Search Results (5)

Background and Clinical Significance: In our case study, the patient experienced approximately a year-long delay in her diagnosis, where her initial diagnosis was mistakenly a primary psychiatric disorder, resulting in undue stress on her family. The aim of this case study is to raise awareness of frontal variant Alzheimer’s dementia (fvAD) and to increase knowledge amongst clinicians about this disorder, its management and the need for long-term follow up in specialized clinics. Case Presentation: In January 2023, a 56-year-old woman first presented with a 4-month history of worsening cognitive symptoms with considerable overlapping mood symptoms. Her Mini-Mental State Examination (MMSE) score was 20/28, whereas her Frontal Assessment Battery (FAB) score was 6/18. Upon neuropsychological evaluation, she demonstrated multidomain cognitive deficits, where impairments were most prominent in executive dysfunction, learning, memory and semantic fluency. There was evidence of progressive neurodegenerative changes, with brain MRI (April 2024) showing predominant bilateral frontal and parietal volume loss, sparing the occipital and temporal lobes. Amyloid positron emission tomography (PET) was diffusely positive. A diagnosis of fvAD (frontal variant Alzheimer’s dementia) with BPSD was made. Other differential diagnoses included a major neurocognitive disorder due to multiple etiologies (AD and dementia with Lewy bodies (DLB)), frontotemporal dementia (bvFTD), primary progressive aphasia (PPA) and the psychiatric disorder of pseudodementia secondary to a mood disorder. Conclusions: This case presented significant challenges given the atypical neuropsychological profile and the complexity of the symptom presentation with significant neuropsychiatric overlay. The preliminary research findings underscore the complexity of fvAD, warranting future research using fundamental approaches. Full article

Even though pseudodementia has been historically linked to depression, other psychiatric conditions may cause reversible cognitive alterations. The purpose of this study is to improve our understanding of pseudodementia occurring throughout the entire bipolar spectrum. A systematic review was conducted according to PRISMA guidelines. PubMed, Scopus, and Web of Science databases were searched up to March 2023. Fifteen articles on patients with pseudodementia and bipolar disorder (BD), mania, hypomania, or mixed depression have been included. Moreover, seven female patients with mood disorders diagnosed with pseudodementia have been described. According to our research, pseudodementia in patients with BD mostly occurs during a depressive episode. However, pseudodementia has also been observed in the context of manic and mixed states. Psychomotor and psychotic symptoms were commonly associated. The most typical cognitive impairments were disorientation, inattention, and short-term memory deficits. Alterations in neuroimaging were frequently observed. Electroconvulsive therapy and lithium, either alone or in combination with antipsychotics, resulted in the most widely used therapies. Cognitive decline may occur in a substantial proportion of patients. Since pseudodementia can manifest along the entire mood spectrum, it should be taken into consideration as a possible diagnosis in BD patients showing cognitive deficits during manic, mixed, and depressive states. Full article

In spite of the uncertainties of its diagnostic framework, pseudodementia may be conceptualized as a condition characterized by depressive symptoms and cognitive impairment in the absence of dementia. Given the controversies on this topic, the aim of the present study was to assess neurological and cognitive dysfunctions in a sample of elderly depressed subjects, and the eventual relationship between cognitive impairment and depressive symptoms. Fifty-seven elderly depressed outpatients of both sexes were included in the study. A series of rating scales were used to assess diagnoses, depressive and cognitive impairment. Comparisons for continuous variables were performed with the independent-sample Student’s t-test. Comparisons for categorical variables were conducted by the χ² test (or Fisher’s exact test when appropriate). The correlations between between socio-demographic characteristics and clinical features, as well as between cognitive impairment and depressive symptoms were explored by Pearson’s correlation coefficient or Spearman’s rank correlation. Our data showed the presence of a mild–moderate depression and of a mild cognitive impairment that was only partially related to the severity of depression. These dysfunctions became more evident when analyzing behavioral responses, besides cognitive functions. A high educational qualification seemed to protect against cognitive decline, but not against depression. Single individuals were more prone to cognitive disturbance but were similar to married subjects in terms of the severity of depressive symptoms. Previous depressive episodes had no impact on the severity of depression or cognitive functioning. Although data are needed to draw firm conclusions, our findings strengthen the notion that pseudodementia represents a borderline condition between depression and cognitive decline that should be rapidly identified and adequately treated. Full article

Recent European guidelines recommend using brain FDG-PET to differentiate between Alzheimer’s disease (AD) and depressive pseudodementia (DP), with specific hypometabolism patterns across the former group, and typically normal or frontal hypometabolism in the latter. We report the case of a 74 years-old man with DP (MMSE 16/30), whose FDG-PET visual rating and semiquantitative analysis closely mimicked the typical AD pattern, showing severe hypometabolism in bilateral precuneus, parietal and temporal lobes, and sparing frontal areas, suggesting the diagnosis of moderate AD. Shortly after starting antidepressant polytherapy, he underwent formal NPS testing, which revealed moderate impairment of episodic memory and mild impairment on executive and visuospatial tests, judged consistent with neurodegenerative dementia and concomitant depression. Over the following two years, he improved dramatically: repeated NPS assessment did not show significant deficits, and FDG-PET showed restoration of cerebral metabolism. The confirmation of PET findings via semiquantitative analysis, and their reversion to normality with antidepressant treatment, proved the non-neurodegenerative origin of the initial AD-like FDG-PET abnormalities. We review similar cases and provide a comprehensive analysis of their implications, concluding that reversible FDG-PET widespread hypometabolism might represent a biomarker of pseudodementia. Therefore, we suggest caution when interpreting FDG-PET scans of depressed patients with cognitive impairment. Full article

Loss of cognitive ability is commonly associated with dementia, a broad category of progressive brain diseases. However, major depressive disorder may also cause temporary deterioration of one’s cognition known as pseudodementia. Differentiating a true dementia and pseudodementia is still difficult even for an experienced clinician and extensive and careful examinations must be performed. Although mental disorders such as depression and dementia have been studied, there is still no solution for shorter and undemanding pseudodementia screening. This study inspects the distribution and statistical characteristics from both dementia patient and depression patient, and compared them. It is found that some acoustic features were shared in both dementia and depression, albeit their correlation was reversed. Statistical significance was also found when comparing the features. Additionally, the possibility of utilizing machine learning for automatic pseudodementia screening was explored. The machine learning part includes feature selection using LASSO algorithm and support vector machine (SVM) with linear kernel as the predictive model with age-matched symptomatic depression patient and dementia patient as the database. High accuracy, sensitivity, and specificity was obtained in both training session and testing session. The resulting model was also tested against other datasets that were not included and still performs considerably well. These results imply that dementia and depression might be both detected and differentiated based on acoustic features alone. Automated screening is also possible based on the high accuracy of machine learning results. Full article