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Using exome sequencing, we analyzed 196 participants of the Cretan Aging Cohort (CAC; 95 with Alzheimer's disease [AD], 20 with mild cognitive impairment [MCI], and 81 cognitively normal controls). The APOE ε4 allele was more common in AD patients (23.2%) than in controls (7.4%; p0.01) and the PSEN2 p.Arg29His and p.Cys391Arg variants were found in 3 AD and 1 MCI patient, respectively. Also, we found the frontotemporal dementia (FTD)-associated TARDBP gene p.Ile383Val variant in 2 elderly patients diagnosed with AD and in 2 patients, non CAC members, with the amyotrophic lateral sclerosis/FTD phenotype. Furthermore, the p.Ser498Ala variant in the positively selected GLUD2 gene was less frequent in AD patients (2.11%) than in controls (16%; p0.01), suggesting a possible protective effect. While the same trend was found in another local replication cohort (n = 406) and in section of the ADNI cohort (n = 808), this finding did not reach statistical significance and therefore it should be considered preliminary. Our results attest to the value of genetic testing to study aged adults with AD phenotype.

Early maladaptive schemas (EMS), illness representations, and coping are associated with clinical outcomes of patients with major depressive disorder (MDD). However, the pathways that link these factors are largely unknown. The present prospective study aimed at investigating the possible mediating role of illness representations and coping in the associations among schema domains, symptom severity, and suicide risk in MDD. Participants were 135 patients diagnosed with MDD, aged 48.13 ± 14.12 (84.4% females). The Young Schema Questionnaire-Short Form 3 was used to measure schema domains at baseline. Illness representations and coping were measured at approximately five months later (mean = 5.04 ± 1.16 months) with the Illness Perception Questionnaire-Mental Health and the Brief COPE Inventory, respectively. MDD outcomes were measured about 10 months after the baseline assessment (mean = 9.44 ± 2.36 months) with the Beck Depression Inventory and the Risk Assessment Suicidality Scale. SPSS AMOS 27 was used to conduct path analysis. Serial mediation Structural Equation Modelling, controlling for age, education, marital status, working status, MDD duration, pharmacotherapy, and psychotherapy, revealed that Impaired Autonomy and Performance was positively linked to suicide risk. Negative MDD impact representations and symptom severity serially mediated the aforementioned association. Finally, problem-focused coping was negatively related to symptom severity and suicide risk. This study’s main limitation was modest sample size. Representations regarding the impact and severity of MDD mediate the effects of Impaired Autonomy and Performance on future suicide risk in MDD. Healing Impaired Autonomy and Performance domain of EMS, restructuring patients’ representations of high MDD impact, and enhancing problem-focused coping could significantly reduce symptom severity and suicide risk in Schema Therapy with MDD individuals.

Introduction We have previously shown that although sleep duration is similar between cognitively non-impaired (CNI) elders and patients with Mild Cognitive Impairment (MCI), long sleep duration is associated with disease severity in patients with multi-domain MCI and Dementia. Our aim was to examine the associations between sleep duration and cognitive status in subjects with CNI and MCI followed 8 years later. Methods A sub-sample of 110 participants (77.3% females) were recruited from a large population-based cohort in Crete, Greece of 3,140 older adults (>60 years). Participants with an initial diagnosis of CNI (n=57) and MCI (n=53) (mean age at baseline=72.7 years [SD=6.8]) were followed-up 8 years later (mean age at follow up=80.5 years [SD=6.7]). All participants underwent neuropsychiatric/neuropsychological evaluation (baseline & follow-up) and a 7-day 24h actigraphy (follow-up). Sleep duration measured as night Total Sleep Time (TST) at follow-up was compared between the diagnostic groups using ANCOVA controlling for age, gender, depression symptom severity, psychotropic medication. Long sleep duration was defined by values >75% percentile of the total sample distribution on each sleep index. Results At follow up 62.7% of participants have deteriorated cognitively with 29 being diagnosed with CNI, 49 with MCI and 32 with dementia. Patients with dementia had significantly longer night TST (mean=493, SD=106 min) than persons with MCI (mean=453, SD=68 min, p=0.05) who in turn had longer night TST than CNI participants (mean=409, SD=58 min, p=0.02). Also, long night sleep duration was significantly more prevalent among dementia patients and MCI compared to CNI individuals [67.7% vs. 36.7% vs. 10.3%, respectively; all p< 0.02)]. Conclusion Our study confirms and expands previous findings that objective long sleep duration is a marker of worse cognitive status in elderly with MCI or Dementia and that this association is even stronger in old-old individuals. Support (if any) National Strategic Reference Framework (NSRF)-Research Funding Program: THALES entitled “UOC-Multidisciplinary network for the study of Alzheimer’s Disease” Grant Cod: MIS 377299 HELLENIC FOUNDATION FOR REASEARCH AND INNOVATION (HFRI)- Research Funding Program: ELIDEK entitled “Sleep Apnea (OSA) and poor sleep as Risk Factors for decreased cognitive performance in patients with Mild Cognitive Impairment: the Cretan Aging Cohort (CAC)”, Grant Cod: HFR1-FM17-4397

Purpose: To evaluate silent reading performance by means of eye fixation analysis before and after anti-VEGF treatment in wet AMD (wAMD) patients.Methods: Twenty wAMD patients who underwent anti-VEGF treatment, in one eye, served as the AMD group. Twenty adults without ocular pathology, served as the control group. Central Retinal Thickness (CRT) and near visual acuity (VA) were assessed at baseline and after 3-4 months following their first visit. Reading performance was evaluated using short passages of 0.4 logMAR print size at 40 cm distance. Eye movements were recorded using a video eyetracker. Data analysis included computation of reading speed, fixation duration, number of fixations, and percentage of regressions.Results: In the AMD group, silent reading speed in the treated eye correlated well with CRT reduction and improved significantly by an average of 15.9±28.5 wpm (p=0.041). This improvement was accompanied by an average reduction of 0.24±0.38 in fixations per word (p=0.023). The corresponding improvement in monocular VA was not statistically significant, as was the case with other eye fixation parameters. Conclusion: VA tests may underestimate the potential therapeutic effects following anti-VEGF treatment. Evaluating silent reading performance and eye fixation parameters may better characterize the effectiveness of therapeutic approaches in wAMD patients.

Being diagnosed with breast cancer (BC) can be a traumatic experience for patients who may experience symptoms of depression. In order to facilitate the prevention of such symptoms, it is crucial to understand how and why depressive symptoms emerge and evolve for each individual, from diagnosis through treatment and recovery. In the present work, data from a multicentric study of 706 BC patients followed for 12 months are analyzed. First, a trajectory-based unsupervised clustering based on K-means is performed to capture the dynamic patterns of change in patients' depressive symptoms after BC diagnosis and to identify distinct trajectory clusters. Then a supervised learning approach was employed to build a classification model of depression progression and to identify potential predictors. Patients were clustered into 4 groups: stable low, stable high, improving, and worsening depressive symptoms. In a nested cross-validation pipeline, the performance of the Support Vector Machine model for discriminating between "good" and "poor" progression was 0.78±0.05 in terms of AUC. Several psychological variables emerged as highly predictive of the evolution of depressive symptoms with the most important ones being negative affectivity and anxious preoccupation. Clinical Relevance-The findings of the present study may help clinicians tailor individualized psychological interventions aiming at alleviating the burden of these symptoms in women with breast cancer and improving their overall well-being.

Introduction Previous cross-sectional studies have shown that insomnia symptoms and objective short sleep are associated with disease severity in patients with Mild Cognitive Impairment (MCI) and Dementia. Our aim was to examine the longitudinal associations between sleep quality/quantity with cognitive progression in non-demented community-dwelling elderly. Methods A sub-sample of 105 participants (77.5% females) from a large population-based cohort in Crete, Greece of 3,140 older adults (>60 years; baseline) were followed up 8 years later (follow-up). All participants underwent neuropsychiatric/neuropsychological evaluation on both phases. At baseline, subjective sleep complaints and objective sleep variables based on 3-day 24 hour actigraphy were assessed. The impact of baseline objective sleep characteristics and/or insomnia (as defined by at least two subjective sleep complaints) on cognitive progression was assessed with univariate and multivariate models controlling for confounders. Results At baseline 50 participants were diagnosed as CNI and 55 as MCI; at follow-up 60 participants (57.1%) displayed clinically significant cognitive deterioration while the remaining showed relatively stable cognitive status. The frequency of persons reporting insomnia symptoms at baseline was higher among those who displayed cognitive deterioration (31.7%) than those in the cognitively stable group (13.3%, p=0.037). Logistic regression analysis showed that participants with insomnia symptoms at baseline, were significantly more likely to deteriorate cognitively at follow-up (p=0.05, OR=2.90). Also, among objective sleep variables added to the model, it was poor sleep efficiency that was associated with cognitive deterioration (p=0.035, OR=3.48). Conclusion More than half of the participants displayed significant cognitive deterioration over 8 years and this decline was predicted by insomnia symptoms and objective poor sleep efficiency at baseline. Improving both quality and quantity of sleep may significantly delay the deterioration of cognitive function in non-demented community-dwelling elderly. Support (if any) National Strategic Reference Framework (NSRF) - Research Funding Program: THALES entitled “UOC-Multidisciplinary network for the study of Alzheimer’s Disease” Grant Cod: MIS 377299 HELLENIC FOUNDATION FOR REASEARCH AND INNOVATION (HFRI)- Research Funding Program: ELIDEK entitled “Sleep Apnea (OSA) and poor sleep as Risk Factors for decreased cognitive performance in patients with Mild Cognitive Impairment: the Cretan Aging Cohort (CAC)”, Grant Cod: HFR1-FM17-4397

There is growing evidence that high basal cortisol levels and systemic inflammation independently contribute to cognitive decline among older people without dementia. The present cross-sectional study examined (a) the potential synergistic effect of cortisol levels and systemic inflammation on executive function and (b) whether this effect is more prominent among older people with mild cognitive impairment (MCI). A sub-sample of 99 patients with MCI and 84 older people without cognitive impairment (CNI) (aged 73.8 ± 7.0 years) were recruited from a large population-based cohort in Crete, Greece, and underwent comprehensive neuropsychiatric and neuropsychological evaluation and a single morning measurement of cortisol and IL-6 plasma levels. Using moderated regression models, we found that the relation between cortisol and executive function in the total sample was moderated by IL-6 levels (b = −0.994, p = 0.044) and diagnostic group separately (b = −0.632, p < 0.001). Moreover, the interaction between cortisol and IL-6 levels was significant only among persons with MCI (b = −0.562, p < 0.001). The synergistic effect of stress hormones and systemic inflammation on cognitive status appears to be stronger among older people who already display signs of cognitive decline. Targeting hypercortisolemia and inflammation may be a promising strategy toward improving the course of cognitive decline.

The current study aimed to track the trajectory of quality of life (QoL) among subgroups of women with breast cancer in the first 12 months post-diagnosis. We also aimed to assess the number and portion of women classified into each distinct trajectory and the sociodemographic, clinical, and psychosocial factors associated with these trajectories. The international sample included 699 participants who were recruited soon after being diagnosed with breast cancer as part of the BOUNCE Project. QoL was assessed at baseline and after 3, 6, 9, and 12 months, and we used Latent Class Growth Analysis to identify trajectory subgroups. Sociodemographic, clinical, and psychosocial factors at baseline were used to predict latent class membership. Four distinct QoL trajectories were identified in the first 12 months after a breast cancer diagnosis: medium and stable (26% of participants); medium and improving (47%); high and improving (18%); and low and stable (9%). Thus, most women experienced improvements in QoL during the first year post-diagnosis. However, approximately one-third of women experienced consistently low-to-medium QoL. Cancer stage was the only variable which was related to the QoL trajectory in the multivariate analysis. Early interventions which specifically target women who are at risk of ongoing low QoL are needed.

Inflammation in elderly is associated with physical and cognitive morbidity and mortality. We aimed to explore the association of modifiable lifestyle parameters with inflammation among non-demented, community-dwelling elderly. A sub-sample of 117 patients with mild cognitive impairment (MCI, n = 63) and cognitively non-impaired controls (CNI, n = 54) were recruited from a large, population-based cohort in Crete, Greece, of 3140 elders (>60 years old). All participants underwent assessment of medical history/physical examination, extensive neuropsychiatric/neuropsychological evaluation, diet, three-day 24-h actigraphy, subjective sleep, physical activity, and measurement of IL-6 and TNFα plasma levels. Associations between inflammatory markers and diet, objective sleep duration, subjective sleep quality, and lack of physical activity were assessed using multivariate models. Regression analyses in the total group revealed significant associations between TNF-α and low vegetable consumption (p = 0.003), and marginally with objective long nighttime sleep duration (p = 0.04). In addition, IL-6 was associated with low vegetable consumption (p = 0.001) and lack of physical activity (p = 0.001). Poor diet and lack of physical activity appear to be modifiable risk factors of inflammation, whereas long sleep appears to be a marker of increased inflammatory response in elderly. Our findings may have clinical implications given the association of inflammatory response with morbidity, including cognitive decline, and mortality in elderly.

Family therapy for schizophrenia has been demonstrated to be effective and is recommended by international clinical guidelines. Reviews of family therapy research conclude that interventions may prevent relapse of the disease, when symptoms are already reduced under psychotropic medication, by reducing family factors associated with relapse. The purpose of this study was to examine the effectiveness of Brief Solution Focused therapy (BSFT) in patients with schizophrenia focusing on the impact of change in family characteristics such as cohesion, conflict, organization and control on patients' psychopathology measured with BPRS. Thirty patients diagnosed with schizophrenia were randomly assigned to the control or intervention group. The intervention group received treatment according to the BSFT model, whereas the control group received the standard care for schizophrenia. The BSFT is a future-oriented psychotherapy model which encourages clients to focus on ''change-talking'' instead of ''problem-talking'' and on instances where a successful solution has been achieved. The intervention was consisted of 5 sessions delivered in 3 months. Main outcomes were patient-rated family characteristics measured by the Family Environment Scale (FES), and psychiatrist-rated symptom severity measured with the Brief Psychiatric Rating Scale (BPRS). The two groups did not differ in terms of age, sex, number of relapses, previous hospital admissions, and BPRS score at baseline. At the end of treatment compared to baseline there was a reduction of the BPRS score in the intervention group (p0.001) whereas no statistically significant changes were noticed in the control group after 3 months. Also, following treatment, patients in the intervention group displayed reduced scores on the Conflict FES scale (p=0.001) accompanied by increased scores on the Cohesion (p=0.004), Expressiveness (p=0.004), and Active Recreational subscales (p=0.001) according to patient's perspective. These preliminary findings suggest that BSFT in patients with schizophrenia, appears to be effective in altering the global properties of the whole family system, specifically cohesion, conflict, organization and control which, in turn, have an impact on reducing patient psychopathology.

Breast cancer diagnosis has been associated with poor mental health, with significant impairment of quality of life. In order to ensure support for successful adaptation to this illness, it is of paramount importance to identify the most prominent factors affecting well-being that allow for accurate prediction of mental health status across time. Here we exploit a rich set of clinical, psychological, socio-demographic and lifestyle data from a large multicentre study of patients recently diagnosed with breast cancer, in order to classify patients based on their mental health status and further identify potential predictors of such status. For this purpose, a supervised learning pipeline using cross-sectional data was implemented for the formulation of a classification scheme of mental health status 6 months after diagnosis. Model performance in terms of AUC ranged from 0.81± 0.04 to 0.90± 0.03. Several psychological variables, including initial levels of anxiety and depression, emerged as highly predictive of short-term mental health status of women diagnosed with breast cancer.

Identifying modifiable factors that may predict long-term cognitive decline in the elderly with adequate daily functionality is critical. Such factors may include poor sleep quality and quantity, sleep-related breathing disorders, inflammatory cytokines and stress hormones, as well as mental health problems. This work reports the methodology and descriptive characteristics of a long-term, multidisciplinary study on modifiable risk factors for cognitive status progression, focusing on the 7-year follow-up. Participants were recruited from a large community-dwelling cohort residing in Crete, Greece (CAC; Cretan Aging Cohort). Baseline assessments were conducted in 2013–2014 (Phase I and II, circa 6-month time interval) and follow-up in 2020–2022 (Phase III). In total, 151 individuals completed the Phase III evaluation. Of those, 71 were cognitively non-impaired (CNI group) in Phase II and 80 had been diagnosed with mild cognitive impairment (MCI). In addition to sociodemographic, lifestyle, medical, neuropsychological, and neuropsychiatric data, objective sleep was assessed based on actigraphy (Phase II and III) and home polysomnography (Phase III), while inflammation markers and stress hormones were measured in both phases. Despite the homogeneity of the sample in most sociodemographic indices, MCI persons were significantly older (mean age = 75.03 years, SD = 6.34) and genetically predisposed for cognitive deterioration (APOE ε4 allele carriership). Also, at follow-up, we detected a significant increase in self-reported anxiety symptoms along with a substantial rise in psychotropic medication use and incidence of major medical morbidities. The longitudinal design of the CAC study may provide significant data on possible modifiable factors in the course of cognitive progression in the community-dwelling elderly.

Introduction Sleep disturbances have been linked with cognitive decline and previous cross-sectional have shown that long sleep duration is a marker of disease severity in patients Mild Cognitive Impairment (MCI) and Dementia.Our aim was to examine the longitudinal associations between sleep quantity and quality indices and cognitive progression in non-demented community-dwelling elderly. Methods A sub-sample of 62 participants (72.6% females) were recruited from a large population-based cohort in the island of Crete, Greece of 3,140 older adults (>60yrs). Participants were followed-up 8 years later (phase III). All participants underwent neuropsychiatric/neuropsychological evaluation (phases II & III) and a 3-day 24h actigraphy (phase II).Participants were diagnosed as CNI(N=38) and MCI(N=24) during phase II, and CNI (N=22), MCI (N=27) and Dementia (N=13) during phase III. On follow-up, 28 participants progressed to a cognitively declined diagnosis compared to phase II (deteriorated group), while 34 did not (non-deteriorated group). Objective sleep variables at phase II were compared between the deteriotated/non-deteriorated groups using ANCOVA controlling for confounders. Also, differences in neuropsychological testing scores (phase II- phase III) were calculated and associations of differences with sleep variables were examined using partial correlation models controlling for confounders. Results The deteriorated group compared to non-deteriorated had significantly longer night total sleep time (TST) (442±72.6min vs. 407±53.6min, p=0.033), 24h-TST (484±8.9min vs. 434±66.4min, p=0.008), night time in bed (TMB) (537±78.7min vs. 497±62.7min, p=0.03), and 24h-TMB (603±85min vs. 539±85min, p=0.005). Episodic memory worsening was moderately correlated with night TST (r=.316), night and 24h-TMB (r=.526, and r= .442 respectively), wake time after sleep onset (r=.351), and average duration of night awakenings (r=.405). Immediate episodic memory recall decline was positively correlated with night TMB (r=.338). Conclusion Preliminary results from the Cretan aging cohort indicate that almost half of the participants deteriorated cognitively in 8 years and this decline was predicted by objective sleep duration at baseline. Long sleep at baseline may predict deterioration of clinical cognitive status in non-demented elderly at follow-up. It appears that prolonged objective sleep duration and time in bed are novel and clinically useful prognostic factors of cognitive deterioration in elderly with or without cognitive deficits. Support (If Any) National Strategic Reference Framework (NSRF) - Research Funding Program: THALES entitled “UOC-Multidisciplinary network for the study of Alzheimer’s Disease” Grant Cod: MIS 377299HELLENIC FOUNDATION FOR REASEARCH AND INNOVATION (HFRI)- Research Funding Program: ELIDEK entitled “Sleep Apnea (OSA) and poor sleep as Risk Factors for decreased cognitive performance in patients with Mild Cognitive Impairment: the Cretan Aging Cohort (CAC)”, Grant Cod: HFR1-FM17-4397

Elevated basal cortisol levels in elderly may indicate dysregulation of the internal stress-related system, as well as dysfunction and structural alterations in brain structures necessary for cognition, such as hippocampus and prefrontal cortex. Because of the close relation of executive functions and episodic memory processing, in this study we explored whether the association of elevated cortisol levels on episodic memory could be partly attributed to cortisol effects on executive functions. In this cross-sectional study we analyzed data from a sample of 236 community-dwelling older adults from the Cretan Aging Cohort aged 75.56 ± 7.21 years [53 with dementia due to probable Alzheimer's disease, 99 with Mild Cognitive Impairment (MCI) and 84 cognitively non-impaired participants (NI)]. Morning serum cortisol levels were higher in the probable AD as compared to the NI group (p = .031). Mediated regression models in the total sample supported the hypothesis that the negative association of basal cortisol levels with delayed memory was fully mediated by the relation of basal cortisol levels with executive functions and immediate memory (adjusted for age and self-reported depression symptoms). Moderated mediation regression models revealed that the direct effect of cortisol on executive function and the effect of executive function on delayed memory performance were statistically significant among participants diagnosed with MCI, while the immediate memory effect on delayed memory was more pronounced in AD patients, as compared to the NI group. The current findings corroborate neuroimaging research highlighting cortisol effects on executive functions and immediate memory and further suggest that dysregulation of systems involved in these functions may account for the purported detrimental long-term effects of high cortisol levels on delayed memory.

Antibodies against glutamate decarboxylase (GAD-Abs), especially GAD65 antibodies, are associated with limbic encephalitis (LE) manifested by temporal lobe epilepsy and neuropsychological deficits. We present the case of a 42-year-old Greek woman with nonparaneoplastic anti-GAD LE, discussing the therapeutic management and highlighting the role of neuropsychological assessment. The patient underwent functional and structural brain studies and was investigated longitudinally over a 6-year period with a battery of neuropsychological tests that were designed to document her intellectual function and verbal and visual memory. The patient suffered from refractory temporal-impaired awareness seizures and memory impairment that was mediated by autoimmune nonparaneoplastic LE and comorbid autoimmune disorders (ie, Hashimoto thyroiditis and vitiligo). Neuroimaging studies demonstrated hyperintensities in the medial temporal lobes bilaterally on T2WI MRI sequences. Serial EEGs showed bitemporal intermittent delta activity as well as epileptiform discharges. Tumor blood markers and onconeural antibodies were negative. Immunological screening revealed extremely high GAD-Abs titers in both serum and CSF, as well as the presence of CSF oligoclonal bands. Neuropsychological testing revealed anterograde amnesia with relative preservation of more remote, premorbid memories. The patient underwent first-line immunotherapy followed by immunosuppressive maintenance treatment that led to a reduction of seizures, EEG improvement, and a significant decline in GAD-Abs titers. Neuropsychological evaluations at 5 months, 1 year, and 6 years posttreatment demonstrated improvement, particularly in recent memory and everyday functionality. In this case of anti-GAD LE, the long-term seizure reduction and the improvement of neuropsychological deficits were most likely related to the immunotherapy.

Chronic pain is one of the most common health problems affecting daily activity, employment, relationships and emotional functioning. Unfortunately, the lack of specialized health care personnel, and the high heterogeneity in terms of clinical manifestation and treatment results contribute in failure to manage efficiently and effectively pain. Information and communication technology (ICT) can be a valuable tool, enabling patients and healthcare professional empowerment and better self-management of pain. To this direction, this article reports on the design of a novel technical infrastructure to support effectively and efficiently chronic pain management, based on an Intelligent Personal Health Record platform on top of already available ICT tools. The designed platform targets, among others, at improving the knowledge on the patient data, effectiveness and adherence to treatment and providing effective communication channels between patients and healthcare professionals.

Adiponectin is the major product of adipose tissue. The aim of this study was to associate adiponectin levels with adipose tissue and metabolic indices.Plasma samples of 274 non-diabetic volunteers were collected to evaluate for adiponectin, inflammatory markers, insulin and lipid parameters. Body fat composition was measured by DEXA.As expected, adiponectin levels correlated with body mass index (BMI) and gender but a wide scattering was evident. When the population was divided into two groups per median levels of adiponectin (11.94 μg/mL), adiponectin was correlated with various metabolic indices. Persons displaying relatively high adiponectin levels [17.7(CI:14.8-21.0]μg/mL; MEDIAN (25%-75%)] exhibited lower levels of inflammatory markers (hs-CRP, plasminogen, erythrocyte sedimentation rate), circulating lipids and markers of insulin sensitivity (fasting blood glucose, insulin, HbA1c and HOMA-IR) compared to those individuals displaying low-adiponectin levels [8.9(CI:6.9-10.6)μg/mL]. The percentage of high-adiponectin individuals decreased from 69.6% in the normal-BMI group to 36.5% in the obese-BMI group. Average adiponectin levels in the high-adiponectin normal-BMI group were significantly higher compared to the high-adiponectin obese-BMI group (p=0.014). Regarding body fat, only the individuals with high adiponectin levels in either the combined population or within the obese-BMI group displayed low levels of waist-to-hip ratio. Interestingly, high-adiponectin levels within the obese-BMI group were associated with higher legs fat than trunk fat as compared to the low-adiponectin obese-BMI group.Our data suggest that the distribution of adiponectin above or below a cutoff level may offer additional clinical information over and above that of BMI grouping regarding inflammatory profile, insulin-sensitivity and adiposity.

In the present study a novel data-driven topological filtering technique is introduced to derive the backbone of functional brain networks relying on orthogonal minimal spanning trees (OMST). The method aims to identify the essential functional connections to ensure optimal information flow via the objective criterion of global efficiency minus the cost of surviving connections. The OMST technique was applied to multichannel, resting-state neuromagnetic recordings from four groups of participants: healthy adults (n=50), adults who have suffered mild traumatic brain injury (n=30), typically developing children (n=27), and reading-disabled children (n=25). Weighted interactions between network nodes (sensors) were computed using an integrated approach of dominant intrinsic coupling modes based on two alternative metrics (symbolic mutual information and phase lag index), resulting in excellent discrimination of individual cases according to their group membership. Classification results using OMST-derived functional networks were clearly superior to results using either relative power spectrum features or functional networks derived through the conventional minimal spanning trees algorithm.

Cognitive impairment and frailty are major problems of older age. This study aims to explore the association between frailty and cognitive impairment in a rural cohort of older subjects in southern Europe (Cretan Aging Cohort).Community-based, primary care, cross-sectional, study in the Heraklion Prefecture, Crete, Greece. Four hundred and two persons aged 60-100 years from the Cretan Aging Cohort [100 with dementia, 175 with mild cognitive impairment (MCI) and 127 cognitively non-impaired] were enrolled, mostly rural dwellers (86.2%). Frailty was assessed with the Simple "Frail" Questionnaire Screening Tool. Demographic data, BMI, Mini-Mental State Examination scores (MMSE), severity of dementia according to the Clinical Dementia Rating Scale, and depressive symptoms according to the Geriatric Depression Scale (GDS) were recorded.Frailty was present in 17% of persons with dementia (73.8% of mild severity), in 6.3% of persons with MCI and in 8.7% of cognitively non-impaired persons (P 0.05). Among the various frailty variables, fatigue and difficulty walking were significantly more frequently reported by persons with dementia. Each frailty variable and the frailty score correlated negatively with MMSE score and positively with GDS score and polypharmacy. Multivariate analysis revealed that reported fatigue improved the identification of dementia in addition to MMSE, significantly and independently of symptoms of depression (P = 0.04).Frailty rates are significantly higher in persons with dementia. In this predominantly rural cohort of older subjects, reported fatigue could serve as a marker of physical decline and a complementary index for referral for further neuropsychological and neuropsychiatric evaluation.

Perceiving and identifying emotions on facial expressions is one of the basic abilities that compose emotional intelligence, and is crucial for normal social functions. It is well documented that facial expression conveys information about felt emotion, and that expressive behavior can activate or regulate the emotion required by a given situation. Instruments measuring emotion perception based on facial expression have been found in literature either as stand-alone scales or as part of other tests. The proposed tool expands existing instruments to combine online availability while affording assessment of emotion recognition on a continuum of intensity. It was founded on Ekman's Facial Action Units, with two Virtual Characters (male and female) portraying five basic emotions Anger, Disgust, Fear, Joy, Sadness, plus Neutral expression. The user can navigate on the custom-made pentagon and choose the emotion and intensity level (1-5) through a single click. The preliminary evaluation of the tool on thirty normal subjects provided threshold data that can later be used as benchmarks to assess emotion perception sensitivity in psychiatric disorders such as depression and schizophrenia characterized by emotional dysfunction.

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The purpose of the present study was threefold: First, to replicate previous findings of changes in local gamma band power as a function of the complexity of a visuo-semantic processing task, second, to extend these findings in tasks delivered in the auditory modality, and third to explore the use of non-linear algorithms as indices of complexity and distant synchronization in the EEG signal. EEG was recorded from 28 scalp locations as participants performed three visual discrimination tasks designed to tap into increasingly more complex operations regularly involved in the recognition of living animate objects. Two auditory processing tasks involving the same stimuli, but requiring no semantic processing, served as controls. The degree of complexity of the semantic decision was associated with the predicted changes in local gamma power, as well as with broadband changes in the non-linear predictability of the signal (an index derived using an artificial neural network algorithm). These changes were observed at all scalp regions, a finding consistent with the wide cortical distribution of component processes involved in the tasks. In addition, the synchronization between temporal and parieto-occipital electrodes and the remaining recording sites was highest in the gamma bands and lowest in the alpha bands for the task that required the most complex visuo-semantic decision. This trend reversed with reduced task complexity, consistent with the view that multidimensional semantic decisions require the involvement of distributed cortical networks in auditory and visual association areas and in the frontal lobes.

Depression is the most prevalent mood disorder worldwide having a significant impact on well-being and functionality, and important personal, family and societal effects. The early and accurate detection of signs related to depression could have many benefits for both clinicians and affected individuals. The present work aimed at developing and clinically testing a methodology able to detect visual signs of depression and support clinician decisions.Several analysis pipelines were implemented, focusing on motion representation algorithms, including Local Curvelet Binary Patterns-Three Orthogonal Planes (LCBP-TOP), Local Curvelet Binary Patterns- Pairwise Orthogonal Planes (LCBP-POP), Landmark Motion History Images (LMHI), and Gabor Motion History Image (GMHI). These motion representation methods were combined with different appearance-based feature extraction algorithms, namely Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), Local Phase Quantization (LPQ), as well as Visual Graphic Geometry (VGG) features based on transfer learning from deep learning networks. The proposed methods were tested on two benchmark datasets, the AVEC and the Distress Analysis Interview Corpus - Wizard of Oz (DAICWOZ), which were recorded from non-diagnosed individuals and annotated based on self-report depression assessment instruments. A novel dataset was also developed to include patients with a clinical diagnosis of depression (n=20) as well as healthy volunteers (n=45).Two different types of depression assessment were tested on the available datasets, categorical (classification) and continuous (regression). The MHI with VGG for the AVEC’14 benchmark dataset outperformed the state-of-the-art with 87.4% F1-Score for binary categorical assessment. For continuous assessment of self-reported depression symptoms, MHI combined with HOG and VGG performed at state-of-the-art levels on both the AVEC’14 dataset and our dataset, with Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) of 10.59/7.46 and 10.15/8.48, respectively. The best performance of the proposed methodology was achieved in predicting self-reported anxiety symptoms in our dataset, with RMSE/MAE of 9.94/7.88.Results are discussed in relation to clinical and technical limitations and potential improvements in future work., La dépression est le trouble de l'humeur le plus répandu dans le monde avec des répercussions sur le bien-être personnel, familial et sociétal. La détection précoce et précise des signes liés à la dépression pourrait présenter de nombreux avantages pour les cliniciens et les personnes touchées. Le présent travail visait à développer et à tester cliniquement une méthodologie capable de détecter les signes visuels de la dépression afin d’aider les cliniciens dans leur décision.Plusieurs pipelines d’analyse ont été mis en œuvre, axés sur les algorithmes de représentation du mouvement, via des changements de textures ou des évolutions de points caractéristiques du visage, avec des algorithmes basés sur les motifs binaires locaux et leurs variantes incluant ainsi la dimension temporelle (Local Curvelet Binary Patterns-Three Orthogonal Planes (LCBP-TOP), Local Curvelet Binary Patterns- Pairwise Orthogonal Planes (LCBP-POP), Landmark Motion History Images (LMHI), and Gabor Motion History Image (GMHI)). Ces méthodes de représentation ont été combinées avec différents algorithmes d'extraction de caractéristiques basés sur l'apparence, à savoir les modèles binaires locaux (LBP), l'histogramme des gradients orientés (HOG), la quantification de phase locale (LPQ) et les caractéristiques visuelles obtenues après transfert de modèle issu des apprentissage profonds (VGG). Les méthodes proposées ont été testées sur deux ensembles de données de référence, AVEC et le Wizard of Oz (DAICWOZ), enregistrés à partir d'individus non diagnostiqués et annotés à l'aide d'instruments d'évaluation de la dépression. Un nouvel ensemble de données a également été développé pour inclure les patients présentant un diagnostic clinique de dépression (n = 20) ainsi que les volontaires sains (n = 45).Deux types différents d'évaluation de la dépression ont été testés sur les ensembles de données disponibles, catégorique (classification) et continue (régression). Le MHI avec VGG pour l'ensemble de données de référence AVEC'14 a surpassé l'état de l’art avec un F1-Score de 87,4% pour l'évaluation catégorielle binaire. Pour l'évaluation continue des symptômes de dépression « autodéclarés », LMHI combinée aux caractéristiques issues des HOG et à celles issues du modèle VGG ont conduit à des résultats comparatifs aux meilleures techniques de l’état de l’art sur le jeu de données AVEC'14 et sur notre ensemble de données, avec une erreur quadratique moyenne (RMSE) et une erreur absolue moyenne (MAE) de 10,59 / 7,46 et 10,15 / 8,48 respectivement. La meilleure performance de la méthodologie proposée a été obtenue dans la prédiction des symptômes d'anxiété auto-déclarés sur notre ensemble de données, avec une RMSE/MAE de 9,94 / 7,88.Les résultats sont discutés en relation avec les limitations cliniques et techniques et des améliorations potentielles pour des travaux futurs sont proposées.