The (late?) modern family: The family's significance for adolescents in Germany and Israel

This study presents German and Israeli youth's heroes and role models. Two hundred twenty-six students from 22 high schools took part in small group interviews. Despite differences in the normative place of the family in the two cultures, the comparison reveals significant similarities between their views and what attributes the subjects assign to their families. Three main models of family members as hero arise from the data: The Self-made Person; sacrifice and pro-social action; and contending with everyday reality. The results point to a) the fading of traditional heroes from the imagination of youth in post-industrial societies; b) the substantive place the family holds in young people's lives. I suggest that the rise of uncertainty and risk in the current socio-historical constellation, contributes to adolescents' choice of figures from their immediate environments that embody safety and a moral framework.     

Attachment and family functioning in patients with Internet addiction

Objective

Although the Internet is used effectively in many areas of life, some users experience problems because of over-use due to a lack of control. The diagnostic criteria for Internet addiction include disruptions in family relationships, but adequate data on the attachment styles and family functioning associated with this condition are limited. This study aimed to investigate the attachment styles and family functioning of patients with Internet addiction.

Method

The sample included 30 male patients consecutively admitted to the Bak?rköy Mental Health and Research Hospital Internet Addiction Outpatient Clinic, who were diagnosed in clinical interviews as having Internet addiction according to Young’s (1998) criteria. Thirty healthy males who were matched with the experimental group in terms of sociodemographic characteristics were included as control subjects. Both groups provided sociodemographic data and completed the Beck Depression Inventory (BDI), the Experiences in Close Relationships Questionnaire-r (ECR-r) and the Family Assessment Device (FAD).

Results

Patients with Internet addiction had higher BDI scores (P< .001) and higher attachment anxiety subscores on ECR-r (P< .001) compared with those in the control group. Patients with Internet addiction evaluated their family functioning as more negative and reported problems in every aspect addressed by the FAD. Scores on the FAD behaviour control, affective responsiveness, and problem-solving subscales (P< .05) and on the FAD communication, roles, and general functioning subscales (P< .001) were significantly higher in the patient compared with the control group.

Conclusion

Patients with Internet addiction have more anxious attachment styles as well as prominent disruptions in family functioning. Thus, it may be important to evaluate the attachment styles and family functioning of patients with Internet addiction. Indeed, comprehensive treatment approaches including other family members may make important contributions to treatment success.     

Learning Markov random walks for robust subspace clustering and estimation

Markov Random Walks (MRW) has proven to be an effective way to understand spectral clustering and embedding. However, due to less global structural measure, conventional MRW (e.g., the Gaussian kernel MRW) cannot be applied to handle data points drawn from a mixture of subspaces. In this paper, we introduce a regularized MRW learning model, using a low-rank penalty to constrain the global subspace structure, for subspace clustering and estimation. In our framework, both the local pairwise similarity and the global subspace structure can be learnt from the transition probabilities of MRW. We prove that under some suitable conditions, our proposed local/global criteria can exactly capture the multiple subspace structure and learn a low-dimensional embedding for the data, in which giving the true segmentation of subspaces. To improve robustness in real situations, we also propose an extension of the MRW learning model based on integrating transition matrix learning and error correction in a unified framework. Experimental results on both synthetic data and real applications demonstrate that our proposed MRW learning model and its robust extension outperform the state-of-the-art subspace clustering methods.     

Healthy Functioning in Families with a Schizophrenic Parent

This is a study using Family Adaptability and Cohesion Evaluation Scales (FACES IV) that surveyed married couples with children where one person was schizophrenic. A surprising finding was that both the patient and their partner rated their family in the healthy range. Using the Circumplex Model where balanced scores are healthy, most of the patients (78%) and spouses (87%) described their family as balanced. Both groups saw their family as “flexible to very flexible” and “connected to very connected.” In terms of the unbalanced aspects of their family (disengaged, enmeshed, rigid, and chaotic), the vast majority of patients and spouses rated these traits as low or very low. Both the patients and spouses described the family communication as more negative than positive. In terms of family satisfaction, it was low for over half of the patients and partners. In summary, these families with a schizophrenic parents generally were functioning well in spite of this major disorder in one parent.     

The relationship of family characteristics and bipolar disorder using causal-pie models

Many family characteristics were reported to increase the risk of bipolar disorder (BPD). The development of BPD may be mediated through different pathways, involving diverse risk factor profiles. We evaluated the associations of family characteristics to build influential causal-pie models to estimate their contributions on the risk of developing BPD at the population level. We recruited 329 clinically diagnosed BPD patients and 202 healthy controls to collect information in parental psychopathology, parent-child relationship, and conflict within family. Other than logistic regression models, we applied causal-pie models to identify pathways involved with different family factors for BPD. The risk of BPD was significantly increased with parental depression, neurosis, anxiety, paternal substance use problems, and poor relationship with parents. Having a depressed mother further predicted early onset of BPD. Additionally, a greater risk for BPD was observed with higher numbers of paternal/maternal psychopathologies. Three significant risk profiles were identified for BPD, including paternal substance use problems (73.0%), maternal depression (17.6%), and through poor relationship with parents and conflict within the family (6.3%). Our findings demonstrate that different aspects of family characteristics elicit negative impacts on bipolar illness, which can be utilized to target specific factors to design and employ efficient intervention programs.     

Lower availability of midbrain serotonin transporter between healthy subjects with and without a family history of major depressive disorder – a preliminary two-ligand SPECT study

PurposeSerotonin transporter (SERT) and dopamine transporter (DAT) levels differ in patients with major depressive disorder (MDD) who are in a depressed state in comparison with healthy controls. In addition, a family history of depression is a potent risk factor for developing depression, and inherited vulnerability to serotonergic and dopaminergic dysfunction is suspected in this. The aim of this study was to examine the availabilities of midbrain SERT and striatal DAT in healthy subjects with and without a first-degree family history of MDD.MethodsEight healthy subjects with first-degree relatives with MDD and 16 sex- and age-matched healthy controls were recruited. The availabilities of SERT and DAT were approximated using SPECT, employing [¹²³I] 2-((2-((dimethylamino)methyl)phenyl)thio)-5-iodophenylamine (ADAM) and [^99mTc] TRODAT-1 as the ligands, respectively. There are missing data for one participant with a first-degree family history of MDD from the ADAM study, due to a lack of the radio-ligand at the time of experiment.ResultsSERT availability in the midbrain was significantly lower in subjects with a first-degree family history of MDD than in healthy subjects. However, DAT availability was no different between two groups.ConclusionsThe results with regard to the midbrain SERT level suggest the heritability of MDD.     

Polymorphisms of BDNF Gene and Autism Spectrum Disorders: Family Based Association Study with Korean Trios

Objective

Autism spectrum disorders (ASDs) are a group of early childhood-onset neurodevelopmental disorders characterized by deficits in social interaction and language skills, and repetitive behaviors. Brain-derived neurotrophic factor (BDNF) plays a critical role in the differentiation of normal neuronal cells during embryonic and postnatal neuronal development through its neurotrophic effects.

Methods

In this study, we performed a family-based association test (FBAT) between single nucleotide polymorphisms (SNPs; rs6265, rs11030101, rs7103411, and rs7103873) or haplotypes in the BDNF gene and affection status or several quantitative traits characterized by ADI-R with151 Korean trios, including a child diagnosed as ASDs.

Results

While no significant association was found between SNPs or haplotypes and the ASDs disease status, a quantitative transmission disequilibrium test (QTDT) by using quantitative traits identified associations of the SNPs (rs6265 and rs11030101) with a domain score for "Restricted, Repetitive and Stereotyped patterns of behavior" (C domain), especially at the subdomain scores for "encompassing preoccupation or circumscribed pattern of interest" (C1) (rs6265A allele, dominant model, p-value=0.019; rs11030101 A allele, additive model, p-value=0.015) and "preoccupations with part of objects or non-functional elements of material" (C4) (rs11030101 A allele, additive model, p-value=0.015) within the ADI-R diagnostic algorithm. In addition, significant associations were also identified between the haplotypes and these quantitative traits (C1, p-value=0.016; C4, p-value=0.012).

Conclusion

We conclude that BDNF gene polymorphisms have a possible role in the pathogenesis of ASDs.     

Unsupervised classification of major depression using functional connectivity MRI

The current diagnosis of psychiatric disorders including major depressive disorder based largely on self‐reported symptoms and clinical signs may be prone to patients' behaviors and psychiatrists' bias. This study aims at developing an unsupervised machine learning approach for the accurate identification of major depression based on single resting‐state functional magnetic resonance imaging scans in the absence of clinical information. Twenty‐four medication‐naive patients with major depression and 29 demographically similar healthy individuals underwent resting‐state functional magnetic resonance imaging. We first clustered the voxels within the perigenual cingulate cortex into two subregions, a subgenual region and a pregenual region, according to their distinct resting‐state functional connectivity patterns and showed that a maximum margin clustering‐based unsupervised machine learning approach extracted sufficient information from the subgenual cingulate functional connectivity map to differentiate depressed patients from healthy controls with a group‐level clustering consistency of 92.5% and an individual‐level classification consistency of 92.5%. It was also revealed that the subgenual cingulate functional connectivity network with the highest discriminative power primarily included the ventrolateral and ventromedial prefrontal cortex, superior temporal gyri and limbic areas, indicating that these connections may play critical roles in the pathophysiology of major depression. The current study suggests that subgenual cingulate functional connectivity network signatures may provide promising objective biomarkers for the diagnosis of major depression and that maximum margin clustering‐based unsupervised machine learning approaches may have the potential to inform clinical practice and aid in research on psychiatric disorders. Hum Brain Mapp 35:1630–1641, 2014. © 2013 Wiley Periodicals, Inc.