Fatma Simsek

Brain regions associated with risk and resistance for bipolar I disorder: a voxel‐based MRI study of patients with bipolar disorder and their healthy siblings

Bipolar I disorder is a highly heritable disorder but not all siblings manifest with the illness, even though they may share similar genetic and environmental risk factors. Thus, sibling studies may help to identify brain structural endophenotypes associated with risk and resistance for the disorder.

Reduced reward-related probability learning in schizophrenia patients

Although it is known that individuals with schizophrenia demonstrate marked impairment in reinforcement learning, the details of this impairment are not known. The aim of this study was to test the hypothesis that reward-related probability learning is altered in schizophrenia patients. Twenty-five clinically stable schizophrenia patients and 25 age- and gender-matched controls participated in the study. A simple gambling paradigm was used in which five different cues were associated with different reward probabilities (50%, 67%, and 100%). Participants were asked to make their best guess about the reward probability of each cue. Compared with controls, patients had significant impairment in learning contingencies on the basis of reward-related feedback. The correlation analyses revealed that the impairment of patients partially correlated with the severity of negative symptoms as measured on the Positive and Negative Syndrome Scale but that it was not related to antipsychotic dose. In conclusion, the present study showed that the schizophrenia patients had impaired reward-based learning and that this was independent from their medication status.

Cortical thickness and VBM in young women at risk for familial depression and their depressed mothers with positive family history

It has been demonstrated that compared to low-risk subjects, high-risk subjects for depression have structural and functional alterations in their brain scans even before the disease onset. However, it is not known if these alterations are related to vulnerability to depression or epiphenomena. One way to resolve this ambiguity is to detect the structural alterations in the high-risk subjects and determine if the same alterations are present in the probands. In this study, we recruited 24 women with the diagnosis of Major Depressive Disorder (MDD) with recurrent episodes and their healthy daughters (the high-risk for familial depression group; HRFD). We compared structural brain scans of the patients and HRFG group with those of 24 age-matched healthy mothers and their healthy daughters at similar ages to the HRFD group; respectively. Both cortical gray matter (GM) volume and thickness analyses revealed that HRFD daughters and their MDD mothers had similar GM differences in two regions: the right temporoparietal region and the dorsomedial prefrontal cortex. These results suggested that the observed alterations may be related to trait clinical and neurophysiological characteristics of MDD and may present before the onset of illness.

Cortical GABA in subjects at ultra-high risk of psychosis: Relationship to negative prodromal symptoms

Abstract Background Whilst robust preclinical and postmortem evidence suggests that altered GABAergic function is central to the development of psychosis, little is known about whether it is altered in subjects at ultra-high risk of psychosis, or its relationship to prodromal symptoms. Methods Twenty-one antipsychotic naïve ultra-high risk individuals and 20 healthy volunteers underwent proton magnetic resonance imaging at 3T. Gamma-aminobutyric acid levels were obtained from the medial prefrontal cortex using MEGA-PRESS and expressed as peak-area ratios relative to the synchronously acquired creatine signal. Gamma-aminobutyric acid levels were then related to severity of positive and negative symptoms as measured with the Community Assessment of At-Risk Mental States. Results Whilst we found no significant difference in gamma-aminobutyric acid levels between ultra-high risk subjects and healthy controls (P=.130), in ultra-high risk individuals, medial prefrontal cortex GABA levels were negatively correlated with the severity of negative symptoms (P=.013). Conclusion These findings suggest that gamma-aminobutyric acidergic neurotransmission may be involved in the neurobiology of negative symptoms in the ultra-high risk state.

Hippocampal shape alterations in healthy young women with familial risk for unipolar depression

BACKGROUND Although reduced hippocampal volume (HCV) is a common finding in depression, it is unclear whether the structural alterations leading to reduction of HCV are pre-existing risk factors before the onset of clinical symptoms or a cumulative process that begins with the onset of clinical symptoms. The aim of the present study was to understand the anatomical status of the hippocampus prior to the clinical symptoms in subjects with high familial risk for depression. METHODS Twenty-seven young women (mean age: 22.3 ± 2.1 years) who were at high risk for familial unipolar depression and 26 age- and gender-matched healthy controls (mean age: 22.1 ± 2.1 years) with low familial risk for depression were included in the study. Total hippocampal volumes were measured by manual tracing. For 3D shape differences, the spherical harmonic basis functions (SPHARM) software was used. The segmented images were parameterized, and the point-to-point based group difference was compared by the Hotellings T-squared test with total brain volume and Beck Depression Scale as covariates. RESULTS Although there was no difference in overall HCVs, shape analyses revealed a contracted area on the Cornu Ammonis (CA) 1 region of the right hippocampus head in the high-risk group compared to the low-risk group. Cross-sectional design and small sample size, including only females, were the main limitations of this study. CONCLUSION This study with shape analyses provided data suggesting that local structural hippocampal alterations in the CA1 region might be associated with depression vulnerability in women at high risk.

Computer based Classification of MR Scans in First Time Applicant Alzheimer Patients

In this study, we aimed to classify MR images for recognizing Alzheimer Disease (AD) in a group of patients who were recently diagnosed by clinical history and neuropsychiatric exams by using non-biased machine-learning techniques. T1 weighted MRI scans of 31 patients with probable AD and 31 age- and gender-matched cognitively normal elderly were analyzed with voxel-based morphometry and classified by support vector machine (SVM), a machine learning technique. SVM could differentiate patients from controls with accuracy of 74% (sensitivity: 70% and specificity: 77%) when the whole brain was included the analyses. The classification accuracy was increased to 79% (sensitivity: 65 % and specificity: 93%) when the analyses restricted to hippocampus. Our results showed that SVM is a promising tool for diagnosis of AD, but needed to be improved.