Remarkable Recovery After Severe Gunshot Brain Injury: A Comprehensive Case Study of Functional Rehabilitation.

BACKGROUND Penetrating traumatic brain injury (TBI) caused by gunshots is a rare type of TBI that leads to poor outcomes and high mortality rates. Conducting a formal neuropsychological evaluation concerning a patient's neurologic status during the chronic recovery phase can be challenging. Furthermore, the clinical assessment of survivors of penetrating TBI has not been adequately documented in the available literature. Severe TBI in patients can provide valuable information about the functional significance of the damaged brain regions. This information can help inform our understanding of the brain's intricate neural network. CASE REPORT We present a case of a 29-year-old right-handed man who sustained a left-hemisphere TBI after a gunshot, causing extensive diffuse damage to the left cerebral and cerebellar hemispheres, mainly sparing the right hemisphere. The patient survived. The patient experienced spastic right-sided hemiplegia, facial hemiparesis, left hemiparesis, and right hemianopsia. Additionally, he had severe global aphasia, which caused difficulty comprehending verbal commands and recognizing printed letters or words within his visual field. However, his spontaneous facial expressions indicating emotions were preserved. The patient received a thorough neuropsychological assessment to evaluate his functional progress following a severe TBI and is deemed to have had a favorable outcome. CONCLUSIONS Research on cognitive function recovery following loss of the right cerebral hemisphere typically focuses on pediatric populations undergoing elective surgery to treat severe neurological disorders. In this rare instance of a favorable outcome, we assessed the capacity of the fully developed right hemisphere to sustain cognitive and emotional abilities, such as language.

Well-being trajectories in breast cancer and their predictors: A machine-learning approach.

OBJECTIVE: This study aimed to describe distinct trajectories of anxiety/depression symptoms and overall health status/quality of life over a period of 18 months following a breast cancer diagnosis, and identify the medical, socio-demographic, lifestyle, and psychological factors that predict these trajectories. METHODS: 474 females (mean age = 55.79 years) were enrolled in the first weeks after surgery or biopsy. Data from seven assessment points over 18 months, at 3-month intervals, were used. The two outcomes were assessed at all points. Potential predictors were assessed at baseline and the first follow-up. Machine-Learning techniques were used to detect latent patterns of change and identify the most important predictors. RESULTS: Five trajectories were identified for each outcome: stably high, high with fluctuations, recovery, deteriorating/delayed response, and stably poor well-being (chronic distress). Psychological factors (i.e., negative affect, coping, sense of control, social support), age, and a few medical variables (e.g., symptoms, immune-related inflammation) predicted patients' participation in the delayed response and the chronic distress trajectories versus all other trajectories. CONCLUSIONS: There is a strong possibility that resilience does not always reflect a stable response pattern, as there might be some interim fluctuations. The use of machine-learning techniques provides a unique opportunity for the identification of illness trajectories and a shortlist of major bio/behavioral predictors. This will facilitate the development of early interventions to prevent a significant deterioration in patient well-being.

Trajectories of Quality of Life among an International Sample of Women during the First Year after the Diagnosis of Early Breast Cancer: A Latent Growth Curve Analysis.

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.

Personalized prediction of one-year mental health deterioration using adaptive learning algorithms: a multicenter breast cancer prospective study.

Identifying individual patient characteristics that contribute to long-term mental health deterioration following diagnosis of breast cancer (BC) is critical in clinical practice. The present study employed a supervised machine learning pipeline to address this issue in a subset of data from a prospective, multinational cohort of women diagnosed with stage I-III BC with a curative treatment intention. Patients were classified as displaying stable HADS scores (Stable Group; n = 328) or reporting a significant increase in symptomatology between BC diagnosis and 12 months later (Deteriorated Group; n = 50). Sociodemographic, life-style, psychosocial, and medical variables collected on the first visit to their oncologist and three months later served as potential predictors of patient risk stratification. The flexible and comprehensive machine learning (ML) pipeline used entailed feature selection, model training, validation and testing. Model-agnostic analyses aided interpretation of model results at the variable- and patient-level. The two groups were discriminated with a high degree of accuracy (Area Under the Curve = 0.864) and a fair balance of sensitivity (0.85) and specificity (0.87). Both psychological (negative affect, certain coping with cancer reactions, lack of sense of control/positive expectations, and difficulties in regulating negative emotions) and biological variables (baseline percentage of neutrophils, thrombocyte count) emerged as important predictors of mental health deterioration in the long run. Personalized break-down profiles revealed the relative impact of specific variables toward successful model predictions for each patient. Identifying key risk factors for mental health deterioration is an essential first step toward prevention. Supervised ML models may guide clinical recommendations toward successful illness adaptation.

Psychological and somatic symptoms among breast cancer patients in four European countries: A cross-lagged panel model.

Psychological and physical health among women with breast cancer are linked. However, more research is needed to test the interrelations between psychological and somatic symptoms, over time and throughout the different phases of breast cancer treatment, to determine when and which interventions should be prioritized. Six hundred and eighty nine women from four countries (Finland, Israel, Italy and Portugal) completed questionnaires during their first clinical consultation following diagnosis with breast cancer, and again after 3 and 6 months. The questionnaires included self-reported measures of psychological symptoms (Hospital Anxiety and Depression Scale; the Positive and Negative Affect Schedule Short Form) and somatic symptoms [selected items from the International European Organization for Research and Treatment of Cancer (EORTC) questionnaires]. Psychological and somatic symptoms were relatively stable across the three time-points. Cross-lagged paths leading from somatic to psychological symptoms (beta coefficients of 0.08-0.10), as well as vice-versa (beta 0.11-0.12), were found to be significant. No evidence was found for cross-cultural differences in mutual effects of psychological and somatic symptoms. The findings of this study call for tailoring personal interventions for breast cancer patients-either from a somatic perspective or a psychological perspective-and adjust them to the specific experiences of the individual patient.

Associations between Physical Exercise, Quality of Life, Psychological Symptoms and Treatment Side Effects in Early Breast Cancer.

Background: Identifying and understanding modifiable factors for the well-being of cancer patients is critical in survivorship research. We studied variables associated with the exercise habits of breast cancer patients and investigated if the achievement of exercise recommendations was associated with enhanced quality of life and/or psychological well-being. Material and Methods. 311 women from Finland, Portugal, Israel, and Italy receiving adjuvant therapy for stage I-III breast cancer answered questions about sociodemographic factors and physical exercise. Quality of life was assessed by the EORTC C30 and BR23 questionnaires. Anxiety and depression were evaluated using the HADS scale. Results: At the beginning of adjuvant therapy and after twelve months, 32% and 26% of participants were physically inactive, 27% and 30% exercised between 30 and 150 minutes per week, while 41% and 45% exercised the recommended 150 minutes or more per week. Relative to other countries, Finnish participants were more likely to be active at baseline and at twelve months (89% vs. 50%, p < 0.001 and 87% vs. 64%, p < 0.001). Participants with stage I cancer were more likely to be active at twelve months than those with a higher stage (80% vs. 70%,p < 0.05). The inactive participants reported more anxiety (p < 0.05) and depression (p < 0.001), lower global quality of life (p < 0.001), and more side effects (p < 0.05) than the others at twelve months. Accordingly, those who remained inactive or decreased their level of exercise from baseline to twelve months reported more anxiety (p < 0.01) and depression (p < 0.001), lower global quality of life (p < 0.001), and more side effects (p < 0.05) than those with the same or increased level of exercise. Conclusion: For women with early breast cancer, exercise was associated with a better quality of life, less depression and anxiety, and fewer adverse events of adjuvant therapy. Trial registration number: NCT05095675. Paula Poikonen-Saksela on behalf of Bounce consortium (https://www.bounce-project.eu/).

Trajectories and Predictors of Depression After Breast Cancer Diagnosis: A 1-year longitudinal study.

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.

Nonparaneoplastic Anti-GAD Limbic Encephalitis: Seizure Outcome and Long-term Neuropsychological Follow-up After Immunotherapy.

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.

Interactions of Mediterranean Diet, Obesity, Polypharmacy, Depression and Systemic Inflammation with Frailty Status.

Objective:Comprehensive characterization of potential frailty determinants, including sociodemographic, clinical, dietary, psychological, cognitive and systemic inflammation parameters. Methods:A rural cohort of 186 subjects aged 60-89 years recruited from a community-based study in Crete, Greece (the Cretan Aging Cohort). Frailty was assessed with the Simple "Frail" Questionnaire Screening Tool. Results:Univariate analyses revealed significant (a) positive associations (p<0.01) between frailty and age, widowhood, Geriatric Depression Scale (GDS) score, waist circumference, polypharmacy, IL-6 and (b) negative associations between frailty and frequency of contact with friends, Mini Mental State Examination (MMSE), and adherence to the Mediterranean diet. Multivariate analyses revealed a significant independent contribution of the following variables to frailty: age (B=0.035, p<0.001), GDS score (B=0.041, p=0.034), polypharmacy (B=0.568, p<0.001), waist circumference (B=0.015, p=0,006), plasma IL-6 levels (B=0.189, p=0.004), and adherence to the Mediterranean diet (B=-0.036, p=0.015). Conclusion:Older age, depression symptoms, polypharmacy, waist circumference, poor adherence to Mediterranean diet and IL-6 plasma levels are associated with increased frailty.

Prediction of Poor Mental Health Following Breast Cancer Diagnosis Using Random Forests1.

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.

Clinical impact of targeted single-photon emission computed tomography/computed tomography (SPECT/CT) bone scintigraphy on the assessment of bone metastasis in cancer patients.

OBJECTIVES: We investigated the clinical impact of single-photon emission computed tomography/computed tomography (SPECT/CT) bone scintigraphy combined with 16-slice CT on metastatic workup and treatment planning in a large cancer patient series. METHODS: Between January 2019 and January 2020, a total of 600 cancer patients were prospectively evaluated with whole-body planar bone scan (wbPBS) for staging or restaging purposes. 272/600 had equivocal lesions on wbPBS and 265/272 underwent additionally a targeted SPECT/CT bone scintigraphy on designated regions. Findings were classified as benign (score 1), metastatic (score 2) and inconclusive (score 3). Findings from SPECT/CT bone scintigraphy were compared with the results of wbPBS. RESULTS: A total of 668 lesions were considered as unclear οn wbPBS and were re-evaluated through targeted SPECT/CT bone scintigraphy. Definite diagnostic findings on SPECT/CT bone scintigraphy were obtained in 227/265 (85.7%) patients and in 592/668 (88.6%) lesions vs. 15.4% of wbPBS alone. On per-patient analyses, 38.9% of patients were considered definitely nonmetastatic and 46.8% as definitely metastatic. On per lesion analyses using SPECT/CT bone scintigraphy, corresponding diagnostic rates were 47.5 and 41.2%. Although the addition of SPECT/CT bone scintigraphy significantly reduced the rate of equivocal wbPBS results (83.1%), it failed to provide a conclusive diagnosis in a relatively small proportion of lesions 76/668 (11.4%) in 38 patients. CONCLUSIONS: SPECT/CT bone scintigraphy afforded a significant reduction of the number of patients with equivocal findings who needed further evaluation with other imaging modalities, preventing unnecessary delays in diagnosis and potential changes in disease staging and treatment planning. Moreover, SPECT/CT bone scintigraphy slightly increased diagnostic sensitivity.

Cognitive, emotional, and behavioral mediators of the impact of coping self-efficacy on adaptation to breast cancer: An international prospective study.

OBJECTIVE: The main objective of this prospective multicenter study was to examine whether illness representations of control, affect, and coping behaviors mediate the effects of self-efficacy to cope with cancer on psychological symptoms and overall quality of life, in breast cancer patients. METHOD: Data from 413 women (Mean age = 54.87; SD = 8.01), coming from four countries (i.e., Finland, Israel, Italy, Portugal), who received medical therapy for their early breast cancer, were analyzed. Coping self-efficacy was assessed at baseline. Potential mediators were assessed three months later, and outcomes after six months. RESULTS: Coping self-efficacy was related to all mediators and outcomes. Illness representations of treatment control, positive and negative affect, and certain coping behaviors (mostly, anxiety preoccupation) mediated the effects of coping self-efficacy. Coping self-efficacy was related to each outcome through a different combination of mediators. CONCLUSIONS: Coping self-efficacy is a major self-regulation factor which is linked to well-being through multiple cognitive, emotional, and behavioral pathways. Enhancement of coping self-efficacy should be a central intervention goal for patients with breast cancer, towards promotion of their well-being.

A machine learning-based pipeline for modeling medical, socio-demographic, lifestyle and self-reported psychological traits as predictors of mental health outcomes after breast cancer diagnosis: An initial effort to define resilience effects.

Displaying resilience following a diagnosis of breast cancer is crucial for successful adaptation to illness, well-being, and health outcomes. Several theoretical and computational models have been proposed toward understanding the complex process of illness adaptation, involving a large variety of patient sociodemographic, lifestyle, medical, and psychological characteristics. To date, conventional multivariate statistical methods have been used extensively to model resilience. In the present work we describe a computational pipeline designed to identify the most prominent predictors of mental health outcomes following breast cancer diagnosis. A machine learning framework was developed and tested on the baseline data (recorded immediately post diagnosis) from an ongoing prospective, multinational study. This fully annotated dataset includes socio-demographic, lifestyle, medical and self-reported psychological characteristics of women recently diagnosed with breast cancer (N = 609). Nine different feature selection and cross-validated classification schemes were compared on their performance in classifying patients into low vs high depression symptom severity. Best-performing approaches involved a meta-estimator combined with a Support Vector Machines (SVMs) classification algorithm, exhibiting balanced accuracy of 0.825, and a fair balance between sensitivity (90%) and specificity (74%). These models consistently identified a set of psychological traits (optimism, perceived ability to cope with trauma, resilience as trait, ability to comprehend the illness), and subjective perceptions of personal functionality (physical, social, cognitive) as key factors accounting for concurrent depression symptoms. A comprehensive supervised learning pipeline is proposed for the identification of predictors of depression symptoms which could severely impede adaptation to illness.

Objective Daytime Napping is Associated with Disease Severity and Inflammation in Patients with Mild to Moderate Dementia1.

BACKGROUND: Patients with dementia report excessive daytime sleep/sleepiness, which is associated with worse cognitive performance. Inflammatory markers may be elevated in patients with dementia and have been proposed as mediators of sleep/sleepiness. OBJECTIVE: To examine the association of objective daytime napping with cognitive performance and peripheral markers of inflammation in patients with dementia as compared to not cognitively impaired (NCI) controls. METHODS: A sub-sample of 46 patients with mild-to-moderate dementia and 85 NCI controls, were recruited from a large, population-based cohort of 3,140 elders (≥60 years) in Crete, Greece. All participants underwent medical history/physical examination, extensive neuropsychiatric and neuropsychological evaluation, 3-day 24 h actigraphy and a single morning measure of IL-6 and TNFα plasma levels. Comparisons of sleep parameters and inflammation markers between diagnostic groups, and between nappers and non-nappers within each diagnostic group, were conducted using ANCOVA controlling for demographics/related clinical factors. Associations between inflammatory markers, sleep variables, and neuropsychological performance were assessed within each group using partial correlation analysis controlling for confounders. RESULTS: Patients with dementia slept 15 minutes longer during the day than NCI. Within dementia patients, nappers had significantly worse performance on autobiographic memory (p = 0.002), working memory (p = 0.007), episodic memory (p = 0.010), and assessment of daily function (p = 0.012) than non-nappers. Finally, IL-6 levels were significantly associated with nap duration within dementia patients who napped (r = 0.500, p = 0.01). CONCLUSIONS: Daytime napping in patients with dementia is associated with worse cognitive performance and increased IL-6 levels. In dementia, objective daytime napping, may be a marker of the severity of the disease.

NEET status among young Greeks: Association with mental health and substance use.

BACKGROUND: Prior studies have shown that young people "not in education, employment or training" ("NEET") are at higher risk for psychopathology and substance abuse. Similar studies are lacking in Southern European populations. We aimed to examine the associations of anxiety and depressive symptoms, and substance use with NEET status in a large, randomized population-based sample in Greece. METHODS: A telephone structured questionnaire was conducted in a representative sample of 2771 young Greeks aged 15-24 years. Anxiety and depressive symptoms were assessed with GAD-7 and PHQ-9 scales, respectively. Substances use and several sociodemographic parameters were also examined. RESULTS: In our sample 16.4% were NEETs. In multivariate analyses, NEETs compared to non-NEETs were older, with lower family income, without insurance and more likely to be married and smoke. Furthermore, NEET status was associated with a higher GAD-7 score among older NEETs and long-term NEETs (NEETs unemployed for more than a year). Moreover, being NEET among older participants was associated with severe symptoms of anxiety and moderate/severe symptoms of depression. LIMITATIONS: This was a telephone survey and a clinical evaluation of the patients was not performed. Furthermore, the structured interview was not designed to detect the level of substance use or the reasons for being NEET. CONCLUSIONS: NEET status is frequent among young Greeks. Older, and long-term NEETs appear to be at higher risk for presenting anxiety/depression symptoms. Whether NEET status is associated with adverse outcomes later in life requires longitudinal studies.

Typical and Aberrant Functional Brain Flexibility: Lifespan Development and Aberrant Organization in Traumatic Brain Injury and Dyslexia.

Intrinsic functional connectivity networks derived from different neuroimaging methods and connectivity estimators have revealed robust developmental trends linked to behavioural and cognitive maturation. The present study employed a dynamic functional connectivity approach to determine dominant intrinsic coupling modes in resting-state neuromagnetic data from 178 healthy participants aged 8-60 years. Results revealed significant developmental trends in three types of dominant intra- and inter-hemispheric neuronal population interactions (amplitude envelope, phase coupling, and phase-amplitude synchronization) involving frontal, temporal, and parieto-occipital regions. Multi-class support vector machines achieved 89% correct classification of participants according to their chronological age using dynamic functional connectivity indices. Moreover, systematic temporal variability in functional connectivity profiles, which was used to empirically derive a composite flexibility index, displayed an inverse U-shaped curve among healthy participants. Lower flexibility values were found among age-matched children with reading disability and adults who had suffered mild traumatic brain injury. The importance of these results for normal and abnormal brain development are discussed in light of the recently proposed role of cross-frequency interactions in the fine-grained coordination of neuronal population activity.

Aberrant resting-state functional brain networks in dyslexia: Symbolic mutual information analysis of neuromagnetic signals.

Neuroimaging studies have identified a variety of structural and functional connectivity abnormalities in students experiencing reading difficulties. The present study adopted a novel approach to assess the dynamics of resting-state neuromagnetic recordings in the form of symbolic sequences (i.e., repeated patterns of neuromagnetic fluctuations within and/or between sensors). Participants were 25 students experiencing severe reading difficulties (RD) and 27 age-matched non-impaired readers (NI) aged 7-14 years. Sensor-level data were first represented as symbolic sequences in eight conventional frequency bands. Next, dominant types of sensor-to-sensor interactions in the form of intra and cross-frequency coupling were computed and subjected to graph modeling to assess group differences in global network characteristics. As a group RD students displayed predominantly within-frequency interactions between neighboring sensors which may reflect reduced overall global network efficiency and cost-efficiency of information transfer. In contrast, sensor networks among NI students featured a higher proportion of cross-frequency interactions. Brain-reading achievement associations highlighted the role of left hemisphere temporo-parietal functional networks, at rest, for reading acquisition and ability.

Self-reported fatigue as a risk index for dementia diagnosis.

INTRODUCTION: 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). METHODS: 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. RESULTS: 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). CONCLUSION: 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.

Mixed language dominance: insights from a case of unexpected fluent aphasia with semantic jargon resulting from massive left perisylvian lesion.

We report a right-handed patient with a massive lesion in left perisylvian language cortex, who unexpectedly presented with fluent aphasia with semantic jargon. Language deficits were assessed with a comprehensive battery of language tests. Comprehension, naming, reading, and writing were severely impaired, and verbal expression was moderately fluent with semantic jargon. Although the patient's lesion included brain areas typically essential for motor speech coordination, he was neither nonfluent nor apraxic. He exhibited strikingly unexpected aphasia with semantic jargon and prominent comprehension deficits, suggesting that this is a case of mixed dominance: the right hemisphere likely controls motor speech and basic syntactic skills, while the severely damaged left hemisphere controls semantic processing, predictably severely impaired.

Data-Driven Topological Filtering Based on Orthogonal Minimal Spanning Trees: Application to Multigroup Magnetoencephalography Resting-State Connectivity.

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 (OMSTs). 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 tree algorithm.