj (at group level). For this purpose we applied methods specific to LSI. The key element of the LSI method is to reduce the dimensionality of the matrix presented in Table 5. Dimensionality reduction can be achieved by reducing Sis considered irrelevant.

Thus, S6, S2, and S5 have been identified as the most relevant (important) symptoms (see Appendix B, Table A1). The other four Sis were considered irrelevant.

The approximation of the matrix of tfidf scores, (see Table 6 and section 2.2.4), by reducing dimensionality enables:

− transformation of Si−Tj matrix into a dense one;

− ranking of the similarity of Tis in relation to Si or groups of Sis.

The approximation matrices of tfidf scores are computed similarly for each patient.

Table 6. Approximation of the matrix of tfidf scores.

Table 6. Approximation of the matrix of tfidf scores.

S/T	T1	T2	T3	T4	T5	T6	T7	T8	T9	T10	T11	T12	T13	T14	T15	T16	T17	T18	T19	T20	T21	T22	T23	T24	T25	T26
S1	2.96	1.49	3.02	0.04	0.40	0.31	0.65	2.33	0.47	0.45	0.89	0.86	0.26	1.40	1.52	1.12	1.38	1.49	1.47	−0.12	−0.02	0.05	0.18	0.78	2.34	0.07
S2	4.04	3.25	6.00	2.21	0.23	0.07	0.49	0.22	0.09	0.10	−0.19	0.01	3.50	1.05	2.79	1.22	0.91	4.12	−0.10	−0.02	1.02	0.25	2.83	0.17	−0.18	0.03
S3	2.68	1.12	2.84	0.25	0.80	0.83	0.69	1.57	0.09	0.27	0.84	0.28	0.68	1.53	1.91	1.25	1.69	2.49	1.61	0.87	0.45	0.26	0.20	0.43	0.72	0.18
S4	2.87	1.35	3.29	0.57	0.85	0.88	0.69	1.24	0.00	0.21	0.71	0.11	1.17	1.54	2.18	1.32	1.71	3.02	1.47	1.02	0.66	0.32	0.56	0.32	0.22	0.19
S5	2.40	0.59	2.38	0.14	1.24	1.40	0.78	1.25	−0.19	0.16	1.00	−0.12	0.63	1.78	2.19	1.43	2.13	3.17	2.07	1.83	0.77	0.44	−0.16	0.22	−0.39	0.30
S6	4.44	2.01	4.40	−0.06	0.84	0.75	1.06	3.54	0.60	0.67	1.50	1.18	0.28	2.31	2.46	1.82	2.37	2.55	2.54	0.25	0.07	0.15	0.03	1.13	3.22	0.16
S7	2.07	1.18	2.32	0.26	0.24	0.17	0.41	1.31	0.27	0.26	0.47	0.48	0.53	0.88	1.13	0.74	0.85	1.26	0.79	−0.09	0.09	0.05	0.42	0.45	1.30	0.04

It is easy to see that the matrix presented in Table 6 is a dense one as it contains non-zero elements. The approximated matrix was obtained as a product of three matrices U(:,1:3)×S(1:3,1:3)×$V^T$(1:3,1:26). A simpler way to obtain the prediction for the null elements in the matrix of tfidf scores can be achieved by calculating the matrix PT=S(1:3,1:3)×$V^T$(1:3,1:26). For example, the prediction of the element (3,6) in Table 5 is obtained by multiplying U(3,:)×PT(:,6). The ranking of the importance of Sis for each Ti is presented in Table 7. The negative values in this table correspond to the positions inTable 5 where Sis are null. At the same time, the representation of Tis in the space {S6,S2,S5} (see Appendix F, Table A6) maintains the same similarity structure as that of the columns in Table 6. This can be easily verified by calculating the cosine similarities of each pair of columns from the two tables.

Table 7. Ranking of the importance of Sis on each Ti.

Table 7. Ranking of the importance of Sis on each Ti.

Si	T1	Si	T2	Si	T3	Si	T4	Si	T5	Si	T6	Si	T7	Si	T8	Si	T9	Si	T10	Si	T11	Si	T12	Si	T13	Si	T14	Si	T15	Si	T16	Si	T17	Si	T18	Si	T19	Si	T20	Si	T21	Si	T22	Si	T23	Si	T24	Si	T25	Si	T26
S6	4,44	S2	3,25	S2	5,98	S2	2,21	S5	1,24	S5	1,40	S6	1,06	S6	3,54	S6	0,60	S6	0,67	S6	1,50	S6	1,18	S2	3,50	S6	2,31	S2	2,79	S6	1,82	S6	2,37	S2	4,12	S6	2,54	S5	1,83	S2	1,02	S5	0,44	S2	2,83	S6	1,13	S6	3,22	S5	0,30
S2	4,04	S6	2,01	S6	4,40	S4	0,57	S4	0,85	S4	0,88	S5	0,78	S1	2,33	S1	0,47	S1	0,45	S5	1,00	S1	0,86	S4	1,17	S5	1,78	S6	2,46	S5	1,43	S5	2,13	S5	3,17	S5	2,07	S4	1,02	S5	0,77	S4	0,32	S4	0,56	S1	0,78	S1	2,34	S4	0,19
S1	2,96	S1	1,49	S4	3,29	S7	0,26	S6	0,84	S3	0,83	S4	0,69	S3	1,57	S7	0,27	S3	0,27	S1	0,89	S7	0,48	S3	0,68	S4	1,54	S5	2,19	S4	1,32	S4	1,71	S4	3,02	S3	1,61	S3	0,87	S4	0,66	S3	0,26	S7	0,42	S7	0,45	S7	1,30	S3	0,18
S4	2,87	S4	1,35	S1	3,02	S3	0,25	S3	0,80	S6	0,75	S3	0,69	S7	1,31	S3	0,09	S7	0,26	S3	0,84	S3	0,28	S5	0,63	S3	1,53	S4	2,18	S3	1,25	S3	1,69	S6	2,55	S1	1,47	S6	0,25	S3	0,45	S2	0,25	S3	0,20	S3	0,43	S3	0,72	S6	0,16
S3	2,68	S7	1,18	S3	2,84	S5	0,14	S1	0,40	S1	0,31	S1	0,65	S5	1,25	S2	0,09	S4	0,21	S4	0,71	S4	0,11	S7	0,53	S1	1,40	S3	1,91	S2	1,22	S1	1,38	S3	2,49	S4	1,47	S2	−0,03	S7	0,09	S6	0,15	S1	0,18	S4	0,32	S4	0,22	S1	0,07
S5	2,40	S3	1,12	S5	2,38	S1	0,04	S7	0,24	S7	0,17	S2	0,49	S4	1,24	S4	0,00	S5	0,16	S7	0,47	S2	0,01	S6	0,28	S2	1,05	S1	1,52	S1	1,12	S2	0,91	S1	1,49	S7	0,79	S7	−0,09	S6	0,07	S7	0,05	S6	0,03	S5	0,22	S2	-0,18	S7	0,04
S7	2,07	S5	0,59	S7	2,32	S6	−0,06	S2	0,23	S2	0,07	S7	0,41	S2	0,22	S5	−0,19	S2	0,10	S2	−0,19	S5	−0,12	S1	0,26	S7	0,88	S7	1,14	S7	0,74	S7	0,85	S7	1,26	S2	−0,10	S1	−0,12	S1	−0,02	S1	0,05	S5	−0,16	S2	0,17	S5	−0,39	S2	0,03

3.4. Rankings of the similarity of Tis in relation to Si or groups of Sis

What is the order of the representativeness of Tis in relation to different Sis or combined Sis? Here representativeness carries the meaning of similarity. The rankings of the similarity of Tis in relation to Sis are presented in the Table 8. These rankings were obtained running the MATLAB commands presented in section 2.2.4.

We notice the highest similarity between S3 and T26. Most rankings at position 26 are recorded by T9.

The most important information is contained in the first and last row of this table. The meanings of this information will be presented and analyzed in the next section.

Table 8. Ranking of the similarities of Tis in relation to Si or groups of Sis (at the group level)

Table 8. Ranking of the similarities of Tis in relation to Si or groups of Sis (at the group level)

S1		S2		S3		S4		S5		S6		S7		S6 & S2 & S5		S3 & S4
T9	0,995965	T23	0,997317	T26	1	T22	0,995868	T20	0,998118	T25	0,989812	T9	0,979403	T13	0,934609	T20	0,989967
T12	0,989301	T4	0,976134	T6	0,999526	T20	0,967097	T26	0,956404	T12	0,98605	T12	0,913588	T4	0,875648	T22	0,984999
T25	0,986681	T13	0,934132	T5	0,994631	T26	0,913931	T6	0,952207	T24	0,978246	T25	0,902476	T3	0,817813	T26	0,97177
T24	0,906217	T2	0,767802	T20	0,971152	T6	0,902173	T22	0,949452	T10	0,962763	T24	0,788894	T23	0,786502	T6	0,964726
T10	0,869069	T3	0,635008	T22	0,921905	T5	0,880936	T5	0,921315	T9	0,924983	T10	0,739049	T18	0,785911	T5	0,950225
T8	0,729452	T21	0,30098	T17	0,870824	T21	0,872964	T21	0,722328	T8	0,892486	T2	0,669705	T15	0,728096	T21	0,778942
T2	0,463941	T18	0,245362	T19	0,794533	T18	0,797319	T17	0,690371	T11	0,487735	T8	0,548356	T21	0,699693	T17	0,767766
T1	0,432782	T1	0,236144	T14	0,753133	T17	0,660874	T18	0,625357	T1	0,455215	T1	0,479856	T2	0,694564	T18	0,750468
T3	0,285165	T15	0,151052	T16	0,71321	T15	0,632086	T19	0,597251	T7	0,316326	T3	0,453271	T1	0,568066	T16	0,636571
T11	0,206558	T9	0,029564	T7	0,708054	T16	0,55462	T14	0,531676	T19	0,294482	T23	0,289191	T22	0,366768	T19	0,635026
T7	0,076089	T12	−0,1715	T18	0,651499	T14	0,521548	T16	0,504182	T2	0,2775	T4	0,047194	T16	0,335539	T14	0,634932
T14	0,014867	T22	−0,19133	T11	0,648323	T19	0,490337	T7	0,474782	T14	0,262896	T13	-0,03904	T7	0,119083	T15	0,630444
T23	0,001208	T25	−0,2017	T21	0,616317	T7	0,468473	T15	0,472042	T3	0,18745	T11	-0,03982	T14	0,114088	T7	0,584223
T19	0,000436	T16	−0,28939	T15	0,597075	T11	0,293451	T11	0,419888	T16	0,169977	T7	-0,0743	T20	0,083479	T11	0,456171
T16	−0,03674	T24	−0,31264	T8	0,105508	T13	0,148896	T13	−0,1088	T17	0,105764	T16	-0,13094	T17	0,065587	T1	0,00915
T17	−0,16352	T10	−0,35753	T1	0,056145	T4	0,014567	T1	−0,17451	T15	−0,15481	T14	−0,13941	T5	0,051444	T13	−0,00596
T4	−0,2398	T20	−0,44278	T10	−0,13426	T1	−0,02681	T8	−0,17868	T5	−0,25879	T15	−0,21727	T26	0,045041	T8	−0,11063
T15	−0,256	T7	−0,477	T3	−0,17007	T3	−0,07686	T4	−0,23521	T23	−0,2641	T19	−0,23342	T6	0,016236	T3	−0,1196
T13	−0,31425	T14	−0,4962	T13	−0,21002	T23	−0,23559	T3	−0,30243	T6	−0,32499	T17	−0,33277	T9	−0,2266	T4	−0,1462
T5	−0,52419	T8	−0,54308	T24	−0,21378	T8	−0,27027	T10	−0,41221	T26	−0,34582	T18	−0,48631	T19	−0,2438	T10	−0,33477
T18	−0,56193	T26	−0,54479	T4	−0,35111	T2	−0,41256	T23	−0,47337	T4	−0,47378	T5	−0,68033	T10	−0,26647	T23	−0,38391
T6	−0,5871	T5	−0,56032	T25	−0,46952	T10	−0,47416	T24	−0,48386	T18	−0,48788	T6	−0,74364	T24	−0,27246	T24	−0,40987
T26	−0,60325	T17	−0,56636	T12	−0,48064	T24	−0,54309	T2	−0,62526	T13	−0,51547	T26	−0,75211	T12	−0,32332	T2	−0,47475
T20	−0,77315	T6	−0,57027	T2	−0,53331	T25	−0,76103	T25	−0,69395	T20	−0,55154	T21	−0,7559	T8	−0,32931	T25	−0,64921
T22	−0,80095	T19	−0,78278	T23	−0,56063	T12	−0,76391	T12	−0,70564	T22	−0,62654	T22	−0,84262	T11	−0,34843	T12	−0,65578
T21	−0,85934	T11	−0,8104	T9	−0,65504	T9	−0,86634	T9	−0,84296	T21	−0,82827	T20	−0,88651	T25	−0,3544	T9	−0,79215

3.6. Spearman correlations of Tis and Sis (at patient/group level)

At the level of the group of patients, Spearman correlations presented in Table A2 highlighted interesting aspects. Out of the 325 correlations between Tis, 118 strong correlations were identified ($\rho$>0.8), 64 of these being higher than 0.9. At the same time, 126 correlations are significant (p<0.05). All 325 correlations refer to both Tis that belong to the same life theme and Tis that do not belong to the same life theme. All correlations between Sis (see Table A4) are significant except S1−S5, S5−S6, S5−S7. Out of the 21 Spearman correlations, 8 correlations have $\rho$>0.8, and 6 of these ones have $\rho$>0.9.

At pacient level, significant Spearman correlations of Sis and Tis are presented in Table 9Table 10.

Table 9. Significant Spearman correlations between Tis

Table 9. Significant Spearman correlations between Tis

Patient	Correlation	$\mathit{\rho }$	p-value
1	T1-T19	1,0000	0,0008
	T1-T5	0,7818	0,0468
	T5-T19	0,7818	0,0468
	T5-T17	0,9636	0,0032
	T17-T18	0,7818	0,0492
	T2-T18	0,9636	0,0040
2	T3-T13	0,9643	0,0028
	T13-T22	1,0000	0,0004
	T13-T21	0,9643	0,0028
	T6-T16	0,9286	0,0067
	T22-T21	0,9643	0,0028
	T3-T22	0,9643	0,0028
	T3-T21	1,0000	0,0004
4	T1-T9	0,9643	0,0028
	T1-T10	0,7857	0,0480
	T1-T3	0,7857	0,0480
	T3-T10	1,0000	0,0004
5	T1-T8	1,0000	0,0024
6	T1-T8	0,8846	0,0119
7	T1-T3	0,8545	0,0222
8	T3-T26	0,9643	0,0028
9	T1-T21	0,9643	0,0028
	T3-T13	0,8929	0,0123
10	T14-T15	0,9636	0,0040

Table 10. Significant Spearman correlations between Sis

Table 10. Significant Spearman correlations between Sis

Patient	Correlation	$\mathit{\rho }$	p-value
1	S1-S6	1,0000	0,0001
	S1-S7	1,0000	0,0001
	S2-S3	0,7831	0,0264
	S6-S7	1,0000	0,0001
2	S1-S4	0,9429	0,0167
	S1-S5	1,0000	0,0028
	S2-S7	1,0000	0,0028
	S4-S5	0,9429	0,0167
4	S1-S2	−1,0000	0,0167
	S1-S7	1,0000	0,0167
	S2-S7	−1,0000	0,0167
	S3-S4	1,0000	0,0167
8	S1-S5	1,0000	0,0167
	S2-S3	1,0000	0,0167
9	S1-S5	1,0000	0,0167
	S2-S3	1,0000	0,0167

4. Discussion

• for P2, the following significant associations were identified: T13 - T3, similarity=0.9902, $\rho$=0.9643 and p=0.0028; T13 - T22, similarity=0.9851, $\rho$=1.0000 and p=0.0004; T13 - T21, similarity=0.9998, $\rho$=0.9643 and p=0.0028; T6 - T16, similarity=0.9023, $\rho$=0.9286 and p=0.0067; T3 - T22, similarity=0.9514, $\rho$=0.9643 and p=0.0028; T3 - T21, similarity=0.9925, $\rho$=1.0000 and p=0.0004; T22 - T21, similarity=0.9819, $\rho$=0.9643 and p=0.0028. These associations suggest a high probability of the manifestation of the dysfunctional subthematic cycles, T3 - T13 - T21 and T3 - T13 - T21 - T22. Additionally, these cycles associate the happiness, unhappiness, and daily themes.

• for P4, the absence of the daily life theme suggests his inability to escape suffering through everyday life, which signifies a severe impairment due to illness. Significant subtheme associations are: T1 - T9, similarity=0.9965, $\rho$=0.9643 and p=0.0028; T1 - T10, similarity=0.9044, $\rho$=0.7857 and p=0.0480; T1 - T3, similarity=0.9640, $\rho$=0.7857 and p=0.0480; T10 - T3, similarity=0.9853, $\rho$=1.0000 and p=0.0004. The results indicate a high probability of the manifestation of the dysfunctional subthematic cycle T1-T3-T10.

• for P5, a significant association T1 - T8, similarity=1.0000, $\rho$=1.0000 and p=0.0024 was identified.

• for P6, a significant association T1 - T8, similarity=0.9390, $\rho$=0.8846 and p=0.0119 was identified.

• for P7, a significant association T1 - T3, similarity=0.9884, $\rho$=0.8545 and p=0.0222 was identified.

• for P8, a significant association T3 - T26, similarity=0.9996, $\rho$=0.9643 and p=0.0028 was identified.

• for P9, two significant associations T1 - T21, similarity=0.9991, $\rho$=0.9643 and p=0.0028; T3-T13, similarity=0.9256, $\rho$=0.8929 and p=0.0123 were identified.

• for P10, a significant association T14 - T15, similarity=0.9801, $\rho$=0.9636 and p=0.004 was identified..

• for P3 and P11, no significant associations were found.

• for P2, there are four significant correlations: S1-S4, similarity=0.9945, $\rho$=0.9429, p=0.0167; S1-S5, similarity=0.9859, $\rho$=1.0000, p=0.0028; S4-S5, similarity=0.963, $\rho$=0.9429, p=0.0167; S2-S7, similarity=0.9997, $\rho$=1.0000, p=0.0028. These data indicate a very high probability of the manifestation of the dysfunctional symptomatic cycle S1-S4-S5. Additionally, with a high probability, the patient simultaneously experiences S2 and S3.

• for P4, there are four significant correlations: S1-S2, disimilarity=−0.9771, $\rho$=−1.0000, p=0.0167; S1-S7, similarity=0.9971, $\rho$=1.0000, p=0.0167; S2-S7, disimilarity=−0.9905, $\rho$=−1.0000, p=0.0167; S3-S4, similarity=0.9999, $\rho$=1.0000, p=0.0167. These data indicate, with a high probability, the patient’s tendency to experience S7 in a pure (undistorted) manner and in combination with S1. Additionally, the patient shows a tendency to experience S2 in a pure manner, and to experience S3 and S4 in a combined manner.

• for P8, there are two significant correlations: S1-S5, similarity=0.9955, $\rho$=1.0000, p=0.0167; S2-S3, similarity=0.9817, $\rho$=1.0000, p=0.0167. These data indicate, with a very high probability, the simultaneous experiencing of S1 and S5, as well as of S2 and S3.

• for P9, there are two significant correlations: S1-S5, similarity=0.9983, $\rho$=1.0000, p=0.0167; S2-S3, similarity=0.9842, $\rho$=1.0000, p=0.0167. These data indicate, with a very high probability, the simultaneous experiencing of S1 and S5, as well as of S2 and S3.

• for P3, P5, P6, P10 and P11 there are no significant correlations.

5. Conclusions

Abbreviations

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